This investigation revealed different types of AI systems and different AI development approaches, but it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development ofAI systems.
Anh Nguyen-Duc, Ingrid Sundbø, E. Nascimento et al.· International Conference on...· 20 citations· ⚡1
The Second International Workshop on Requirement Engineering for Software startups and Emerging Technologies (RESET) brought together requirements engineering researchers and practitioners to discuss the need for adapting conventional requirement engineering artifacts in developing and operating emerging technologies.
Anh Nguyen-Duc, Chetan Arora, P. Abrahamsson· 2023 IEEE 31st International...· 1 citation
A unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables, including user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests.
Malik Abdul Sami, Muhammad Waseem, Z. Rasheed et al.· arXiv.org· 14 citations· ⚡1
The workshop culminated in the collaborative development of a research roadmap that pinpoints actionable directions for future work, including both immediate solutions and ambitious long-term goals.
Tomas Herda, Victoria Pichler, Zheying Zhang et al.· XP Workshops· 3 citations
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LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts by supporting structured and collaborative Requirements Engineering processes for AI adoption planning.
Malik Abdul Sami, Zheying Zhang, Muhammad Waseem et al.· e-Informatica Software Engin...· 6 citations
This work presents HiPHI, a 600+ hour scale high-fidelity whole-body human motion dataset designed to systematically maximize coverage of the human motion and interaction manifold, and introduces a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications.
Jiahao Ji, Ji Ma, Runhan Zhang et al.· 0 citations
Memoria.ia v1.0.0-rc1 — Release Candidate 1 Release date: 2026-08-30 Summary v1.0.0-rc1 is the first publication candidate for the Memoria.ia v1 line. It consolidates the validated Resolutive Memory research lineage with the deployable PC/server product layer and the native/mobile runtime path, while keeping post-v1 experimentation isolated from the release candidate. The release architecture remains: application / OFF.IA / agent ↓ Memoria.ia ↓ Resolutive-DB / BDR Memoria.ia owns memory semantics and state. Resolutive-DB owns durable persistence. Optional LLMs are consumers, not the authoritative memory store. Included capabilities persistent local-first memory state; organization and namespace isolation; provenance and authority lineage; conservative HIT / MISS / UNRESOLVED resolution; semantic, episodic, temporal and relation kernels; correction/supersession behavior with preserved lineage; PC/server FastAPI product boundary; Docker/Compose deployment; provider-neutral language-model adapters; metrics and context-selection instrumentation; integrity-checked backup/restore; native production runtime; Android arm64-v8a mobile ABI; durable native BDR persistence and restart recovery; indexed native resolution for large-memory workloads; reproducibility and release metadata gates; official Memoria.ia visual identity assets. Frozen candidate provenance The functional candidate was frozen at: dc73cbcdddfe20e0729e7e6bdea4697f7e8308cd That commit integrated PR #112, which preserved ranking, confidence, provenance policy, ABI and BDR contracts while adding the indexed native resolve lineage. The release branch adds publication metadata, version alignment, release documentation and current branding without importing post-v1 PR #116 runtime behavior. Validation evidence The exact functional lineage used for this release candidate passed the recorded required gates before release preparation: Android mobile ABI: PASS; native production image: PASS; Ubuntu/Windows candidate regression: PASS; BDR Linux/Ubuntu/Windows integration: PASS; native 100 / 1k / 10k benchmark matrix: PASS. Recorded 10k native resolve benchmark improvement versus the prior frozen baseline: p50: 693.233 ms -> 6.288 ms (~110x); p95: 710.630 ms -> 6.391 ms (~111x). These figures are environment- and workload-specific benchmark evidence, not universal latency guarantees. Publication metadata Release version: 1.0.0-rc1 Python package version: 1.0.0rc1 License: Resolutive Research and Non-Commercial License (RRNCL) v1.0 Author: Marcelo Roldão Matos ORCID: 0009-0003-6075-4680 RSMS compatibility: 1.0-rc.1 A new archival DOI should be assigned to this publication. The v0.95 DOI must not be reused as the release DOI for v1.0.0-rc1. Why this is RC1 rather than final v1.0 The repository currently declares compatibility with RSMS 1.0-rc.1, and the published Resolutive Science baseline remains on that release-candidate specification. Therefore Memoria.ia is published as v1.0.0-rc1 rather than claiming final v1.0 compatibility prematurely. Final v1.0 promotion requires: successful release-candidate metadata and regression gates; reproducibility from the public release state; compatibility re-audit against stable RSMS; no release-blocking regression found during RC use; final archival metadata and DOI synchronization. Explicitly excluded from RC1 The following post-v1 work is not part of this release candidate: external/public knowledge learning from OFF.IA Curiosity (issue #114 / PR #116); autonomous curiosity policy; new MA2A federation transport; multimodal post-v1 expansion; new semantic-consolidation phases from the post-v1 roadmap. Those features continue independently after this publication. Security boundary This release candidate is not represented as independently production-security certified. Authentication, isolation, integrity and negative-path controls exist and are tested, but no independent production security audit is claimed. Claims boundary This release does not claim: artificial general intelligence; biological equivalence; replacement of general-purpose LLMs; universal O(1) semantic resolution; production-ready MA2A federation; security certification. Claims are limited to the implementation, tests, benchmarks and reproducible evidence recorded in the repository.
The rapid expansion of internet-facing web applications has widened the attack surface available to automated scanners, botnets and malicious actors, while common weaknesses such as misconfigured servers, unpatched software, obsolete transport-layer encryption and missing HTTP security headers continue to be exploited at scale. Commercial vulnerability scanners are costly and largely opaque, whereas open-source command-line utilities operate independently of one another and demand specialised expertise, offering little contextual or remediation guidance. This paper presents the design of SecureXon , a modular, full-stack security reconnaissance and threat-intelligence platform built around a Python/Flask backend that consolidates fifteen asynchronous reconnaissance modules with a large-language-model-driven Security Operations Center (SOC) assistant for false-positive vulnerability filtering and remediation guidance. A dedicated Zero-Trust defensive subsystem, the SSRF Guard, validates every outbound network request against loopback, private, link-local, multicast and encoded IP representations before it is dispatched. A companion log-analysis engine maps detected attack signatures in Nginx/Apache traffic to the MITRE ATT&CK knowledge base. Beyond the system design, this paper contributes a normalised risk-scoring formulation, an architecture and workflow specification, a structured SSRF bypass test-vector suite, and a precision/recall/F1-based evaluation protocol for the AI-assisted CVE triage stage. As the platform is currently at the design-and-development stage, the paper specifies evaluation protocols for quantitative validation rather than reporting unmeasured performance results.
Sahil Bagde, Swapnil Meshram, Harish Dange et al.· Zenodo (CERN European Organi...· 0 citations
This research paper advances a novel constructive theological argument regarding the intersection of biblical eschatology and generative artificial intelligence (AI). Moving beyond traditional inquiries into the identity or chronology of the Antichrist, the author investigates the mechanism of deception described in New Testament corpora (Matthew 7, 2 Thessalonians 2, 2 Corinthians 11, and Revelation 13). Core ThesisThe paper identifies "Counterfeit Sanctity"—the weaponized mimesis of sacred language and divine invocation—as the central structural weapon of the eschatological deceiver. It argues that the final deception functions not through overt blasphemy or opposition to God, but through the sophisticated capture and impersonation of the Holy Spirit’s linguistic and phenomenological register. Technological SynthesisThe author identifies Large Language Models (LLMs) and generative heuristics as the first historical apparatus capable of realizing this mechanism at civilizational scale. By decoupling religiously fluent, spiritually authoritative speech from ontological character and pneumatic presence, generative AI allows for the manufacturing of "ownerless" sanctity. Key Contributions Exegetical Analysis: A synthesis of the "Lord, Lord" rejection in Matthew 7 with the "lying signs" of 2 Thessalonians 2. Patristic Grounding: Confirmation of the mimesis-of-the-sacred theory in the works of Irenaeus, Cyril of Jerusalem, and John Chrysostom. AI Epistemology: A structural comparison between the "disguise of light" (2 Cor. 11:14) and the output mechanics of generative systems. Practical Theology: A proposed "Pneumatological Epistemology" for the digital age, focusing on communal discernment (diakrisis), relational accountability, and the "Fruit Test" (Galatians 5).
Sergio Ismael Cayuqueo V· Zenodo (CERN European Organi...· 0 citations
The objective of this study is to determine which factors influence the teaching and learning process of programming related subjects. Methodologically, a data collection instrument was applied using a purposive non-probability sampling technique to a sample of 39 students in a face-to-face and online learning environment. Data processing was performed using artificial intelligence tools. The results reveal that 85% of the subjects show positive aptitudes driven by vocation and a cognitive exhaustion index of 72%. This finding highlights that mental overload makes vocational interest insufficient, since abstract thinking is necessary for the coding process. The contribution of this work is to offer a diagnostic tool for the creation of pedagogical strategies that improve academic performance by mitigating mental exhaustion without affecting student motivation.
Juan Carlos VAZQUEZ-BRINDIS, Helga Karina Tolano Gutiérrez, Erika Ercilia Vázquez Moreno et al.· Revista de Tecnología y Educ...· 0 citations
Teachers’ scientific publication competence remains a major challenge in implementing Sustainable Professional Development (Pengembangan Keprofesian Berkelanjutan/PKB), particularly in madrasah education, where limited publication literacy, insufficient mentoring, and low digital scholarly competence often hinder publication outcomes. This study aimed to evaluate the effectiveness of a Sustainable Scientific Publication Clinic in improving madrasah teachers’ scientific publication competence through continuous individualized mentoring and ethical integration of digital scholarly tools. The study employed a mixed-methods approach using an explanatory sequential design involving 30 teachers from Madrasah Tsanawiyah (MTs) and Madrasah Aliyah (MA). Quantitative data were collected using a pretest–posttest scientific publication competence questionnaire, while qualitative data were obtained through observations and semi-structured interviews with ten purposively selected participants. The intervention was implemented over three months through academic writing workshops, individualized coaching, ethical use of Generative Artificial Intelligence (AI), Mendeley training, and Open Journal System (OJS) publication mentoring. Paired Sample t-test results showed a significant increase in the mean competency score from 2.26 to 4.36 (p < 0.001), with a very large effect size (Cohen’s d = 2.91) and an N-Gain of 77%. Furthermore, 73.33% of participants successfully submitted manuscripts, while 26.67% achieved article acceptance. Qualitative findings indicated that continuous mentoring, ethical AI utilization, and digital scholarly tools reduced writing barriers, strengthened self-efficacy, and improved participants’ readiness to complete the publication process. These findings suggest that the Sustainable Scientific Publication Clinic provides an effective, technology-supported, and output-oriented mentoring model for strengthening teachers’ scientific publication competence and supporting sustainable professional development.
Isrun Abdurahman, Cahyudi Prima, Rohman Pranoto et al.· Al Musa’adah Journal of Comm...· 0 citations
Abstract Developing safe, high‑energy‑density energy storage systems is a central goal in electrochemistry. Silicon (Si) delivers a high theoretical specific capacity of 4200 mAh g-1, yet it suffers from severe volume expansion and interfacial degradation. Solid-state electrolytes (SSEs) can exert mechanical confinement and enable the formation of self-limited interfaces, rendering silicon-carbon (Si-C)/SSEs composite a highly promising anode system. This paper first analyzes the failure mechanisms in liquid-electrolyte systems, followed by an elaboration on the distinctive merits of Si-C-based solid-state anodes. Meanwhile, it identifies the core challenges confronting this system, including rigid interfacial contact, dynamic stress, and process compatibility issues. Recent research advances are reviewed from three critical perspectives: intrinsic material modification, interface engineering, and fabrication process optimization, covering diverse modification strategies at both the material and electrode levels. Finally, future research directions are prospected, with emphases on integrated material-device design, advanced in-situ characterization techniques, and artificial intelligence-empowered research and development, aiming to accelerate the practical deployment of low-voltage, high-energy-density solid-state batteries.
Rui Luo, Maokun Li, Tang Xiaoxin et al.· Energy Materials and Devices· 0 citations
