Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by mental abnormalities and motor dysfunction. Its early classification and prediction of clinical scores have been major concerns for researchers. Currently, multi-view data learning has become an essential research area due to the capacity of multiple views to provide complementary insights from various perspectives. However, the discontinuous distribution, data missing complexity, small sample size, and redundant features in multi-view datasets pose a substantial obstacle, and most existing multi-view learning methods are unable to handle these challenges effectively. In this study, we propose a novel incomplete multi-view data learning framework (IMVDL) via dynamic embedding and partiall2,1norm constraints for PD diagnosis. Specifically, multi-view dynamic embedding can adapt to any view missing scene, thereby linearly/nonlinearly mapping incomplete multi-view data to low-dimensional manifold spaces and generating complete multi-view data representations. The partiall2,1norm constraint can ignore larger feature weight values and performl2,1norm sparse on the remaining weights, thereby avoiding the sparse bias problem caused by larger weight values. An efficient iterative algorithm is derived to find the optimal solution of the IMVDL method. We conduct extensive experiments using multi-modal neuroimage data from the Parkinson's Progression Markers Initiative (PPMI) database. The results demonstrate that the IMVDL method is superior to other comparative methods. The source code for IMVDL is available at https://github.com/a610lab/IMVDL/.

Clinical Decision Support Systems (CDSS) have become an indispensable element in modern healthcare delivery by providing clinicians with evidence-based recommendations that enhance diagnostic accuracy, facilitate treatment planning, and ultimately enhance patient outcomes. Traditional CDSS implementations typically depend on manually curated clinical practice guidelines and domain ontologies, which are encoded in Structured Query Language (SQL) or rule-based formats. While these approaches ensure transparency and traceability, they suffer from two major limitations: (i) the labor-intensive process of converting narrative guidelines into machine- executable knowledge, and (ii) the delay between publication of new clinical evidence and its integration into the system. Moreover, as the volume of biomedical literature grows exponentially, these bottlenecks compromise the scalability and timeliness of CDSS deployments in rapidly evolving clinical domains. Recent advances in large language models (LLMs) and retrieval- augmented generation (RAG) have opened a compelling approach to alleviating these limitations by automating the extraction, validation, and integration of novel clinical evidence. However, indiscriminate application of LLMs to unstructured clinical guideline texts may yield hallucinations, inconsistencies, and unsupported assertions that jeopardize patient safety. To mitigate these hazards, it is imperative to develop a robust provenance- and- alignment framework that systematically evaluates, organizes, and integrates new insights with preexisting guideline repositories.

Clinical Decision Support Systems (CDSS) play a crucial role in medical decision-making by integrating medical knowledge with patient data; however, existing systems face limitations in terms of knowledge representation, interoperability, scalability, and maintenance costs. This paper proposes a generalized modeling approach based on the Resource Description Framework (RDF), designed to enhance knowledge sharing and system interoperability through standardized knowledge representation. The effectiveness of this approach is validated through a case study on asthma management, demonstrating a universal framework for the conceptual design of CDSS. Furthermore, the paper explores the potential of utilizing large models to enable the automated modeling of guideline knowledge in future work, offering a strategic direction for the continued development of intelligent CDSS.

Deep learning-based multi-view stereo (MVS) methods enable dense point cloud reconstruction in texture-rich areas. However, existing methods incur significant computational costs to capture pixel dependencies for complete reconstruction in low-texture regions. Additionally, discrete depth layers in occluded environments hinder the cost volume’s ability to model object information effectively. To address these issues, we propose a spatial-aware multi-view stereo network with attention cost volume, termed SA-MVSNet. The network introduces the pixel-driven spatial interaction (PDSI) module, which integrates the hierarchical spatial location enhancement mechanism (HSLE) and the spatial context aggregation mechanism (SCA). Leveraging an efficient parallel architecture, the PDSI module captures pixel-level spatial dependencies with the HSLE and strengthens global contextual information through the SCA. This design improves the network’s ability to represent features in low-texture regions while maintaining high inference efficiency. Furthermore, SA-MVSNet incorporates an attention weight generation branch that refines the cost volume by aggregating multi-scale depth cues, effectively mitigating the impact of occlusion. Experiments on the DTU dataset and the Tanks and Temples dataset show that our method outperforms other learning-based methods, achieving superior performance and strong generalization ability.

There are two main barriers to using large language models (LLMs) in clinical reasoning. Firstly, while LLMs exhibit significant promise in Natural Language Processing (NLP) tasks, their performance in complex reasoning and planning falls short of expectations. Secondly, LLMs use uninterpretable methods to make clinical decisions that are fundamentally different from the clinician’s cognitive processes. This leads to user distrust. In this paper, we present a multi-agent framework called ArgMed-Agents, which aims to enable LLM-based agents to make explainable clinical decision reasoning through interaction. ArgMed-Agents performs self-argumentation iterations via Argumentation Scheme for Clinical Discussion (a reasoning mechanism for modeling cognitive processes in clinical reasoning), and then constructs the argumentation process as a directed graph representing conflicting relationships. Ultimately, use symbolic solver to identify a series of rational and coherent arguments to support decision. We construct a formal model of ArgMed-Agents and present conjectures for theoretical guarantees. ArgMed-Agents enables LLMs to mimic the process of clinical argumentative reasoning by generating explanations of reasoning in a self-directed manner. The setup experiments show that ArgMed-Agents not only improves accuracy in complex clinical decision reasoning problems compared to other prompt methods, but more importantly, it provides users with decision explanations that increase their confidence.

Clinical decision support is aimed at delivering the best evidence available encapsulated in practice guidelines. The current challenges in reaching this goal include keeping guidelines up-to-date, linking them to address multi-morbidity, and flexible customization to fit patient preferences. Although a variety of solutions have been proposed to address these challenges, a comprehensive approach for their systematic integration remains absent. We propose PICOAS, a knowledge linking model composed of three modes, to establish the relationship between knowledge sources of guidelines, medical literature, and patient reviews. An LLM-Agent architecture is developed, which is capable of understanding and providing decision support informed by the knowledge link model. We demonstrate the feasibility and effectiveness of our approach using a study of breast cancer. An experiment was conducted for evaluating its capabilities in addressing the three challenges.

The continuous development in the medical field faces multiple challenges in managing a large amount of literature and research results using traditional ontology and knowledge graph construction methods. These challenges include high labor costs, limited coverage, and poor dynamism of traditional ontology and knowledge graph construction methods. Large language models (LLMs) can solve various natural language processing tasks and can understand and generate human-like natural language, which makes automated construction of ontology expansion and knowledge graphs (KGs) possible. This paper proposes an ontology expansion method based on LLMs, using LLMs to formulate competency questions (CQs) to extend the initial ontology, and then constructing the knowledge graph based on the extended ontology. We demonstrated the feasibility of the method by creating a knowledge graph for breast cancer treatment. The combination of LLMs-based medical ontology and knowledge graph can achieve more efficient medical knowledge management and application, promoting the informatization and intelligent development of the medical field.

Clinical decision support systems (CDSS) assist physicians in making medical decisions by relying on fixed rules and guidelines. However, these systems often lack sufficient consideration of individual differences and struggle to adapt to complex clinical scenarios. This paper proposes a novel CDSS based on an extended argumentation framework, integrating patient data and individual preferences to provide more personalized treatment recommendations. By employing critical questions and argument schemes from computational argumentation, and utilizing multiple data sources—including official medical guidelines, comprehensive drug information databases, and patient reviews—we enhance the system's decision-making capability. We developed a prototype system to validate this approach and its outcomes.

Independently developed clinical guidelines present a systematic challenge in managing patients with multi-morbidity in a consistent and integrated manner. Existing approaches mainly focus on combining multiple guidelines and lack approaches that combine with additional medical resources. The correlations and conflicts between treatment plans in the management of multi-morbidity are well-documented in medical literature but are less explored in the Clinical Decision Support line of research. In this paper, we propose a literature-based guideline interlinking method to address these challenges through the integration of clinical guidelines and the harmonization of conflicting recommendations, thereby providing a more holistic and efficient way to manage patients with multi-morbidity conditions. This method employs an ontology model and knowledge graph technology to represent and analyze the complexity and interrelations of diseases, with the aim of transcending the limitations of traditional single disease guidelines and providing a holistic and integrated framework for multi-morbidity management. The objective is to construct a multi-morbidity knowledge graph by correlating medical literature with clinical guidelines and to provide optimal decision support for patients with multi-morbidity complications in a clinical decision support system (CDSS).

Sequential recommendations aim to capture user preferences through user historical behavior interaction data in order to make accurate recommendations. Recently, graph convo-lutional networks have achieved remarkable results in the field of sequential recommendations. However, most of them only utilize the original interactive items, ignoring the influence of time and noise information in their interaction sequences. In particular, some of them may pay attention to the time domain information, they also neglect the frequency domain information which can also be utilized to analyze user interests. To address these limitations, considering both time domain and frequency domain perspectives, we propose a novel model, named DFT-SR in short. First, our approach incorporates a timestamp embedding based on a window function to capture the temporal representations of user interaction sequences. Afterward, we replace the self-attention layer in the encoder with a learnable filter module, which comprises two components such as high-frequency and low-frequency functions, utilizing different neural network layers in the frequency domain to hierarchically cover specific frequency ranges. Experimental results demonstrate the superiority of DFT-SR model over other sequence models. The incorporation of frequency-aware filtering and timestamp embedding enhances the performance of sequential recommendations, increasing the HR@20 from 23.71% to 35.70%, and increasing the NDCG@20 from 26.54% to 51.28%.

Online hospital appointment systems provide patients the flexibility to select their preferred physicians. However, these systems face challenges due to sparse datasets in doctor-patient interaction scenarios, which compromises the precision of their doctor recommendation features. To improve this, we propose a novel doctor recommendation model called the Contextual Semantic Information and Attribute-based Graph Convolutional Network (CSIA-GCN). This model leverages graph structures of doctor and patient information, along with their interaction attributes, using a Graph Convolutional Network (GCN) for efficient message propagation and aggregation. The proposed CSIA-GCN model uniquely integrates high-order correlation features with internal characteristics of doctors and patients. It also considers their specific and dynamic interaction information, aiding in the accurate learning of vector representations for each node in the graph. This process is further enhanced by incorporating Bio-BERT, a pre-trained large language model, to assimilate comprehensive prior knowledge into CSIA-GCN model, significantly boosting its performance. In our empirical study, we analyzed 8,000 textual interactions from 12 departments within an online hospital system. The experimental results demonstrate that CSIA-GCN achieves significant improvements over baseline models, with a 16.32% increase in Precision, 5.40% in Recall, and 15.89% in AUC. These results demonstrate CSIA-GCN's enhanced capability in providing precise doctor recommendations, effectively addressing the personalized requirements and preferences of patients.

Hourly Himawari-8 (H8) Advanced Himawari Imager Level 3 Ocean Color (L3 OC) products have been recently released; however, a thorough evaluation and uncertainty analysis of L3 OC data spanning full disk, as well as applicability to studies on photosynthetically active radiation (PAR) have not yet been conducted. This study evaluates the accuracy of L3 OC products, including normalized water-leaving radiance (Lwn) at 470, 510, and 640 nm, Chlorophyll-a concentration (Chlor-a), aerosol optical thickness (AOT) at 510 nm, and Ångström exponent (AE), by comparing them to ground-based measurements obtained from Ocean Color Component of the AErosol RObotic NETwork (AERONET-OC). Our results demonstrate a general agreement with the ground-based measurements, especially Lwn510. Chlor-a and AOT510 also demonstrate an overall consistency, whereas AE shows a larger discrepancy. Uncertainty analysis shows that Lwn remained accurate under different conditions, although increased uncertainties were observed in turbid water and periods of severe air pollution. Spatial analysis revealed that the distribution of L3 OC and Aqua-MODIS L2 OC products were strongly correlated. Lwn and Chlor-a in the Yellow and Bohai Seas exhibit seasonal variations, with both parameters decreasing in summer and increasing in winter. The impact of aerosols and Chlor-a on PAR calculations was investigated by developing a sophisticated algorithm for estimating PAR under clear-sky conditions using a coupled radiative transfer (RT) model. An analysis of the May 2021 dust event in the Southern Yellow Sea, which exhibited an AOT of 0.82, showed a notable increase in Chlor-a levels —one to two days later, while the average daytime PAR forcing was −42.469 W/m2 under clear-sky conditions.

In today’s complex healthcare environment, shared decision making is increasingly being emphasized as an ideal model for healthcare decision making. The interaction of a set of arguments can represent the beliefs of agents, and the process of reaching consensus in a multi-agent system can be facilitated by argument merging. However, existing argumentation models are insufficient to support the application of shared decision making in healthcare. To fill this gap, this paper, we construct a cognitive representation model of agents supporting the computational task of argumentation and propose a novel interactive argumentation framework merging method based on it as a solution for shared decision support in healthcare environment. We used the Lightweight Social Calculus (LSC) to describe and standardize our interaction model. The usability of the method is demonstrated by definitional proofs and case study.

Clinical decision support systems (CDSS) are knowledge-driven tools that have the potential to improve diagnosis and decision-making for clinicians. However, no clinical decision model is infallible. Therefore, CDSS should enable clinicians to validate each decision recommendation and reject any incorrect ones while selecting the right ones. Although previous studies have aimed to explain the arguments behind each decision candidate, these arguments are often drawn from a knowledge base that is modeled from clinical guidelines and may not always be up-to-date with the latest medical research.To address this issue, we propose a different approach that provides accurate and relevant scientific evidence from the biomedical literature. Our proposed knowledge extension engine uses the BioBERT tool to efficiently identify clinical trial reports based on a range of clinical questions. This feature enables the system to identify clinical trials that are relevant to diagnostic or treatment hypotheses. Furthermore, the knowledge extension engine can extract essential information from clinical trial summaries, such as patient populations, interventions, and outcomes. This capability allows clinicians to quickly identify matches between clinical questions and clinical trials, and understand key elements of clinical trials without extensive reading.Additionally, we have designed a knowledge modeling approach that facilitates the rapid updating of the knowledge base, enabling domain experts to quickly update the knowledge base based on the latest literature provided by the system. By following this approach, we aim to provide clinicians with up-to-date and accurate information to improve the effectiveness of CDSS in clinical decision-making.

It is important to incorporate patient values in healthcare decision-making systems so that the system gives patient-centered decision solutions in the decision-making process. The extant studies show less integration of both. Patient values can be summarized in several medical community review data. The existing knowledge of values is distilled and then combined with existing knowledge of clinical guidelines and rehabilitation. The patient's personalized information is obtained during the consultation process and matched with the general medical data, resulting in personalized recommendations that incorporate the patient's values. An ontology model is constructed based on five domains: population features, medical treatments, personal values, side effects and rehabilitation. Clinical guideline data, rehabilitation data and patient review data are mapped and linked through the ontology to obtain a general medical knowledge graph. The patient's voice, emotion and gesture information during the consultation process are analyzed and recognized as weighted entities, which are passed into the general medical knowledge graph to form a personalized multi-modal knowledge graph of the patient. A prototype system in a man with breast cancer was designed and implemented to demonstrate the method's feasibility.

The sequential recommendation aims to capture the user's dynamic interest characteristics according to the historical interaction sequences of users, to provide users with much more personalized recommendations. Although the popular transformer-based sequential recommendation models can effectively capture items dependencies between sequences, they also have some problems such as the noisy information and data sparseness in reality. Therefore, this paper proposes a novel sequential recommendation model based on Filter and Model Augmentation Strategies, named FMAS in short. In particular, during the embedding process, the proposed FMAS utilizes the Fast Fourier Transform (FFT) to convert the original user sequence embedding into the style of the frequency domain, which can be filtered the noise information via a learnable filter without destroying the sequence correlations; Moreover, to alleviate the problem of data sparsity, the FMAS proposes two model augmentation strategies, named Layer Drop and neural masking, to construct contrastive learning views. Experimental results demonstrate that the FMAS model outperforms many sequential recommendation approaches.

Aspect-Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity of certain aspect words in a sentence.Recently, it is a popular approach to fuse the sentences' syntactic information via the dependency tree into the graph neural network.However, how to efficiently utilize the obtained syntactic information is still a challenging problem of this kind of approach.Therefore, this paper proposes a novel Aspect-level Sentiment Analysis model based on Convolutional network with Dependency Tree, named ASAC-DT in short.First, the attention mechanism is utilized to obtain the attention score of the sentence and the aspect word respectively, to improve the connection of the words related to the aspect word in the sentence.Afterwards, by relying on the syntactic information obtained from the dependency tree, the connections of words that are not related to the aspect words are reduced.Finally, the feature information most relevant to the aspect words in the proposed model is extracted through the graph convolutional neural network and the interactive network.Through extensive experimental baselines the proposed ASAC-DT model shows effectiveness in aspect-level sentiment classification and outperforms baselines in accuracy.

Traditional medical guidance becomes increasingly unsatisfactory, as the care of patients should be centered around not just clinical symptoms but also their values and preferences. A method is proposed, in this paper, to fuse clinical knowledge and patient preferences into an integrated knowledge graph. Objective data was extracted from semi-structured online medical service interfaces, and subjective emotional data from patient review pages. A prototype system was designed and implemented to demonstrate the feasibility of the method. The system can recommend a ranked list of doctors with the best matched clinical background as well as patient preferences. An evaluation was conducted via carrying out a survey of user groups upon the medical guidance options of a human nurse, the “We Doctor” system, and our prototype system.

Recently, most relational extraction models usually mitigate the adverse effects of noise in sentences for the prediction results, utilizing different tools of natural language processing that to capture high-level features in sentences combined.However, these attention mechanisms do not manage to exploit as much as possible the semantic information of certain keywords that have relational expressive information in the sentence.Therefore, this paper proposes a model based on the keyword's attention mechanism, which is a novel attention mechanism based on the keywords of relational expression related.In particular, the proposed attention mechanism utilizes a linear-chain conditional random field that combines entity-pair features, similarity features between entity-pair features, and its hidden vectors to compute each word's marginal distribution defined as the attention weight.Experimental results show that the method can focus on keywords with relational expression semantics in sentences without using sophisticated tools and achieves performance improvements on the SemEval-2010 Task 8 dataset.

Sentence semantic matching is one of critical research in various NLP tasks such as natural language inference, paraphrase identification, and question answering, in which similarity of input sentences has always been a key aspect to determine the semantic relations of sentences.One of the most popular models is to utilize single word granularity to address the semantic similarity.However, it is not appropriate for Chinese sentences semantic matching.This is because there are various meanings following various granularities such as characters or word segmentation in a Chinese sentence.In addition, it is difficult for the sentence semantic matching due to its own short contents and sparse features.Inspired by Siamese Neural Network, an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors, this paper proposes a Multi-Granularity Fusion neural network, which enables preserving semantic features from both the character-granularity and the word-granularity in Chinese sentences.The paper evaluates the proposed architecture on highly competitive benchmark datasets related to sentence matching.Experimental results show that the proposed architecture, which retains both characters and words features of sentences, and achieves state-of-the-art performances for most of the tasks.

Recent years have seen a surge in many deep learning research approaches to predict links in structured network data, however, these approaches seem to falter in its applicability. This paper seeks to propose a new model, named HLPGAM (Heuristics Link Prediction Graph Attention Mechanism), which combines probabilistic heuristics and attention mechanism to learn a more suitable way of predicting links in a given structured-network without relying on sophisticated feature engineering based on the statistical properties of a given node. The paper first aligns graphs and performs graph2Vec conversion using graph convolutions operation then it overcomes entity classification and link prediction limitation via an attention mechanism, i.e. to replace the normalization with data-dependent attention weights. For the entity classification problem, the experimental results have demonstrated that the HLP-GAM model can act as a competitive, end-to-end trainable graph-based encoder. For link prediction, the HLP-GAM model outperformed direct optimization of the factorization model and achieved competitive results on standard link prediction benchmarks. Our model achieves much better performance than other algorithms when he experimented both based on AIFB and AM dataset.

Plan recognition is a ubiquitous task in artificial intelligence and pervasive computing research. The multigoal recognition problem presents a major challenge in the real world of plan recognition. Users often pursue several goals in a concurrent and interleaving manner, where the pursuit of goals may spread over different parts of an activity sequence and may be pursued in parallel. Existing approaches for multigoal problems are probabilistic approaches. They all assume the existence of plan libraries, which require a lot of human efforts in predicting and formalizing plans and may be impractical in many cases. In this paper, we present a novel logic-based approach to solve the multigoal recognition problem efficiently, without the need of plan libraries, using a state-of-the-art heuristic search planner LAMA. In particular, we first propose the formulation of a multigoal recognition problem based on automated planning. Then we present a bilevel probabilistic plan recognition approach that deals with both concurrent and interleaving goals from observed activity sequences. Experimental results over several domains show that our method has great flexibility and scalability.

Increased commodity use of mobile devices has the potential to enable mission-critical monitoring applications. However, these mobile-enabled monitoring applications have to often work in environments where a delay-tolerant network (DTN) is the only feasible communication paradigm. Detection of complex (composite) events is fundamental to monitoring applications. However, the existing plan-based CED techniques are mostly centralized, and hence are inherently unscalable for DTNs. In this paper, we create Comet â" a decentralized plan-based, efficient and scalable CED for DTNs. Comet shares the task of detecting complex events (CEs) among multiple nodes, with each node detecting a part of the CE by aggregating two or more primitive events or sub-CEs. Comet uses a unique h-function to construct cost and delay efficient CED trees. As finding an optimal CED plan requires exponential-time, Comet finds near-optimal detection plans for individual CEs through a novel multi-level push-pull conversion algorithm. Performance results show that Comet reduces cost by up to 89% compared to pushing all primitive events and over 60% compared to a two-level exhaustive search algorithm.

The current peer-to-peer (P2P) content distribution systems are constricted by their simple on-demand content discovery mechanism. The utility of these systems can be greatly enhanced by incorporating two capabilities, namely a mechanism through which peers can register their long term interests with the network so that they can be continuously notified of new data items, and a means for the peers to advertise their contents. Although researchers have proposed a few unstructured overlay-based publish-subscribe systems that provide the above capabilities, most of these systems require intricate indexing and routing schemes, which not only make them highly complex but also render the overlay network less flexible toward transient peers. This paper argues that for many P2P applications, implementing full-fledged publish-subscribe systems is an overkill. For these applications, we study the alternate continuous query paradigm, which is a best-effort service providing the above two capabilities. We present a scalable and effective middleware, called CoQUOS, for supporting continuous queries in unstructured overlay networks. Besides being independent of the overlay topology, CoQUOS preserves the simplicity and flexibility of the unstructured P2P network. Our design of the CoQUOS system is characterized by two novel techniques, namely cluster-resilient random walk algorithm for propagating the queries to various regions of the network and dynamic probability-based query registration scheme to ensure that the registrations are well distributed in the overlay. Further, we also develop effective and efficient schemes for providing resilience to the churn of the P2P network and for ensuring a fair distribution of the notification load among the peers. This paper studies the properties of our algorithms through theoretical analysis. We also report series of experiments evaluating the effectiveness and the costs of the proposed schemes.

The publish-subscribe (pub-sub) paradigm is maturing and integrating into community-oriented collaborative applications. Because of this, pub-sub systems are faced with an event stream that may potentially contain large numbers of redundant and partial messages. Most pub-sub systems view partial and

The utility and the effectiveness of peer-to-peer (P2P) content distribution systems can be greatly enhanced by augmenting their ad-hoc content discovery mechanisms with two capabilities, namely a mechanism to enable the peers to register their queries and receive notifications when corresponding data-items are added to the network and a means for the peers to advertise their new content. While P2P-based publish-sub scribe systems can infuse these capabilities, developing full-fledged publish-subscribe systems on top of unstructured P2P networks requires complex techniques, and it is often an overkill for many P2P applications. For these applications, we study the alternate continuous query paradigm, which is functionally similar to publish-subscribe systems, but provides best-effort notification guarantees. This paper presents CoQUOS - a scalable and lightweight middleware to support continuous queries in unstructured P2P networks. A key strength of the CoQUOS system is that it can be implemented on any unstructured overlay network. Moreover, CoQUOS preserves the simplicity and flexibility of the overlay network. Central to our design of the CoQUOS middleware is a completely decentralized scheme to register a query at different regions of the P2P network. This mechanism includes two novel components, namely cluster resilient random walk algorithm for propagating query to various regions of the network and dynamic probability-based query registration technique for ensuring that the registrations are well distributed. Our experiments show that the proposed techniques are highly effective and their overheads are low.

Many unstructured overlay-based peer-to-peer (P2P) applications require techniques that can effectively send messages to various topological regions of the overlay. While searching in unstructured P2P networks has been widely studied in literature, the problem of diffusing messages to various parts of an arbitrary overlay network has received surprisingly little research attention. In this paper we analyze the message diffusion problem and make two technical contributions towards addressing it. First, we propose a novel message propagation technique called the cluster resilient random walk (CRW). While the CRW technique preserves the overall framework of random walks, at each step of message forwarding, it favors the neighbors that are more likely to send the message deeper into the network. Second, in order to ensure effective message diffusion in networks with small cuts, we introduce a unique message fission technique in which messages are split when they reach peers connecting two or more topological regions of the network. Our experiments show that the proposed technique are very effective in diffusing messages across overlay networks of various topologies.

Cooperation among individual caches has proven to be an effective strategy to improve the scalability and performance of edge cache networks delivering dynamic Web content. To date, research in the area of cooperative edge caching has mainly focused on serving client requests and maintaining freshness of cached documents. However, designing mechanisms to effectively manage the available resources is an important challenge that can have significant impact on the performance of an edge cache network. In this paper we propose a novel data placement scheme, called the utility-based placement scheme, which is not only sensitive to the ongoing cooperation in the edge cache but also takes into account the various costs and benefits of storing a data-item at an individual edge cache. At the heart of proposed scheme is a utility function that quantifies the usefulness of storing a data-item at a particular edge cache. Experiments show that the proposed scheme provides significant performance benefits

The exponential growth of dynamic Web content has posed serious challenges to the scalability of the World Wide Web. While caching on the edge of the Internet has emerged as a popular technique to address these challenges, many of the present-day edge caching systems do not harness the complete benefits of edge computing. Our research efforts in the cooperative edge cache grid project are aimed at utilizing collaboration among edge caches as a means to further enhance the capabilities and the performance of edge cache network. This paper outlines the cooperative EC grid project including its architecture, fundamental concepts, and various techniques that have been designed for supporting low-cost cooperation among the edge caches