Search Results (150)

String matching is a foundational problem in computer science with applications that span data mining, text retrieval, bioinformatics, and software engineering. Traditional string matching algorithms often face limitations with space and time efficiency, particularly in large-scale data applications. Sampled string matching is a recent approach designed to improve efficiency by addressing the high space requirements for index construction and reducing search time for online solutions. Parameterized pattern matching, a generalized form of string matching, introduces flexible matching criteria, initially developed to detect duplicate code and plagiarism in software maintenance. Its utility has since expanded to various domains, enabling adaptable matching across diverse applications. In this paper, we propose a novel algorithm for parameterized string matching that takes advantage of character distance sampling. This technique samples the distances between consecutive occurrences of a specified pivot character, facilitating an efficient online search within the sampled data for pattern occurrences before performing verification in the full text. Through extensive experimentation with diverse datasets, we demonstrate that our algorithm outperforms existing parameterized string matching methods in terms of speed. These results suggest that character distance sampling is a promising approach to enhance the efficiency and scalability of parameterized string matching algorithms, opening new avenues for application in large-scale and complex data environments. Full article

Source code plagiarism is a significant challenge in software engineering and computer science education, affecting academic integrity, intellectual property rights, and software quality assurance. However, Source Code Plagiarism Classification (SCPC) remains difficult because labelled training data are limited, mainly due to the sensitivity of plagiarism cases. This restricts the effective use of machine learning (ML) and deep learning (DL) methods, especially in low-resource settings. This work investigates low-resource SCPC using Pre-trained Code Models (PCMs). We first examine Full Fine-Tuning (FFT), where all model parameters are updated, across multiple public datasets. We then evaluate Parameter-Efficient Fine-Tuning (PEFT), where only a small subset of parameters is trained. Specifically, we apply three adapter-based PEFT methods and compare them with FFT in terms of classification performance, training time, inference time, GPU usage, trainable parameter percentage, and model size. The results show that, when labelled training data are available, fine-tuned PCMs achieve strong SCPC performance and higher $F_1$ scores than the unsupervised open-source plagiarism-detection tools in our evaluation, JPlag and Dolos. Overall, PEFT achieves a performance that is similar, comparable to, or slightly lower than that of FFT, while requiring fewer trainable parameters and lower GPU usage, at the cost of slightly higher inference time. Full article

This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there. Full article

This paper presents a novel platform for automated self-paced learning in Computer-Aided Design (CAD) courses within engineering education. The platform fully automates the entire learning cycle, including exercise generation, submission, scheduling, test design, and grading. The central hypothesis posits that complete automation reduces repetitive tasks for instructors, allowing them to dedicate more time to individualized student support. The system also provides key advantages: it generates unique exercises for each student to prevent plagiarism while maintaining comparable complexity; it delivers instantaneous grading and feedback to enhance motivation; and it enables students to work with almost any CAD software, as the evaluation relies on physical properties rather than commercial tools. After several years of successive testing and refinement, the tool can generate and accurately grade frequent activities in large student cohorts, providing abundant data points. Their statistical analysis, via multiple approaches, confirms that the system reliably produces individualized exercises, reduces grading errors, and offers prompt, consistent feedback, thereby supporting a more efficient and engaging learning process. Full article

In the field of engineering, the advancement of automated scoring systems for laboratory reports has been significantly hampered by three persistent challenges: scarcity of high-quality annotated data, high domain-specific complexity, and insufficient model interpretability. To address these limitations, this study proposes an AdaBoost regression model based on multi-level feature engineering and threshold control, denoted as MFTC-ABR. This method constructs a multi-dimensional feature set using a lightweight neural network, which evaluates laboratory reports across four core dimensions: comprehension of experimental principles, completion of experimental procedures, depth of result analysis, and plagiarism detection. At the scoring algorithm level, a dynamic threshold adjustment mechanism is integrated into the AdaBoostReg ensemble learning framework. By redesigning the sample weight update rule, the prediction errors of samples are divided into three intervals: the acceptable region, the stable learning range, and the focus range. Accordingly, a differentiated weight update strategy is implemented, and a history-aware mechanism is introduced to further regulate the attention allocated to individual samples. Finally, experimental results on the power electronics laboratory report dataset show that MFTC-ABR model achieves a mean absolute error (MAE) of 3.09 and a scoring consistency rate of 82% within a five-point error tolerance. These findings validate the effectiveness and practicability of the proposed method for automatic assessment in specialized domains with limited data availability. Full article

The aim of this study was to design and validate a questionnaire to assess students’ perceptions of the use of Artificial Intelligence (AI) tools in academic tasks (PEHIA-TA). To determine the psychometric properties of the PEHIA-TA, a descriptive, exploratory and confirmatory factor analysis was carried out. The sample used in this study consisted of 546 students. The results confirmed that it is a valid and reliable scale with a five-factor structure: “Uses of Artificial Intelligence (AI)” (student opinion, knowledge and experience in relation to AI); “Perceptions of skills needed to use AI” (type of skills they consider necessary to work with this type of tool); “Plagiarism and lack of academic integrity” (issues related to what the student considers plagiarism and lack of academic integrity in order to identify possible risks or associated moral dilemmas); “Perception of the benefits of AI” (assessment of the beneficial aspects of the use of AI in the academic context by students); and “Perception of the problems of AI” (analyses how students assess the problems associated with the use of AI tools in the development of their tasks). The instrument allows for the traceability of training needs in digital literacy, as well as the formulation of institutional policies on the use of AI that contribute to the prevention of behaviours associated with academic dishonesty and ensure critical reflection by students on the risks and opportunities of AI in their educational process. Full article

This work addresses critical challenges to academic integrity, including plagiarism, fabrication, and verification of authorship of educational content, by proposing a Natural Language Processing (NLP)-based framework for authenticating students’ content through author attribution and style change detection. Despite some initial efforts, several aspects of the topic are yet to be explored. In contrast to existing solutions, the paper provides a comprehensive analyzis of the topic by targeting four relevant tasks, including (i) classification of human and machine text, (ii) differentiating in single and multi-authored documents, (iii) author change detection within multi-authored documents, and (iv) author recognition in collaboratively produced documents. The solutions proposed for the tasks are evaluated on two datasets generated with Gemini using two different prompts, including a normal and a strict set of instructions. During experiments, some performance reduction is observed for the proposed solutions on the dataset generated by the strict prompt, demonstrating the complexities involved in detecting machine-generated text with cleverly crafted prompts. The generated datasets, code, and other relevant materials are made publicly available on GitHub, which are expected to provide a baseline for future research in the domain. Full article

Generative AI (GenAI) in education has become a divisive topic. While some teachers view GenAI as a transformative tool, others caution against harms and impacts on teaching and learning. Since the usefulness of a tool depends on the user, how teachers view and use GenAI and their perceptions of its role in teaching and learning may influence its benefits and risks. The study adopts a qualitative approach, interviewing 23 higher education teachers with teaching experience across disciplines. Using the Technological, Pedagogical, and Content Knowledge (TPACK) and sensemaking frameworks to guide and analyse the interviews, a set of opportunities, challenges, and threats associated with integrating GenAI in higher education was identified. Teachers highlighted opportunities, including using GenAI as a study buddy. Concerns were raised about students’ overreliance on GenAI, GenAI undermining the teaching and learning process, and issues like undetected plagiarism. The findings suggest a need for professional development to help teachers understand GenAI and how it can be effectively used in teaching. Some teachers warned against the paradox of using a tool to save time, only to find that it might increase workload and frustration. These insights contribute to developing guidelines and informing policymaking to ensure integration of GenAI in education. Full article

With the proliferation of online examination systems, protecting academic integrity and reliably detecting cheating have become significant research problems. Current AI-based online monitoring systems can achieve high accuracy by analyzing visual behavioral cues; however, their often black-box nature limits their explainability, reliability, and legal compliance (e.g., GDPR). In contrast, while rule-based approaches are interpretable, they are insufficient for generalizing complex and ambiguous human behaviors. This study proposes an explainable neuro-symbolic framework combining data-driven learning with symbolic reasoning for cheating detection in online exams. The proposed framework comprises three main layers: a neural perceptron layer that generates a suspicious behavior score; a symbolic reasoning layer comprising ANFIS and ILP methods to increase explainability and manage ambiguity; and a neuro-symbolic fusion layer that integrates these two layers. The success of the proposed framework for plagiarism detection was evaluated using a dataset containing visual–behavioral features such as gaze behavior, head pose, hand-object interaction, and device usage, along with the XGBoost method at the neural perceptron layer. Experimental results show that the proposed approach achieves high detection success and supports decision-making using logical rules, thereby reducing false positives. In this respect, the study offers an ethical, transparent, and reliable solution for online exam security. Full article

Peer review is the cornerstone of scholarly publishing and, in medicine, the ultimate guarantor of the reliability of clinical evidence that informs guidelines, therapeutic strategies, and patient care. However, the current peer review system is increasingly strained by bias, abuse, and reviewer overload. Favoritism toward prominent authors, editorial “nepotism,” coercive citation practices, superficial evaluations, and even documented cases of idea theft from confidential manuscripts undermine the trustworthiness of the scientific literature upon which clinical decisions depend. In this paper, we argue that artificial intelligence (AI) and large language models (LLMs) offer a transformative opportunity to strengthen the integrity and efficiency of medical peer review. AI-driven tools can perform rapid consistency checks, detect statistical errors or plagiarism, and enforce compliance with ethical and methodological standards across thousands of manuscripts. Early implementations of AI-guided review platforms, plagiarism detectors, and citation-anomaly algorithms demonstrate that machine assistance can make reviews more thorough, objective, and reproducible. At the same time, we acknowledge the limitations of AI, including hallucinations, a lack of human judgment, and risks to confidentiality if misused. To address these concerns, we propose a hybrid model in which AI handles routine screening and technical tasks under strict safeguards, while human experts retain final responsibility for scientific evaluation. This human–AI partnership may represent an essential step toward improving the quality, fairness, and reliability of the clinical evidence base. Full article

Generative Artificial Intelligence (GenAI) has been a viable technology for decades, yet widespread adoption in healthcare and academic settings has remained limited to research. One possible explanation for this is limited understanding about the beliefs around GenAI use amongst faculty and students training in biomedical disciplines that frequently lead to non-physician healthcare careers, including physical therapy (PT), occupational therapy (OT), allied health (AH), and biomedical engineering (BME). Furthermore, no known studies exist assessing differences that may exist across those disciplines. Given the significant number of professionals in those disciplines and the outsized impact they have on the healthcare system, investigating their beliefs around GenAI use is vital before widespread adoption. Accordingly, we investigated the perceptions of GenAI among students and faculty in the aforementioned fields that frequently lead to careers in healthcare. We found that knowledge of GenAI significantly influences comfort with its use completing college coursework including whether respondents believed it contributed to the process of completing that coursework and whether use of GenAI enhances learning. Interestingly, however, there were no statistically significant differences in perceptions of GenAI across disciplines, roles, or institution sizes. Qualitative findings revealed concerns about plagiarism, decline of critical thinking skills, and ethical challenges, while also recognizing GenAI’s potential to enhance learning efficiency and idea generation. Critically, the study results emphasize the need for proper training and guidelines to ensure GenAI is integrated responsibly into healthcare-related education. Full article

The style change detection (SCD) task involves identifying the locations of writing style changes in multi-authored documents. This task can be applied to plagiarism detection, security, and commerce applications. Introducing decoder-based Large Language Models (LLMs) marks a pivotal shift in applications. The segment boundaries for SCD models can be represented by concatenating two consecutive segments as pairs. However, LLMs usually restrict their input lengths, where the long-length inputs may exceed the restricted length. This paper seeks to bridge this gap and exploit the power of LLMs by introducing boundary-focused LLM Adaptation for SCD (BF-LLMA-SCD). The proposed solution adapts decoder-based LLMs for SCD using QLoRA. BF-LLMA-SCD truncates long-length input by preserving texts near an examined boundary while removing those at the other sides. BF-LLMA-SCD was trained on three PAN datasets. Comparison results with the top-performing SOTA solutions show that BF-LLMA-SCD achieved the best performance results in terms of F₁ on PAN 2021 and PAN 2022/D1, while obtaining competitive results on PAN 2022/D3. BF-LLMA-SCD was also trained on an Arabic SCD dataset comprising three difficulty levels. It achieved an F₁ score above 0.99 on easy instances. Full article

The evaluation of research and development (R&D) project proposals plays a critical role in shaping national scientific and technological priorities. However, existing expert review systems are often characterized by fragmented digital workflows, limited traceability of decisions, and a strong reliance on manual coordination, which reduces transparency and auditability. This paper proposes a conceptual and methodological framework for a national research project evaluation system that integrates artificial intelligence and blockchain technologies as complementary decision-support and data integrity mechanisms. The framework formalizes the complete evaluation lifecycle, including applicant authorization, formal compliance verification, originality and plagiarism analysis, expert selection and assessment, analytical consolidation of reviews, and fixation of final decisions. Artificial intelligence modules are introduced to support thematic classification, compliance checking, expert matching, and analytical processing of expert evaluations, while blockchain technology is incorporated as an immutable integrity layer for recording critical evaluation events and ensuring data provenance. The proposed approach focuses on architectural design, governance principles, and process modeling rather than system implementation or empirical validation. The framework is intended to serve as a reference model for the design and future development of transparent, accountable, and scalable research project evaluation platforms at national and institutional levels. Full article

Background: Integrity, encompassing honesty, accountability, and ethical conduct, is a cornerstone of the dental profession, essential for patient trust and safety. Despite its importance, academic dishonesty remains a pervasive issue in dental education globally. This review examines the prevalence, causes, and long-term career implications of academic dishonesty in dental education and explores institutional strategies to cultivate a culture of integrity. Method: The study was conducted using PubMed, Scopus, Web of Science, and Google Scholar to identify studies published between 1970 and 2025 on academic dishonesty in dental education. Search terms included dental students, cheating, plagiarism, and clinical falsification. Eligible studies reported prevalence, drivers, or consequences of dishonest behaviors. Data were extracted and thematically synthesized to highlight common patterns and professional implications. Results: Self-reported data indicate alarmingly high rates of cheating among dental students, ranging from 43% to over 90%. Common forms include exam fraud, plagiarism, and the falsification of clinical records. Key drivers include intense academic pressure, competitive environments, and a perception of weak enforcement. Such behaviors are not merely academic violations—they have profound professional consequences. A history of academic dishonesty can damage a student’s reputation, hinder licensure and credentialing processes, and limit postgraduate opportunities. Crucially, studies indicate that unethical behavior in school can normalize dishonesty, predicting a higher likelihood of future professional misconduct, such as insurance fraud or malpractice, thereby jeopardizing patient care and public trust. Conclusions: Academic integrity is a critical predictor of professional ethical conduct. Dental schools must move beyond punitive policies to implement proactive, multi-faceted approaches. This includes integrating comprehensive ethics curricula, fostering reflective practice, promoting faculty role modeling, and empowering student-led initiatives to uphold honor codes. Cultivating an unwavering culture of integrity is essential not only for academic success but for developing trustworthy practitioners committed to lifelong ethical patient care. Full article

Open access (OA) has expanded scholarly publishing, yet concerns remain about the sustainability of journals indexed in selective databases. This study analyzes editorial predictors of discontinuation among 8730 journals simultaneously registered in the Directory of Open Access Journals (DOAJ) and indexed in Scopus, including 58 (0.66%) discontinued titles as of June 2025 (latest available update at the time of data extraction). The analyses revealed that a journal’s history of prior discontinuation was the strongest and most consistent predictor of future instability, confirming that discontinuation follows a path-dependent pattern rather than isolated events. Financial structure also played a decisive role: journals applying other editorial fees beyond standard article processing charges (APCs) were nearly four times more likely to experience discontinuation (IRR = 3.877, p = 0.048), while those following standardized APC models showed a protective but non-significant tendency (IRR = 0.378, p = 0.084). Journal age exhibited a modest yet significant positive effect (IRR = 1.032, p = 0.031), suggesting that older titles face a gradual accumulation of risk over time. By contrast, editorial practices such as plagiarism detection, waiver policies, and turnaround time showed no significant association. Overall, the findings indicate that discontinuation in Scopus-indexed OA journals is statistically associated with historical trajectories, financial transparency, and governance capacity, rather than by routine editorial procedures. Full article