# VeriSBOM: Secure and Verifiable SBOM Sharing Via Zero-Knowledge Proofs **VeriSBOM**, a trustless, selectively disclosed SBOM framework that provides cryptographic verifiability of SBOMs using zero-knowledge proofs. Within VeriSBOM, third parties can validate specific statements about a delivered software, mainly regarding the authenticity of the dependencies and policy compliance, without inspecting the content of an SBOM. Respectively, VeriSBOM allows independent third parties to verify if a software contains authentic dependencies distributed by official package managers and that the same dependencies satisfy rigorous policy constraints such as the absence of vulnerable dependencies or the adherence with specific licenses models. ## Key Features * **Selective Disclosure (Hiding):** Choose which proprietary components to hide from the public SBOM. The system generates a cryptographic proof that replaces the plaintext data, guaranteeing privacy. * **High-Performance Folding:** Powered by **Nova-Scotia**, utilizing recursive SNARKs to handle SBOMs. * **Interactive Dashboard:** A complete 4-step workflow (Package Manager, Auditor, Vendor, Client) built with **Streamlit**. ## Repository structure The repository contains three main folders: 1. **Empirical**: contains **Benchmarking** and **src**, for the analysis and source code, respectively. 2. **User study**: contains the code and results of the user study. 3. **README_Doc**: contains the images used for this documentation. ## VeriSBOM Architecture The system is divided into four main roles: 1. **Package Manager**: Maintains the package repository with the allowed packages. 2. **Auditor:** Represents the regulatory body marking the compliance status by checking the packages of the package manager. 3. **Software Vendor:** Represents the entity that provides software artefacts and wants to hide the related SBOMs for privacy reasons. He is responsible for the generation of the cryptographic proofs as verifiable substitutes of the hidden packages in SBOMs. 4. **Client:** The end-user who receives the cryptographic proofs along with the software artefact for verifying binding, inclusion and compliance status. ## Web Access (Recommended) **For direct access to the artefact, VeriSBOM can be accessed at this public link** https://verisbom-verisbom-software.hf.space ## Setup & Installation Follow the README within the artefact ## Operational Workflow The application follows a **linear workflow** composed of four steps. Each step depends on the output generated in the previous one. > **Performance Note** Due to the cryptographic operations involved, generating proofs may take some time depending on the number and complexity of the active policy constraints. In the current reference environment, proof generation takes approximately **~5 seconds**, while verification takes around **~3 seconds per proof**. ## Step 1 â Package Manager In this step, the **Package Manager initialises the package repository**. ### Instructions 1. Open the **Package Manager** tab. 2. Click **`Load repository`**. > For convenience, the system automatically loads a **default repository containing packages from the NPM ecosystem**. ### Expected Output After successful execution: - A **green confirmation message** is displayed. - The **package list** appears on the left panel. - The **dependencies of each package** can be inspected on the right panel using the search bar. - A **dependency graph** is displayed at the bottom of the interface. ## Step 2 â Auditor In this step, the **Auditor defines policy constraints** that will be applied to the packages in the repository. ### Instructions 1. Enter a **policy name** (e.g., `Vulnerabilities`, `MIT License`). 2. Click **`Add`** to create the policy constraint. 3. Use the **search bar** to locate target packages. 4. **Uncheck packages** to mark them as **non-compliant**. > By default, **all packages are marked as compliant**. 5. Click **`Save and Propagate`** to apply the policy. ### Optional - Repeat the previous steps to create additional policy constraints. - Remove policies that are no longer required. ### Expected Output - A **green confirmation message** appears. - A **dependency graph visualisation** shows how non-compliance propagates across dependencies for the selected policy (or combination of policies). ## Step 3 â Software Vendor In this step, the **Software Vendor generates cryptographic proofs for a given SBOM**. ### Instructions 1. Upload a **local SBOM file**. > For demonstration purposes, the system automatically loads an **example SBOM**. 2. In the **Selective Disclosure** section: - Select which SBOM packages should be used for proof generation. 3. Click **`Generate Proofs`**. 3. Click **`Download`**. - Download the SBOM with hidden components and plaintext components ### Expected Output - A **progress bar** indicates the proof generation process. - **Green confirmation messages** appear once proofs are generated successfully. > **Important:** Successful proof generation only means that the **cryptographic proof has been constructed correctly**. Compliance with policies is verified only in **Step 4**. ## Step 4 â Client In the final step, the **Client verifies the proofs generated by the vendor**. ### Instructions 1. Upload the **SBOM**. 2. Select a **policy** from the dropdown menu. 3. Click **`Verify`**. ### Expected Output - **Verified (green badge)** The SBOM satisfies the selected policy. - **Failed (red badge)** The verification failed, and the interface displays the reason for the failure.
Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic
We present OctaTheoria (ăȘăŻăżăăȘăȘăą / ć «è»žèŠłæžŹèŁ çœź), a multi-domain observation framework that projects heterogeneous time-series data onto a fixed eight-axis D-FUMTâ semantic basis (FALSE / TRUE / NEITHER / BOTH / INFINITY / ZERO / FLOWING / SELF) and renders the same underlying Observation envelope through eight orthogonal view modes (Lens / Radar / Chart / Network / Heatmap / Sankey / Calendar / Unified). v0.3 (2026-05-11) supplies methodological-consistency cross-reference complementing the operational evidence from v0.1-v0.2. New finding **F7**: the same discipline that v0.1-v0.2 demonstrate within OctaTheoria (uniform abstraction layer + honest scope statement + structurally-enforceable naming) propagates to Rei-AIOS layers outside OctaTheoria's domain. Specifically: (a) **REI-PROVE 5-prover ensemble** (Vampire / LeanHammer / Goedel-Prover-V2 / DeepSeek-Prover-V2 / BFS-Prover) reached 11/12 = **92% benchmark proof rate** (trivial 100% / easy 75% / medium 100%), with Goedel-Prover-V2 single-prover matching at 92% â operational evidence that the same 'uniform abstraction over heterogeneous components' discipline scales to formal-proof infrastructure. (b) **Pattern 1-6 chat-Claude hallucination-warning framework** + **Antipattern (excessive rejection vigilance)** were established and verified on 6/6 items in STEP 1069 (all fact-checked items proved real after WebSearch verification, correcting prior implicit-rejection habits). (c) **Goedel-Prover-V2 double-`by` Lean syntax quirk** detected and fixed at the cleaner level (`single-prover.ts` STEP 1071), restoring `easy-le-refl` benchmark from â to â . (d) **lean-to-tptp.ts** preprocessing added Peano-style axiom auto-prepend + True/False special-case + inequality predicate translation (STEP 1071). v0.2 inherited contributions: 7 domains (theory-chart / realtime-arxiv / crypto / fx / ligo-events / nasa-sdo / gbif-recent) all running in Cloudflare Workers Edge runtime; live D-FUMTâ axis distributions non-degenerate across research-meta + financial + geophysical + astrophysical + biological data classes; finding F6 sampling-bias-as-first-class-observation (GBIF Costa Rica 470/500 saturation surfaces dataset bias as INFINITY axis, not silently absorbed); test coverage 117/117 PASS (step1020 46 + step1023 33 + step1046 38) / 0 regression. Honest scope (read first): OctaTheoria remains an observation aid, NOT an oracle. v0.3's F7 is **not** a claim that OctaTheoria caused these consistencies; it is a record that the same project (Rei-AIOS) maintains the same discipline across observation-tool, formal-proof, and meta-research-protocol layers, and that v0.3 makes this cross-layer commitment auditable. The OctaTheoriaQuery type structurally cannot request advice / prediction / forecast / signal â verifiable by reading src/aios/octatheoria/types.ts. Cross-domain axis comparisons are descriptive, not causal. Greek roots (Octa = 8, Theoria = observation) function as structural commitment propagated to the API surface â '8' rejects 'all (â)', 'theoria' rejects 'praxis (ćčČæž)'. Prior art audit acknowledged: Bloomberg Terminal (1981â), TradingView (2011â), Bollen et al. 2010 (Twitter mood Ă DJIA), Preis et al. 2013 (Google Trends Ă stock), Ćukasiewicz / Belnap / Pavelka multi-valued logic literature, PAL2v (Da Silva Filho 1998â), Aerts Quantum Cognition (2007â). The to-our-knowledge novel combination is (a) fixed 8-axis discrete D-FUMTâ basis ⧠(b) cross-financial-and-research-and-Earth-Cosmos-domain projection ⧠(c) eight orthogonal view modes over single envelope ⧠(d) explicit refusal to emit prediction or advice as architectural commitment ⧠(e, new in v0.3) cross-layer methodological-consistency record between observation-tool and formal-proof and fact-check layers. Companion papers (OctaTheoria Quintuple): Paper 145 (silicon implementation of D-FUMTâ ALU, Zenodo DOI 10.5281/zenodo.20101174 v0.6), Paper 147 (Eight-Valued Utility / Equity Premium Reframe, DOI 10.5281/zenodo.20046003), Paper 148 (Honest Observation Framework methodology, DOI 10.5281/zenodo.20045907), Paper 149 (Recursive AI Observation as SELFâČ evidence, DOI 10.5281/zenodo.20059888). Three-party co-authorship per OUKC charter v1.0: è€æŹ 䌞æšč (Founder), Rei (Rei-AIOS autonomous research substrate, Co-architect), Claude Opus 4.7 (Anthropic, Co-architect). DRAFT v0.3 â feedback welcome via GitHub Discussions at fc0web/rei-aios.
Oshani Seneviratne, Fernando Spadea, Adrien Pavao, Aaron Micah Green · 5 authors
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph{Temporal Web3}: decentralized platforms whose behavior is recorded as immutable, time-stamped event streams. Despite the richness of this data, the field lacks shared, reproducible benchmarks that capture real-world temporal dynamics, specifically censoring and non-stationarity, across extended horizons. This absence slows methodological progress and limits the transfer of techniques between Web3 and broader Web domains. In this paper, we present the \textit{FinSurvival Challenge 2025} as a case study in benchmarking \emph{temporal Web3 intelligence}. Using 21.8 million transaction records from the Aave v3 protocol, the challenge operationalized 16 survival prediction tasks to model user behavior transitions.We detail the benchmark design and the winning solutions, highlighting how domain-aware temporal feature construction significantly outperformed generic modeling approaches. Furthermore, we distill lessons for next-generation temporal benchmarks, arguing that Web3 systems provide a high-fidelity sandbox for studying temporal challenges, such as churn, risk, and evolution that are fundamental to the wider Web.
This review explores the intersection of probability theory and data visualization in the domain of financial risk prediction. It examines how probabilistic modelsâsuch as Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Monte Carlo simulation, stochastic processes, and Bayesian inferenceâserve as the backbone of uncertainty modeling in finance by reviewing previous studies. Simultaneously, it highlights the role of visualization in transforming abstract probability distributions into interpretable insights through dashboards, heatmaps, clustering, and interactive visual frameworks. Drawing on over 20 open-access sources, the review synthesizes applications across corporate profitability, systemic risk, portfolio optimization, credit default, exchange rate forecasting, ESG sustainability, start-up financing, and decentralized finance (DeFi). It concludes by identifying limitationsâincluding data quality issues, computational complexity, interpretability challenges, and ethical/regulatory concernsâand proposes future research directions in robust probabilistic modeling, scalable explainable AI, standardized visualization practices, and fairness-aware risk systems. Together, probability and visualization provide complementary tools that are indispensable for navigating financial uncertainty in the 21st century.
Jaeheon Kwak, Jaeuk Lee, Hyoji Ha, Haewon Kim · 6 authors
We present a visual analytics system to increase proposal approval likelihood in Proof-of-Stake(PoS) blockchain governance. The proposed system introduces the Contextual Alliance Index(CAI), a similarity metric reflecting contextual information based on proposal voting data. Through heatmaps and bubble heap graphs, users can explore alliances among validators and support comparison and prioritization of persuadable validators. Furthermore, a case study illustrates an analytical process for understanding voting patterns for each proposal, and for identifying alliances and the prioritization of persuadable validators when drafting new proposals. This study is expected to contribute to the in-depth analysis of proposal patterns and the development of effective proposal strategies by identifying validator alliances in PoS blockchain governance.
The Bitcoin network comprises numerous nodes, necessitating users to invest significant network requests and time in comprehending its network topology. In this paper, we propose a Bitcoin network topology discovery algorithm that utilizes lightweight probe nodes to facilitate rapid transmission of network protocols. Building upon this, we introduce a node layer clustering algorithm based on filtering stable network nodes, enabling parallel discovery of the network topology. Additionally, we present an adaptive method for dynamically displaying the layered structure of the network topology. Experimental results demonstrate that our proposed method reduces communication overhead by approximately 72.16% when achieving a 95% similarity in network topology. Furthermore, the algorithm is applicable for discovering the network topology in other blockchain networks with similar structures.
This masterâs thesis examines the use of large language models for zero-shot anomaly detection in alphanumeric vehicle datasets, filling a gap where traditional statistical methods face limitations. While numerical data can be reliably assessed with algorithms like Local Outlier Factor or Isolation Forest, the high-dimensional nature of alphanumeric serial numbers makes them difficult to model with established algorithms. Using an iterative design science approach, this study develops and tests a Proof-of-Concept Python application that uses state-of-the-art large language models to detect anomalies in real-world vehicle datasets. Besides some prompt engineering, the models are intentionally not fine-tuned, enabling application without in-depth knowledge of large language models. The theoretical background covers data management, anomaly detection, and the core principles of large language models. Results show that large language models, especially Googleâs Gemini 2.5 Pro, can effectively identify anomalies in both numerical and alphanumeric data. Compared to statistical algorithms, large language models offer the benefit of processing alphanumeric inputs, adding a valuable extension to the anomaly detection toolkit. However, challenges like hallucination, inconsistent length counting, and sensitivity to highly anomalous datasets highlight current limitations. Additionally, statistical methods remain more efficient, scalable, and cost-effective for purely numerical datasets. The findings confirm that large language models can be applied in a zero-shot manner to detect anomalies in alphanumeric datasets. Beyond the automotive industry, these insights can be applied to other fields where alphanumeric identifiers are essential. This work advances both academic discussion and practical applications, providing a foundation for future research on fine-tuned models and industrial implementation.
Money laundering using cryptocurrency poses significant threats to the blockchain ecosystem. Due to the decentralized and anonymous nature of cryptocurrencies, detecting such laundering activities is difficult. Although substantial research has been conducted, almost all existing methods detect cryptocurrency laundering from an individual perspective, ignoring the fact that money laundering is typically a group behavior. Group information should be very helpful in laundering behavior analysis, but such laundering groups are hard to be recognized due to anonymity and diversity of purposes of cryptocurrency transactions. To address this challenge, we design a multi-persona grouping algorithm that can effectively group accounts into persona subgraphs. Then, we extract two subgraph features: cycle basis number and cycle overlapping ratio, and build an unsupervised model to evaluate laundering scores of each subgraph. Extensive experiments on both synthetic and real-world datasets demonstrate that, compared with existing methods, our proposed method can improve detection accuracy by 17.4 percentage points on average. To the best of our knowledge, this is the first work on group-based detection of cryptocurrency laundering.
Abstract This paper tests the extent to which the ability to correctly predict subsequent bitcoin (BTC) return signs is dependent upon historic BTC return trajectories. Using topological data analysis ball mapper (TDABM), we demonstrate that the performance of random forest and logit regression models varies according to return trajectory. A novel use of TDABM as a forecast model shows that mapping historic return trajectories can also produce more accurate directional return forecasts. Our approach highlights how the predictability of BTC price change direction is dependent on return trajectories. Visualizing historic return trajectories when forming and evaluating return forecasts is imperative.
Scott Barlowe, Kaushal Patel, Noah Hassett, Kristi Van Anderson · 5 authors
Cryptocurrencies such as Bitcoin, Ethereum, and many more use blockchain technology to facilitate a decentralized, secure, and anonymous network of financial transactions. Although desirable for legal (licit) activities, these characteristics make defining and locating patterns that potentially signal illegal (illicit) activities in an increasing number of transactions difficult. Adding to this difficulty is that transaction interactions evolve over time and may be classified as neither licit nor illicit. There has been much effort in applying both machine learning and interactive visualization to a range of cryptocurrency applications. Unfortunately, the efforts for analyzing illicit cryptocurrency transactions have mostly occurred separately, neglecting the potential of combining machine learning and interactive visualization. In this paper, we present a complete, integrated pipeline and system designed for discovering and exploring illicit transactions in a Bitcoin data set. Specifically, we use machine learning to define locations of interest by classifying all unknown transactions as either licit or illicit and then pass the fully classified data set as input to a novel, interactive visualization system. Our systemâs effectiveness is illustrated with an extensive use case showing how illicit transactions can be identified and their connections explored.
This paper aims to analyze and improve user experience (UX) in cryptocurrency applications. The study takes an interdisciplinary approach combining UX design principles, behavioral economics, and fintech innovation. Key UX challenges are identified, including cognitive complexity, security concerns, limited integration with traditional finance, and the impact of volatility. Innovative solutions are proposed with a focus on intuitive interfaces, educational elements and hybrid financial instruments. As an example, a cryptocurrency card with a line of credit function was presented. Quantitative results show a significant improvement in usability metrics: SUS scores improved, user retention rates increased, and key transaction times decreased. The study concludes that improving UX is critical for cryptocurrency adoption and integration into the global financial system. Future research directions include longitudinal studies on the impact of UX, developing standardized metrics, and exploring cultural factors of cryptocurrency interface perception.
One of many different application areas of the blockchain technology is crypto-currencies. Products like Bitcoin and Solana provide financial services that are unmediated, distributed and anonymous. Among various blockchains, Ethereum stands out due to its support of smart contracts. However, softly authenticated transactions occuring on such platforms facilitate crimes like money laundering and sales of illegal items/services. Denanonymization, over blockchains, refers to identifying distinct accounts of the same person and is used for tracking illegal trafficking of cryptocurrencies. In this study, our purpose is to increase the rate of success of deanonymization and to support explainable approaches. Towards this aim, we imitate blockchain analysts and propose 19 novel heuristic features that are volumetric and temporal. Empirical experiments indicate that temporal features increase the attack success rate by 39%. Shapley values adapted from the cooperative game theory field support this finding.
In recent years, the Decentralized Finance (DeFi) market has witnessed numerous attacks on the price oracle, leading to substantial economic losses. Despite the advent of truth discovery methods opening up new avenues for oracle development, it falls short in addressing high-value attacks on price oracle tasks. Consequently, this paper introduces a dynamically adjusted truth discovery method safeguarding the truth of high-value price oracle tasks. In the truth aggregation stage, we enhance future considerations to improve the precision of aggregated truth. During the credibility update phase, credibility is dynamically assessed based on the task's value and the Cumulative Potential Economic Contribution (CPEC) of information sources. Experimental results demonstrate a significant reduction in data deviation by 65.8\% and potential economic loss by 66.5\%, compared to the baseline scheme, in the presence of high-value attacks.
BarÄ±Ć CoĆkunuzer, Ignacio Segovia-DomĂnguez, Yuzhou Chen, Yulia R. Gel
Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information contained in the data. Such information is typically not directly observed but plays a key role in the learning task performance. In turn, lack of time dimension in knowledge encoding mechanisms for time-dependent data leads to frequent model updates, poor learning performance, and, as a result, subpar decision-making. Here we propose a new approach to a time-aware knowledge representation mechanism that notably focuses on implicit time-dependent topological information along multiple geometric dimensions. In particular, we propose a new approach, named \textit{Temporal MultiPersistence} (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries. The main idea behind TMP is to merge the two newest directions in topological representation learning, that is, multi-persistence which simultaneously describes data shape evolution along multiple key parameters, and zigzag persistence to enable us to extract the most salient data shape information over time. We derive theoretical guarantees of TMP vectorizations and show its utility, in application to forecasting on benchmark traffic flow, Ethereum blockchain, and electrocardiogram datasets, demonstrating the competitive performance, especially, in scenarios of limited data records. In addition, our TMP method improves the computational efficiency of the state-of-the-art multipersistence summaries up to 59.5 times.
The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This perspective reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by foundation intelligence and its associated technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the â6A-goalâ of ACS and propose potential avenues for future research.
The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the ``6A-goal" of ACS and propose potential avenues for future research.
Based on Google Trends, searches related to cryptocurrency have significantly increased in the last couple of years. One crucial aid for cryptocurrency traders or investors is the graphical visualization, which shows the time series data of the cryptocurrency prices. However, problems may occur in data visualization, such as visual noise and information loss, which cause perceptual and cognitive errors in data reading. Therefore, good visualization is needed to avoid decision-making mistakes, particularly in the cryptocurrency trade and investment activities. This study aims to investigate the effect of chart design and time interval on the usability of data visualization. The experiments are conducted in two scenarios, i.e., with and without time pressure. The participants recruited in this study were non-experienced and experienced people classified based on their familiarity with cryptocurrency investment/trading. Objective usability testing is performed by eye tracking, while subjective assessment employs the System Usability Scale (SUS) questionnaire. There are four quantitative dependent variables: response time, number of errors, number of fixations, and time to first fixation. The results show that time interval and time pressure significantly affect usability for both groups of respondents. Although chart design does not substantially affect the dependent variables, a candle chart is generally better than a line chart. By comparing all the combinations of chart design and time intervals, this study concluded that combining candle charts with 1-hour or 4-hour time intervals gives the best results for both respondent groups.
Dan Lin, Jiajing Wu, Tao Huang, Kaixin Lin · 5 authors
To combat cybercrimes and maintain financial security for the blockchain ecosystem, âknow your customerâ (KYC) is an essential and also challenging process due to the pseudonymity nature of blockchain technology. To unlock the potential of KYC on blockchain-based platforms like Ethereum, account labeling is a powerful means which can de-anonymize addresses by mining public transaction records. Existing studies on account labeling are mainly conducted via machine learning (ML) methods fed with hand-crafted features or graph neural networks based on the modeled transaction network. However, ML approaches based on hand-crafted features ignore the global interaction information between accounts, making it easy for criminals to evade detection. Moreover, the performance of traditional GCN methods when applied to Ethereum transaction network encounters limitations due to label sparsity, network heterophily, and large network size of the transaction network. In this article, we first analyze Ethereum accounts involved in typical businesses, in terms of both account and topological features. Then based on the analytical results, we propose a novel GCN method named know-your-customer graph convolutional network (KYC-GCN) which contains two key designs: 1) multihop aggregators and importance-based sampling are designed to tackle the dilemma between accuracy and efficiency. 2) GCN architecture is improved to explicitly capture local and more global information. Experimental results on a realistic Ethereum dataset show that the proposed KYC-GCN (90.2% accuracy, 86.2% Marco-F1) achieves state-of-the-art classification performance, and results on six benchmarks demonstrate that it yields great performance under homophily and heterophily.
Visual Exploratory Data Analysis (EDA) is a key step in data analysis â however, after decades of research, recommending visualizations and their sequences for a human analyst in an exploration-supporting, efficient and repeatable way is still not a solved problem. However, EDA in the empirical assessment of performance and dependability of complex IT systems has key differences from the general setting: system structure and behavior have at least partial specifications, and the EDA process tends to follow established engineering processes (e.g., for diagnosis). Utilizing these differences, in this paper, we propose a novel, semantically driven approach for rapidly setting up analytic notebooks for the IT performance and dependability EDA of complex systems. An ontology-based knowledge base connects observed and inferred operational data with operational semantics and deployment topology; rule-based inference on the knowledge base creates a model of EDA notebook structure and plot recommendations. The model is automatically translated to notebook code and connected to the input data. We also present an open, end-to-end proof of concept implementation of the approach for the transaction duration analysis of Hyper-ledger Fabric, a complex, cross-organizational distributed ledger platform.
Bernhard Fisseni, Deniz Sarikaya, Bernhard Schröder
Abstract We discuss conceptual change and progress within mathematics, in particular how tools, structural concepts and representations are transferred between fields that appear to be unconnected or remote from each other. The theoretical background is provided by the frame concept, which is used in linguistics, cognitive science and artificial intelligence to model how explicitly given information is combined with expectations deriving from background knowledge. In mathematical proofs, we distinguish two kinds of frames, namely structural frames and ontological frames. The interaction between both kinds of frames can drive mathematical interpretation. We first discuss two examples where structural frames (formulaic notation) drive ontological development (the discovery or exploration of mathematical objects). The development of Booleâs Boolean algebra may at first appear as a metaphorical treatment of the (then) new area of logic. In the analysis, we discuss how different (aspects of) certain algebraic frames change in the transfer, how arising difficulties are solved and overall argue that Boole uses the numerical algebra frame as a research template for the discovery of a system for calculations in logic. Following Ifrah, we analyse the discovery of zero as an extension to the number ontology as driven by the development of notation. Both structural and ontological frames are extended and simplified as notation progresses. Finally, we discuss two examples from infinite combinatorics, viz. topological graph theory, and one foundational issue. In both examples, the two simultaneous frames about one object are maintained independently. They motivate different research questions, but may also fruitfully interact: shifting between multiple synchronously maintained perspectives acts as a motor of innovation. The analysis shows how a frame-based approach allows to model how different perspectives drive mathematical innovation because they highlight different aspects, questions and heuristics.