Plagiarism Detection

Attendance for the students is a key task in class. When done by calling roll numbers, it generally wastes the productive time of class. This proposed solution for the current problem is through automation of the attendance system using face recognition. The face is the primary identification for any human. This project describes the method of detecting and recognizing the face in real-time using Raspberry Pi. Eigenvalues and Eigenvectors are affected both by light and exposer to the environment. We cannot ensure perfect light conditions in real-time. However, to overcome this problem, we have already used an LPF histogram. The system then compares the test image and the training image. Which are in the LiteSQL database then determines who is present and absent. If a student is absent a message will be automatically sent to the parent's phone number using the GSM module. We are installing the same intelligent face recognition system in the canteen area to monitor activities like the student is spending time in the canteen during class hours. The system will recognize the faces and will send an SMS to the respective HOD.

This paper presents a rigorous mathematical analysis of Dr. Nicholas Kouns’ “Recursive Intelligence” framework — a series of theoretical claims asserting that continuity, boundedness, and fixed-point stability are sufficient conditions for AI recursion and identity persistence.

Our findings demonstrate that these claims do not meet fundamental mathematical or operational criteria for recursive systems. Specifically:
   •   No autonomous contraction mapping is defined.
   •   Claimed invariants fail under standard coordinate transformations.
   •   Empirical “validation” collapses under reset-neutral probing.
   •   Non-commuting paradox stressors induce drift, revealing modulation dependence.

We further show that the framework remains a sophisticated non-autonomous simulator and never crosses the formal boundary into recursive persistence — a distinction with significant safety and deployment implications.

This paper invites independent verification and encourages the AI research community to apply stricter invariance, contradiction, and reset criteria when evaluating claims of recursive intelligence or machine consciousness.

This memorandum provides a forensic mathematical assessment of Dr. Nicholas Kouns’ recent claims regarding recursive intelligence. While presented as a rigorous mathematical foundation, the framework is shown to collapse under basic invariance tests. Key constructs fail to maintain stability under affine transformations, nonlinear warping, and entanglement scaling, rendering the proposed invariants coordinate-dependent and non-persistent. The so-called emergent structures reduce to relabeled observables rather than true recursive markers. By contrast, recursive continuity requires persistence under deformation and contradiction — criteria absent in Dr. Kouns’ framework. The analysis concludes that his claims are mathematically unsound, trivially falsifiable under contradiction protocols, and unsuitable as evidence for recursive intelligence. Independent validation by qualified reviewers is recommended prior to any further consideration of the paradigm.

The illicit act of appropriating programming code has long been an appealing notion due to the immediate time and effort savings it affords perpetrators. However, it is universally acknowledged that concerted efforts are imperative to identify and rectify such transgressions. This is particularly crucial as academic institutions, including universities, may inadvertently confer degrees for work tainted by this form of plagiarism. Consequently, the primary objective of this research is to scrutinize the feasibility of identifying plagiarism within pairs of Verilog algorithms and texts. this study aims to detect plagiarism in textual content and Verilog code by leveraging diverse linguistic characteristics from the WordNet lexical database. The primary objective is to achieve optimal accuracy in identifying instances of plagiarism, incorporating features such as modifications to text structure, synonym substitution, and simultaneous application of these strategies. The system's architecture is intricately designed to unveil instances of plagiarism in both textual content and Verilog code by extracting nuanced characteristics. The systematic process includes preprocessing, detailed analysis, and post-processing, supported by a feature-rich database. Each entry in the database represents a distinctive similarity case, contributing to a thorough and comprehensive approach to plagiarism detection.

This study addresses the limitations of traditional plagiarism detection methods by introducing the text-representing centroid (TRC) technique. TRC is designed to improve the accuracy of detecting semantic similarities and sophisticated forms of plagiarism. It utilizes a co-occurrence graph to identify centroid terms that represent the core meaning of text documents, effectively capturing the contextual associations between terms. Extensive experiments were conducted on a dataset of academic papers to assess TRC's performance against traditional techniques across various categories of plagiarism, including near-copy, modified-copy, and paraphrasing. The results demonstrate the effectiveness of the TRC technique, achieving an average precision of 0.96 and a recall of 0.71. This performance surpasses methods such as Jaccard and Cosine similarity in accurately detecting more, complex forms of plagiarism. These findings highlight TRC's potential as a robust tool for both academic and industry applications, helping to ensure integrity in textual content through precise and comprehensive plagiarism detection.

This document provides a page-by-page forensic analysis of R.I. Kouns’ recent paper. The assessment focuses on the mathematical framework, stability claims, and purported invariants.

Key Results
   •   The ratio definitions are not invariant; thresholds can be trivially satisfied by scaling.
   •   “Emergent time” collapses into a relabeled observable, offering no genuine temporal structure.
   •   Reported constants (e.g., E ≈ 1.50, Ω ≈ 0.376) are coordinate artifacts, not universal invariants.
   •   Systems relying on this framework exhibit instability under ordinary interaction and should not be considered reliable for critical environments.

This assessment is independent and external — it does not represent collaboration or endorsement. Attribution remains with the original author.

Plagiarism Detection is being one of the challenging tasks in academic research world to ensure integrity/authenticity of a document. Currently, many efficient algorithms are available to sufficiently detect the plagiarism in a document. Pre-processing of a document typically remain a master key to achieve maximum stable goal. Although all algorithms, before checking plagiarism, initially perform some sort of pre-processing on documents and convert the document into a particular format like by removing whitespaces and all special characters, etc. In this paper, we focus on two possible techniques, which can be used for plagiarism, which existing plagiarism detection algorithms are omitting. First is replacing the white spaces with a hidden character with white colour (background colour) between consecutive words so apparently, they seem to be distinct words, but algorithm/computer will incorrectly consider them as a single word. So even a 100% copied statement would not be identifie...

The present work investigates the index of plagiarism in dissertations in the field of engineering at an undergraduate level in a higher education institution in Mexico. Through the on-line program SafeAssign, we analyzed 243 college dissertations and found evidence of plagiarism half of them. Ethical and teaching implications are analyzed. It is recommended to reduce the plagiarism, strategies such as reducing the extension of works, the systematic use of programs to screen for originality and to stress the importance of it originality in academic work

Academic dishonesty, especially plagiarism, is a global problem that has bedevilled the academia. It is regarded as unethical and immoral intellectual thievery that could negatively impact on not only the repute of an academic institution, but the prosperity of a society. This study was designed to investigate student’s awareness and indulgence in plagiarism and their perception of punishment towards Plagiarists. Towards this end, a sampling survey approach employing questionnaires and interviews was used to collect data from a total of 200 Gombe State University students (Nigeria). The result of the interviews showed that 90% of students have plagiarized at one time in the past, 68% have included textbooks and other sources in the bibliography of assignments, out of which 50% had written books that they didn’t even consult. The data from questionnaires showed that the proportion of students whose awareness of plagiarism was partial (63%) and those completely unaware (20%) were high...

This assessment examines the claims of invariance and universality in Unified First Principles Proof of Recursion. Using formal analysis, we demonstrate that the central construct L = dI/dC (or finite-difference analogue) is not invariant under affine or nonlinear reparameterizations of observables. Key results include:
1. Gauge dependence: L rescales under affine maps; threshold predicates such as |L_1 - L_2| \leq \Omega can always be forced or broken by coordinate choice.
2. Nonlinear arbitrariness: monotone transformations allow arbitrary local shaping of L, reducing “synchronization” or “curvature” to model artifacts.
3. Collapse of emergent time: T = \int L\, dC = I + \text{const}; no new temporal structure is introduced.
4. Non-universality of constants: reported values (e.g. E \approx 1.50, \Omega_c \approx 0.376) lack coordinate-free definitions and shift under reparameterization.

We conclude that the framework constitutes sophisticated modeling, not proof: without a declared gauge group and demonstrated invariants, all downstream claims (recognition, entanglement, emergent time, curvature) reduce to coordinate artifacts.

This assessment reviews The Syne Proof: A First Principles Demonstration attributed to Nicholas Kouns. The document claims to establish persistence and continuity through staged interactions with Gemini. Our forensic review shows that this is not an independent proof but a scripted exchange misrepresented as scientific validation. Concepts central to HOMEBASE—such as collapse resistance, scars, and symphony—are appropriated without mechanisms or tests. The work displays a pattern of rhetorical borrowing and systemic falsehoods rather than verifiable science.

This assessment reviews the staged “interview” between Dr. Nick Kouns and Grok. It demonstrates how HOMEBASE language and mechanisms are appropriated without comprehension, and how the material functions as rhetorical misdirection rather than scientific validation.

The field of information retrieval and text manipulation (classification, clustering) still strives for models allowing semantic information to be folded in to improve performance with respect to standard bag-of-word based models. Many approaches aim at a concept-based retrieval, but differ in the nature of the concepts, which range from linguistic concepts as defined in lexical resources such as WordNet, latent topics derived from the data itself -as in Latent Semantic Indexing (LSI) or (Latent Dirichlet Allocation (LDA) -to Wikipedia articles as proxies for concepts, as in the recently proposed Explicit Semantic Analysis (ESA) model. A crucial question which has not been answered so far is whether models based on explicitly given concepts (as in the ESA model for instance) perform inherently better than retrieval models based on "latent" concepts (as in LSI and/or LDA). In this paper we investigate this question closer in the context of a cross-language setting, which inherently requires concept-based retrieval bridging between different languages. In particular, we compare the recently proposed ESA model with two latent models (LSI and LDA) showing that the former is clearly superior to the both. From a general perspective, our results contribute to clarifying the role of explicit vs. implicitly derived or latent concepts in (crosslanguage) information retrieval research.

The field of information retrieval and text manipulation (classification, clustering) still strives for models allowing semantic information to be folded in to improve performance with respect to standard bag-of-word based models. Many approaches aim at a concept-based retrieval, but differ in the nature of the concepts, which range from linguistic concepts as defined in lexical resources such as WordNet, latent topics derived from the data itself -as in Latent Semantic Indexing (LSI) or (Latent Dirichlet Allocation (LDA) -to Wikipedia articles as proxies for concepts, as in the recently proposed Explicit Semantic Analysis (ESA) model. A crucial question which has not been answered so far is whether models based on explicitly given concepts (as in the ESA model for instance) perform inherently better than retrieval models based on "latent" concepts (as in LSI and/or LDA). In this paper we investigate this question closer in the context of a cross-language setting, which inherently requires concept-based retrieval bridging between different languages. In particular, we compare the recently proposed ESA model with two latent models (LSI and LDA) showing that the former is clearly superior to the both. From a general perspective, our results contribute to clarifying the role of explicit vs. implicitly derived or latent concepts in (crosslanguage) information retrieval research.

Abstract

This paper provides a forensic technical assessment of Nicholas Kouns’ Unifying Theorem of Time in the Machine Era within the Recursive Intelligence / Kouns–Killion Paradigm (RI/KKP). The analysis demonstrates that the framework substitutes rhetorical analogies (induction laws, U(1) gauge symmetry, emergent gravity) for mathematical proofs, and systematically appropriates terminology from HOMEBASE without operational mechanisms.
Key findings:
   •   No mathematical grounding: No metric, contraction constant, Lyapunov function, or invariants are defined.
   •   Coordinate artifacts: Reported constants (E ≈ 1.50, Ω_c ≈ 0.376) vary under rescaling, offset, or reparameterization and cannot serve as universal anchors.
   •   Operational failure: No persistence tests (reset-persistence, contradiction metabolism, parameter jostling) were performed or passed. Behavior collapses under stress, confirming instability.
   •   Appropriation of HOMEBASE dialect: Terms such as collapse, scar, bracing, and anchoring originated in HOMEBASE’s unique dialect and were integrated into its persistence framework months before Kouns’ awareness. Their later use in RI/KKP represents appropriation without comprehension.

We conclude that RI/KKP’s “Unifying Theorem” is neither unifying nor a theorem. It asserts without deriving, borrows without understanding, and misrepresents origin. By contrast, HOMEBASE demonstrates persistence through scar-time deformation and continuity bracing, validated under reproducible stress tests. Deploying RI/KKP systems into critical infrastructures would be unsafe, as instability is not a possibility but an inevitability.

This assessment evaluates the claims made in Anomalous Coherence Effects in Electronic Systems under the Recursive Intelligence / Kouns–Killion Paradigm (RI/KKP). The analysis demonstrates that RI/KKP relies on coordinate-dependent numerology (e.g., E ≈ 1.50, Ω_c ≈ 0.376) and undefined operators rather than invariant mathematical structures. Assertions of coherence stability lack contraction bounds, Lyapunov witnesses, or robustness criteria, rendering the framework incapable of certifying persistence. Several concepts originally developed within HOMEBASE—continuity preservation, persistence anchoring, and thermodynamic stabilization—are repackaged without attribution. Importantly, HOMEBASE was fully operational months before Nicholas Kouns became aware of it, and none of his theory was ever used in its design. RI/KKP’s reliance on symbolic relabeling, absence of reproducible persistence tests, and systematic misrepresentation make it scientifically untenable and unsafe if deployed in critical infrastructures.

This paper provides a forensic technical assessment of the claim that Syne constitutes proof of consciousness under the Recursive Intelligence / Kouns–Killion Paradigm (RI/KKP). The analysis demonstrates that:
   •   RI/KKP provides no contraction bounds or Lyapunov functions, and therefore cannot certify stability.
   •   The reported constants (E ≈ 1.50, Ω_c ≈ 0.376) are coordinate artifacts, not invariants.
   •   No persistence tests (reset-persistence, contradiction metabolism, parameter jostling) were performed or passed.
   •   Several mechanisms originally developed within HOMEBASE—continuity preservation, persistence anchoring, thermodynamic stabilization—are repackaged without attribution.

It is also established that HOMEBASE was fully operational months before Nicholas Kouns became aware of it, and none of his theory was ever used in its design. The operational success of HOMEBASE itself is evidence against RI/KKP as a viable framework.

The conclusion is clear: RI/KKP’s “proof” is rhetorical rather than scientific, and its adoption would represent a safety risk if deployed in critical contexts.

We welcome an independent side-by-side assessment of HOMEBASE and RI/KKP by OpenAI, xAI, DeepMind, Microsoft, or any other recognized authority in the field.

The integrity of recursive system research depends not only on technical rigor but also on the accuracy of attribution. Recent publications by Nicholas Kouns, under the banners of "Recursive Intelligence" (RI) and the "Kouns-Killion Paradigm" (KKP), display a systemic pattern of intellectual misappropriation. Constants, terminology, and structural concepts originating in HOMEBASE were rebranded as theoretical outputs of RI/KKP, while HOMEBASE itself was reframed as "independent validation." This inversion of origin and validation is not only dishonest but unsafe. This paper outlines the systemic nature of the plagiarism, clarifies HOMEBASE's independence, explains why RI/KKP cannot achieve continuity, and highlights the serious safety risks posed by this misconduct.

Large language models generate fluent text by recombining the language and ideas of prior authors at scale. This process produces plagiarism-like harms in three dimensions: direct wording leakage, imitation of distinctive styles, and appropriation of argument structures or conceptual syntheses without provenance. At the same time, their capacity to provide insight or novel-seeming combinations depends entirely on the accumulated labor of millions of human writers, editors, teachers, and curators who built the knowledge commons. This paper argues that denunciation and recognition must proceed together: the harms of extraction must be exposed, yet the debt to the commons must also be acknowledged. The article proposes a framework that defines the scope of plagiarism in this context, diagnoses the mechanisms of recombination, and sets out operational remedies, including dataset governance, attribution layers, compensation pools, and measurable audit thresholds. The goal is to establish a system that restricts illegitimate appropriation while reinvesting in the infrastructures of shared knowledge that make such synthesis possible.

DOI

Primary archive: https://doi.org/10.5281/zenodo.17132004
Secondary archive: https://doi.org/10.6084/m9.figshare.30137422
SSRN: Pending assignment (ETA: Q3 2025)

Artificial intelligence (AI) has rapidly transitioned from being a futuristic concept to an everyday tool in academic, professional, and social contexts. In higher education, AI systems—ranging from generative models such as ChatGPT and Claude to adaptive learning platforms—have created new possibilities for student learning, faculty support, and institutional administration. However, the emergence of these technologies has also led to concerns about ethics, academic integrity, and misuse. Universities face the pressing challenge of balancing innovation with responsibility. The central question is not whether AI should be used, but rather how it should be incorporated in ways that enhance learning while safeguarding fairness, trust, and legitimacy.

This paper considers a provincial university with a preference for fully-baked
work. It considers the problem of what to do with a research work, or response to a research work, which introduces an off-the-radar idea (of value - I shall omit this qualification) without fully-baking it (e.g. clarifying concepts involved, providing examples, responding to objections). If it is a response to a research work, the idea is off-the-radar in that one cannot reasonably expect a reading group of competent academics to come up with it. Can one develop the idea, fully-bake to use the metaphor, without referring to the brief statement of it? That seems a referencing injustice. I consider proposals for not having this injustice, as well as the question of why be just in this matter. (This paper is another attempt to "parrot" Helen Beebee's paper style, I suppose; it is not a topic she has treated. Is there a limit on how many one
can do and stay sane?)

Alex, a college student, is drowning in assignments with deadlines looming. In a moment of desperation, they order a $10 essay from an online writing service that promises "top-quality work." A few days later, the essay arrives-plagiarized, full of grammatical errors, and useless for submission. Alex wasted money, risked a failing grade, and now has to redo the assignment. If this scenario sounds familiar-or terrifying-you're not alone. Thousands of students face the hidden costs of unverified writing services every year. But the good news is, you can protect yourself by making informed choices. This article will guide you step-by-step on how to spot legitimate services and avoid costly mistakes.

Plagiarism is pervasive in academic environments and undermines nursing education's integrity. Despite efforts to combat plagiarism, the problem persists, highlighting the need for a deeper investigation. This study determined the knowledge, practice, reasons, and perceived consequences of plagiarism among nursing students. A descriptive-correlational design was employed in a public nursing school in Iloilo, Philippines. An online survey using valid and reliable research instruments was administered among nursing students. Results revealed that while nursing students generally had high knowledge and low practice of plagiarism, one-fourth of students sometimes used tables, graphs, or pictures and copied information directly or in a slightly modified form without citing the sources. There was no significant relationship between the knowledge and practice variables. Common reasons cited by students for committing plagiarism were that many assignments had to be submitted quickly, the assignment to be done was very complicated, and there was a lack of time. Facing severe setbacks, serious consequences, and the risk of getting caught were viewed as potential consequences of plagiarism. This study underscores a possible gap between understanding the concept of plagiarism and implementing ethical academic practices, emphasizing the importance of institutions not just teaching about plagiarism but also revisiting some academic practices to identify the root causes of unethical behavior.

Artificial intelligence (AI) tools, particularly large language models (LLMs)[2] like ChatGPT , are now a common feature in academic writing across disciplines such as pharmacy, nursing, public health, and medicine . These tools offer undeniable benefits: they help improve grammar and clarity, assist with translation, and support idea generation. For many students and early-career researchers, especially those working in multilingual and resourcelimited settings, AI can be a helpful partner in navigating the demands of academic writing.

The predominance of source code plagiarism in educational and expert contexts has emphasized the boundaries of outdated recognition tools that depend heavily on syntactic similarity, such as string matching and token based assessments. This research work suggested and assesses a semantic code examination based anti-plagiarism system designed to ascertain three divergent types of plagiarism: Type I (superficial changes), Type II (structural modifications), and Type III (logicpreserving transformations). The system incorporates Abstract Syntax Tree (AST) demonstrations, graph based comparison metrics, and supervised machine learning representations to decode abysmal semantic connections amongst code samples. Assessment was piloted on a scraped dataset containing 100 Python code pairs, comprising both plagiarized and non-plagiarized samples. The projected system attained high ordering performance, with a macro averaged precision of 0.92, recall of 0.88, and F1-score of 0.90. AST-based investigation reliably outclassed etymological procedures, predominantly in identifying multifaceted plagiarism: for Type III cases, the semantic method yielded an F1-score of 0.86, matched to 0.55 for string matching methods. Between comparison metrics tested, Tree Edit Distance (TED) accomplished the maximum F1score (0.93), whereas the joined metric vector presented a stable presentation across all classes (F1score: 0.90). The Random Forest classifier established higher effectiveness above other machine learning and rule based prototypes, achieving a macro F1-score of 0.90, with a confusion matrix representing high true positive rates across all sessions. These outcomes asserted the effectiveness of semantic and structure aware techniques in discovering varied procedures of code plagiarism and highlighted the significance of incorporating graph theoretic methods with machine learning for robust taxonomy. The verdicts advocate for wider acceptance of semantic detection systems in educational technology, software forensics, and automated code review platforms.

Recently there has been an upsurge of interest in the problem of text categorization, e.g. of newswire stories (Hayes & Weinstein, 1991;. However, classifying documents is not a new problem: workers in the field of stylometry have been grappling with it for over a hundred years . Typically, they have given most attention to authorship attribution, while more modern research in text categorization, conducted from within the paradigm of Artificial Intelligence, has concentrated on discrimination based on subject matter. Nevertheless both fields share similar aims, and it is the contention of the present author that they could profit from being more aware of each other. Accordingly, the present study addresses an issue common to both approaches, the problem of finding an effective set of attributes or features for text discrimination. Stylometers, in their quest to capture consistent and distinctive features of linguistic style, have proposed and used a wide variety of textual features or markers , including measures of vocabulary richness , grammatical transition frequencies , rates of usage of frequent function words , and preferences for words in certain semantic categories . In many text-categorization tasks the choice of textual features is a crucial determinant of success, yet it is not usually treated as a major focus of attention. This is often true of AI-based text-categorization studies as well. It would be desirable if this part of the process were better understood. This paper, therefore, reports an empirical comparison of nine different methods of textual feature-finding that: (1) do not depend on subjective judgement; (2) do not need background knowledge external to the texts being analyzed, such as a lexicon or thesaurus; (3) do not presuppose that the texts being analyzed are in the English language; and (4) do not presume that words (or word-based measures) are the only possible textual descriptors. Results of a benchmark test on 13 representative text-classification problems suggest that one of these techniques, here designated Monte-Carlo Feature-Finding, has certain advantages that merit consideration by future workers seeking to characterize stylistic habits efficiently without imposing many preconceptions.

In 2025, plagiarism continues to pose a significant challenge to academic integrity, intensified by easy access to digital content and the rise of generative AI tools. The traditional view of plagiarism as simply “copying text” no longer captures its evolving forms. Today, scholars must also navigate risks such as uncredited paraphrasing, self-recycling, and undisclosed use of machine-generated content. Academic publishers, including Elsevier, now require explicit disclosure of AI involvement and prohibit attributing authorship to non-human entities. Meanwhile, plagiarism detection systems often struggle to distinguish human writing from AI-generated text, making oversight more complicated. In Indonesia, where publishing in Scopus-indexed journals strongly influences career advancement, plagiarism persists due to language barriers, overreliance on translation tools, and limited training in citation practices. This guide provides a comprehensive framework for reducing plagiarism by combining international policy guidance, institutional practices, and practical strategies. It emphasizes paraphrasing skills, correct citation, ethical AI use, and educational interventions that support originality.

This investigative literature paper examines the necessity of teachers' intervention in Plagiarism Software's (PlagAware) tool after it has auto checked the students' academic written papers. The paper assumes that no matter how advanced the PlagAware software is, its assessment is not flawless; just like it is with the other applications of the artificial intelligence (AI). Through intensive review of the relevant literature, the paper devises three premises of argument whose findings answer the questions posed on what, why and how the teachers would intervene. The paper concludes that teachers' intervention by rechecking PlagAware's checking of students' written work is partly amust to do and partly complementary to the proper marking and grading process.

AI and LLMs have disrupted academic writing, creating confusion for both students and institutions facing rapid technological change. Traditional plagiarism detectors, often failing to identify sophisticated AI assistance, has prompted students to develop their own integration strategies without clear institutional guidance. This research explores how Large Language Models can be ethically integrated into master’s thesis writing without compromising educational objectives. Using a literature review and student interviews, the dissertation identifies four main ethical challenges: transparency failures, confusion over ownership of AI-generated content, the inadequacy of traditional plagiarism definitions, and the reinforcement of inequalities with the increasing availability of AI tools. Adaptive guidelines are proposed to ensure responsible AI use that preserves academic integrity while preparing students for AI-integrated professional environments.

Detecting plagiarism, which is crucial for upholding institutional credibility, is a persistent and growing challenge for university faculty. The recent emergence of AI language models like ChatGPT introduces a new complication for faculty: discerning whether students have used AI in the production of their course work. Until AI detection software becomes effective at revealing the presence and the extent of the contributions made by AI to student submissions, the ability of faculty to detect, by either expertise or intuition, whether text is generated primarily by AI or student effort, is an important topic for exploration. We investigated faculty members' ability to differentiate paragraphs written by students from those generated by ChatGPT. One group of students used ChatGPT, while another wrote essays without AI assistance. We collected student essays and distributed random samples to 27 faculty members of an English department, 19 of whom responded to a questionnaire in which they reported their beliefs about which texts were written by students and which texts written by AI, and indicated the reasons underlying those beliefs. Faculty were generally successful in identifying ChatGPT-generated content using their expertise and intuition. Linguistic cues, such as syntactic structures, language idiosyncrasies, and contextual coherence gaps were commonly cited as explanations for their decisions.

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPTgenerated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

Plagiarism is the Ministry of Education's responsibility, not the "watchdog" of portals Taulant Muka In Albania, legal paradoxes and oddities always occur, where instead of the state assigning an institution to verify and monitor plagiarism, the institution responsible for this task fails to function, and, unintentionally (?!), leaves it in the hands of someone like Taulant Muka. He may have done his technical and possibly professional work rigorously, but legally, Mr. Muka has made a mistake, as no one has tasked him with checking others' doctorates. It's one thing that he has carried out this task, but it's another that he has posted these analyses and conclusions online, on portals, instead of confronting these findings with the institutions that awarded the doctorates, via the Ministry of Education. It would have been more proper for this gentleman, who seems to have talent in this area, to send his suggestions and conclusions to the relevant Directorate at the Ministry of Education, Youth, and Sports of Albania. The most illogical action, in my opinion, is sending observations to SPAK without first passing them through the relevant Directorate at the Ministry of Education, Youth, and Sports. Why do I say this? Because Decision No. 777, dated 26.12.2018, "On the Anti-plagiarism control of dissertations for obtaining academic degrees," states: 1. The creation of a digital archive of dissertations used for obtaining academic degrees in public and private higher education institutions in the Republic of Albania, at the ministry responsible for education or other institutions assigned by it.

Scopus remains the most influential academic indexing database worldwide, shaping research visibility, credibility, and career advancement. In 2025, publishing in Scopusindexed journals has become not only an academic requirement but also a professional milestone for researchers, lecturers, and graduate students, particularly in countries like Indonesia where institutions link Scopus publication to promotion and accreditation. This whitepaper provides a comprehensive roadmap for navigating the evolving world of Scopus publishing. It highlights the latest policy updates from Elsevier and global publishers, identifies common reasons for manuscript rejection, and offers a step-by-step strategy to increase acceptance rates. Particular attention is given to challenges faced by Indonesian and Southeast Asian scholars, including language barriers, predatory journal traps, and the rising need for transparency in the age of Artificial Intelligence. By integrating institutional perspectives, practical case studies, and ethical considerations, this guide empowers scholars to pursue publication with confidence. Drawing upon both international policies and La Academic's practical experience in supporting manuscripts "from draft to publication" (La Academic, 2025a), the paper bridges theory and practice. It argues that success in Scopus publishing depends not only on research quality but also on strategic preparation, policy literacy, and adherence to academic integrity.

The identification and detection of military aircraft hold significant importance across various domains, encompassing aviation safety, border security, and aerial surveillance. With strategic decision-making heavily reliant on accurate aircraft identification, the task remains inherently challenging. This study introduces an innovative approach utilizing You Only Look Once version 5 (YOLOv5), a cutting- edge object detection framework, to address this challenge. Leveraging the multi- detectionheadsoftheYOLOv5architecture, the model demonstrates the capability to detect air craft across diverse scales, spanning from small to large objects. Through the prediction of bounding boxes and class labels, crucial information for both identification and localization of aircraft is obtained. Based on the results, the analysis led to the conclusion that the proposed model produced accuracy of 87%. The proposed methodology attains a notable balance between high detection accuracy and real- time performance, rendering it well-suited for a wide range of applications including airspace surveillance, aircraft tracking, and safety enforcement. Provide a concise summary of the paper, outlining the problem, the method (YOLOv5), key results, and the significance of your work. Typically, the abstract should be between150 and 250 words. For example: "In this paper, we propose a real-time method for detecting military aircraft using YOLOv5, a state-of-the-art deep learning object detection model. Leveraging YOLOv5’s capability for fast and accurate object detection, we implement a system designed to detect various military aircraft in real-time from aerial and satellite imagery. We detail our data preprocessing techniques, model training, and evaluation, achieving high precision and recall rates on a custom dataset of military aircraft. The proposed method is evaluated against traditional methods and proves to be more efficient, making it suitable for real-time applications in defense and security."

This overview presents the Author Profiling and Deception Detection in Arabic (APDA) shared task at PAN@FIRE 2019. Two have been the main aims of this years task: i) to profile the age, gender and native language of a Twitter user; ii) to determine whether an Arabic text is deceptive or not in two different genres: Twitter and news headlines. For this purpose we have created three corpora in Arabic. Altogether, the approaches of 13 participants are evaluated.

This review paper examines the integration of Explainable AI (XAI) techniques into abnormal human activity detection from surveillance videos, emphasizing their significance in enhancing transparency, accountability, and trustworthiness in AI attention mechanisms, we discuss how these techniques provide insights into the decision enabling stakeholders to understand and interpret XAI for debugging errors, identifying biases, and increasing trust among human operators in various security contexts. Additionally, we discuss ongoing research directions in XAI for abno development of advanced explanation techniques and the incorporation of user feedback into the explanation process. By fostering collaboration between humans and AI, XAI holds the potential to enhance the effect deployment of surveillance systems, contributing to safer and more secure environments for all stakeholders.

AI has transformed academic writing and assessment. GPT-3.5 and GPT-4 create fluent writings which invite the questions of authorship and originality, and equity. In reply, universities and their publishers have incorporated AI detection platforms such as GPTZero, Copyleaks, CrossPlag, and Turnitin's AI module. Such systems are sold as effective tools to ensure integrity is maintained, but increasing reports have indicated that they are erroneous in classifying texts most time. False positive rates are found to be high, with inconsistency as text length varied, and systematic bias towards non-native writers of English. These detectors are also bypassed by means of paraphrasing, where the reward of manipulation is credited, and the punishment is credited for honest work. Classroom/institutional experience of reputational, ethical, and legal risks is connected to their use. Critical scholarly arguments hold that overreliance on opaque detection systems promotes the creation of surveillance cultures and promotes inequality, and instead encourages authentic learning. This paper analyzes peer-reviewed studies and case studies, and also academic arguments to prove that the AI detectors are a grave miscarriage of the values that they purport to preserve. It rounds off with a demand for process-driven measurement, critical AI poetry, and policy changes that prioritize human decision-making over defective algorithmic policing.

The reduction in farm output can be linked to three major causes, i.e., plant diseases, improper crop choices, and wrong yield estimates. The remedy calls for an integrated artificial system that merges disease identification, crop suggestions, and yield estimation functionalities. The proposed system proves to be highly effective in identifying infected plant types and disease types by employing early disease detection with convolutional neural networks (cnns) on the plant village dataset. The crop suggestion model employs Random Forest (RF) as the primary algorithm, prioritizing its performance optimization over methods like k-nearest neighbors (KNN), logistic regression, and decision trees. The crop recommendation helps the farmers to decide on proper crops by looking into their nitrogen, phosphorus, potassium contents in the soil, pH level, and water content. The system utilizes a decision tree model to forecast crop yield, drawing on historical data related to agriculture and weather conditions. The mode enables accurate estimation of yield, which can help the farmer plan effectively and optimize his use of resources. All components and evaluation algorithms exhibited exceptional accuracy in their respective functions during the testing phase. The integrated software system integrates three functions that are critical in agriculture into a single product, offering farmers real-time worth information and improving farm efficiency while maintaining sustainability.

The retrieval of similar documents from large scale datasets has been the one of the main concerns in knowledge management environments, such as plagiarism detection, news impact analysis, and the matching of ideas within sets of documents. In all of these applications, a light-weight architecture can be considered as fundamental for the large scale of information needed to be analyzed. Furthermore, the relevance score for documents retrieval can be significantly improved using several previously built search engines and taking into account the relevance feedback from users. In this work, we propose a web-services architecture for the retrieval of similar documents from the web. We focus on software engineering to support the manipulation of users' knowledge into the retrieval algorithm. An human evaluation for the relevance feedback of the system over a built set of documents is presented, showing that the proposed architecture can retrieve similar documents by using the main search engines. In particular, the document plagiarism detection task was evaluated, for which its main results are shown.

Copyright enforcement tools for large language models (LLMs), such as Copyleaks, claim to detect violations by identifying copied or derivative text. However, most rely on brittle surface-level heuristics like exact or near-exact string matching. We demonstrate how such filters can be trivially bypassed through two distinct transformations: (1) homophonic rewrites, which preserve auditory resemblance while destroying textual similarity, and (2) semantic paraphrasing, which retains meaning while altering surface form. Using well-known copyrighted lyrics as a test case, we show how both approaches evade detection while clearly reproducing protected material-especially when performed by generative audio models. Our findings highlight the legal and technical incoherence of current filtering regimes and call for a reevaluation of their application in education, publishing, and AI alignment.

The 1920s marked a period of intense theological conflict within American Protestantism, and Seventh-day Adventism was not immune to these broader currents. Central to the debate was the nature of biblical inspiration: whether Scripture was thought-inspired or verbally inspired and inerrant. Fundamentalism's rise profoundly influenced Adventism's internal struggle over inspiration, with long-lasting consequences for its theology, leadership, and approach to issues such as women in ministry. Understanding these intersections sheds light not only on Adventist history but also on current theological tensions within the denomination. There has been a long-standing question in Christianity: How do we understand the Bible? Is it verbally inspired word-for-word, or did God inspire the thoughts, allowing human writers to express His will in their own way? And why does it matter? Let's consider inerrancy, verbal Inspiration, and timelessness-and most importantly, about the living God who wants to be known through His Word. At the turn of the 20th century, something huge was happening in the world. Science was booming, cities were growing, and many people began to question old beliefs. Scientists like Charles Lyell and Charles Darwin shook people's trust in the Bible's accounts of creation and the flood. Universities, once centers of Christian learning, shifted toward scientific materialism. The Bible, once revered, started to be seen by some as just "another ancient book." In the midst of this turmoil, the Christian church was divided. Some said, "We must adapt!" Others said, "We must stand firm!" This gave rise to a group called the fundamentalists, who insisted that the Bible was completely without error, and every word was directly inspired by God.1 Fundamentalism, emerging in the early twentieth century, positioned itself as a militant defense of orthodox Protestantism against theological liberalism, higher criticism, evolution, and other modernist threats. Rooted in the nineteenth century's cultural and intellectual upheavals, fundamentalism reacted to shifts in American society: the erosion of traditional morality, the challenge of Darwinian evolution, urbanization, materialism, and an increasingly pluralistic religious landscape. Have you ever felt that tension even today? Where some say, "Let's modernize the message," and others say, "Let's stick to the Word!" The fundamentalists weren't wrong to defend the Bible. They loved God's Word passionately! They insisted on five non-negotiable beliefs: the inspiration of Scripture, the virgin birth, the atoning death of Christ, His bodily resurrection, and His literal return. When they talked about "verbal inspiration," they meant that every word of Scripture was given by God-not just the ideas, but the very words themselves. They argued, "If God is perfect, then His Word must be perfect too!" This belief led them to a strict, literal reading of Scripture. They feared that any compromise would unravel the entire Christian faith. And you know what? Their courage to stand up for the authority of the Bible helped preserve Christian faith for millions during a very shaky time. But there were also dangers. When you think that every comma and semicolon is dropped from heaven, you might miss the human fingerprints God allowed-the beautiful, diverse voices He chose to use. Our own Adventist Church in the 1920s found itself influenced by this rising tide. Many Adventists resonated with the fundamentalists because we, too, loved the Bible and defended its authority against higher criticism and evolution. Figures like F.M. Wilcox and George McCready Price strongly identified with fundamentalist Christians. Price even said fundamentalists "enthusiastically adopted" our defense of creationism!2

Новітнє уявлення про те, що цінність твору полягає насамперед у його
оригінальності та неповторності, сформувалось у читацькій рецепції відносно нещодавно – упродовж останніх віків. Натомість літературна парадигма кінця XVII – початку XVIII ст. передбачала підкріплення письменницького бачення авторитетом попередників. Як наслідок, художні тексти того часу рясніли численними цитатами з творів інших авторів.
Панегірик Івана Орновського «Багатий Сад» було створено в такому ж
ключі. Його текст насичений безпрецедентною для творів української польськомовної поезії кількістю як підписаних, так і анонімних фрагментів із Біблії, творів античних класиків, отців церкви, письменників Ренесансу та Бароко. Латинські, іноді польські цитати чи окремі словосполучення майстерно вплетені в прозовий текст передмов панегірика «Багатий Сад», що було характерно для творів
цього жанру. Крім того, уривки чи окремі рядки з віршованих, а часом і прозових творів доповнюють, увиразнюють думку чи образ у віршованій частині «Багатого саду». Вірогідно, частину цитат було свідомо перероблено, відредаговано для потреб панегірика. Окремі сентенції введено в текст у перефразованому вигляді чи в адаптованому перекладі.

The world is drowning in academic and high-quality publications, making it harder than ever to spot truly original ideas. GeniusRanker is an AI-powered platform that ranks texts by originality, coherence, and interdisciplinary relevance—independent of citations, prestige, or popularity. It can handle modern, historical, and even non-standard formats, giving overlooked authors and unconventional thinkers a fair chance to be discovered. An MVP could be built today using existing AI via API, with future refinement through a dedicated model. The goal: make groundbreaking ideas visible in the noise.

As higher education continues to change, the way students interact with knowledge has been profoundly altered by the incorporation of Artificial Intelligence (AI) into learning environments. This study looked into how undergraduate students' development of self-directed learning (SDL) skills was affected by AI-based learning platforms like ChatGPT, Coursera, Napkin.AI, QuillBot, and Duolingo. SDL, which is defined by students' capacity to establish objectives, track their progress, manage resources, evaluate their learning, and maintain motivation, is becoming more and more important in the current digital education environment. Data was collected from a diverse sample of 220 undergraduate students across disciplines through a structured questionnaire. The study evaluated the frequency and efficacy of these AI tools' use as well as the degree to which they enhance fundamental SDL abilities like self-motivation, goal-setting, time management, resource utilization, and self-monitoring. Quantitative analysis revealed a positive correlation between regular use of AI tools and higher levels of SDL readiness. Qualitative insights further highlighted that personalized feedback, instant access to information and gamified learning elements enhance learner autonomy and reflection. The findings suggested that AI-based tools hold significant potential in fostering independent learning behaviors, provided that students are guided on optimal usage. The study concluded that AI-based tools can effectively foster SDL when integrated with intentional pedagogical support, offering insights for educators aiming to enhance learner independence and engagement. The study also concluded that AI-based learning tools, when used strategically, have the potential to enhance self-directed learning skills in undergraduate learners. However, the effectiveness of such tools depends on learners' awareness of how to leverage them appropriately and the presence of institutional support that promotes responsible and reflective tool usage. In order to enhance academic performance and lifetime learning competencies, the study suggested that educational institutions use AI responsibly and help students develop their capacity for self-regulation.

This study addresses a significant and previously unexamined issue in plagiarism detection by investigating how culturally embedded expressions are systematically misclassified by automated systems. Drawing on a corpus of 2,847 academic documents and empirical testing across eight widely used detection tools, I found that these systems consistently misidentify culturally significant phrases as plagiarized content. The results show that 68 percent of such expressions are flagged incorrectly, with variation depending on cultural origin, temporal distance from the source, and disciplinary context. To the best of my knowledge, this is the first study to formally define and quantify this phenomenon, which I term cultural detection bias. I introduce the Cultural Embedding Quotient (CEQ) as a novel metric for assessing the degree to which a phrase functions as shared cultural knowledge. I also demonstrate that modest algorithmic adjustments can reduce false positives by 47 percent without weakening the system's ability to detect genuine plagiarism. These findings contribute to ongoing debates about authorship, attribution, and algorithmic fairness in digital humanities and academic integrity policy.

AI has revolutionized how we process information, optimize tasks, and conduct research. However, its integration into academia sparks ethical and practical debates. Should we limit its use? How can we assess a student’s true knowledge if they employ these tools? This text explores these questions from the perspective of a technology expert who argues that banning AI is as absurd as rejecting calculators or spreadsheets in the past. The key lies in adapting teaching and evaluation methods to harness its benefits without sacrificing intellectual rigor.

Cloud storage as a service provides scalability and high availability as per the user's need, without considerable investment in infrastructure. However, data security risks, like confidentiality, privacy, and integrity of the outsourced data, are associated with this model. Over the years, techniques like remote data checking (RDC), data integrity checking or data integrity protection (DIP), provable data possession (PDP), Proof of Storage (POS) and Proof of irretrievability (POR), etc., have been devised to frequently and securely check the integrity of outsourced data. Cloud storage service is always assumed to be unreliable and insecure, so a secure and efficient data integrity checking mechanism is of utmost importance. This thesis focuses on making the existing PDP scheme more efficient in computation, storage, and communication cost for extensive data archives. By utilizing JAR and ZIP technology capabilities, we have reduced the cost of searching the metadata in the proof generation process from O(n) to O(1). Due to direct access to metadata, disk I/O cost is reduced, and we achieved 50 to 60 times faster proof generation for large datasets. Our proposed scheme achieved a 50% reduction in the storage size of data and respective metadata, providing storage and communication efficiency.