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All Journal Jurnal Ilmu Komputer dan Informasi
Adila Alfa Krisnadhi
Universitas Indonesia
Computer Science & IT Library & Information Science

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Reducing Adversarial Vulnerability through Adaptive Training Batch Size Ken Sasongko; Adila Alfa Krisnadhi; Mohamad Ivan Fanany
Jurnal Ilmu Komputer dan Informasi Vol 14, No 1 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v14i1.907

Abstract

Neural networks possess an ability to generalize well to data distribution, to an extent that they are capable of fitting to a randomly labeled data. But they are also known to be extremely sensitive to adversarial examples. Batch Normalization (BatchNorm), very commonly part of deep learning architecture, has been found to increase adversarial vulnerability. Fixup Initialization (Fixup Init) has been shown as an alternative to BatchNorm, which can considerably strengthen the networks against adversarial examples. This robustness can be improved further by employing smaller batch size in training. The latter, however, comes with a tradeoff in the form of a significant increase of training time (up to ten times longer when reducing batch size from the default 128 to 8 for ResNet-56). In this paper, we propose a workaround to this problem by starting the training with a small batch size and gradually increase it to larger ones during training. We empirically show that our proposal can still improve adversarial robustness (up to 5.73\%) of ResNet-56 with Fixup Init and default batch size of 128. At the same time, our proposal keeps the training time considerably shorter (only 4 times longer, instead of 10 times).
LexID: The Metadata and Semantic Knowledge Graph Construction of Indonesian Legal Document Nur Siti Muninggar; Adila Alfa Krisnadhi
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1096

Abstract

The Legal Fiction principle stipulates that the government needs to ensure the public availability of all of their legal documents. Unfortunately, the text-based search services they provide cannot return satisfactory answers in retrieval scenarios requiring proper representation of relationships between various legal documents. A key problem here is the lack of explicit representation of such relationships behind the employed retrieval engines. We aim to address this problem by proposing LexID knowledge graph (KG) that provides an explicit knowledge representation for Indonesian legal domain usable for such retrieval purposes. The KG contains both legal metadata information and semantic content of the legal clauses of the legal document's articles, modeled using formal vocabulary from the LexID ontology also presented in this paper. The KG is constructed from thousands of Indonesian legal documents. Since the procedure of writing a legal document regulated by the government is clear and detailed, we use a rule-based approach to construct our KG. At the end, we describe several use cases of the KG to address different retrieval needs. In Addition, we evaluated the quality of our KG by measuring its ability to answer questions and got that LexID can answer questions with the macro average F1 score is about 0.91.
Co-Authors Ken Sasongko Mohamad Ivan Fanany Nur Siti Muninggar