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DeepVec: State-Vector Aware Test Case Selection for Enhancing Recurrent Neural Network

IEEE Transactions on Software engineering 2025年第6期51卷 1702-1723页

作者： Jiang, Zhonghao Yan, Meng Huang, Li Sun, Weifeng Liu, Chao Sun, Song Lo, David Chongqing University School of Big Data and Software Engineering China Chongqing Normal University School of Computer and Information Science China Singapore Management University School of Information Systems Singapore

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable. In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonl

关键词： Model checking

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Feature Selection for GPSR Based on Maximal Information Coefficient and Shapley Values 13

Feature Selection for GPSR Based on Maximal Information Coef...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Rimas, Mohamad Anfar, Mohamad Chen, Qi Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington Centre for Data Science and Artificial Intelligence PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

Feature selection is a critical aspect of improving the interpretability of machine learning models. Genetic Programming (GP) has a built-in feature selection mechanism that explores the search space to include informative features in models. However, this built-in mechanism is insufficient for identifying important features, when dealing with high-dimensional feature spaces. To overcome this limitation, the paper introduces a novel feature importance measurement based on the Maximal Infor-mation Coefficient and Shapley Values. The proposed algorithm operates in two stages. In the first stage, it identifies the best individuals from different populations. In the second stage, the best individuals from the first stage are utilized for the calculation of the novel individual feature importance measurement. The new feature importance measurement offers valuable insights into the significance and relevance of the selected features. Regression experiments were conducted on six datasets to assess the effectiveness of the proposed method. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach enhances GP performance for high dimensional datasets while maintaining GP trees of similar size compared to standard GP. © 2024 IEEE.

关键词： Genetic programming

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LearnSC: An Efficient and Unified Learning-Based Framework for Subgraph Counting Problem 40

LearnSC: An Efficient and Unified Learning-Based Framework f...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Hou, Wenzhe Zhao, Xiang Tang, Bo National University of Defense Technology Laboratory for Big Data and Decision China Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Graphs are valuable data structures used to represent complex relationships between entities in a wide range of applications, such as social networks and chemical reactions. Subgraph counting problem is a well-known hard problem, as its core subroutine, the subgraph matching, is NP-complete. In this work, we propose an efficient and unified deep learning-based solution framework LearnSC, which solves the subgraph counting problem approximately. This framework offers two key advantages: (i) it is a generic solution that is orthogonal to the existing techniques of learning-based solutions;and (ii) it is equipped with a suite of optimizations to significantly improve the accuracy of the estimated results. Our experimental results on 7 datasets demonstrate that our proposal is highly accurate, robust, and scalable, making it an excellent solution for subgraph counting problem among all statistics-based and learning-based competitors. © 2024 IEEE.

关键词： Deep learning

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ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-Objective Genetic-Programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules 13

Multi-Objective Genetic-Programming Hyper-Heuristic for Evol...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Pang, Junwei Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

The job shop scheduling problem is an important combinatorial optimisation problem in the real world. Genetic programming hyper-heuristic has been successfully applied to automatically evolve effective dispatching rules to make a schedule in real time without much domain knowledge. However, the interpretability of GP-evolved rules has been largely neglected, which could lead to the lack of reliability and trustworthiness of the evolved rules in practice. Current work related to interpretable genetic programming algorithms primarily uses the model size as the interpretability metric. This could not fully reflect the interpretability of evolved rules. To overcome the limitation, we employ structural complexity and dimension gap as more comprehensive interpretability measures. In addition, a new multi-objective genetic programming algorithm, which applies the a non-dominated sorting method to solve the objective selection bias issue, is proposed to optimise the makespan (scheduling objective), structural complexity and dimension gap simultaneously. A variety of experiments demonstrates the competitive performance of our proposed algorithm based on effectiveness, convergence and diversity. Furthermore, the semantics of evolved dispatching rules are analysed to show their better interpretability. © 2024 IEEE.

关键词： Genetic programming

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset 9

Anatomization of Neural Networks based models for Semantic A...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bharath, B. Sai Bollineni, Jahnavi Mandala, Sandeep Preetham Ganguly, Tania Amudha, J. Shukla, Nikhil Joseph, Raj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Intellectyx Data Science Pvt Ltd India

ISBN: (纸本)9798350394474

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models' performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP. © 2024 IEEE.

关键词： Neural network models

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Graph decision transformer for offline reinforcement learning

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science China(Information sciences) 2025年第6期68卷 395-396页

作者： Shengchao HU Li SHEN Ya ZHANG Dacheng TAO School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Shanghai AI Laboratory School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University School of Computer and Data Science Nanyang Technological University

Recent advances [1, 2] in offline reinforcement learning(RL)have taken a new perspective on the problem, departing from conventional methods that concentrate on learning value functions or policy gradients. Instead, the problem is viewed as a generic sequence modeling task, where past experiences consisting of state-action-reward triplets are input to the Transformer.

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Assessing the efficiency of Gene Expression data for cancer diagnosis with Artificial Intelligence Techniques 10

Assessing the efficiency of Gene Expression data for cancer ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Chandrakanth, G. Mathu, T. Thanka, M. Roshni Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Science and Cybersecurity Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350353068

Sparse multi-dimensional gene expression data refers to datasets that has a vast number of features and observations, where a substantial portion of the entries are zero or missing values. In such datasets, the number of observations is typically fewer than the number of features. Analyzing such data poses significant challenges, particularly in the context of making accurate diagnoses. In this study, we conducted a comparative evaluation of a Deep Learning(DL) model (BiLSTM) and five well-known Machine Learning(ML) models, including Logistic Regression, Ensemble Learning, Random Forest, and Bagging models. Our comparison led to the conclusion that the DL model (Bi-LSTM) outperformed the ML models, achieving an average accuracy of 98.13% which asserts that DL models are superior to ML in terms of diagnosis. © 2024 IEEE.

关键词： Long short-term memory

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis 3

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Roshan Ahmed, N. Shridevi, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Center for Advanced Data Science Chennai India

ISBN: (纸本)9798350372120

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis. © 2024 IEEE.

关键词： Embeddings

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