The Android operating system provides functions and methods to handle sensitive data to secure users' data. The Android security literature extracts binary features from a method and classifies the method into one...
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The Android operating system provides functions and methods to handle sensitive data to secure users' data. The Android security literature extracts binary features from a method and classifies the method into one of the Security Relevant Method's classes, adding information about how the method handles sensitive data. However, the usage of binary features hinders the performance of some classifiers due to the high collision rate between instances. Although previous works have explored Security Relevant Method classification, an extensive study of machine learning algorithms over this problem has not been conceived. This work fills this gap, analyzing Monolithic classifiers, Multiple Classifier Systems, and Embedding algorithms to transform binary features into real-valued features, aiming to facilitate the classifier's work by minimizing the ambiguity promoted by the collision. Our analyzes show that META-DES, using a pool of Decision Trees trained with the Random Forest algorithm, statistically has the best results. We also find that, in general, distance-based classifiers have a disadvantage in binary features. Moreover, embedding techniques such as deep metric learning with triplet loss can reduce geometrical instance ambiguity, improving the performance of the weakest learning algorithms. However, its usage was detrimental to the performance of more robust techniques, such as dynamic ensemble models better suited for handling difficult cases.
As Large Language Models (LLMs) advance in natural language processing, there is growing interest in leveraging their capabilities to simplify software interactions. In this paper, we propose a novel framework that in...
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Application Programming Interfaces (apis) enable interaction, integration, and interoperability among applications and services, contributing to their adoption and proliferation. However, discovering apis has relied o...
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ISBN:
(纸本)9783031790584;9783031790591
Application Programming Interfaces (apis) enable interaction, integration, and interoperability among applications and services, contributing to their adoption and proliferation. However, discovering apis has relied on manual, time-consuming, costly processes that jeopardize their reuse potential and accentuate the need for effective api retrieval mechanisms. Leveraging the Openapi Specification as a basis, this paper presents an exploratory study that combines BERT and GPT machine learning models to propose a novel api classifier. Our investigation explored the zero-shot learning capabilities of GPT-4 and GPT-3.5 using relevant terms extracted from api descriptions using BERT. The evaluation of our approach on two datasets comprising 940 api descriptions sourced from public repositories yielded an F1-score of 100% in the small dataset (17 apis) and 39.1% in the large dataset (923 apis). These results surpass state-of-the-art on the small dataset with an impressive 29-point improvement. The large dataset showed GPT can suggest labels not in the provided list. Manual analysis revealed that GPT's suggested labels fit the api intent better in 18 out of 20 cases, highlighting its potential for unknown classes and mismatch detection. This emphasizes the need to improve dataset quality and availability for api research. Our findings show the potential of automated api retrieval and open avenues for future research.
Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data s...
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ISBN:
(纸本)9781728187860
Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data sparsity. However, beside functional description documents, the Web api ecosystem has accumulated a wealth of information that can be used to improve the accuracy of Web service (api) classification. At the moment, there is an absence of a unified way to combine functional description documents with other sources of information (e.g., attributes, interactions and external knowledge) accumulated in the Web api ecosystem for api classification. To address this issue, we present a dual-GCN framework that can effectively suppress the noise propagation of textual contents by distinguishing functional description documents and other sources of information (specifically Mashup-api coinvocation patterns by default in this paper) for api classification. This framework is extensible with the ability to include different sources of information accumulated in the Web api ecosystem. Comprehensive experiments on a real-world public dataset demonstrate that our proposed method can outperform various representative methods for api classification.
(1)H NMR spectroscopy, combined with pattern recognition techniques (PCA and SOM) was used in discriminating base oils and refinery-intermediate products. Both PCA and SOM enabled correct oil discrimination into group...
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(1)H NMR spectroscopy, combined with pattern recognition techniques (PCA and SOM) was used in discriminating base oils and refinery-intermediate products. Both PCA and SOM enabled correct oil discrimination into groups in accordance with api 1509. Moreover, information about the structural compositions of the samples, correlated with their physical and chemical properties, was provided. The PCA score plot enabled component identification and semiquantitative analysis of binary mixtures of base oils, including semisynthetic oils. (C) 2009 Elsevier Ltd. All rights reserved.
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