Transition Graph Research Articles

Assessment of QOS indicators of a network with UDP and TCP traffic under a node peak load mode

The object of research is Markov models of network nodes with UDP (User Datagram Protocol) and TCP (Transmission Control Protocol) traffic and their differences. The task solved is the lack of Markov models of network nodes describing the behavior of TCP traffic from the point of view of packet retransmissions and packet delivery guarantees. Markov models of network nodes describing traffic behavior with guaranteed packet delivery have been further advanced. Given the comparison of the models, the differences from the classic models serving TCP traffic were shown, for each packet flow, an additional dimensionally was added to the graph of states and transitions, which takes into account the retransmission of a lost packet. The comparison graph shows similar behavior of queue length and packet loss for both types of traffic. But the nature of the curves is different. With TCP traffic, packet loss can exceed 5 percent. In addition, lost packets must be retransmitted, which increases the load on the network node. More failures and packet queue lengths at a network node during peak load typically occur with TCP traffic compared to UDP traffic. At peak load, the difference in service failures can reach 20–30 percent. The main reason is that TCP uses flow control and rate-limiting mechanisms to avoid network congestion and ensure efficient data transfer between nodes. The Markov model of TCP traffic requires an additional dimensionally on the graph of states and transitions, which affects the behavior of queues and packet failures. The investigated problem was solved due to the universality and diversity of the mathematical apparatus of Markov mass service systems. The results could be used in network modeling software products for building and reengineering the topology of electronic communications networks at enterprises and organizations

Career Mobility Analysis With Uncertainty-Aware Graph Autoencoders: A Job Title Transition Perspective

Career mobility analysis aims at discovering the movement patterns of employees across different job positions or grades, which can benefit various human resource-related applications. Indeed, recent studies in this direction mainly focus on modeling individual career trajectories, while the macroguidance for labor market assessment has been largely ignored. To this end, in this article, we propose to study career mobility from a market-driven perspective based on large-scale online professional networks (OPNs). Specifically, we propose an uncertainty-aware graph autoencoders (UnGAEs) framework, which can simultaneously discover potential job title transition patterns and predict job durations. In this phase, we first construct a job title transition graph based on massive career trajectory data from OPNs. Then, considering the inherent uncertainty in career mobility, we introduce a novel uncertainty-aware graph encoder (UnGE) to represent job titles as Gaussian embeddings. Furthermore, we design two task-specific decoders that can preserve the asymmetric relationships between job titles, namely the gravity-inspired decoder (GID) and the energy-inspired decoder (EID), for predicting potential transition patterns and corresponding duration, respectively. In particular, both tasks are modeled through a specially designed multitask learning approach. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of UnGAE compared with state-of-the-art baselines, as well as some potential applications such as job title benchmarking and career path planning.

Exploiting dynamic social feedback for session-based recommendation

Since people with close relationships are easily influenced by each other, social friends usually have more preferences of higher similarities than others. For this reason, social recommendation methods are proposed to adopt social links to improve the degree of preference matching between users and recommended items. Although current social recommendation methods have captured the general preference similarities among social friends, it is still difficult to model the evolution of dynamic social influence among friends, especially in session-based scenarios. In reality, when users’ dynamic preferences are changing, social feedback from their friends is also changing over time. So that the dynamic social feedback is an important social influence, which has not been considered in current studies. To this end, we propose a social feedback-enhanced session-based recommendation (SFRec) method, which not only utilizes the similarity of general preferences among friends but also captures the friends’ influence which reflects people’s dynamic preferences. Specifically, we first coordinate similarity relations via information propagation on social graph, item transition graph and user–item interaction graph. To capture social feedback based on users’ dynamic preferences, we then construct a social feedback generation module that consists of preference extraction, feedback generation and feedback aggregation. Finally, we construct a preference fusion module to obtain the final preference representation and make personalized recommendation. We conduct comprehensive experiments on three datasets. Results demonstrate that SFRec surpasses the state-of-the-art models on recommendation performance.

Multi-agent cooperative operation based on cross-domain zero-shot learning

Multi-agent systems (MAS) based on reinforcement learning have shown impressive performance in complex tasks. However, they are still weak when it comes to addressing tasks without any samples. In this paper, we design a 2D ground and a 3D underwater cooperative task, which serve as the source and target tasks, respectively. The MAS requires learning prior knowledge that can be generalized to the target task from the source task. To solve this problem, we propose cross-domain zero-shot learning (CDZSL) for MAS cooperative operation. Specifically, we first construct a state transition graph by decomposing the source tasks into multiple state phases. Subsequently, we describe the task description document for MAS and construct the task-related semantic embedding space, which effectively combats the semantic gap. Then, we propose zero-shot learning based on feature synthesis mapping (FSM), which synthesizes the feature centers of the unseen classes by the features of seen classes. During knowledge transfer, FSM is employed to assist MAS's decisions-making in the target task. Finally, extensive experiments show that CDZSL, compared with MAX-Q, improves the adaptability of MAS, where the cost of physical time for the first time to complete the new task is reduced by eight times.

Comparing Structure and Dynamics of Transition Graphs by the Symmetric Difference Metric Over an Edge-Filtration

Comparing Structure and Dynamics of Transition Graphs by the Symmetric Difference Metric Over an Edge-Filtration

Mathematical modeling of food production technological processes in a computer training complex for operator education

A computer training complex (CTC) structure has been designed for training food production operators, comprising interconnected subsystems generating output parameters. This CTC structure enables the configuration and storage of knowledge on specific technological processes, facilitating the customization of exercises by instructors. Individualized exercises are generated for students to perform in a virtual production environment, utilizing computer programs, tablets, smartphones, or simulated production equipment, including VR/AR technologies. During exercises, student actions are automatically recorded, and a quantitative assessment is provided based on standards, forming a comprehensive evaluation. A mathematical model, represented by a Markov transition graph, captures various states of the virtual production environment during simulated processes at the CTC. This model serves as the basis for constructing specific technological process models in the food industry, tailored to CTC hardware, software, and training methodologies. The methodology presented streamlines the development of training courses for CTC exercises, enhancing personnel knowledge and skills in food production. By utilizing these models, methods, and algorithms, CTCs can be tailored for training operators in diverse food production sectors. The approach outlined simplifies course creation, ensuring personnel development at the desired proficiency level through exercise quality assessment algorithms.

Conical Transition Graphs for Analysis of Asymptotic Stability in Hybrid Dynamical Systems

A method is proposed for analyzing asymptotic stability in the conical approximation of a hybrid system. Specifically, this paper introduces the conical transition graph (CTG) to simplify the analysis of asymptotic stability in conical approximations by converting solutions to a hybrid system into walks through a discrete graph. By exploiting the fact that pre-asymptotic stability in a conical approximation implies pre-asymptotic stability in the original system, a CTG-based approach can establish asymptotic stability in hybrid systems that have nonlinear flow maps and jump maps without needing to construct a Lyapunov function.

A Nonaugmented Method for the Minimal Observability of Boolean Networks.

This article proposes a nonaugmented method for investigating the minimal observability problem of Boolean networks (BNs). This method can be applied to more general BNs and reduce the computational and space complexity of existing results. First, unobservable states concerning an unobservable BN are classified into three categories using the vertex-colored state transition graph, each accompanied by a necessary and sufficient condition for determining additional measurements to make them distinguishable. Then, an algorithm is designed to identify the additional measurements that would render an unobservable BN observable using the conditions. Next, to determine the minimum added measurements, a necessary and sufficient condition and an algorithm based on a constructed matrix are presented. Finally, the results obtained are compared with existing literature and illustrated with examples.

Researching opportunities to improve efficiency detection of unmanned aerial vehicles

The article considers options for the formation of radio electronic systems for detecting UAVs using radar, optoelectronic, thermal imaging and acoustic systems with specified values of performance indicators in their autonomous use. The results of comparing the effectiveness of eight variants of the formation of electronic systems for detecting UAVs of the Lancet, Shahed-136, and Cube types are shown. In these variants, two electronic complexes are considered, which provide for the combination of 3 systems, and 6 electronic complexes, which provide for the combination of two systems. To evaluate the effectiveness of the radio electronic complex for detecting unmanned aerial vehicles of each type, the indicator "probability of UAV detection" is used. The fact of UAV detection is recorded in three variants of partial task completion. The first embodiment of the partial solution of the task takes into account unmanned aerial vehicles detected by three systems. The second embodiment of the task takes into account unmanned aerial vehicles detected by any two of the three systems. The third variant of the partial solution of the task takes into account unmanned aerial vehicles detected by any one system out of three. To determine the performance indicator, a discrete-continuous stochastic model of the operational functional behavior of an electronic complex consisting of radar, optoelectronic or thermal imaging and acoustic systems with a given algorithm for its functioning is proposed. The development of a probabilistic graph of states and transitions is shown, on the basis of which a stochastic model of the operational functional behavior of the UAV detection electronic complex is formed in the form of a system of Kolmogorov-Chopman differential equations. The degree of adequacy of the proposed discrete-continuous stochastic model of the operational functional behavior of the electronic complex is determined by taking into account the performance indicators of the systems that are part of it. As indicators of system efficiency, we used "the probability of detecting a UAV by the system of the electronic complex when the UAV is in the area controlled by this system" and "the average values of the time intervals from the moment of UAV appearance in the controlled area until the moment of its detection by the corresponding system". To evaluate the increase in the efficiency of the created UAV detection electronic systems, an indicator is used in the form of the ratio of the probability of non-detection of UAVs by one system when it is used autonomously and the probability of non-detection of UAVs by the created electronic system.

Design and self-diagnostics of cyberphysical control devices on SOC platform

The subject of research in this article is models, methods, and procedures for designing and self-diagnosing automated models of logic control devices implemented in SoCs. The object of work is the procedures for automated design and diagnosis of digital devices on the SoC technology platform. The aim of the study is to develop models and procedures for designing and self-testing in the cycle of automated design of automatic logic control systems on the SoC technology platform, which will significantly increase the reliability of their operation. The article solves the following tasks: consideration of the procedures for interacting the processor core with programmable logic as part of the SoC; improvement of the procedures for designing and testing software and hardware systems based on SoC; further development of procedures for automated design, verification, and diagnosis of cyber-physical logic control systems using programming languages and hardware description languages; implementation of the procedure for hardware self-testing of control automata on the SoC technology platform. The following methods are implemented: synthesis of control automata based on graph models, implementation of control automata models in the C programming language using an automata template, diagnostic experiment by traversing the automata transition graph. Results achieved. Based on the analysis of the procedures for the interaction of the processor core and programmable logic on the selected SoC platform, a model of a cyber-physical logic control system was designed. The practical implementation was carried out on the basis of the Vivado/Vitis/Vitis HLS CAD toolkit. The method of hardware self-testing of control automata on the technological platform of SoC ZYNQ-7000 was implemented. Conclusions. The article analyzes the principles of designing embedded cyber-physical systems implemented in system-on-chip. The principles of building verification systems and embedded self-diagnostics of system-on-chip systems containing software and hardware are considered. The developed methods are tested on a model of a traffic light logic control device on the SoC FPGA platform of the ZYNQ-7000 family by Xilinx. The Moore's control automaton is implemented in the PL block in the C programming language, and the operational automaton is implemented in the PS block. During the organization of the self-diagnosis process, a non-destructive diagnostic experiment was performed by traversing all arcs of the transition graph, starting from the initial vertex. In this case, the tester was an operational automaton, the reference logic and time values of which were stored in the memory of the PS unit. Visual observation of the diagnostic experiment was carried out using the LED panel of the ZedBoard board.

Основні питання створення моделі процесу технічної експлуатації повітряного судна військового призначення

The subject of the research in this article is the creation of a mathematical model of the process of technical operation (PTO) of a military aircraft in the system of information support for the operation of an aircraft. The aircraft is a complex technical system that includes many systems, subsystems, and elements for reliable and error-free functioning, which require constant monitoring of its technical condition to determine the possibility of its intended use. It is proposed to promote PTO in the system of information support for the operation of aircraft operating in real time and to create a mathematical model of the process. We will construct a graph of states and transitions using the transition matrix to describe the PTO of a military aircraft. The purpose of this study is to create a mathematical model of the PTO of a military aircraft in a system of information support for the operation of the aircraft operating on a real time scale. The task: to determine the role and advantage of using the mathematical model of the PTE of a military aircraft in the information support system for the operation of a CMMS-type aircraft operating in real time for its use in forecasting and managing the effectiveness of the PTE. The scientific and practical novelty of the obtained results is as follows: the advantages of the PTE mathematical model of a military aircraft in the CMMS-type aircraft operation information support system, which works in real time before the PTO model by resource and the model when decisions are made at the moment, have been determined state changes. The conclusion of this study is that the use of the proposed PTO model will allow us to evaluate complex reliability indicators in real time, that is, continuously. The considered PTO model of a military aircraft considers all the features of military aircraft operation at the current stage. This allows us to assume the possibility of its use for the entire aircraft fleet of the Air Force Base of the Armed Forces of Ukraine.

State Transition Graph-Based Spatial–Temporal Attention Network for Cell-Level Mobile Traffic Prediction

Mobile traffic prediction enables the efficient utilization of network resources and enhances user experience. In this paper, we propose a state transition graph-based spatial–temporal attention network (STG-STAN) for cell-level mobile traffic prediction, which is designed to exploit the underlying spatial–temporal dynamic information hidden in the historical mobile traffic data. Specifically, we first identify the semantic context information over different segments of the historical data by constructing the state transition graphs, which may reveal different patterns of random fluctuation. Then, based on the state transition graphs, a spatial attention extraction module using graph convolutional networks (GCNs) is designed to aggregate the spatial information of different nodes in the state transition graph. Moreover, a temporal extraction module is employed to capture the dynamic evolution and temporal correlation of the state transition graphs over time. Such a spatial–temporal attention network can be further integrated with a parallel long short-term memory (LSTM) module to improve the accuracy of mobile traffic prediction. Extensive experiments demonstrate that the STG-STAN can better exploit the spatial–temporal information hidden in the state transition graphs, achieving superior performance compared with several baselines.

Using SAT Solvers to Reverse-Engineer FSM Models of Digital Devices

Inferring a functional specification from an existing digital design is a challenge that is suitable with reverse-engineering methods. One of the most widely used functional specification formats is a finite state machine (FSM). This article studies the possibility of blind passive specification mining for a digital device, where the device is treated as a “black box”. The presented approach treats an input and output signal waveform as the transition graph of an incomplete deterministic FSM and learns the FSM through FSM minimization. It employs a Boolean satisfiability problem (SAT) solver to find a minimal FSM that complies with observed object behavior. The known approach to identifying state machines in discrete event systems is adapted to operate with variables in the form of coloring and transition tables. The developed implementation produces a synthesizable specification in hardware description language (HDL) and a state diagram in unified modeling language (UML). The proposed approach for inferring an FSM from a waveform trace can serve as a supplementary tool during reverse engineering to provide developers with meaningful insight regarding the analyzed device. The presented case study defines metrics of successful FSM inference and applies them to a synthetic FSM and a real-world example FSM to demonstrate the applicability of the approach.

Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at https://github.com/SUSTechBruce/RESTC-Source-code .

FSMx-Ultra: Finite State Machine Extraction From Gate-Level Netlist for Security Assessment

Numerous security vulnerability assessment techniques urge precise and fast finite state machines (FSMs) extraction from the design under evaluation. Sequential logic locking, watermark insertion, fault-injection assessment of a System-on-a-Chip (SoC) control flow, information leakage assessment, and reverse engineering at gate-level abstraction, to name a few, require precise FSM extraction from the synthesized netlist of the design. Unfortunately, no reliable solutions are currently available for fast and accurate extraction of FSMs from the highly unstructured gate-level netlist for effective security evaluation. The major challenge in developing such a solution is precise recognition of FSM state flip-flops in a netlist having a massive collection of flip-flops. In this paper, we propose FSMx-Ultra, a framework for extracting FSMs from extremely unstructured gate-level netlists. FSMx-Ultra utilizes state-of-the-art graph theory concepts and algorithms to distinguish FSM state registers from other registers and then constructs gate-level state transition graphs (STGs) for each identified FSM state register using automatic test pattern generation (ATPG) techniques. The results of our experiments on 14 open-source benchmark designs illustrate that FSMx-Ultra can recover all FSMs quickly and precisely from synthesized gate-level netlists of diverse complexity and size utilizing various state encoding schemes.

Learning Multiagent Options for Tabular Reinforcement Learning using Factor Graphs

Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios, where only sparse reward signals are available. It aims to connect the most distant states identified through the Fiedler vector of the state transition graph. However, the approach cannot be directly extended to multi-agent scenarios, since the joint state space grows exponentially with the number of agents thus prohibiting efficient option computation. Existing research adopting options in multi-agent scenarios still relies on single-agent algorithms and fails to directly discover joint options that can improve the connectivity of the joint state space. In this paper, we propose a new algorithm to directly compute multi-agent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as the Kronecker product of individual agents' state spaces, based on which we can directly estimate the Fiedler vector of the joint state space using the Laplacian spectrum of individual agents' transition graphs. This decomposition enables us to efficiently construct multi-agent joint options by encouraging agents to connect the sub-goal joint states which are corresponding to the minimum or maximum of the estimated joint Fiedler vector. Evaluation on multi-agent collaborative tasks shows that our algorithm can successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher cumulative rewards.

ПРОГРАМНА АНАЛІТИЧНА СТОХАСТИЧНА МОДЕЛЬ ЕКСПЛУАТАЦІЙНОЇ ФУНКЦІЙНОЇ ПОВЕДІНКИ РАДІОЕЛЕКТРОННОГО КОМПЛЕКСУ ВИЯВЛЕННЯ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

The article considers an electronic system for detecting unmanned aerial vehicles, which includes a radar system, an optoelectronic system, a thermal imaging system, and an acoustic system. In information technology, the development of a software discrete-continuous stochastic model of the operational behavior of an electronic complex is an important stage in the creation of a structural-automatic model. The creation of a structural-automatic model is described in the article in the following sequence: description of the selected algorithm for the functioning of the electronic complex; verbal model of the operational functional behavior of the electronic complex; according to the verbal model, the development of a reference graph of states and transitions is described; on the basis of the reference graph of states and transitions, a structural-automatic model of operational behavior is formed; the need to verify the developed structural-automatic model is indicated. The combination of the structural-automatic model of operational behavior with the ASNA-2 software module forms a software stochastic model. The software stochastic model is designed to solve the problems of system engineering design (analysis and synthesis) of an electronic complex. The structural-automatic model allows the Designer to set any values of the performance indicators of the systems that are included in the electronic complex. The ASNA-2 software module automates the construction of state graphs based on the structural automated model; according to the state graph, it generates and solves the Kolmogorov-Chopman system of differential equations. By validating the software stochastic model, the reliability of the results to be obtained by the Projector was verified.

A Spatio-Temporal Task Allocation Model in Mobile Crowdsensing Based on Knowledge Graph

With the increasing popularity of wireless networks and the development of smart cities, the Mobile Crowdsourcing System (MCS) has emerged as a framework for automatically assigning spatiotemporal tasks to workers. The study of mobile crowdsourcing makes a valuable research contribution to community service and urban route planning. However, previous algorithms have faced challenges in effectively addressing task allocation issues with massive spatial data. In this paper, we propose a novel solution to the spatiotemporal task allocation problem using a knowledge graph. Firstly, we construct a robust spatiotemporal knowledge graph (STKG) and employ a knowledge graph embedding algorithm to learn the representations of nodes and edges. Next, we utilize these representations to build a task transition graph, which is a weighted and learning-based graph that highlights important neighbors for each task. We then apply a simplified Graph Convolutional Network (GCN) and an RNN-based model to enhance task representations and capture sequential transition patterns on the task transition graph. Furthermore, we design a similarity function to facilitate personalized task allocation. Through experimental results, we demonstrate that our solution achieves higher accuracy compared to existing approaches when tested on three real datasets. These research findings are significant as they contribute to an 18.01% improvement in spatiotemporal task allocation accuracy.

Implicit contact-rich manipulation planning for a manipulator with insufficient payload

Purpose This paper aims to study using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot’s maximum payload. Design/methodology/approach This paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method’s help, the authors plan contact-rich manipulation without particularly analyzing an object’s contact modes and their transitions. The planner and optimizer determine them automatically. Findings The authors conducted experiments and analyses using simulations and real-world executions to examine the method’s performance. The method successfully found manipulation motion that met contact, force and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. Originality/value This paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.

Complete Bifurcation Analysis of the Vilnius Chaotic Oscillator

The paper is dedicated to the numerical and experimental study of nonlinear oscillations exhibited by the Vilnius chaotic generator. The motivation for the work is defined by the need for a comprehensive analysis of the dynamics of the oscillators being embedded into chaotic communication systems. These generators should provide low-power operation while ensuring the robustness of the chaotic oscillations, insusceptible to parameter variations and noise. The work focuses on the investigation of the dependence of nonlinear dynamics of the Vilnius oscillator on the operating voltage and component parameter changes. The paper shows that the application of the Method of Complete Bifurcation Groups reveals the complex smooth and non-smooth bifurcation structures, forming regions of robust chaotic oscillations. The novel tool—mode transition graph—is presented, allowing the comparison of experimental and numerical results. The paper demonstrates the applicability of the Vilnius oscillator for the generation of robust chaos, and highlights the need for further investigation of the inherent trade-off between energy efficiency and robustness of the obtained oscillations.