Computer System Validation (CSV) has evolved into one of the most critical operational and regulatory disciplines within modern digital enterprises. Organizations operating in highly regulated sectors including pharmaceuticals, biotechnology, healthcare, banking, aerospace, food manufacturing, industrial automation, and medical device production increasingly rely upon computerized systems for managing operational workflows, manufacturing environments, electronic records, laboratory infrastructures, process automation, and compliance documentation. Consequently, ensuring the reliability, integrity, traceability, security, and regulatory compliance of computerized systems has become a mandatory organizational requirement. The rapid expansion of cloud computing, Artificial Intelligence (AI), machine learning, Industrial Internet of Things (IIoT), distributed microservices, DevOps ecosystems, real-time analytics platforms, and blockchain infrastructures has significantly transformed the complexity of validation ecosystems. Traditional validation methodologies based upon static documentation, sequential testing approaches, and manual compliance management are increasingly inadequate for supporting continuously evolving enterprise architectures. Modern organizations require intelligent validation frameworks capable of supporting automated deployment pipelines, continuous compliance monitoring, predictive risk analytics, cybersecurity governance, and autonomous validation operations. This survey paper presents a comprehensive and research-oriented analysis of Computer System Validation including validation lifecycle methodologies, regulatory compliance frameworks, data integrity governance, cybersecurity integration, cloud-native validation systems, AI-assisted validation architectures, automated testing ecosystems, and emerging intelligent compliance technologies. The paper critically examines major challenges including scalability limitations, audit trail management, cloud infrastructure validation, AI explainability, cybersecurity threats, distributed architecture complexity, and validation documentation overload. Furthermore, the paper investigates open research issues involving explainable artificial intelligence, blockchain-enabled audit systems, autonomous validation ecosystems, predictive compliance analytics, quantum-resistant security frameworks, and edge-native validation architectures. Several modern tools and technologies including Apache Hadoop, Apache Spark, Apache Kafka, TensorFlow, Kubernetes, Jenkins, Terraform, Docker, and cloud- native validation platforms are comparatively analyzed with respect to architecture, operational capabilities, advantages, limitations, and industrial applications. Finally, future research directions emphasizing intelligent continuous validation, AI-augmented compliance systems, digital twins, decentralized audit infrastructures, and autonomous quality assurance ecosystems are discussed in detai
Elvis Konjoh Selabi, Maurizio Murgia, António Ravara, Emilio Tuosto
The companion paper proposes a formal approach for specifying and implementing decentralised coordination in distributed systems, with a focus on smart contracts. The model captures dynamic roles, data-driven transitions, and external coordination interfaces, enabling high-level reasoning about decentralised workflows. A toolchain supports formal model validation, Solidity code generation (extensible to other smart contract languages), and automated test synthesis. Although targeting blockchain platforms, the methodology is platform-agnostic and may generalise to other service-oriented and distributed architectures. The expressiveness and practicality of the approach are demonstrated through modelling and realising coordination patterns in smart contracts. This artifact accompanies our paper [Elvis Konjoh Selabi et al., 2026]. It provides a toolchain for generating smart contract code from EDAM (Extended Data-Aware Machines) specifications. The artifact includes the complete source code, a Docker image for easy deployment, pre-generated experiment data (generated code, automated tests, and mutation testing results), and reproduction scripts.
Smart contracts are high-stakes software: their immutable, publicly accessible code may govern assets worth millions. This means that even minor defects can have severe consequences. Unit tests often miss edge cases. Although formal verification is the only route to full correctness, it demands substantial time and expertise. Property-based testing bridges this gap by exploring large input spaces and shrinking failures to minimal counterexamples. Used early, it filters defects and prioritises verification effort on code already validated by tests. Although Solidity benefits from a mature testing ecosystem, comparable support for other languages such as Daml remains limited. This dissertation addresses this gap by introducing Hypothesis2Daml, an open-source library that brings property-based testing to Damlby connecting Hypothesis withthe Daml JSON API. Hypothesis2Daml enables developers to specify invariants, pre/postconditions, and stateful workflows over realistic ledger interactions. It provides automatic input generation, shrinking to minimal counterexamples, party isolation per example, and request helpers that keep tests focused on properties rather than HTTP/JSON wiring. The approach is validated on a benchmark of eight contracts and twenty-seven properties that range from simple invariants to multi-step, role-sensitive workflows. All properties held under testing, with runs completing within practical time bounds, demonstrating that property-based testing is feasible and effective for Daml applications. A comparative evaluation situates Hypothesis2Daml among established tools and methods, highlighting strengths in usability and expressiveness, and acknowledging a throughput trade-off due to JSON-API transport overhead. Contributions include the Hypothesis2Daml library and a reusable benchmark with representative properties. Limitations concern transport overhead, ecosystem specificity, and dependence on developer-authored properties and generators. Future work targets automation, performance, andscope expansion. Together, these results establish a practical path for making property-based testing a routine part of building Daml applications.
Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
We present the availability and reproducibility report of the ACM SIGMOD 2025 paper titled ''InTime: Towards Performance Predictability In Byzantine Fault Tolerant Proof-of-Stake Consensus''. Following the instructions provided the authors, we evaluated the artifacts hosted on Github. The reviewers confirmed that the codebase is functional and evaluatable. Reproducibility was straightforward as the authors provided automated scripts that simplified the execution of experiments. Experiments on single machine were reproduced while experiments requiring a large cluster of machines are constrained by limited resource capacity and instructions. Hence, the paper's core claims were partially reproduced.
Xingchen Chen, Baizhu Wang, Mengjun Zhang, Yaqin Cao · 5 authors
In recent years, web application development has become more efficient, yet vulnerabilities still pose significant risks. Traditional static and dynamic detection techniques are prone to false positives and negatives, making it challenging for small and medium-sized developers with limited security knowledge to accurately assess the results. To address these challenges, we introduced VulKiller, an automated vulnerability detection tool powered by large language models (LLM). VulKiller leverages static analysis to convert application code into Code Property Graphs (CPG) and utilizes Neo4j to identify high-risk method call chains. By designing structured interactions with ChatGPT, these call chains and corresponding code are transformed into Proofs of Concept (PoCs), which are then parsed into attack payloads and evaluated by a vulnerability monitor for effectiveness. In comparison with traditional tools, VulKiller excels in reducing false positives and negatives. Additionally, in zero-day vulnerability detection experiments, VulKiller identified 12 zero-day vulnerabilities. Our results offer significant encouragement for using LLM to enhance vulnerability detection.
With the G-20 prioritising the development of a solution that will address the challenges of cross-border payment, a lot of focus and research has been exploring ways of using a decentralised ledger technology (DLT) solution to address this challenge. Some of the issues that have been identified with the DLT option are interoperability, scalability, security, and privacy. Central Bank Digital Currencies (CBDCs) implementing DLT have also been acknowledged by experts as having the potential to address some of these challenges. Given the focus on CBDCs as an alternative technology that could address the shortcomings of traditional cross-border payment systems, some of the design challenges that are impacting their widespread adoption have come to the fore. Considering that early CBDC projects were in reaction to emerging digital currencies and therefore were meant to address the domestic needs of each implementing jurisdiction, the lack of a common development framework for developing the technology contributed to the challenge of the interoperability of the various CBDCs across jurisdictions and has impacted it’s fullscale adoption as an alternative to existing cross-border payment systems. With specific focus on regulatory frameworks used by various jurisdictions and also an identified challenge of CBDCs, this paper explores Customer Due Diligence (CDD) regulation as a mechanism for combating anti-money laundering (AML) and counter-terrorist financing (CFT). As part of the efforts to adapt cross-border payment system into a world of distributed ledgers relying on smart contracts, it reviews the impact of divergent regulatory frameworks that limit the interoperability of CBDCs. It discusses some of the existing techniques in place for conducting CDD, such as Know Your Customer (KYC) and how they fit with the new technology. It concludes with a proposal on how the incongruence of legal frameworks and AML/CFT regulations, can be addressed and standardised using new technologies such as Large Language Models (LLMs).
This paper aims to discuss the function of software in satellite system and the reliability analysis of satellite software. Firstly, the overview of satellite system and the importance of software in it are introduced. Then, the concept of reliability, reliability evaluation method and factors affecting reliability of satellite software are expounded in detail. Then, the strategy to improve the reliability of satellite software is discussed. Finally, the article looks forward to the future development trend of satellite software reliability, including the application of artificial intelligence, machine learning, and distributed ledger technology. These new technologies are expected to further improve the reliability of satellite pieces and provide a solid guarantee for the safety of satellite communication.
The ability to create decentralized applications without the authority of a single entity has attracted numerous developers to build applications using blockchain technology. However, ensuring the correctness of such applications poses significant challenges, as it can result in financial losses or, even worse, a loss of user trust. Testing smart contracts introduces a unique set of challenges due to the additional restrictions and costs imposed by blockchain platforms during test case execution. Therefore, it remains uncertain whether testing techniques developed for traditional software can effectively be adapted to smart contracts. In this study, we propose a multi-objective test selection technique for smart contracts that aims to balance three objectives: time, coverage, and gas usage. We evaluated our approach using a comprehensive selection of real-world smart contracts and compared the results with various test selection methods employed in traditional software systems. Statistical analysis of our experiments, which utilized benchmark Solidity smart contract case studies, demonstrates that our approach significantly reduces the testing cost while still maintaining acceptable fault detection capabilities. This is in comparison to random search, mono-objective search, and the traditional re-testing method that does not employ heuristic search.
A smart legal contract is a legally binding contract in which some or all of the contractual obligations are defined and performed automatically by a computer program. As its software requirement, the legal contract is composed of legal clauses expressing the execution logic and time constraints between events in natural language. When formally verifying a smart legal contract to ensure the requirements’ conformance, it is necessary to translate the time-constrained functional requirements (TFRs) into property specifications like Metric temporal logic (MTL) as the input of a model checker. Instead of costly and error-prone manual writing, this work automates the TFR detection and the specification generation using deep learning, named AutoMTL-Spec. We separate the MTL specification generation approach into four tasks: TFR detection, intermediate representation structure extraction, event sequence/time point extraction, and MTL generation, respectively. We construct a dataset including 43 contracts of four categories, 4608 terms, and 277 TFRs. The experimental results showed that all three models significantly outperform the baselines. Most of the indicators of the three learning tasks reached near to or more than 90%.
Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphs’ structures. MANDO extracts customized meta-paths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score.
Traditional management practices applied to the cybersecurity realm are causing significant delays to the product release cycle for major organizations and government agencies. These delays are often seen as a necessary part of the security landscape to ensure appropriate approvals, audits, and risks are addressed prior to fielding software. However, the exact opposite outcome has been observed; delayed releases decrease overall security and functionality. This paper presents an alternative approval and review process using blockchain technology that complements agile development and continuous delivery paradigms. An example implementation is provided to demonstrate the behavior of the proposed system under hypothetical auditing and threat assessment scenarios.
The rapid evolution of distributed systems during the 2010s fundamentally altered how software systems were designed, deployed, and operated, particularly in cloud-based and service-oriented environments. As organizations increasingly decomposed monolithic applications into microservices and event-driven components, traditional monitoring approaches centered on host-level metrics and reactive alerting proved insufficient. Failures became probabilistic rather than deterministic, symptoms emerged far from root causes, and system behavior could no longer be fully inferred from static architecture diagrams or predefined dashboards. Within this context, observability emerged not merely as an operational concern but as an engineering discipline that directly influences how systems are designed, instrumented, and evolved over time. Observability driven engineering refers to the practice of designing software systems such that their internal states can be inferred through externally visible signals under real-world operating conditions. By 2019, this concept had gained traction across distributed systems research and industry practice, informed by earlier control theory definitions and reinforced by practical challenges in debugging production microservices. Rather than treating telemetry as an afterthought added during operations, observability driven engineering integrates metrics, logs, and distributed traces into the development lifecycle itself, shaping interface contracts, failure semantics, and deployment strategies. This shift reflects a recognition that correctness, reliability, and performance in complex systems cannot be validated solely through pre-production testing. In regulated domains such as financial services, the need for observability carries additional significance. Payment processing systems, fraud detection pipelines, and ledger services operate under strict latency, consistency, and auditability requirements, while simultaneously being subject to partial failures, traffic bursts, and external dependencies. In such environments, the inability to explain system behavior during anomalies is not merely an inconvenience but a material operational and regulatory risk. Observability driven engineering therefore intersects with compliance obligations, incident response processes, and risk management practices, extending its relevance beyond purely technical concerns. This paper examines observability driven engineering as understood and practiced by May 2019, situating it within the broader evolution of software architecture from monolithic systems to distributed, cloud-native platforms. It synthesizes academic literature and industry experience to articulate a conceptual model for observability-aware system design, emphasizing the relationship between instrumentation, architectural layering, and operational feedback loops.
Driven by the surge of interest generated around blockchain technologies over the last years, a new category of systems, called Blockchain-Oriented Software (BOS), which are strictly tied to Blockchain distributed environment, has become increasingly popular. Yet, there is not a thorough understanding of their structure and behaviour and if and to which extent they differ from traditional software systems. The present work provide a first statistical characterisation of BOS. We analysed and compared 5 C++ open source Blockchain-Oriented and 5 Traditional Java software systems, aiming at detecting potential differences between the two categories of projects, and specifically in the statistical distribution of 10 software metrics. Although, in general, the statistical distributions for Traditional software and Blockchain software show similarities, the distribution of Average Cyclomatic and Ration Comment To Code metrics reveal significant differences in their queue, whereas the Number of Statements metric shows meaningful differences on the double Pareto distribution.
Jake McCarthy, Ruben O Callaghan, Christopher B. Stanley
Distributed Ledger Technology (DLT) acts as a medium for transferring value amongst members of a shared network. Tokenization is a prominent factor enabling this type of value transfer by allowing non-DLT based assets to undergo a digital transformation to a distributed ledger environment. With the advent of new and emerging asset classes, it is necessary to explore the suitability of migrating these non-DLT based assets from their conventional data forms to a distributed ledger environment. In this paper, assets are examined to evaluate if their investment appeal is increased after tokenization, by analyzing changes in the underlying assets’ structure and potential disruption to external parties. This study reviews the existing body of knowledge at the intersection of DLT, digitalization and finance to map the benefits of tokenization on different asset classes. Additionally, this paper aims to contribute to existing research by providing a framework to assess the suitability of tokenizing an asset. For this paper, tokenization is viewed as the consolidation of embedding financial functionality (improvements to an asset’s liquidity, composability or available information)(Merton & Bodie, 1995) and digitalization (disruption to the actors, settings or offerings) of an asset (Hagberg et al. 2016). The following question will be addressed: How can the tokenization suitability framework derived in this paper be used as a decision support tool?
It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that blockchain is decentralized and is not controlled by anyone. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.
Cooperation of vehicular systems is the stepping stone towards both road and indoor smart transportation systems. It aims at increasing transportation efficiency and safety compared to the stand-alone vehicular systems. The usage of wireless communication as the foundation of such safety-critical cooperation needs to be embraced with all its benefits and flaws compared to the wired communication. The cooperative functions need to be designed to adapt to the varying reliability of the wireless communication channels such that both the stand-alone vehicles as well as the smart transportation system formed by their cooperation are deemed sufficiently safe. In this paper we build upon a contract-based runtime monitoring architecture and propose a methodology for assuring adaptive behaviour of transportation with respect to the wireless communication channel failures. More specifically, we elaborate how safety analysis of the interaction of the wirelessly connected vehicles can be used as the basis for derivation of the adaptive modes and the corresponding contracts. Furthermore, we discuss how such contracts can be used as the basis for assurance of the adaptive wireless cooperation. We illustrate the proposed methodology on a smart transportation system of a factory.
FigureWASHINGTON, DC—The ambitious continuous learning database project of the American Society of Clinical Oncology known as CancerLinQ (OT, 8/25/12) has demonstrated its feasibility for the first time, according to speakers at a news briefing at the National Press Club here. The new prototype, demonstrated for briefing attendees on a hypothetical post-surgical patient with hormone-responsive breast cancer, included anonymous data from 100,000 breast cancer patients treated at U.S. cancer care sites. CancerLinQ is not the only cancer continuous learning database—Georgetown University has pioneered a similar project (see box). The prototype CancerLinQ, which makes available to oncologists via computer massive amounts of data to inform clinical decision-making and improve the quality of cancer care, has now demonstrated through a real-time testing process that it can work in actual practice, said ASCO President Sandra M. Swain, MD, Medical Director of the Washington Cancer Institute at MedStar Washington Hospital Center. Swain noted that the majority of oncologists, about 60 percent, are currently using electronic health records (EHRs), a necessity for CancerLinQ. Swain explained that when ASCO embarked on this multi-stage project about a year and a half ago—which she described as “very bold” and “scary”—it was with the continuous learning vision of the Institute of Medicine (IOM) in mind. “Our work is really grounded in the work of the IOM over the last few years,” she said.Figure: ASCO President-Elect CIFFORD HUDIS, MD, noted that one key benefit of the new prototype is that it can accept data from different electronic health records: “The system is independent of the EHR that the physician is using. We will work with anyone; we hope all vendors will end up with transformable data.”The vision, as set forth in a number of IOM reports, seeks to help clinicians both learn from and contribute to diagnostic and treatment data through a health information technology (HIT) computerized database containing electronic health records (EHRs). Now, she said, “the physicians are just clamoring to give us the data,” because they realize its importance in making informed clinical decisions. She said use of the large data set should help to counter the fragmentation in cancer care that makes it very difficult to draw insights from the collective clinical experience with cancer patients. “It means having the whole medical community available for an opinion. It confirms that every cancer patient can be an information donor.” The database makes available a vast amount of valuable patient data that cannot now be mined because it is hidden away—since only about three percent of adult cancer patients participate in clinical trials. “The worst situation is not having information,” Swain continued. “Every time I see a patient, there are one or two things that make that patient different. This helps us to get more answers.” She said it isn't just oncology that will benefit, but that the data gathered will likely be relevant to other diseases as well. ‘Proof-of-Principle Prototype’ “This is a proof-of-principle prototype,” said ASCO President-Elect Clifford A. Hudis, MD, Chief of the Breast Cancer Medicine Service and Attending Physician at Memorial Sloan-Kettering Cancer Center and Professor of Medicine at Weill Medical College. “It's a real-time, push-of-the-button load of the patient data upfront.” Hudis said much work on the prototype remains, and that over the next year “we're going to write white papers on what we've learned.” He noted that right now ASCO's Quality Oncology Practice Initiative (QOPI), is paper-based—an initiative that could become much more streamlined and efficient if CancerLinQ is eventually widely adopted. One key benefit of the new prototype is that it can accept data from different EHRs, he said. “The system is independent of the EHR that the physician is using. We will work with anyone; we hope all vendors will end up with transformable data.”Figure: ASCO President SANDRA M. SWAIN, MD, said use of the large data set should help to counter the fragmentation in cancer care that makes it very difficult to draw insights from the collective clinical experience with cancer patients.In the hypothetical breast cancer case demonstrated, the patient is put on an aromatase inhibitor but develops arthralgia. The CancerLinQ database prototype tells her physician to consider using tamoxifen as an alternative, and provides supporting data for that treatment choice. “For 25 years I've been doing one-on-one medicine,” said another speaker, W. Charles Penley, MD, a partner with Tennessee Oncology, PLLC, Board Chair of the Conquer Cancer Foundation, and a member of the Dean's Advisory Board of the College of Arts and Sciences at the University of Tennessee. “Patients have been telling me, ‘Doctor, I want you to learn from my case to help other patients.’ This [CancerLinQ] is that taken to the modern information age.” Penley, who is one of about 25 clinicians in the network testing the ASCO database prototype and whose practice contributed breast cancer patient data to it, added, “This tool really can be a game changer in that regard.” What it means for cancer patients, he said, is that they can have confidence that they are receiving the highest quality care no matter where they are located. The database prototype, which he called “a remarkable step forward,” offers “an opportunity to query not just a few experts known to us, but the collective experience of treating clinicians—thus adding “second opinions times multiples.” Lessons from Pediatric Oncology Lynn M. Etheredge, who leads the Rapid Learning Project at George Washington University, said lessons from pediatric oncology can be valuable for CancerLinQ as it moves forward. Pediatric oncologists built a system to capture data from every patient as if he or she were on a clinical trial and then learn from that experience, noted Etheredge, who worked for the White House Office of Management and Budget in the Carter and Reagan Administrations, and who proposed the concept of the “rapid learning health system” in a special issue of Health Affairs in 2007 (26: w107-w118). “Pediatric oncologists realized early on that there were genetic differences,” he said. “Hopefully we will have the same success in treating adult patients.” Asked by OT if he could have envisioned his concept of a rapid learning health system coming to this database prototype point, Etheredge said, “I'm an optimist,” but noted that “This is astonishing.” He said that in the past physician groups have largely been reactive—responding to “things done to them,” and he praised ASCO for being proactive, innovative, and forward-thinking. “What we are saying now is that we have put the stake in the ground; we have demonstrated everything we wanted to demonstrate,” Joshua Mann, ASCO's Associate Director for Oncology Technology Solutions, Quality and Guidelines, said in an interview. “Now we're ready to engage the broader audience.” For the full CancerLinQ system, “we plan to siphon off data feeds from anyone,” including small oncology practices, not just large cancer centers. The message is: “Send us whatever you have however you can.” He noted that “machine-learning algorithms” convert data into a standardized format, thus allowing practices using different EHRs to participate in the continuous learning database. Lombardi's G-DOC Integrates New Knowledge with Practice At Georgetown University's Lombardi Comprehensive Cancer Center, Director Louis M. Weiner, MD, has pioneered a continuous learning system similar to CancerLinQ called Georgetown Database of Cancer, known as G-DOC. This system uses both local data and publicly available data sets to put the concept of personalized medicine into practice, Weiner explained. Commenting on ASCO's CancerLinQ prototype proof-of-principle, Weiner—a member of the Board of Scientific Advisors of the National Cancer Institute—said, “CancerLinQ is very ambitious. Currently, cancer specialists have access to only limited data to help them make critical life-altering decisions for their patients. In particular, it is very difficult to knowledgeably personalize therapies based upon a person's particular circumstances that are dictated by their genetics, comorbidities, and molecular properties of the cancers that afflict them. G-DOC has been designed as a first step towards that goal.”FigureWeiner noted that while there are patient confidentiality issues that need to be overcome in drawing on large databases to make treatment decisions, the concept is sound. It is clear that “it would be logical and desirable to link multiple datasets and then to create physician- and patient-friendly user interfaces that allow for shared decision-making that is based on a nuanced understanding of who to treat, what to use, and when to use it.”
Machine Learning and Algorithms
Software Reliability and Analysis Research
Intelligent Tutoring Systems and Adaptive Learning
Despite the considerable efforts spent on researching and developing Multi-Agent Systems (MAS) there is a noticeable absence of deployed systems. In the past the MAS research community ignored this problem - arguing that it is not a genuine MAS problem and consequently of lesser importance than other unsolved issues like cooperation, coordination, negotiation and communication. However, as the field matures, empirical evaluations of techniques and systems are more commonly used and deployment issues like the management of a MAS become increasingly important. This paper introduces a generic framework for fault-management in MAS that has been successfully tested in a large scale MAS. 1. Motivation MAS are decentralized self-organizing systems consisting of autonomous entities called agents that are designed to solve tasks by cooperating with each other. Using structured messages for communication the agents negotiate and coordinate in a decentralized manner the actions required to solve a common task. Decentralization and self-organization ensure that there is no need for a central coordination and consequently no single point of failure resulting in a more robust design. But with the absence of centralized coordination it becomes difficult to determine the current state of the system or to predict the effects of actions. This difficulty concerning the determination and prediction of states is made worse by the fact that the functional dependencies between the agents change over time as a result of negotiations at run-time. Consequently MAS can appear to behave chaotically, which is one of the reasons for the lack of successful deployments. To overcome these difficulties it is necessary to introduce management functions that will support the monitoring and controlling of individual agents as well as the MAS. ISO/OSI defines five basic management functions for any distributed system namely: accounting management, configuration management, performance management, security management and fault management. While all five are important management functions we view fault management as the most relevant in the deployment of a MAS based on our experiences [1].