This study investigates the implementation and impact of mastery learning in a computerscience course, particularly during the transition from traditional teaching methods to mastery learning amidst the COVID-19 pand...
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In the dynamic educational context of Malaysia, this study examines the impact of integrating Unplugged Activities (UA) with Block-Based programming (BBP) on improving the computational thinking (CT) skills of seconda...
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In the dynamic educational context of Malaysia, this study examines the impact of integrating Unplugged Activities (UA) with Block-Based programming (BBP) on improving the computational thinking (CT) skills of secondary students in full boarding schools in Northern Peninsular Malaysia. Using a quasi-experimental design and mixed-methods analysis, the research evaluates the impact of these teaching methods on students' CT skills and attitudes toward programming. This research compares the results between a group that uses only BBP and another that combines both UA and BBP. The results indicate that CT skills improved in both groups, while students in the UA + BBP group showed more significant gains in confidence and a more positive attitude toward programming. These results provide valuable insights into pedagogical strategies within digital education and highlight the benefits of an integrated approach that combines tactile learning experiences with digital technologies. By combining hands-on activities with technology-based instruction, this approach not only deepens students' understanding of CT concepts but also positively changes their perception and engagement with programming.
State-of-the-art large language models (LLMs) have demonstrated an extraordinary ability to write computer code. This ability can be quite beneficial when integrated into an IDE to assist a programmer with basic codin...
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ISBN:
(纸本)9798400705793
State-of-the-art large language models (LLMs) have demonstrated an extraordinary ability to write computer code. This ability can be quite beneficial when integrated into an IDE to assist a programmer with basic coding. On the other hand, it may be misused by computerscience students for cheating on coding tests or homework assignments. At present, knowledge about the exact capabilities and limitations of state-of-the-art LLMs is still inadequate. Furthermore, their capabilities have been changing quickly with each new release. In this paper, we present a dataset of 559 programming exercises in 10 programming languages collected from a system for evaluating coding assignments at our university. We have experimented with four well-known LLMs (GPT-3.5, GPT-4, Codey, Code Llama) and asked them to solve these assignments. The evaluation results are intriguing and provide insights into the strengths and weaknesses of the models. In particular, GPT-4 (which performed the best) is currently capable of solving 55% of all our exercises and achieved an average score of 86% on exercises from the introductory programming course (using the best of five generated solutions).
One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’de...
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One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware *** previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their ***,attackers are continuously developing more sophisticated methods to bypass ***,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile ***,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent *** overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different *** results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler ***,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET *** global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and *** the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_*** is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust.
P4 serves as a programming language for configuring flexible and programmable network data planes, facilitating the development of custom protocols and programmable switches, and driving innovation in software-defined...
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P4 serves as a programming language for configuring flexible and programmable network data planes, facilitating the development of custom protocols and programmable switches, and driving innovation in software-defined networking and network function virtualization. While the Linux container based network emulator, Mininet, coupled with the BMv2 software P4 switch, is widely used for rapid prototyping of P4-based applications, BMv2's diminished performance raises fidelity concerns under high traffic and large network scenarios. In this paper, we introduce a lightweight virtual time system integrated into Mininet with BMv2 to enhance fidelity and scalability. By applying a time dilation factor (TDF) to interactions between containers and the physical machine, we optimize the emulated P4 network's perceived speed from the application processes' perspective. System evaluation demonstrates accurate emulation of significantly larger networks under high loads with minimal system overhead. We showcase our system's utility through two network applications: an emulation of a TCP SYN flood attack and an ECMP load balancer. Evaluating against a production-grade software switch, Open vSwitch, and a physical testbed, we highlight the virtual time system's improvement in temporal fidelity despite the observed performance degradation in BMv2 software switches.
We propose an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set programming (ASP). The general task is to study the solution spaces of combina...
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ISBN:
(数字)9789819705665
ISBN:
(纸本)9789819705658;9789819705665
We propose an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set programming (ASP). The general task is to study the solution spaces of combinatorial problems and to decide whether or not there are sequences of feasible solutions that have special properties. The resulting recongo solver covers all metrics of the solver track in the most recent international competition on combinatorial reconfiguration (CoRe Challenge 2022). recongo ranked first in the shortest metric of the single-engine solvers track. In this paper, we present the design and algorithm of bounded combinatorial reconfiguration, and also present ASP encodings of the independent set reconfiguration problem under the token jumping rule that is one of the most studied combinatorial reconfiguration problems. Finally, we present empirical analysis considering all instances of CoRe Challenge 2022.
Program synthesis offers an attractive alternative to the intricate and tedious process of writing assembly programs manually. Assembly program synthesis automatically generates implementations, given a high-level for...
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ISBN:
(纸本)9783031712937;9783031712944
Program synthesis offers an attractive alternative to the intricate and tedious process of writing assembly programs manually. Assembly program synthesis automatically generates implementations, given a high-level formal specification and a machine description. However, its limited scalability prevents widespread adoption. Automatic parallelization improves program synthesis in general, but parallelizing assembly synthesis is nontrivial as the realities that data are untyped and all state is global lead to an enormous search space and prevent straightforward decomposition into separable sub-problems that can be run in parallel. We present PASSES, a Parallel Assembly Synthesis System Exploiting Subspaces. PASSES uses five heuristics to transform an original assembly synthesis problem into a set of sub-problems;it runs multiple synthesis sub-problems in parallel and constructs the final result by combining them. We evaluate PASSES on 26 general bit manipulation assembly programming problems and 140 machine-dependent use cases from two operating systems. Compared to an existing assembly synthesis tool and a state-of-the-art parallel SMT solver, all five heuristics in PASSES significantly improve assembly synthesis scalability.
Lexicase selection is a parent selection method that has been successfully used in many application domains. In recent years, several variants of lexicase selection have been proposed and analyzed. However, it is stil...
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ISBN:
(纸本)9783031569562;9783031569579
Lexicase selection is a parent selection method that has been successfully used in many application domains. In recent years, several variants of lexicase selection have been proposed and analyzed. However, it is still unclear which lexicase variant performs best in the domain of symbolic regression. Therefore, we compare in this work relevant lexicase variants on a wide range of symbolic regression problems. We conduct experiments not only over a given evaluation budget but also over a given time as practitioners usually have limited time for solving their problems. Consequently, this work provides users a comprehensive guide for choosing the right selection method under different constraints in the domain of symbolic regression. Overall, we find that down-sampled is an element of-lexicase selection outperforms other selection methods on the studied benchmark problems for the given evaluation budget and for the given time. The improvements with respect to solution quality are up to 68% using down-sampled is an element of-lexicase selection given a time budget of 24 h.
Developing programming competencies is essential for systems, information science, computerscience, and electrical engineering students. Engineering students usually face the complexity of working with programming la...
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Developing programming competencies is essential for systems, information science, computerscience, and electrical engineering students. Engineering students usually face the complexity of working with programming languages that demand compliance with syntactic and semantic rules, which typically represent a daunting task for novice students. Watching textual messages on the screen only, like the classic "hello world," is no longer attractive in the current information society, a missing motivation and possible obstacle to developing programming competencies. Students would like to interact with hardware and appreciate environmental reactions. Arduino board permits developing solutions like that. This article presents the academic experience of first-year students of Ingenieria de Sistemas e Informatica at the Universidad Continental (ISI-UC) of Huancayo, Peru, using the Arduino microcontroller board for the teaching-learning process to develop programming competencies. The results obtained show a positive impact regarding the experience of previous using traditional text-based programming languages. Using Arduino, students create digital circuits and computational electronics competencies, another significant benefit. This experience used an online simulator, and the results obtained permit us to plan future online education strategies for this major. The next step will be the application of Arduino and the online simulator to deepen programming skills, including recursivity, real-time constraints, multitasking features, data structure, data-oriented programming, and object-oriented programming. The primary limitations encountered in this experiment were the students' lack of experience with electronics concepts to build circuits and, in some cases, the low internet speeds to assist in the programming process of online education. Realizing simulated experiences in classroom experiences was not a significant challenge for teachers and most students. However, proble
In this contribution we study how to effectively evolve programs tailored for biomedical image segmentation by using an Active Learning approach in Cartesian Genetic programming (CGP). Active Learning allows to dynami...
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ISBN:
(纸本)9783031700545;9783031700552
In this contribution we study how to effectively evolve programs tailored for biomedical image segmentation by using an Active Learning approach in Cartesian Genetic programming (CGP). Active Learning allows to dynamically select training data by identifying the most informative next image to add to the training set. We study how different metrics for selecting images under active learning impact the searchability of CGP. Our results show that datasets built during evolution with active learning improve the performance of Cartesian GP substantially. In addition, we found that the choice of the particular metric used for selecting which images to add heavily impacts convergence speed. Our work shows that the right choice of the image selection metric positively impacts the effectiveness of the evolutionary algorithm.
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