UMBC Information Systems Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/51

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APEM — Approximate Performance Evaluation for Multi-Core Computers
(World Scientific Publishing Company, 2019-01) Zhang, Lei; Down, Douglas G.
Mean Value Analysis (MVA) has long been a standard approach for performance analysis of computer systems. While the exact load-dependent MVA algorithm is an efficient technique for computer system performance modeling, it fails to address multi-core computer systems with Dynamic Frequency Scaling (DFS). In addition, the load-dependent MVA algorithm suffers from numerical difficulties under heavy load conditions. The goal of our paper is to find an efficient and robust method which is easy to use in practice and is also accurate for performance prediction for multi-core platforms. The proposed method, called Approximate Performance Evaluation for Multi-core computers (APEM), uses a flow-equivalent performance model designed specifically to address multi-core computer systems and identify the influence on the CPU demand of the effect of DFS. We adopt an approximation technique to estimate resource demands to parametrize MVA algorithms. To validate the application of our method, we investigate three case studies with extended TPC-W benchmark kits, showing that our method achieves better accuracy compared with other commonly used MVA algorithms. We compare the three different performance models, and we also extend our approach to multi-class models.
A Stable Mean Value Analysis Algorithm for Closed Systems with Load-dependent Queues
(ACM, 2017-05-03) Zhang, Lei; Down, Douglas G.
The load-dependent Mean Value Analysis (MVA) algorithm suffers from numerical instability. Different techniques have been adopted to avoid this issue, however, they either have large complexities or restrictive assumptions. In this paper, we introduce a numerically Stable MVA (SMVA) algorithm for product-form networks that allows for load-dependent queues. The SMVA algorithm offers an efficient and accurate approximate solution. We validate SMVA by comparing it to other MVA algorithms in a concrete example, and analyze its errors. We also extend it to a multi-class model.
Self-Attention Policy Optimization for Task Offloading and Resource Allocation in Low-Carbon Agricultural Consumer Electronic Devices
(IEEE, 2025-04-23) Huang, Yi; Zeng, Jisong; Wei, Yanting; Chen, Miaojiang; Xiao, Wenjing; Yang, Yang; Liu, Zhiquan; Farouk, Ahmed; Song, Houbing
In recent years, the widespread use of edge agricultural consumer electronics has greatly contributed to the level of intelligence in agricultural production, bringing higher efficiency and quality. However, offloading all tasks to the cloud incurs significant latency and resource waste, while relying solely on edge computing fails to meet the computational demands of the entire system. To solve the above problems, we introduce the device-edge-cloud (DEC) three-layer architecture, where agri-consumer electronics devices can partially offload tasks to the edge, and the edge can partially offload tasks to the cloud, i.e., agri-consumer electronics can realize device-edge-cloud collaborative computation. Second, we model the joint computation offloading and resource allocation optimization problem as a non-convex optimization and propose a novel Self-Attention Policy Optimization (SAPO) algorithm to solve it. Experiments show that the joint optimization performance of the proposed SAPO exceeds the baseline, and it is suitable for many different models. Compared with fully connected networks, it has better convergence and robustness, with a convergence speed 50% faster than the fully connected networks. The proposed SAPO algorithm has good scalability and adaptability, and has the potential to be extended to smart agricultural computing scenarios with non-convex optimization.
Augmenting Personalized Memory via Practical Multimodal Wearable Sensing in Visual Search and Wayfinding Navigation
(2025-04-28) Ghosh, Indrajeet; Jayarajah, Kasthuri; Waytowich, Nicholas; Roy, Nirmalya
Working memory involves the temporary retention of information over short periods. It is a critical cognitive function that enables humans to perform various online processing tasks, such as dialing a phone number, recalling misplaced items' locations, or navigating through a store. However, inherent limitations in an individual's capacity to retain information often result in forgetting important details during such tasks. Although previous research has successfully utilized wearable and assistive technologies to enhance long-term memory functions (e.g., episodic memory), their application to supporting short-term recall in daily activities remains underexplored. To address this gap, we present Memento, a framework that uses multimodal wearable sensor data to detect significant changes in cognitive state and provide intelligent in situ cues to enhance recall. Through two user studies involving 15 and 25 participants in visual search navigation tasks, we demonstrate that participants receiving visual cues from Memento achieved significantly better route recall, improving approximately 20-23% compared to free recall. Furthermore, Memento reduced cognitive load and review time by 46% while also substantially reducing computation time (3.86 seconds vs. 15.35 seconds), offering an average of 75% effectiveness compared to computer vision-based cue selection approaches.
RDPP-TD: Reputation and Data Privacy-Preserving based Truth Discovery Scheme in Mobile Crowdsensing
(2025-05-07) Wu, Lijian; Xie, Weikun; Tan, Wei; Wang, Tian; Song, Houbing; Liu, Anfeng
Truth discovery (TD) plays an important role in Mobile Crowdsensing (MCS). However, existing TD methods, including privacy-preserving TD approaches, estimate the truth by weighting only the data submitted in the current round, which often results in low data quality. Moreover, there is a lack of effective TD methods that preserve both reputation and data privacy. To address these issues, a Reputation and Data Privacy-Preserving based Truth Discovery (RDPP-TD) scheme is proposed to obtain high-quality data for MCS. The RDPP-TD scheme consists of two key approaches: a Reputation-based Truth Discovery (RTD) approach, which integrates the weight of current-round data with workers' reputation values to estimate the truth, thereby achieving more accurate results, and a Reputation and Data Privacy-Preserving (RDPP) approach, which ensures privacy preservation for sensing data and reputation values. First, the RDPP approach, when seamlessly integrated with RTD, can effectively evaluate the reliability of workers and their sensing data in a privacy-preserving manner. Second, the RDPP scheme supports reputation-based worker recruitment and rewards, ensuring high-quality data collection while incentivizing workers to provide accurate information. Comprehensive theoretical analysis and extensive experiments based on real-world datasets demonstrate that the proposed RDPP-TD scheme provides strong privacy protection and improves data quality by up to 33.3%.
GCN-LG-Trust Model for Attack Detection and Cluster Optimization in Underwater Wireless Sensor Networks
(IEEE, 2025) Jiang, Bin; Feng, Jiacheng; Cui, Xuerong; Luo, Fei; Wang, Huihui Helen; Song, Houbing
The researchers focus on the security issues of underwater wireless sensor networks (UWSNs) due to the specificity of network environment. In recent years, researchers have proposed trust models to assess the reliability of each node in the network. However, nodes in UWSNs are usually managed in clusters. Existing trust model evaluation algorithms and model training methods are not well adapted to the network clustering structure. For this reason, we propose a trust model optimization based on graph convolutional network (GCN) and LightGBM for cluster management in this paper, which we named GCN-LG-Trust. This trust model designs a trust parameter estimation method based on the node activity characteristics of the cluster network. And the network trust evidence is used to train the trust model by GCN-LG deep learning method. The GCN-LG-Trust model is tested in single attack and hybrid attack scenarios. The experimental results show that the proposed method has better results in terms of trust evaluation and accuracy.
A Tri-Fusion Approach for Brainwave Based Biometric Authentication, Using Consumer EEG Devices
(IEEE, 2025-05-20) Adil, Muhammad; Song, Houbing; Jin, Zhanpeng
Brainwave biometrics holds promise for secure authentication, but its real-world adoption faces challenges. Most existing models are tested on small datasets, which include less than 50 participants. In addition, many studies either analyze complete brainwave patterns or concentrate on a single wave associated with rest or sleep, which makes them impractical for authentication. These limitations restrict the real-world use of brainwave biometrics. To address these challenges, this paper proposes a novel brainwave-based authentication model known as "TFG-SVM" utilizing the most important brainwaves to set a step forward for futuristic biometrics using advanced consumer EEG devices. This model uses a Tri-Fusion framework in coordination with a Support Vector Machine (SVM) and k-nearest Neighbors (k-NN) algorithm. i) First, we begin with the Early Fusion in coordination with k-NN algorithms to develop a graph-based representation of local relationships between brainwave signal instances across both spatial and temporal dimensions to facilitate nuanced learning processes within the GNN layers. ii) Next, we apply late fusion to combine outputs from separately processed signal channels at the decision level. iii) We then use weighted fusion to dynamically optimize the contribution of each pathway based on their predictive reliability, which significantly improves the accuracy of the model. iv) Finally, a Support Vector Machine (SVM) classifier refines the decision boundary, effectively distinguishing between authentic users and imposters by maximizing the margin between them. We evaluate our proposed model on different datasets and compared its results with state-of-the-art methods and other algorithms implemented in this work. This comparison shows how effectively our model ensures legitimate user authentication by achieving high accuracy approximately 97.9%, and making it suitable for real world applications.
BACON: A fully explainable AI model with graded logic for decision making problems
(2025-05-22) Bai, Haishi; Dujmovic, Jozo; Wang, Jianwu
As machine learning models and autonomous agents are increasingly deployed in high-stakes, real-world domains such as healthcare, security, finance, and robotics, the need for transparent and trustworthy explanations has become critical. To ensure end-to-end transparency of AI decisions, we need models that are not only accurate but also fully explainable and human-tunable. We introduce BACON, a novel framework for automatically training explainable AI models for decision making problems using graded logic. BACON achieves high predictive accuracy while offering full structural transparency and precise, logic-based symbolic explanations, enabling effective human-AI collaboration and expert-guided refinement. We evaluate BACON with a diverse set of scenarios: classic Boolean approximation, Iris flower classification, house purchasing decisions and breast cancer diagnosis. In each case, BACON provides high-performance models while producing compact, human-verifiable decision logic. These results demonstrate BACON's potential as a practical and principled approach for delivering crisp, trustworthy explainable AI.
Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments
(IEEE, 2025-05-08) Rahimi, Fatema; Sadeghi-Niaraki, Abolghasem; Song, Houbing; Wang, Huihui; Choi, Soo-Mi
This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts.
"If We Had the Option": Infrastructuring for Access to Online Subscription-Based Services in Bangladesh
(ACM, 2025-05-02) Prottoy, Hasan Mahmud; Yao, Yaxing; Hamidi, Foad
As online access to entertainment, education, news, and information increasingly becomes mitigated through subscription-based services, it is important to study how inequities in access impact users in low and middle-income countries (LMICS), and what infrastructuring strategies they employ to overcome obstacles. In this paper, we present findings from an interview study with 22 participants from Bangladesh who use and share online subscription-based services. Due to the lack of availability and limitations using formal international payment methods, procedural difficulties, and infrastructural challenges in Bangladesh, we found an emergence of a distinct informal ecosystem of accessing, sharing, and using subscription-based services. We report a detailed analysis of the adoption, sharing practices, and dynamics of sharing online subscription-based services in Bangladesh, that builds on and extends previous HCI literature on informality, informal marketplace, intermediaries, and media sharing in the Global South. Our findings show how a vibrant and growing user base of subscription-based online services is using creative and sometimes risky ways to gain access to media and information through informal intermediaries and administrators. Finally, we discuss potential directions for practice and policy innovations that include facilitating international payments for online services and platforms and reconsidering their policies and service delivery mechanisms to better support users in the Global South context.
Black-box Adversarial Attack Method for Object Detection Under Multi-View Conditions
(IEEE, 2025) Zhang, Yun; Yu, Zhenhua; Yin, Zheng; Ye, Ou; Cong, Xuya; Song, Houbing
Deep learning-based object detection has become an important application in industrial IoT. However, studies have shown that adversarial attacks may cause object detection to output incorrect detection results. Such vulnerabilities can threaten the robustness of object detection systems and lead to security problems. To address the issue of low attack effectiveness on target detection from different perspectives using the existing adversarial attack methods, this paper proposes an adversarial attack method with multi-view adaptive weight-balancing. First, a multi-view channel is constructed for training, and the target features under different viewpoints are comprehensively considered to enhance the robustness of the attack method. Then, the model is optimized by combining the model shake drop and patch cut-out algorithms during the training process, so that the attack method no longer relies on a single model, thus enhancing its generalization ability. Finally, by dynamically adjusting the weights of each viewpoint, a weight-balancing strategy is constructed, which adaptively adjusts the preference of different perspectives during the training process to enhance the attack effect of the attack method in each viewpoint. To verify the performance of the method, experiments are conducted on multiple benchmarks, specifically the PKU-Reid dataset. Compared with the mainstream methods, the proposed method improves the attack success rate by 3.78% and 19.26% under white-box and black-box conditions, respectively, while reducing the mean average precision of the object detection model by 2.18% and 11.12%, respectively. The experimental results demonstrate that the proposed method effectively enhances attack performance on targets from different viewpoints and exhibits better viewpoint robustness.
Causal Feedback Discovery using Convergence Cross Mapping from Sea Ice Data
(2025-05-13) Nji, Francis Ndikum; Mostafa, Seraj Al Mahmud; Wang, Jianwu
The Arctic region is experiencing accelerated warming, largely driven by complex and nonlinear interactions among time series atmospheric variables such as, sea ice extent, short-wave radiation, temperature, and humidity. These interactions significantly alter sea ice dynamics and atmospheric conditions, leading to increased sea ice loss. This loss further intensifies Arctic amplification and disrupts weather patterns through various feedback mechanisms. Although stochastic methods such as Granger causality, PCMCI, and VarLiNGAM estimate causal interactions among atmospheric variables, they are limited to unidirectional causal relationships and often miss weak causal interactions and feedback loops in nonlinear settings. In this study, we show that Convergent Cross Mapping (CCM) can effectively estimate nonlinear causal coupling, identify weak interactions and causal feedback loops among atmospheric variables. CCM employs state space reconstruction (SSR) which makes it suitable for complex nonlinear dynamic systems. While CCM has been successfully applied to a diverse range of systems, including fisheries and online social networks, its application in climate science is under-explored. Our results show that CCM effectively uncovers strong nonlinear causal feedback loops and weak causal interactions often overlooked by stochastic methods in complex nonlinear dynamic atmospheric systems.
MorphoLayerTrace (MLT): A Modified Automated Radio-Echo Sounding Englacial Layer-tracing Algorithm for Englacial Layer Annotation in Ice Penetrating Radar Data
(ACM, 2025-03-31) Tama, Bayu Adhi; Purushotham, Sanjay; Janeja, Vandana
Modeling ice flow is a critical component of sea level rise projections, yet the datasets available to enhance our understanding of large-scale ice dynamics remain limited. Extracting the englacial layer configuration of the Greenland ice sheet offers valuable insights into the age of the ice, which can inform studies of past snow accumulation, glacier sliding, and provide context for modern glacier change. Although these englacial layers have been extensively surveyed using ice-penetrating radar/radio-echo sounding, the resulting radargram imagery, with fine grained ice layers, is often labeled manually or semi-automatically. This is a laborintensive process that hinders integration into glacier models. In this paper, we propose an improved automatic annotation method, MorphoLayerTrace (MLT), building upon the Automated RadioEcho Sounding Englacial Layer-tracing Package ( ARESELP). Our approach enhances englacial layer tracing by utilizing peak distance thresholds and morphological image processing to select reliable seed points, significantly improving layer continuity and reducing discontinuities. Our technique is designed to operate effectively on both individual radargram frames and multi-frame sets, enabling better performance over extended distances. We evaluate the method using 100 radargram frames collected across North Greenland, demonstrating its ability to trace more layers and maintain greater continuity compared to previous methods. Furthermore, we introduce novel validation metrics, such as the Layer Proportion Score (LPS) and the Multi-Frame Layer Consistency (MF LC) score, which provide a more robust and ground truth-independent evaluation of annotation quality. Our results show that while the method excels in short-range layer detection over the prior layer tracing methods, further refinement is needed for maintaining long-range continuity across multiple frames, offering a promising direction for future development in automatic englacial layer annotation
Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corpora
(2025-05-16) Majurski, Michael; Matuszek, Cynthia
Language Models (LMs) continue to advance, improving response quality and coherence. Given Internet-scale training datasets, LMs have likely encountered much of what users may ask them to generate in some form during their training. A plethora of evaluation benchmarks have been constructed to assess model quality, response appropriateness, and reasoning capabilities. However, the human effort required for benchmark construction is rapidly being outpaced by the size and scope of the models under evaluation. Having humans build a benchmark for every possible domain of interest is impractical. Therefore, we propose a methodology for automating the construction of fact-based synthetic data model evaluations grounded in document populations. This work leverages the same LMs to evaluate domain-specific knowledge automatically, using only grounding documents (e.g., a textbook) as input. This synthetic data benchmarking approach corresponds well with human curated questions producing a Spearman ranking correlation of 0.97 and a benchmark evaluation Pearson accuracy correlation of 0.75. This novel approach supports generating both multiple choice and open-ended synthetic data questions to gain diagnostic insight of LM capability. We apply this methodology to evaluate model performance on two recent arXiv preprints, discovering a surprisingly strong performance from Gemma-3 models on open-ended questions. Code is available at https://github.com/mmajurski/grounded-synth-lm-benchmark
Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling
(AMLDS, 2025-05-08) Mostafa, Seraj Al Mahmud; Wang, Chenxi; Yue, Jia; Hozumi, Yuta; Wang, Jianwu
Object localization in satellite imagery is particularly challenging due to the high variability of objects, low spatial resolution, and interference from noise and dominant features such as clouds and city lights. In this research, we focus on three satellite datasets: upper atmospheric Gravity Waves (GW), mesospheric Bores (Bore), and Ocean Eddies (OE), each presenting its own unique challenges. These challenges include the variability in the scale and appearance of the main object patterns, where the size, shape, and feature extent of objects of interest can differ significantly. To address these challenges, we introduce YOLO-DCAP, a novel enhanced version of YOLOv5 designed to improve object localization in these complex scenarios. YOLO-DCAP incorporates a Multi-scale Dilated Residual Convolution (MDRC) block to capture multi-scale features at scale with varying dilation rates, and an Attention-aided Spatial Pooling (AaSP) module to focus on the global relevant spatial regions, enhancing feature selection. These structural improvements help to better localize objects in satellite imagery. Experimental results demonstrate that YOLO-DCAP significantly outperforms both the YOLO base model and state-of-the-art approaches, achieving an average improvement of 20.95% in mAP50 and 32.23% in IoU over the base model, and 7.35% and 9.84% respectively over state-of-the-art alternatives, consistently across all three satellite datasets. These consistent gains across all three satellite datasets highlight the robustness and generalizability of the proposed approach. Our code is open sourced at https://github.com/AI-4-atmosphere-remote-sensing/satellite-object-localization.
Groundbreaking taxonomy of metaverse characteristics
(Springer, 2025-05-13) Sadeghi-Niaraki, Abolghasem; Rahimi, Fatema; Azlan, Nur Alya Emanuelle Binti; Song, Houbing; Ali, Farman; Choi, Soo-Mi
The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier.
Blue Sky: Expert-in-the-Loop Representation Learning Framework for Audio Anti-Spoofing: Multimodal, Multilingual, Multi-speaker, Multi-attack (4M) Scenarios
(SIAM International Conference on Data Mining, 2025) Khanjani, Zahra; Janeja, Vandana; Mallinson, Christine; Purushotham, Sanjay
Audio spoofing has surged with the rise of generative artificial intelligence, posing a serious threat to online communication. Recent studies have shown promising avenues in detecting spoofed audio specifically those that use human expert knowledge in representation learning, but more work is needed to evaluate performance across various realistic scenarios that tend to pose challenges in spoofed audio detection. In this paper, we introduce a comprehensive framework for expert-in-the-loop representation learning for audio anti-spoofing that is robust enough to address four specific challenging scenarios. Multimodal, Multilingual, Multi-speaker, and Multi-attack (4M). Preliminary results demonstrate the framework’s potential effectiveness in audio anti-spoofing.
QLP-DCS: A Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service for Mobile Crowd Sensing
(IEEE, 2025-04-29) Huang, Yajiang; Guo, Jialin; Yang, Shihao; Liu, Jiali; Liu, Anfeng; Tang, Jianheng; Wang, Tian; Dong, Mianxiong; Song, Houbing
In the service of Mobile Crowd Sensing (MCS), High-quality Data Collection (HDC), Bilateral Location Privacy Preservation (BLPP), and sensing cost are three pivotal issues. It is widely believed that HDC necessitates the recruitment of workers with high Quality of Service (QoS), which is related to the sensing data capabilities of the recruited workers and the worker-task distances. However, submitting high-quality data demands more resources from the workers, incurring higher costs. Meanwhile, BLPP techniques, aiming to conceal the locations of the workers and tasks, may impede the evaluation of the workers' QoS. Therefore, there is still a lack of a low-cost and BLPP high QoS data collection research. Motivated by this, we propose a Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service (QLP-DCS) for MCS. First, we propose a matrix perturbation-based approach to achieve BLPP while preserving the partial order relationship of distances. Subsequently, we employ the Upper Confidence Bound indexes-based reverse auction recruiting workers to balance exploration and exploitation with the low sensing cost. Then, we propose a multi-level truth discovery approach and establish an effective trust verification mechanism. Theoretical analysis and extensive experiments validate the superior performance of our QLP-DCS.
SmartShift: A Secure and Efficient Approach to Smart Contract Migration
(2025-04-12) Hossain, Tahrim; Bappy, Faisal Haque; Zaman, Tarannum Shaila; Hasan, Raiful; Islam, Tariqul
Blockchain and smart contracts have emerged as revolutionary technologies transforming distributed computing. While platform evolution and smart contracts' inherent immutability necessitate migrations both across and within chains, migrating the vast amounts of critical data in these contracts while maintaining data integrity and minimizing operational disruption presents a significant challenge. To address these challenges, we present SmartShift, a framework that enables secure and efficient smart contract migrations through intelligent state partitioning and progressive function activation, preserving operational continuity during transitions. Our comprehensive evaluation demonstrates that SmartShift significantly reduces migration downtime while ensuring robust security, establishing a foundation for efficient and secure smart contract migration systems.
CrossLink: A Decentralized Framework for Secure Cross-Chain Smart Contract Execution
(2025-04-12) Hossain, Tahrim; Bappy, Faisal Haque; Zaman, Tarannum Shaila; Islam, Tariqul
This paper introduces CrossLink, a decentralized framework for secure cross-chain smart contract execution that effectively addresses the inherent limitations of contemporary solutions, which primarily focus on asset transfers and rely on potentially vulnerable centralized intermediaries. Recognizing the escalating demand for seamless interoperability among decentralized applications, CrossLink provides a trustless mechanism for smart contracts across disparate blockchain networks to communicate and interact. At its core, CrossLink utilizes a compact chain for selectively storing authorized contract states and employs a secure inter-chain messaging mechanism to ensure atomic execution and data consistency. By implementing a deposit/collateral fee system and efficient state synchronization, CrossLink enhances security and mitigates vulnerabilities, offering a novel approach to seamless, secure, and decentralized cross-chain interoperability. A formal security analysis further validates CrossLink's robustness against unauthorized modifications and denial-of-service attacks.

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