RIDGE (Residual-Informed Diagnosis from Graph-Structured Evidence) is a two-stage temporal graph neural network architecture for root cause analysis (RCA) in computer networks. A probabilistic emulator forecasts the next telemetry snapshot in a fault-free network. Comparing the observations with that forecast produces residuals in units of the emulator's predictive uncertainty. An RCA model reads histories of these residual graphs, ranks candidates comprising the no-fault case, devices, and links, and classifies the fault category. Contents Each archive extracts into the artifact root that the code reads through its artifacts/ symlink. ridge-v1.0.0-stage1-dataset.zip: the raw dataset of 4,000 emulated network episodes, extracting to stage1-dataset/. Includes the generation provenance, the run manifest, the timing validation, and the exploratory analysis outputs. ridge-v1.0.0-stage2-normal.zip : the normal-emulator training windows, extracting to stage2-normal/. 209,145 windows with history length 6 and horizon 1, train-only normalization, and run-level splits of 2800/600/600. ridge-v1.0.0-stage4-residual-standardized.zip : the standardized residual windows, extracting to stage4-residual-standardized/. 316,000 windows built with the Stage 3 checkpoint. ridge-v1.0.0-stage4-residual-raw.zip : the matched raw-telemetry arm, extracting to stage4-residual-raw/. The same windows, labels, and splits, with the emulator skipped. ridge-v1.0.0-checkpoints.zip : the trained emulator (stage3-emulator/best_normal_emulator.pt) and the nine RCA checkpoints (stage5-rca-{standardized,raw,nograph}-seed{42,43,44}/best.pt), with their training histories and feature schemas. ridge-v1.0.0-evaluations.zip : the test-split evaluations, the per-seed aggregates, and the threshold baseline, extracting to stage6-evaluations/, stage6-aggregates/, and stage6-threshold-baseline/. ridge-v1.0.0-run-logs.zip : the per-stage console logs, exit codes, and wall-clock metadata for the campaign. stage1_generation_profile.json : the fully resolved Stage 1 generation configuration, readable without downloading an archive. MANIFEST.md and SHA256SUMS.txt : the archive inventory and checksums. The Dataset The 4,000 episodes were generated in a Mininet emulation running FRRouting under randomized traffic, with faults injected through tc and Open vSwitch. The composition is 2,000 healthy episodes and 500 each of drain, fiber_cut, link_degradation, and link_flap. No episode failed. Generation used seed 42 across 14 workers, on a 16-core CPU host with Python 3.12.3 and PyTorch 2.11.0+cu128, and took roughly 27 to 30 hours. Each episode covers 180 seconds at a 2-second telemetry cadence, recording node, interface, queue, route, neighbor, host, and ping statistics as CSV, with the topology and the run metadata as JSON. Provenance All telemetry was generated in an emulated network and contains no production or personal data. The absolute host paths, process identifiers, and generating hostname that the pipeline wrote into manifest.csv, the .meta files, and the simulator logs are preserved as produced.
Jakub Jeck· Zenodo (CERN European Organi...· 0 citations
RIDGE (Residual-Informed Diagnosis from Graph-Structured Evidence) is a two-stage temporal graph neural network architecture for root cause analysis (RCA) in computer networks. A probabilistic emulator forecasts the next telemetry snapshot in a fault-free network. Comparing the observations with that forecast produces residuals in units of the emulator's predictive uncertainty. An RCA model reads histories of these residual graphs, ranks candidates comprising the no-fault case, devices, and links, and classifies the fault category. Contents Each archive extracts into the artifact root that the code reads through its artifacts/ symlink. ridge-v1.0.0-stage1-dataset.zip: the raw dataset of 4,000 emulated network episodes, extracting to stage1-dataset/. Includes the generation provenance, the run manifest, the timing validation, and the exploratory analysis outputs. ridge-v1.0.0-stage2-normal.zip : the normal-emulator training windows, extracting to stage2-normal/. 209,145 windows with history length 6 and horizon 1, train-only normalization, and run-level splits of 2800/600/600. ridge-v1.0.0-stage4-residual-standardized.zip : the standardized residual windows, extracting to stage4-residual-standardized/. 316,000 windows built with the Stage 3 checkpoint. ridge-v1.0.0-stage4-residual-raw.zip : the matched raw-telemetry arm, extracting to stage4-residual-raw/. The same windows, labels, and splits, with the emulator skipped. ridge-v1.0.0-checkpoints.zip : the trained emulator (stage3-emulator/best_normal_emulator.pt) and the nine RCA checkpoints (stage5-rca-{standardized,raw,nograph}-seed{42,43,44}/best.pt), with their training histories and feature schemas. ridge-v1.0.0-evaluations.zip : the test-split evaluations, the per-seed aggregates, and the threshold baseline, extracting to stage6-evaluations/, stage6-aggregates/, and stage6-threshold-baseline/. ridge-v1.0.0-run-logs.zip : the per-stage console logs, exit codes, and wall-clock metadata for the campaign. stage1_generation_profile.json : the fully resolved Stage 1 generation configuration, readable without downloading an archive. MANIFEST.md and SHA256SUMS.txt : the archive inventory and checksums. The Dataset The 4,000 episodes were generated in a Mininet emulation running FRRouting under randomized traffic, with faults injected through tc and Open vSwitch. The composition is 2,000 healthy episodes and 500 each of drain, fiber_cut, link_degradation, and link_flap. No episode failed. Generation used seed 42 across 14 workers, on a 16-core CPU host with Python 3.12.3 and PyTorch 2.11.0+cu128, and took roughly 27 to 30 hours. Each episode covers 180 seconds at a 2-second telemetry cadence, recording node, interface, queue, route, neighbor, host, and ping statistics as CSV, with the topology and the run metadata as JSON. Provenance All telemetry was generated in an emulated network and contains no production or personal data. The absolute host paths, process identifiers, and generating hostname that the pipeline wrote into manifest.csv, the .meta files, and the simulator logs are preserved as produced.
Jakub Jeck· Zenodo (CERN European Organi...· 0 citations
Machine learning is often advanced by scaling models, data, and computation, yet scaling encounters basic limits of learning. A model may require more memory than a machine can provide, more data than can realistically be collected, or a representation that does not match the structure of the problem. These difficulties recur throughout the history of pattern recognition in different forms, indicating persistent constraints of learning across technological regimes. This dissertation studies these constraints through discrete Bayesian classifiers, full-joint probability distribution memory models, and N-tuple subspace methods, formalizing and extending the author's prior research on structured N-tuple extensions published in IEEE Transactions on Systems, Man, and Cybernetics: Systems (2021). These systems provide a transparent setting in which learning can be examined directly, because training is based on stored statistical evidence and inference is based on retrieval and comparison. Within this setting, the dissertation introduces the Three Walls theory as a unified conceptual and operational framework for recurring limits of learning. The theory identifies limits imposed by physical capacity, finite data, and representational structure, while treating noise as a separate performance ceiling. The dissertation shows that the full-joint discrete model provides a precise reference for identifying when learning becomes physically infeasible because memory requirements grow too rapidly, statistically unreliable because available training data are too sparse relative to the discrete state space, and structurally limited when the chosen discrete representation does not adequately capture the organization of the data. The dissertation further shows that class-labeled observations possess exploitable statistical and relational structure, so lower-order subspace models can preserve important dependencies while substantially reducing storage and execution demands. Feasibility-constrained optimization can then systematically improve these structured subspace models and reduce the empirical gap to the full-joint reference. To resolve the repeated-evidence multiplicity inherent in overlapping subspaces, this dissertation introduces the V-Tuple architecture–an exact graph-induced clique–separator factorization of the discrete N-tuple network. By regulating shared coordinates through a running-intersection ordering, the V-Tuple lifts the classical subspace approximation into a decomposable graphical model. The empirical profiles confirm the theoretical guarantees: on controlled high-entropy and arithmetic regimes where uncorrected aggregation collapses to baseline guessing, the V-Tuple reconstructs the required dependence structure. In these regimes, the V-Tuple restores test performance from random-guessing baselines to the Full-Joint Bayesian reference level, reaching 1.0000 on Modulo Sum and matching the Full-Joint reference mean on XOR Parity. This closes the structural approximation gap in these regimes while explicitly quantifying the realized statistical support and hardware storage burdens required by the clique–separator factorization. The same separator-corrected probability head is also evaluated on quantized Transformer attention-head summaries. In an undertrained XOR Parity encoder regime, the V-Tuple head raises Transformer linear-readout test accuracy from 0.6703 to 0.9612, reduces negative log-likelihood from 0.5500 to 0.0829, and reduces expected calibration error from 0.0939 to 0.0231, with paired sign-flip values p < 0.0001. Taken together, these results show that learning feasibility is governed jointly by physical capacity, finite data, representational structure, and noise. Discrete Bayesian learning admits a hierarchy of structured alternatives under fixed resource constraints: the full-joint reference for identifying fundamental limits, lower-order subspace models for feasible execution, feasibility-constrained optimization for systematic performance improvement, the V-Tuple architecture for exact overlap-consistent dependency retention, and separator-corrected V-Tuple probability heads for intermediate neural representations.
Ahmet Yuksel· CUNY Academic Works (City Un...· 0 citations
Driven by artificial intelligence, or AI, today's medicinal science has the potential of rapidly transforming a doctor's knowledge of patient reactions to a variety of medications. In clinical practice, one has seen that sometimes a given medication is effective and sometimes ineffective in a patient. This difference could be caused by genetics, lifestyle, disease conditions, environmental exposure or none of the above. With the advent of AI, drug response prediction and optimization are now better supported with many various approaches. Random Forest (RF) and Support Vector Machine (SVM) are some of the common machine learning algorithms employed to predict the activity of a particular drug, identify the biomarkers and classify patients as responders and non-responders. These techniques aid doctors with the informed decisions, particularly regarding chemotherapy. Gradient Boosting models also predict clinical risks and bad drug reactions with lot of precision, such as XGBoost, and LightGBM. The use of complex biological patterns by deep learning models is enhancing the field of pharmacology research further. The use of Artificial neural Networks (ANNs) to explore drug interactions with the target and to simulate Pharmacokinetics – the mechanisms of body uptake, distribution and disposition of drugs. CNNs also are extremely effective when performing image analysis tasks, such as detecting tumors or modeling and assessing the effectiveness of cancer medications with medical images. The Recurrent Neural Networks (RNNs) are designed to see data changing over time and so help doctors stay abreast of patients' progress and predict their long-term outcomes. More sophisticated AI techniques are making a large impact too. Graphical models are used to describe complex molecular and drug–drug network data and graphical networks are used in Graph Neural Networks (GNNs) to understand the relationships between different molecules and drugs. This is really important to identify safe and effective combined therapies. Drug Safety Monitoring and Evidence-Based Decision Making can be enhanced through the use of Natural Language Processing (NLP), as this technology can extract valuable information from clinical notes, research articles, and electronic health records. Using Reinforcement Learning (RL), flexible treatment plans can be developed by optimising the dose of drug administration in real-time. Pro-bayarian networks: Bayes models when a doctor doesn’t know what to do. Combination of clinical with multi-omics data (including genomic, transcriptomic, and proteomic data) is one of the most powerful of AI. Such a combination helps understand disease mechanisms and patient variability better, resulting in improved patient stratification, personalized dosages and therapeutic outcomes.
Driven by artificial intelligence, or AI, today's medicinal science has the potential of rapidly transforming a doctor's knowledge of patient reactions to a variety of medications. In clinical practice, one has seen that sometimes a given medication is effective and sometimes ineffective in a patient. This difference could be caused by genetics, lifestyle, disease conditions, environmental exposure or none of the above. With the advent of AI, drug response prediction and optimization are now better supported with many various approaches. Random Forest (RF) and Support Vector Machine (SVM) are some of the common machine learning algorithms employed to predict the activity of a particular drug, identify the biomarkers and classify patients as responders and non-responders. These techniques aid doctors with the informed decisions, particularly regarding chemotherapy. Gradient Boosting models also predict clinical risks and bad drug reactions with lot of precision, such as XGBoost, and LightGBM. The use of complex biological patterns by deep learning models is enhancing the field of pharmacology research further. The use of Artificial neural Networks (ANNs) to explore drug interactions with the target and to simulate Pharmacokinetics – the mechanisms of body uptake, distribution and disposition of drugs. CNNs also are extremely effective when performing image analysis tasks, such as detecting tumors or modeling and assessing the effectiveness of cancer medications with medical images. The Recurrent Neural Networks (RNNs) are designed to see data changing over time and so help doctors stay abreast of patients' progress and predict their long-term outcomes. More sophisticated AI techniques are making a large impact too. Graphical models are used to describe complex molecular and drug–drug network data and graphical networks are used in Graph Neural Networks (GNNs) to understand the relationships between different molecules and drugs. This is really important to identify safe and effective combined therapies. Drug Safety Monitoring and Evidence-Based Decision Making can be enhanced through the use of Natural Language Processing (NLP), as this technology can extract valuable information from clinical notes, research articles, and electronic health records. Using Reinforcement Learning (RL), flexible treatment plans can be developed by optimising the dose of drug administration in real-time. Pro-bayarian networks: Bayes models when a doctor doesn’t know what to do. Combination of clinical with multi-omics data (including genomic, transcriptomic, and proteomic data) is one of the most powerful of AI. Such a combination helps understand disease mechanisms and patient variability better, resulting in improved patient stratification, personalized dosages and therapeutic outcomes.
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
The field of software engineering has evolved significantly over the past decades. It can be argued that software has become the vital element of any system or service. Interestingly, even in the field of automotive industry, 90% of recent innovations stem from software and electronics (ITEA2 2005). Ever since the classic NATO conference in 1968, however, software has been argued to be in some state of crisis. From the 1990s, the crisis has been labeled as a chronic one. It is quite evident that several attempts to improve the state of software industry have taken place with varying levels of success. Software process improvement approaches have been developed since many decades ago. The results have been impressive, on the one hand, but many problems still exist. As an example, the business impact of software process improvement endeavors has been difficult to pinpoint, several of the improvement initiatives appear to fall short of their goals, and overemphasis on comparing processes with fixed reference models may lead an organization astray regarding the product or service perspective. The Profes conference series was established in 1999 to provide a venue for industry and academia to present and discuss the shift from model-based improvement to a product-oriented perspective in the field of software engineering and software process improvement. This includes a context-oriented understanding of the process-product relationships. Recently, the focus in practice and, consequently, in research has been on viewing software development as a global endeavor rather than a local one. There are several reasons for this. To name but a few: The distribution of software development enables the optimum use of competence regardless of the geographical location. The large software-intensive organizations operate in the global space and market, which makes distribution a necessity rather than a choice. On the positive side, distribution enables the development of software and services close to the prominent market area. Despite these relatively clear advantages and 2 benefits, global software development has brought with it several challenges due to increased complexity and cultural as well as technology issues. These challenges are now being tackled by means of research taking place in Europe and elsewhere as witnessed by this special issue of Profes conference research papers. Profes Conference series was established at the end of European IST project called PROFES (PROduct Focused Software process improvement) for disseminating the research results of the project area to industry. The PROFES Consortium included …
P. Abrahamsson, Jürgen Münch, P. Kuvaja· Software Process: Improvemen...· 4 citations
Context: While there are many success stories of achieving high reuse and improved quality using software platforms, there is a need to investigate the issues and challenges organizations face when transitioning to a software platform strategy. Objective: This case study provides a comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms. The study also reveals how new trends in software engineering (i.e. agile methods, distributed development, and flat management structures) interplayed with the chosen platform strategy. Method: We used an ethnographic approach to collect data by spending time at a medium-scale company in Scandinavia. We conducted 16in-depth interviews with representatives of eight different teams, three of which were working on three separate platforms. The collected data was analyzed using Grounded Theory. Results: The findings identify four classes of challenges, namely: business challenges, organizational challenges, technical challenges, and people challenges. The article explains how these findings can be used to help researchers and practitioners identify practical solutions and required tool support. Conclusion: The organization's decision to adopt a software platform strategy introduced a number of challenges. These challenges need to be understood and addressed in order to reap the benefits of reuse. Researchers need to further investigate issues such as supportive organizational structures for platform development, the role of agile methods in software platforms, tool support for testing and continuous integration in the platform context, and reuse recommendation systems.
Yaser Ghanam, F. Maurer, P. Abrahamsson· Information and Software Tec...· 41 citations· ⚡3
This article is a contribution towards an understanding of open access (OA) publishing. It proposes an analysis framework of 18 core attributes, divided into the areas of bibliographic information, activity metrics, economics, accessibility, and predatory issues. The framework has been employed in a systematic analysis of 30 OA journals in software engineering (SE) and information systems (IS), which were selected from among 386 OA journals in Computer Science from the Directory of OA Journals. An analysis was performed on the sample of the journals, to provide an overview of the current situation of OA journals in the fields of SE and IS. The journals were then compared between-group, according to the presence of article processing charges. A within-group analysis was performed on the journals requesting article processing charges from authors, in order to understand what is the value added according to different price ranges. This article offers several contributions. It presents an overview of OA definitions and models. It provides an analysis framework born from the observation of data and the existing literature. It raises the need to study OA in the fields of SE and IS while offering a first analysis. Finally, it provides recommendations to readers of OA journals. This paper highlights several concerns still threatening the adoption of OA publishing in the fields of SE and IS. Among them, it is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· Scientometrics· 21 citations· ⚡1
Instead of relying on huge and expensive data centers for rolling out cloudbased services to rural and remote areas, we propose a hardware platform based on small single-board computers. The role of these micro-data centers is twofold. On the one hand, they act as intermediaries between cloud services and clients, improving availability in the case of network or power outages. On the other hand, they run community-based services on local infrastructure. We illustrate how to build such a system without incurring high costs, high power consumption, or single points of failure. Additionally, we opt for a system that is extendable and scalable as well as easy to deploy, relying on an open design.
P. Abrahamsson, S. Helmer, Tosin Daniel Oyetoyan et al.· arXiv.org· 2 citations
Software Engineering practitioners work using highly diverse methods and practices, and general theories in software engineering are lacking. One attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering, which can be considered a method-agnostic project management tool for software engineering. Essence supports the use of any development practices and provides a framework for building a suitable method for any software engineering context. However, Essence presently suffers from low practitioner adoption that is partially considered to be caused by a lack of proper tooling. In this paper, we present Essencery, a tool for essentializing software engineering methods and practices using the Essence graphical syntax. Essencery aims to facilitate adoption of Essence among potential future users. We present an empirical evaluation of the tool by means of a qualitative, quasi-formal experiment and, based on the experiment, confirm that the tool is easy to use and useful for its intended purpose.
A. Evensen, Kai-Kristian Kemell, Xiaofeng Wang et al.· arXiv.org· 5 citations
The main principle of the Lean Startup movement is that static business planning should be replaced by a dynamic development, where products, services, business model elements, business objectives and activities are frequently changed based on constant customer feedback. Our ambition is to empirically measure if such changes of the business idea, the business model elements, the project management and close interaction with customers really increases the success rate of entrepreneurs, and in what way. Our first paper; “Does Lean Startup really work? Foundation for an empirical study” presented the first attempt to model the relations we want to measure. This paper will focus on how to build and set up a test harness (from now on called the Entrepreneurship Platform or EP) to gather empirical data from Companies and how to store these data together with demographical and financial data from the PROFF-portal in the Entrepreneurial Data Warehouse (from now called the EDW). We will end the paper by discussing the potential methodological problems with our method, before we document a test run of our set-up to verify that we are actually able to populate the Data Warehouse with time series data.
Yngve Dahle, M. Steinert, Anh Nguyen-Duc et al.· International Conference on...· 4 citations
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
