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IConFuzz:Constraint-Aware Argument Mutation for Effective Smart Contract Fuzzing 认领 引用
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作者 Hojin Choi Jaeseung Choi 《Computers, Materials & Continua》 SCIE EI 2026年第9期2025-2045,共21页
Recently,extensive research has focused on addressing the unique challenges of smart contract fuzzing.Nevertheless,existing fuzzers still struggle to generate adequate function call arguments that can explore the deep... Recently,extensive research has focused on addressing the unique challenges of smart contract fuzzing.Nevertheless,existing fuzzers still struggle to generate adequate function call arguments that can explore the deep smart contract states.In this paper,we introduce novel classes of argument constraints that capture the inter-argument relationships required to exercise meaningful contract logic.We propose a static analysis algorithm to extract these constraints from Solidity source code.In addition,we design a constraint-aware argument mutation strategy that leverages the identified constraints to guide test case generation for smart contract fuzzing.We implement our approach in a fuzzer named IConFuzz.Our evaluation on realistic benchmarks with integer overflow,suicidal contract,and ether leakage vulnerabilities demonstrates that IConFuzz outperforms state-of-the-art testing tools in both the number of bugs discovered and the speed of bug detection. 展开更多
关键词 Software testing static analysis fuzzing smart contract security
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Language Model Using Differentiable Neural Computer Based on Forget Gate-Based Memory Deallocation 认领 引用
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作者 Donghyun Lee Hosung Park +4 位作者 Soonshin Seo Changmin Kim Hyunsoo Son Gyujin Kim Ji-Hwan Kim 《Computers, Materials & Continua》 SCIE EI 2021年第7期537-551,共15页
A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized metho... A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems.In this study,we apply a DNC to a language model(LM)task.The LM task is one of the reasoning problems,because it can predict the next word using the previous word sequence.However,memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory,which degrades performance.Therefore,we propose a forget gatebased memory deallocation(FMD)method,which searches for the minimum value of elements in a forget gate-based retention vector.The forget gatebased retention vector indicates the retention degree of information stored in each external memory address.In experiments,we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example.For LM tasks,we evaluated DNC using the Penn Treebank and enwik8 LM tasks.Although it does not yield SOTA results in LM tasks,the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character.For the speech recognition rescoring tasks,FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate. 展开更多
关键词 Forget gate-based memory deallocation differentiable neural computer language model forget gate-based retention vector
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TP-MobNet: A Two-pass Mobile Network for Low-complexity Classification of Acoustic Scene 认领 引用
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作者 Soonshin Seo Junseok Oh +3 位作者 Eunsoo Cho Hosung Park Gyujin Kim Ji-Hwan Kim 《Computers, Materials & Continua》 SCIE EI 2022年第11期3291-3303,共13页
Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networ... Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networks(CNNs)proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models.When using ASC systems in the real world,model complexity and device robustness are essential considerations.In this paper,we propose a two-pass mobile network for low-complexity classification of the acoustic scene,named TP-MobNet.With inverse residuals and linear bottlenecks,TPMobNet is based on MobileNetV2,and following mobile blocks,coordinate attention and two-pass fusion approaches are utilized.The log-range dependencies and precise position information in feature maps can be trained via coordinate attention.By capturing more diverse feature resolutions at the network’s end sides,two-pass fusions can also train generalization.Also,the model size is reduced by applying weight quantization to the trained model.By adding weight quantization to the trained model,the model size is also lowered.The TAU Urban Acoustic Scenes 2020 Mobile development set was used for all of the experiments.It has been confirmed that the proposed model,with a model size of 219.6 kB,achieves an accuracy of 73.94%. 展开更多
关键词 Acoustic scene classification low-complexity device robustness two-pass mobile network coordinate attention weight quantization
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Joint On-Demand Pruning and Online Distillation in Automatic Speech Recognition Language Model Optimization 认领 引用
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作者 Soonshin Seo Ji-Hwan Kim 《Computers, Materials & Continua》 SCIE EI 2023年第12期2833-2856,共24页
Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these... Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs. 展开更多
关键词 Automatic speech recognition neural language model Mogrifier long short-term memory pruning distillation efficient deployment optimization joint training
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Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus 认领 引用 被引量：10
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作者 Donghyun Lee Minkyu Lim +4 位作者 Hosung Park Yoseb Kang Jeong-Sik Park Gil-Jin Jang Ji-Hwan Kim 《China Communications》 SCIE CSCD 2017年第9期23-31,共9页
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force... A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method. 展开更多
关键词 acoustic model connectionisttemporal classification large-scale trainingcorpus long short-term memory recurrentneural network
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Decomposing class responsibilities using distance-based method similarity 认领 引用 被引量：1
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作者 Junha LEE Dae-Kyoo KIM +1 位作者 Suntae KIM Sooyong PARK 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第4期612-630,共19页
Cohesion is a design quality that has a great im- pact on the posterior development and maintenance. As software evolves, the cohesion of the system becomes weaker due to the changes introduced during evolution. Over ... Cohesion is a design quality that has a great im- pact on the posterior development and maintenance. As software evolves, the cohesion of the system becomes weaker due to the changes introduced during evolution. Over evolution, a single responsibility class may be unintentionally assigned other responsibilities, which makes the class less cohesive and more complex and consequently increases the complexity of the entire system. There has been much work on decomposing class responsibilities based on internal class relationships such as method-attribute referencing and internal method calls. However, object-oriented systems involve significant external class relationships carrying important behavioral semantics, which should be taken into account in identifying class responsibilities. In this paper, we present a novel approach for identifying and decomposing classes responsibilities based on method similarity using both internal and external class relationships. We extend the existing work for measuring similarity of internal class relationships and present a distance-based method for measuring external class relationships. We evaluate the approach using three open source applications -- JMeter, JHotDraw, and ArgoUML. The evaluation shows that the presented approach improves precision over the existing work. We validate the results using independent samples T-test and ANOVA applied to a set of hypotheses. The validation confirms that the results are statistically significant. 展开更多
关键词 class decomposition cohesion external rela-tionships identifying responsibility method similarity
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