10,989 research outputs found

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors

    Get PDF
    Aufgrund der steigenden Anzahl der Informatikstudierenden verlassen sich Dozenten auf aktuelle Werkzeuge zur Erkennung von Quelltextplagiaten, um zu verhindern, dass Studierende plagiierte Programmieraufgaben einreichen. Während diese auf Token basierenden Plagiatsdetektoren inhärent resilient gegen einfache Verschleierungen sind, ermöglichen kürzlich veröffentlichte Verschleierungswerkzeuge den Studierenden, ihre Abgaben mühelos zu ändern, um die Erkennung zu umgehen. Der Vormarsch von ChatGPT hat zusätzliche Bedenken hinsichtlich seiner Verschleierungsfähigkeiten und der Notwendigkeit wirksamer Gegenstrategien aufgeworfen. Bestehende Verteidigungsmechanismen gegen Verschleierung sind oft durch ihre Spezifität für bestimmte Angriffe oder ihre Abhängigkeit von Programmiersprachen begrenzt, was eine mühsame und fehleranfällige Neuimplementierung erfordert. Als Antwort auf diese Herausforderung führt diese Arbeit einen neuartigen Verteidigungsmechanismus gegen automatische Verschleierungsangriffe namens Match-Zusammenführung ein. Er macht sich die Tatsache zunutze, dass Verschleierungsangriffe die Token-Sequenz ändern, um Übereinstimmungen zwischen zwei Abgaben aufzuspalten, sodass die gebrochenen Übereinstimmungen vom Plagiatsdetektor verworfen werden. Match-Zusammenführung macht die Auswirkungen dieser Angriffe rückgängig, indem benachbarte Übereinstimmungen auf der Grundlage einer Heuristik intelligent zusammengeführt werden, um falsch positive Ergebnisse zu minimieren. Die Widerstandsfähigkeit unserer Methode gegen klassische Verschleierungsangriffe wird durch Evaluationen anhand verschiedener realer Datensätze, einschließlich Studienarbeiten und Programmierwettbewerbe, in sechs verschiedenen Angriffsszenarien demonstriert. Darüber hinaus verbessert sie die Erkennungsleistung gegen KI-basierte Verschleierung signifikant. Was diesen Mechanismus auszeichnet, ist seine Unabhängigkeit von Sprache und Angriff, während sein minimaler Laufzeit-Aufwand ihn nahtlos mit anderen Verteidigungsmechanismen kompatibel macht

    UMSL Bulletin 2023-2024

    Get PDF
    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    The Structure and Function of the Retina in Multiple Sclerosis

    Get PDF
    Background: Multiple sclerosis (MS) is a complex heterogenous autoimmune inflammatory disease with a prolonged and variable time course. The visual system is frequently implicated, either as the presenting symptom, or, with advancement of the disease. This has been documented in the literature with changes in visual acuity (VA) that are accompanied by functional changes in the optic nerve, measured with the visual evoked potential (VEP) and possible retrograde degeneration involving the retinal ganglion cells in the retina, measured with the pattern reversal electroretinogram (PERG). However, inflammatory episodes may be clinical or subclinical in nature and may go unrecognised. Originating from the same embryological origins, the effect of inflammation in MS on the on the retina is less well known. The research hypothesis was that there is a measurable difference in the function of retinal cells in patients with newly diagnosed multiple sclerosis, suggestive of inflammatory retinopathy compared to healthy controls. The overall aim was to investigate any differences in the electrophysiological function of the visual pathway of patients newly diagnosed with MS compared to healthy controls. Methods: The visual system is explored with clinical (VA), electrophysiology (VEP and electroretinography (ERG – pattern and flash) and structural (OCT) measures, in patients presenting with symptoms suggestive of MS to a specialist service. This prospective case control study investigates the visual pathway at the earliest stage of the disease to look for differences in structure and function between patients and healthy volunteers that might serve as a biomarker in the future. Results: There were a number of variables that were significantly different between the two groups, logistic regression analysis found that VA (p 0.038) and VEP P100 peak-time (p 0.014) from the right eye as significant. Dividing the participants by prolongation of the VEP P100 peak-time as defined in clinical practice, found a number of ERG amplitude variables as well as VA that were consistently different between the groups regardless of symptoms. Conclusion: The study confirms optic nerve involvement in MS with VEP and VA abnormalities consistent with the literature in this cohort. Additionally, VA and some ERG amplitude variables were significantly reduced in participants with MS, when grouped according to VEP P100 peak-time, suggesting inner and outer retinal changes. Further work would be required to confirm these findings. No OCT structural changes were found in any of the analysis that included the macula thickness, ganglion cell layer or retinal nerve fibre layer. Keywords: multiple sclerosis (MS), visual evoked potential (VEP), pattern electroretinogram (PERG), electroretinogram (ERG), optical coherence tomography (OCT

    Deep Learning Techniques for Electroencephalography Analysis

    Get PDF
    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Optimizing digital archiving: An artificial intelligence approach for OCR error correction

    Get PDF
    Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThis thesis research scopes the knowledge gap for effective ways to address OCR errors and the importance to have training datasets adequated size and quality, to promote digital documents OCR recognition efficiency. The main goal is to examine the effects regarding the following dimensions of sourcing data: input size vs performance vs time efficiency, and to propose a new design that includes a machine translation model, to automate the errors correction caused by OCR scan. The study implemented various LSTM, with different thresholds, to recover errors generated by OCR systems. However, the results did not overcomed the performance of existing OCR systems, due to dataset size limitations, a step further was achieved. A relationship between performance and input size was established, providing meaningful insights for future digital archiving systems optimisation. This dissertation creates a new approach, to deal with OCR problems and implementation considerations, that can be further followed, to optimise digital archive systems efficiency and results

    UMSL Bulletin 2022-2023

    Get PDF
    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections

    Full text link
    Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers' related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list

    A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

    Full text link
    Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table
    corecore