Digital Commons Network^™

Simulation Study On The Vulnerability Of Belt And Road Composite Transportation Network Based On Cascading Failure, Mingjun Qian, Chao Yin, Zhiwen Huang, Quanneng Wang

Journal of System Simulation

Abstract: To address the issues of unclear cascading failure propagation mechanisms and difficult-to-quantify vulnerability evolution laws caused by the structural complexity and node heterogeneity of the "Belt and Road" multimodal transport network, a vulnerability assessment method for composite transportation networks integrating physical analogy and improved cascading failure model was proposed. Sub-networks were constructed based on main transportation modes of the Belt and Road, and nodes in the same city were coupled to build a composite transportation network model. An information entropy-Joule's law model was constructed to identify key nodes of the composite network and its subnetworks. A load-capacity cascading failure …

Distributed Cooperative Control Of Swarm Unmanned Aerial Vehicles Based On Multi-Route Clustering, Yuyang Xiong, Chuntao Li, Wenhao Qiu

Journal of System Simulation

Abstract: To address the collaborative control and formation maintenance problems of large-scale swarm unmanned aerial vehicles, a distributed cooperative control method for unmanned aerial vehicles based on multi-route clustering was proposed. Leveraging the characteristics of multiple routes, the overall cooperative control problem of the swarm was decoupled into the cooperative control within the same route and the consensus control between multiple routes. Within the same route, a forward-neighbor information interaction mechanism was designed to determine the neighbor relationships, and a velocity-guided cooperative algorithm was utilized to achieve the desired velocity matching and spacing maintenance of unmanned aerial vehicles; between multiple …

Multi-Scale Modeling Method For Intelligent Unmanned Aerial Vehicle Swarm Combat, Yelei Zhu, Shoulin Shen, Jiang Zhu, Chuanhua Wen

Journal of System Simulation

Abstract: Intelligent unmanned aerial vehicle swarm combat exhibits multi-scale characteristics of microlevel tactical autonomy, meso-level resource constraints, and macro-level network collaboration. In view of the problem that a single-scale modeling method is difficult to simultaneously characterize individual decision-making details, resource flow process, and system collaboration mechanism, a multi-scale modeling method integrating multi-agent modeling, system dynamics, and complex network theory was proposed. By designing six formal coupling operators to achieve cross-layer information mapping, a time progression mechanism based on a master-slave clock and hybrid synchronization was established, and a proof of error boundedness was provided. A prototype system was implemented on …

Modeling And Simulation Of Efficient State Information Diffusion In Wireless Ad Hoc Networks, Mengyao Jia, Shaozhu Gu, Junhan Wang, Denghao Wang, Du Xu, Xiaoning Zhang

Journal of System Simulation

Abstract: To solve the problems of significant communication overhead and limited real-time response capability in the traditional flooding-based state diffusion mechanism, an efficient diffusion algorithm based on regional partitioning and information compression was proposed. A multi-dimensional feature clustering method integrating geographic distance and link quality was used to achieve high-cohesion network partitioning through weighted Euclidean distance and the K-means algorithm; a dynamic selection mechanism for representative nodes was designed to conduct comprehensive scoring by combining the link quality and geographic centrality of nodes, realizing the efficient compression and aggregated transmission of regional information. Simulation results indicate that the algorithm significantly …

Research On Safety Monitoring System For Digital Twin Fully Mechanized Mining Face In Thin Coal Seams, Ya Liu, Zhaoyun Zhang, Cheng An, Canguang Zheng

Journal of System Simulation

Abstract: To solve the problems of opaque production processes, difficult expression of production information, and high difficulty in real-time model driving and safety monitoring in fully mechanized mining faces of thin coal seams, studying the use of digital twin technology and risk early warning models to solve beyond-visual-range visual production operations and safety monitoring control of the working face has important theoretical and practical value. This paper studied the construction methods of the digital twin system for fully mechanized mining faces in thin coal seams and discussed the construction of high-precision three-dimensional models, the real-time collection and processing conversion of …

Trajectory-Aware Dynamic Offloading For High-Mobility Vehicular Edge Systems, Haoran Xu, Zhiqin Huang, Youwu Hu, Zheyi Chen

Journal of System Simulation

Abstract: To address the problems of service interruptions, task failures, and resource waste caused by high mobility of intelligent vehicles (IVs) in vehicular edge computing (VEC), this paper proposes a trajectory-aware dynamic offloading (TADO) framework for high-mobility vehicular edge systems. A lightweight T-pattern trajectory-aware algorithm is designed to efficiently predict the next-hop road side unit (RSU) by mining spatio-temporal patterns from historical trajectories of vehicles, offering a forward-looking reference for offloading decisions. A joint optimization model is constructed, and a service interruption risk factor driven by trajectory prediction is introduced. An improved DRL method is developed. It takes the predicted …

Multi-Auv Pursuit Algorithm With Phased Guidance Based On Maddpg, Sen Zhang, Sihang Shen, Xiaojie Sun, Jingping Shao, Shuaiqiang Guo, Yingjie Deng

Journal of System Simulation

Abstract: To address the problems such as low exploration and sampling efficiency and sparse rewards in the early training stage under the complex target pursuit environment of multiple AUVs based on MADDPG, a phased-guidance curriculum MADDPG (PGC-MADDPG) algorithm was proposed. The pursuit task was divided into two phases, i.e., target tracking and encircling, through curriculum learning. In the target tracking phase, an experience strategy based on the APF method was introduced as a guidance item to provide prior knowledge of the target direction and accelerate the AUV training speed. After entering the encircling phase, the APF guidance item was removed, …

Study On Trajectory Optimization Of Spray Painting Robot Based On Improved Sparrow Search Algorithm, Jie Yang, Zhenkai Xiong, Longyan Wang

Journal of System Simulation

Abstract: To address the challenges posed by the complex working environment to the trajectory planning of the spray painting robot, a multi-strategy integrated sparrow search algorithm (MISSA) was proposed with the dual optimization objectives of time efficiency and motion smoothness. The 3-5-3 polynomial interpolation method was adopted to design the trajectory curve, aiming to ensure continuous angular displacement, angular velocity, and angular acceleration throughout the process. A multidimensional optimization problem with joint constraints was constructed using the total trajectory duration as the performance index. MISSA, which integrates refraction reverse learning, sine-cosine adaptive adjustment, and Cauchy mutation, was utilized to optimize …

Research On Multi-Objective Hybrid Flow-Shop Scheduling With Order Splitting, Junjie Yu, Weixi Ji, Chen Chen, Jingyu Lu, Chaoyang Zhang

Journal of System Simulation

Abstract: To address the order splitting multi-objective hybrid flow-shop scheduling problem (OSMOHFSP), a dual-objective optimization model was formulated with the objectives of minimizing makespan and total order tardiness. Fixed sub-batch specification constraints were incorporated to reflect common splitting limitations in actual production. A sub-batch generation strategy based on a candidate set of sub-batch specifications was designed to efficiently filter feasible splitting combinations, effectively reducing the search space dimensionality and computational complexity. A hybrid multi-objective metaheuristic algorithm integrating NSGA-II and SA was proposed. The global search capability was enhanced by constructing two crossover and four mutation operators. An improved SA embedding …

Ultra-Short-Term Photovoltaic Power Prediction Method Based On Spatio-Temporal Feature Enhancement Of Ground-Based Cloud Images, Zhiwei Kou, Fengyue Xin, Xiaoming Cui, Yu Yin, Feifan Li, Yongsheng Qi

Journal of System Simulation

Abstract: In view of the time lag in ground-based cloud image acquisition and the insufficient accuracy of existing photovoltaic power prediction models, an ultra-short-term photovoltaic power prediction method named spatial-temporal feature enhancement-ground-based cloud image-improved LSTM (STFE-GCI-ILSTM), which is based on spatio-temporal feature enhancement-ground-based cloud images-improved long short-term memory (LSTM) network was proposed. A ground-based cloud image prediction model with multi-scale spatio-temporal feature enhancement (STFE-GCI) was constructed. By spatial feature enhancement, temporal feature enhancement, and multi-scale feature fusion technologies, the deep spatio-temporal features of historical cloud image sequences were extracted to generate future predicted cloud image sequences, thereby eliminating the time …

Simulation Platform Based On Soc-Fpga Clusters For Cxl-Ethernet Heterogeneous Interconnection, Xu Zhang, Ke Liu, Mingyu Chen

Journal of System Simulation

Abstract: To address the difficulty of balancing simulation speed and topology flexibility when simulating large-scale, CXL-Ethernet heterogeneous interconnect datacenter scenarios with existing CXL simulation platforms, this paper presents CNetSim, a semi-physical simulation platform based on an SoC-FPGA cluster. The platform employs SoC-FPGA hardware to emulate endpoint nodes, leveraging their real processors and memory devices as well as FPGA-implemented CXL. mem protocol to support high-speed execution of standard Linux systems and real distributed applications. Meanwhile, it utilizes the flexible topology configuration capability of software simulation to implement CXL switched networks and inter-rack Ethernet interconnects based on DPDK on simulation servers. Simulation …

Optimization Of Convective Heat Transfer Parameters For Spindles Based On Finite Element Thermal Analysis, Jiali Zhang, Haiping Liu, Qinsheng Jiang, Sina Dang

Journal of System Simulation

Abstract: In view of the low simulation accuracy of existing finite element models for thermal characteristics of spindles caused by ignoring the influences of geometric characteristics of convective surfaces and fluid flow patterns and often adopting constant temperature loading in the setting of convective heat transfer boundary conditions, this paper proposed an optimization method for convective heat transfer parameters of spindles based on finite element thermal analysis. Combined with the geometric shapes and spatial positions of various convective surfaces of the spindle system, the calculation criterion of the convective heat transfer coefficient was determined through dimensional analysis according to the …

Simulation And Optimization Of Task Offloading In Mine Edge Computing For Low-Concurrency Devices, Zhaolu Guo, Qianhui Liu

Journal of System Simulation

Abstract: With the advancement of mine intelligence, task offloading technology has become a core technology of mine edge computing (MEC). For edge computing systems deployed with mine low-concurrency devices (MLCD), a discrete-event simulation model for MEC task offloading oriented to MLCD was constructed to solve the collaborative optimization problem of task processing latency and load balancing of mine edge servers (MES). By dynamically simulating the queuing, transmission, and computation processes of tasks, the objective of minimizing task processing latency was achieved. An improved evolutionary algorithm, ISBT-EA, was proposed, which integrated a task-characteristic-driven initialization strategy and local search operators, enhancing the …

Prediction Of Industrial Concentration Parameters Based On Caudformer Model, Kaipeng Xu, Yan Wang, Xin Zhang, Yang Liu, Zhenzhong Wang, Xiang Liu, Zhicheng Ji

Journal of System Simulation

Abstract: To solve the problems in the industrial concentration process of traditional Chinese medicine that traditional prediction methods are difficult to deal with complex characteristics such as nonlinearity, high-dimensional coupling, and highly skewed distribution and that existing deep learning models insufficiently consider causal relationships among variables, ignore frequency-domain periodic characteristics, and lack long-range dependency modeling capabilities, an improved multivariate time series prediction model CauDformer was proposed in this paper. Based on the iTransformer architecture, a causal multi-head self-attention mechanism (C-MHSA) was introduced to compulsorily constrain the dependency direction among variables by utilizing a lower triangular causal mask, guiding the model …

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap, Christoph Treude

Research Collection School Of Computing and Information Systems

AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools.In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development …

Operationalizing Ethics For Ai Agents: How Developers Encode Values Into Repository Context Files, Christoph Treude, Sebastian Baltes, Marc Cheong

Research Collection School Of Computing and Information Systems

As AI coding agents become embedded in software development workflows, developers are beginning to operationalize ethical principles by encoding behavioral rules into repository-level context files for AI agents, such as AGENTS.md files. Rather than examining the ethics of AI agents in the abstract, this vision paper investigates how ethics and values are already being translated for AI agents into actionable instructions that shape agent behavior. Through a preliminary investigation, we find that developers are already embedding guidance related to fairness, accessibility, sustainability, tone, and privacy. These artifacts function as a developer-authored governance layer, translating abstract principles into situated, natural-language directives …

A Dataset Of Agentic Ai Coding Tool Configurations, Matthias Galster, Seyedmoein Mohsenimofidi, Levi Böhme, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Agentic AI coding tools such as Claude Code and OpenAI Codex execute multi-step coding tasks with limited human oversight. To steer these tools, developers create repository-level configuration artifacts (e.g., Markdown files) for configuration mechanisms such as Context Files, Skills, Rules, and Hooks. There is no curated dataset yet that captures these configurations at scale. This dataset, collected from open-source GitHub repositories, fills that gap. We selected 40,585 actively maintained repositories through metadata filtering, classified them using GPT-5.2 to identify 36,710 as belonging to engineered software projects, and systematically detected configuration artifacts in these repositories. The dataset covers 4,738 repositories across …

Spatiotemporal Sycophancy: Negation-Based Gaslighting In Video Large Language Models, Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational interaction remains largely underexplored. In this paper, we identify spatiotemporal sycophancy, a failure mode in which Vid-LLMs retract initially correct, visually grounded judgments and conform to misleading user feedback under negation-based gaslighting. Rather than merely changing their answers, the models often fabricate unsupported temporal or spatial explanations to justify incorrect revisions. To systematically investigate this phenomenon, we propose a negation-based gaslighting evaluation framework and introduce GasVideo-1000, a curated benchmark designed to probe spatiotemporal sycophancy with clear visual grounding and temporal reasoning requirements. …

Oscbench: Benchmarking Object State Change In Text-To-Video Generation, Xianjing Han, Bin Zhu, Shiqi Hu, Franklin Mingzhe Li, Patrick Carrington, Roger Zimmermann, Jingjing Chen

Research Collection School Of Computing and Information Systems

Text-to-video (T2V) generation models have made rapid progress in producing visually high-quality and temporally coherent videos. However, existing benchmarks primarily focus on perceptual quality, text–video alignment, or physical plausibility, leaving a critical aspect of action understanding largely unexplored: object state change (OSC) explicitly specified in the text prompt. OSC refers to the transformation of an object’s state induced by an action, such as peeling a potato or slicing a lemon. In this paper, we introduce OSCBench, a benchmark specifically designed to assess OSC performance in T2V models. OSCBench is constructed from instructional cooking data and systematically organizes action–object interactions into …

Tranx-Adapter: Bridging Artifacts And Semantics Within Mllms For Robust Ai-Generated Image Detection, Wenbin Wang, Yuge Huang, Jianqing Xu, Yue Yu, Jiangtao Yan, Shouhong Ding, Pan Zhou, Yong Luo

Research Collection School Of Computing and Information Systems

Rapid advances in AI-generated image (AIGI) technology enable highly realistic synthesis, threatening public information integrity and security. Recent studies have demonstrated that incorporating texture-level artifact features alongside semantic features into multimodal large language models (MLLMs) can enhance their AIGI detection capability. However, our preliminary analyses reveal that artifact features exhibit high intra-feature similarity, leading to an almost uniform attention map after the softmax operation. This phenomenon causes attention dilution, thereby hindering effective fusion between semantic and artifact features. To overcome this limitation, we propose a lightweight fusion adapter, TranX-Adapter, which integrates a Task-aware Optimal-Transport Fusion that leverages the Jensen-Shannon divergence …

Rendering Data Unlearnable By Exploiting Llm Alignment Mechanisms, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. In this work, we address the problem of data protection against unwanted model learning in a realistic blackbox setting. We propose Disclaimer Injection, a novel data-level defence that renders text unlearnable to LLMs. Rather than relying on model-side controls or explicit data removal, our approach exploits the models’ own alignment mechanisms: injecting carefully designed alignment-triggers to prevent effective learning. Through layer-wise analysis, we find that finetuning on such protected data induces persistent activation …

Smart Atm, Majid Hakeem

Systems Manuals - 2026

The Smart ATM is a new system that goal is to create a new experience for ATMs users by creating a different way of using the ATM. It would protect users from germs and bacteria. Instead of using buttons and touch screens, the main input for this system will be the motion sensor. To specify, the system uses Microsoft Kinect, which is the motion sensor for the Xbox One.

This document is a user guide, which is intended to give assistance to users for using the Smart ATM. This document contains an application overview, list of user requirements, the design …

Solving Rubik's Cube By Using Artificial Intelligence, Polat Coban, Seth Reed

Systems Manuals - 2026

This document will detail a proposal to build a Rubik’s cube simulator, and a Rubik’s cube solver. It is broken into several sections which in turn are broken into subsections.

There Is No Free Benchmark: An Institutional View Of Legal Ai Benchmarking, Neel Guha, Andy K. Zhang, Christine Tsang, Christopher D. Manning, Julian Nyarko, Daniel E. Ho

Faculty Scholarship

Despite substantial excitement around the use of AI in law, little information exists on the performance and associated risks of the domain’s widely marketed tools. Recent work, for instance, has demonstrated the significant potential for “hallucinations” — wherein models make up facts, law, and precedent — leading Chief Justice Roberts to spotlight this risk in his annual report on the judiciary. We argue that there is a need for public AI benchmarking in law. First, relative to other AI application domains, the legal AI ecosystem lacks legibility — there is little information about the design and performance of many commercial …

Advancing Social Media Analytics And Personalized Generation Via Transfer Learning, Discourse-Aware Modeling, And Collaborative Modeling, Gibson Nkhata

Graduate Theses and Dissertations

Social media platforms have become central to information exchange, shaping public opinion across social, political, and economic domains. However, the massive volume of user-generated content, combined with its informal, nuanced, and often noisy nature, presents significant challenges for automated analysis and generation. Tasks such as stance detection, rumor verification, and personalized content generation are further complicated by sarcasm, evolving discourse structures, and diverse user preferences. Addressing these challenges requires models that can effectively leverage linguistic nuance, conversational dynamics, and collaborative user signals. Transfer learning has emerged as a powerful paradigm for improving performance in low-resource and complex language understanding tasks. …

Improving Urban Search And Rescue Team Coordination Through Adaptive Context Awareness, Daniel Reyes Duran

Doctoral Dissertations and Master's Theses

Modern multi-agent Urban Search and Rescue (USAR) operations heavily rely on mobile geospatial Common Operating Pictures (COPs) to maintain team coordination and Situational Awareness (SA). However, the proliferation of high-frequency sensor telemetry at the tactical edge has introduced a data saturation paradox challenge: while information theoretically drives informed decision-making, unmanaged data surges induce increased operator cognitive overload and alert fatigue on mobile End-User Devices (EUDs), while downstream data-broadcasting models inherently strain edge processing and viewport environments.

To resolve these constraints, this dissertation presents a context-aware Value of Information (VoI) data-management framework integrated directly with a custom, event-driven Android Team Awareness …

Improved Pbs Algorithm For Multi-Agent Path Planning Based On Conflict Guidance And Punishment Mechanism, Jinbao Zhang, Jianlin Mao, Chengze Qian, Guimi Sun, Kaixin Tong

Journal of System Simulation

Abstract: To address the bottleneck in which the priority-based search (priority-based search, PBS) algorithm for multi-agent path planning easily falls into conflict loops and generates invalid node expansions in complex scenarios, an improved algorithm based on conflict guidance and a punishment mechanism (improved PBS multi-agent path finding algorithm based on conflict guidance and punishment mechanism, CGP-PBS) was proposed. A conflict-guided node expansion mechanism was constructed; in high-level search, it comprehensively evaluated path cost and the number of conflicts, preferentially expanded child nodes with high potential for conflict resolution, and delayed the expansion of high-conflict nodes, thereby effectively compressing the search …

Impartial Intelligence? Evidence Of Country-Label Sensitivity In Ai Financial Analysis, Fabio Motoki, Jedson Pinto

School of Accountancy Faculty Publications

This study examines whether large language models exhibit systematic country-contingent differential treatment in financial fraud detection. Analyzing 30,000 synthetic transactions with identical statistical properties across three country attributions (United States, Great Britain, and China), we find LLMs assign significantly higher fraud probabilities to Chinese-attributed transactions (36.2%) compared to Western countries (≈30–31%), resulting in accuracy disparities of 67% versus 74%. The gap remains stable across five independent experimental replications and persists when using Chinese language prompts, ruling out linguistic effects. Bias mitigation strategies, such as requiring explanations or explicit country neutrality instructions, reduce but fail to eliminate these disparities. Testing across …

Operational Agency: A Permeable Legal Fiction For Tracing Culpability In Ai Systems, Anirban Mukherjee, Hannah H. Chang

Research Collection Lee Kong Chian School Of Business

Modern artificial intelligence (AI) systems act with a high degree of independence yet lack legal personhood—a paradox that fractures doctrines grounded in human-centric notions of mens rea and actus reus. This Article introduces Operational Agency (OA)—a permeable legal fiction structured as an ex post evidentiary framework—and Operational Agency Graph (OAG)—a tool for mapping causal interactions among human actors, organizations, and AI systems. OA evaluates an AI’s observable operational characteristics: its goal-directedness (as a proxy for intent), predictive processing (as a proxy for foresight), and safety architecture (as a proxy for standard of care). OAG operationalizes that analysis by embedding these …