Fresh Repositories Watch: Education Technology — AI Tutoring and Assessment Tools

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19245772  58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]1,909✗Minimum 2,000 words for a full research article. Current: 1,909
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19245772
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]15%✗≥60% of references from 2025–2026. Current: 15%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)

The open-source education technology landscape has undergone rapid transformation in early 2026, driven by the convergence of large language model capabilities with established pedagogical frameworks. This article surveys emerging open-source repositories created within the past 60 days that address AI-powered tutoring, automated assessment, and multi-agent classroom simulation. We evaluate fiv...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.19245772 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic73%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]1,909✗Minimum 2,000 words for a full research article. Current: 1,909
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19245772
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]15%✗≥60% of references from 2025–2026. Current: 15%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)
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Digital Payment Adoption and Shadow Economy Reduction: Evidence from Ukraine’s Diia Platform

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19242241  51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources24%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,363✓Minimum 2,000 words for a full research article. Current: 2,363
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19242241
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

This article examines the relationship between digital payment adoption and shadow economy reduction in Ukraine, with particular focus on the Diia government services platform as a digitalization catalyst. Drawing on National Bank of Ukraine transaction data (2015–2025), cross-country panel evidence, and sector-level informality estimates, we investigate whether cashless payment penetration cau...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19242241 51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources24%○≥80% from editorially reviewed sources
[t]Trusted52%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,363✓Minimum 2,000 words for a full research article. Current: 2,363
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19242241
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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The UIB Composite Score: Integrating Eight Intelligence Dimensions into a Unified Benchmark

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Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19238245  63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed79%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,969✗Minimum 2,000 words for a full research article. Current: 1,969
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19238245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)

Current artificial intelligence benchmarks measure isolated capabilities — reasoning, coding, knowledge retrieval — yet no single metric captures the multidimensional nature of machine intelligence. This article presents the Universal Intelligence Benchmark (UIB) Composite Score, integrating eight previously defined intelligence dimensions (reasoning, causal, temporal, social, efficiency, trans...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19238245 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed79%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,969✗Minimum 2,000 words for a full research article. Current: 1,969
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19238245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (76 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)
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Quarterly Benchmark: Q1 2026 Open-Source Trust Score Evolution

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19233040  43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted35%○≥80% from verified, high-quality sources
[a]DOI24%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed41%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,039✓Minimum 2,000 words for a full research article. Current: 2,039
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19233040
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (33 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

Open-source software underpins more than 90% of modern application stacks, yet systematic measurement of project trustworthiness remains fragmented across competing frameworks. This article presents the first quarterly benchmark of the Trusted Open Source Index, evaluating 20 high-impact repositories across eight trust dimensions derived from OpenSSF Scorecard, CHAOSS community health metrics, ...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.19233040 43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted35%○≥80% from verified, high-quality sources
[a]DOI24%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed41%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,039✓Minimum 2,000 words for a full research article. Current: 2,039
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19233040
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (33 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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Tax Evasion Mechanisms in Ukraine: A Typology of Shadow Economy Channels

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19229248  55stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted48%○≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef35%○≥80% indexed in CrossRef
[i]Indexed48%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access91%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]2,522✓Minimum 2,000 words for a full research article. Current: 2,522
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19229248
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (53 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

Ukraine's shadow economy remains one of the largest in Europe, with wartime conditions creating both new evasion channels and shifting the composition of existing ones. This article develops a comprehensive typology of tax evasion mechanisms operating in Ukraine, classifying shadow economy channels along three dimensions: mechanism type, sectoral concentration, and detection difficulty. Drawing...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19229248 55stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted48%○≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef35%○≥80% indexed in CrossRef
[i]Indexed48%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access91%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]2,522✓Minimum 2,000 words for a full research article. Current: 2,522
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19229248
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (53 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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Fresh Repositories Watch: Financial Technology — Open-Source Trading and Risk Engines

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19227945  60stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources23%○≥80% from editorially reviewed sources
[t]Trusted77%○≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed77%○≥80% have metadata indexed
[l]Academic62%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]1,984✗Minimum 2,000 words for a full research article. Current: 1,984
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19227945
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]18%✗≥60% of references from 2025–2026. Current: 18%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)

The financial technology open-source ecosystem experienced rapid growth in early 2026, driven by the convergence of AI-powered trading agents, prediction market infrastructure, and quantitative research frameworks. This article surveys 89 newly created repositories (January-March 2026) across trading automation, risk management, portfolio optimization, and payment infrastructure. We evaluate re...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.19227945 60stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources23%○≥80% from editorially reviewed sources
[t]Trusted77%○≥80% from verified, high-quality sources
[a]DOI62%○≥80% have a Digital Object Identifier
[b]CrossRef23%○≥80% indexed in CrossRef
[i]Indexed77%○≥80% have metadata indexed
[l]Academic62%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]1,984✗Minimum 2,000 words for a full research article. Current: 1,984
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19227945
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]18%✗≥60% of references from 2025–2026. Current: 18%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (2/5 × 30%) + Optional (2/4 × 10%)
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Fresh Repositories Watch: Developer Infrastructure — Build Tools and CI/CD Innovations

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19226630  56stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted63%○≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,142✓Minimum 2,000 words for a full research article. Current: 2,142
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226630
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (55 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)

The developer infrastructure landscape is undergoing a fundamental transformation driven by two converging forces: the rapid adoption of AI-augmented development pipelines and the escalating frequency of software supply chain attacks targeting CI/CD systems. This article surveys open-source repositories created within the past 60 days (January-March 2026) that address build tooling, pipeline au...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.19226630 56stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted63%○≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic56%○≥80% from journals/conferences/preprints
[f]Free Access94%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,142✓Minimum 2,000 words for a full research article. Current: 2,142
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226630
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (55 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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GROMUS: A Unified AI Architecture for Pre-Publication Music Virality Prediction

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19226416  44stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed40%○≥80% have metadata indexed
[l]Academic40%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]4,128✓Minimum 2,000 words for a full research article. Current: 4,128
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226416
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The music industry faces a persistent and costly challenge: determining whether a track will achieve viral reach before it is released to the public. Conventional approaches to music popularity prediction rely on post-publication engagement signals — streams, likes, shares, and historical interaction data — making them structurally incapable of informing pre-release decisions. GROMUS addresses ...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.19226416 44stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed40%○≥80% have metadata indexed
[l]Academic40%○≥80% from journals/conferences/preprints
[f]Free Access80%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]4,128✓Minimum 2,000 words for a full research article. Current: 4,128
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226416
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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FLAI: An Intelligent System for Social Media Trend Prediction Using Recurrent Neural Networks with Dynamic Exogenous Variable Injection

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19226414  67stabilfr·wdophcgmx
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Social media platforms — foremost TikTok and Instagram — generate billions of interaction events daily, creating stochastic, high-velocity Big Data streams whose trend trajectories prove notoriously difficult to forecast with classical statistical models. This paper presents FLAI, an intelligent information-analytical system for predicting the behaviour of social-network objects, with emphasis ...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.19226414 67stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources56%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed63%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access81%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]5,080✓Minimum 2,000 words for a full research article. Current: 5,080
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226414
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (78 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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ScanLab: Explainable Diagnostic AI — A Local Architecture for Training, Inference, and Visual Explanation of Medical Image Analysis

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DOI: 10.5281/zenodo.19226407  49stabilfr·wdophcgmx
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Medical artificial intelligence has long suffered from a critical epistemic gap: models produce predictions without producing justifications. Clinicians, regulators, and patients cannot evaluate the validity of a decision if they can only see its output. ScanLab addresses this gap through a deliberate architectural choice — making explainability a mandatory, non-negotiable layer of the inferenc...

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DOI: 10.5281/zenodo.19226407 49stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed50%○≥80% have metadata indexed
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19226407
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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