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Cheating Detection in Online Exams Using Deep Learning and Machine Learning

APPLIED SCIENCES-BASEL 2025年第1期15卷

作者： Erdem, Bahaddin Karabatak, Murat Bitlis Eren Univ Adilcevaz Vocat Sch Dept Comp Programming TR-13000 Bitlis Turkiye Firat Univ Fac Technol Dept Software Engn TR-23119 Elazig Turkiye

Featured Application The proposed model can contribute to the field in revealing the best performance with the least error rate in deep learning and machine learning applications. It can be useful in classifying and detecting unethical behavior patterns in online distance education *** This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. One hundred twenty-nine online exam data were analyzed by the researcher with three different scenarios to reveal the best model performance in regression and classification. For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. In the regression analysis conducted within the scope of Scenario-1, the model we proposed to detect "cheating" behavior, which is one of the unethical learner behaviors, was found to be a 5-layer DNN model with a test performance success of 80.9%. In the binary classification analysis for Scenario-2, students who "copied" from unethical behaviors were obtained with an accuracy rate of 96.9% by the model established by the 10-layer DNN algorithm we proposed. In the triple classification analysis for Scenario-3 defined in the study, the XGBoost model was found to have the highest accuracy rate of 97.7% for students who "cheated" due to unethical behaviors and the highest performance in all other metric values. In addition, SHAP and LIME methods, which are explanatory methods for the XGBoost model, which is one of the best-performing models, were applied, and the attributes and percentages affecting the model were shared. As a result of this study, it has been shown that the application of the most

关键词： deep learning machine learning cheating academic integrity online examination

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Parallel PSO for Efficient Neural Network Training Using GPGPU and Apache Spark in Edge Computing Sets

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ALGORITHMS 2024年第9期17卷 378页

作者： Capel, Manuel I. Salguero-Hidalgo, Alberto Holgado-Terriza, Juan A. Univ Granada Software Engn Dept ETSIIT Granada 18071 Spain Univ Malaga Dept Comp Sci & Programming Languages ETSII Malaga 29010 Spain

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate *** paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO.

关键词： Apache Spark classification recall deep neural networks GPU parallelism optimization research particle swarm optimization (PSO) predictive accuracy

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Deep Learning-Based Refactoring with Formally Verified Training Data

INFOCOMMUNICATIONS JOURNAL

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INFOCOMMUNICATIONS JOURNAL 2023年 15卷 2-8页

作者： Szalontai, Balazs Bereczky, Peter Horpacsi, Daniel Eotvos Lorand Univ Dept Software Technol & Methodol Budapest Hungary Eotvos Lorand Univ Dept Programming Languages & Compilers Budapest Hungary

Refactoring source code has always been an active area of research. Since the uprising of various deep learning methods, there have been several attempts to perform source code transformation with the use of neural networks. More specifically, Encoder-Decoder architectures have been used to transform code similarly to a Neural Machine Translation task. In this paper, we present a deep learning-based method to refactor source code, which we have prototyped for Erlang. Our method has two major components: a localizer and a refactoring component. That is, we first localize the snippet to be refactored using a recurrent network, then we generate an alternative with a Sequence-to- Sequence architecture. Our method could be used as an extension for already existing AST-based approaches for refactoring since it is capable of transforming syntactically incomplete code. We train our models on automatically generated data sets, based on formally verified refactoring definitions and by using attribute grammar-based sampling.

关键词： Deep learning Formally verified training data Neural Machine Translation Sequence-to-Sequence

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WIP: An Engaging Undergraduate Intro to Model Checking in software Engineering Using TLA+ 54

WIP: An Engaging Undergraduate Intro to Model Checking in So...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Loyola University Software and Systems Laboratory Department of Computer Science Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

ISBN: (纸本)9798350351507

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive 'failuresy' through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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An empirical study of token-based micro commits

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EMPIRICAL software ENGINEERING 2024年第6期29卷 148页

作者： Kondo, Masanari German, Daniel M. Kamei, Yasutaka Ubayashi, Naoyasu Mizuno, Osamu Kyushu Univ Principles Software Engn & Programming Languages P Fukuoka Japan Univ Victoria Dept Comp Sci Victoria BC Canada Inamori Res Inst Sci Kyoto Japan Waseda Univ Tokyo Japan Kyoto Inst Technol Software Engn Lab SEL Kyoto Japan

In software development, developers frequently apply maintenance activities to the source code that change a few lines by a single commit. A good understanding of the characteristics of such small changes can support quality assurance approaches (e.g., automated program repair), as it is likely that small changes are addressing deficiencies in other changes;thus, understanding the reasons for creating small changes can help understand the types of errors introduced. Eventually, these reasons and the types of errors can be used to enhance quality assurance approaches for improving code quality. While prior studies used code churns to characterize and investigate the small changes, such a definition has a critical limitation. Specifically, it loses the information of changed tokens in a line. For example, this definition fails to distinguish the following two one-line changes: (1) changing a string literal to fix a displayed message and (2) changing a function call and adding a new parameter. These are definitely maintenance activities, but we deduce that researchers and practitioners are interested in supporting the latter change. To address this limitation, in this paper, we define micro commits, a type of small change based on changed tokens. Our goal is to quantify small changes using changed tokens. Changed tokens allow us to identify small changes more precisely. In fact, this token-level definition can distinguish the above example. We investigate defined micro commits in four OSS projects and understand their characteristics as the first empirical study on token-based micro commits. We find that micro commits mainly replace a single name or literal token, and micro commits are more likely used to fix bugs. Additionally, we propose the use of token-based information to support software engineering approaches in which very small changes significantly affect their effectiveness.

关键词： Empirical study Micro commits Tokens Mining software repositories

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Aggregate Update Problem for Multi-clocked Dataflow Languages 22

Aggregate Update Problem for Multi-clocked Dataflow Language...

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20th IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

作者： Kallwies, Hannes Leucker, Martin Scheffel, Torben Schmitz, Malte Thoma, Daniel Univ Lubeck Inst Software Engn & Programming Languages Lubeck Germany

ISBN: (纸本)9781665405843

Dataflow languages have, as well as functional languages, immutable semantics, which is often implemented by copying values. A common compiler optimization known from functional languages involves analyzing which data structures can be modified in-place instead of copying them. This paper presents a novel algorithm to this so called Aggregate Update Problem for multi-clocked dataflow languages, i.e. those that allow streams to have events at disjoint timestamps, like e.g. Lucid, Lustre and Signal. Unrestricted multi-clocked languages require a static triggering analysis on how events and hence data values are read, written and replicated. We use TeSSLa as a generic stream transformation language with a small set of operators to develop our ideas. We implemented the solution in a TeSSLa compiler targeting the Java VM via Scala code generation which combines persistent data structures and mutable data structures for those data values which allow in-place editing. Our empirical evaluation shows considerable speedup for use cases where queues, maps or sets are dominant data structures.

关键词： Java Codes Aggregates Semantics Static analysis Performance gain Data structures

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Effectiveness of Performance Visualizations for Declarative Model Transformations

Effectiveness of Performance Visualizations for Declarative ...

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2024 IEEE Working Conference on software Visualization

作者： Groner, Raffaela Tichy, Matthias Chalmers Univ Gothenburg Dept Comp Sci & Engn Gothenburg Sweden Ulm Univ Inst Software Engn & Programming Languages Ulm Germany

ISBN: (纸本)9798331528492;9798331528485

Several profilers for general-purpose languages like Java offer visualizations to support users in understanding the execution of a program and identifying the causes of a performance issue. Unfortunately, these performance visualizations are difficult to reuse in profilers for declarative model transformations since they cannot display transformation-specific information. For example, a profiler for a declarative model transformation language must provide information on the traversal of the input model since it impacts the performance but is hidden from the developer. Moreover, the respective visualization must scale for input models that consist of several thousand model elements. Hence, we developed performance visualizations for the declarative model transformation language Henshin that provide insights into the transformation execution. Subsequently, we performed a mixed methods study with 18 Henshin novices to evaluate the effectiveness of our visualizations. In our study, the participants tried to improve the execution performance of four transformations by performing a root cause analysis using our visualizations. The results of our study show that depending on the task, between 16 and 18 participants understood the execution of a transformation correctly based on our visualizations. Moreover, between 12 and 18 participants proposed effective optimizations using our visualizations.

关键词： Performance Visualization Mixed Methods Study Declarative Model Transformation Henshin

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A Distributed Particle Swarm Optimization Algorithm Based on Apache Spark for Asynchronous Parallel Training of Deep Neural Networks 24

A Distributed Particle Swarm Optimization Algorithm Based on...

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53rd International Conference on Parallel Processing (ICPP)

作者： Capel, Manuel, I Holgado-Terriza, Juan A. Galiana-Velasco, Sergio Salguero, Alberto G. Univ Granada Software Engn Dept Granada Andalucia Spain Univ Malaga Dept Comp Sci & Programming Languages Malaga Andalucia Spain

ISBN: (纸本)9798400718021

Deep Learning Neural Networks (DLNN) require an immense amount of computation, especially in the training phase when multiple layers of intermediate neurons need to be built. The situation is even more dramatic today with the proliferation of applications with intelligence at the edge, not just in the cloud. Therefore, to meet the new requirements of edge computing, it is imperative to accelerate the execution phases of neural networks as much as possible. In this paper, we will focus on the algorithm known as Particle Swarm Optimization (PSO). It is a bio-inspired, stochastic optimization approach whose goal is to iteratively improve the solution to a given (usually complex) problem by attempting to approximate a given objective. The use of PSO in an edge computing environment has the potential to make the training of the DLNN there without the need to transfer resource-intensive tasks to the cloud. However, implementing an efficient PSO is not a straightforward process due to the complexity of the computations performed on the swarm of particles and the iterative execution until until a near-target solution with minimal error is achieved. In the present work, two parallelizations of the PSO algorithm have been implemented, both designed for a distributed execution environment (Apache Spark). The first PSO parallelization follows a synchronous scheme;i.e., the best global position found by particles is globally updated before the execution of the next iteration of the algorithm. This implementation proved to be more efficient for medium-sized datasets (<40000 data points). In contrast, the second implementation is an asynchronous parallel variant of the PSO algorithm, which showed lower execution time for large datasets (> 170,000 data points) compared to the first one. Additionally, it exhibits better scalability and elasticity with respect to increasing dataset size. Both variants of the PSO have been implemented to distribute the computational load (particle fit

关键词： Apache Spark Classification recall Deep Neural Networks GPU Parallelism Optimization research Particle Swarm Optimization (PSO) Predictive Accuracy

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Improving Collaborative Modeling by an Operation-Based Versioning Approach

Improving Collaborative Modeling by an Operation-Based Versi...

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2024 software Engineering, SE 2024

作者： Exelmans, Joeri Pietron, Jakob Raschke, Alexander Vangheluwe, Hans Tichy, Matthias University of Antwerp Flanders Make Antwerp Belgium Ulm University Institute of Software Engineering and Programming Languages Ulm Germany

ISBN: (纸本)9783885797371

The presented work concerns our recent research on advanced collaboration and versioning techniques supporting blended modeling, originally published in the Journal of Computer Languages in August 2023 [Ex23]. Collaboration is a rising topic not only but also in the domain of model-driven engineering. The more collaborators working together on a joint model or artifact, the more adequate tooling becomes important, especially if the collaborators use different, but for their specific task most appropriate notations/concrete syntaxes (CS) simultaneously. The presented approach supports collaboration in this context by applying and extending operation-based versioning to support bidirectional change propagation between multiple CSs and a common single abstract syntax (AS). We further broaden the topic of our talk by presenting benefits for the user of modeling tools introduced by operation-based versioning and the information included in the recorded operations. We especially focus on how we facilitate the comprehension of a model’s evolution over time and the impact of a distinct edit operation on the whole model. This allows modelers to understand how, why, when, and by whom the model or specific elements were modified [PFT22]. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Blended Modeling Collaboration Evolution Modeling Understandability Versioning

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Optimizing Trans-Compilers in Runtime Verification Makes Sense - Sometimes 1

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16th International Symposium on Theoretical Aspects of software Engineering (TASE)

作者： Kallwies, Hannes Leucker, Martin Prilop, Meiko Schmitz, Malte Univ Lubeck Inst Software Engn & Programming Languages Lubeck Germany

ISBN: (数字)9783031103636

ISBN: (纸本)9783031103636;9783031103629

This paper considers two kinds of optimizations for a specification language compiler for stream-based runtime verification: (i) the manual addition of core functions with dedicated translation schemas and (ii) an improved initialization that simplifies subsequent constant propagation. We employ both optimizations within the open source runtime verification framework TeSSLa, which comes with a trans-compiler as synthesis tool which translates TeSSLa specifications to Scala code eventually running on the JVM. Our evaluation shows that the first optimization improves the efficiency of the resulting monitor significantly while the second gets lost within the variety of optimizations present for the back end systems.

关键词： Specifications

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