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Aspect Summarization for Product Reviews based on Attention-based Aspect Extraction
Jun-Nyeong Jeong, Sang-Young Kim, Seong-Tae Kim, Jeong-Jae Lee, Yuchul Jung
http://doi.org/10.5626/JOK.2021.48.12.1318
Recently, document summaries such as articles and papers through machine learning and summary-related research on online reviews are active. In this study, unlike the existing simply summarizing content, a technique was developed for generating an aspect summary by considering various aspects of product reviews. By refining the earphone product review data crawled to build the learning data, 40,000 reviews were obtained. Moreover, we manually constructed 4,000 aspect summaries to be used for our training and evaluation tasks. In particular, we proposed a model that could summarize aspects using only text data using the aspect-based word expansion technique (ABAE). To judge the effectiveness of the proposed technique, we performed experiments according to the use of words related to aspects and the masking ratio during learning. As a result, it was confirmed that the model that randomly masked 25% of the words related to the aspect showed the highest performance, and during verification, the ROUGE was 0.696, and the BERTScore was 0.879.
A Digital Forensic Process for Ext4 File System in the Flash Memory of IoT Devices
Junho Jeong, Beomseok Kim, Jinsung Cho
http://doi.org/10.5626/JOK.2021.48.8.865
With the recent rapid advances in digital communication technology, the spread of IoT(Internet of Things) has accelerated and IoT devices can be utilized to investigate crimes and accidents due to the close connection between human society and IoT devices. Accordingly, with the increasing importance of digital forensics, numerous studies have been conducted. However, most digital forensics research proposed only abstract methodologies due to the various types of IoT devices. In addition, binwalk, which is actively used as a firmware analysis tool, does not adequately analyze and extract the ext4 file system. To solve these problems, this paper proposes a proper extraction and analysis method and a practical process that could extract the ext4 file system from the flash memory of IoT devices using the binwalk with the proposed method. This study also verifies the proposed process with DJI Phantom 4 Pro V2.0 drone.
A New Light-Weight and Efficient Convolutional Neural Network Using Fast Discrete Cosine Transform
http://doi.org/10.5626/JOK.2020.47.3.276
Recently proposed light-weight neural networks maintain high accuracy in some degree with a small amount of weight parameters and low computation cost. Nevertheless, existing convolutional neural networks commonly have a lot of weight parameters from the Pointwise Convolution (1x1 convolution), which also induces a high computational cost. In this paper, we propose a new Pointwise Convolution operation with one dimensional Fast Discrete Cosine Transform (FDCT), resulting in dramatically reducing the number of learnable weight parameters and speeding up the process of computation. We propose light-weight convolutional neural networks in two specific aspects: 1) Application of DCT on the block structure and 2) Application of DCT on the hierarchy level in the CNN models. Experimental results show that our proposed method achieved the similar classification accuracy compared to the MobileNet v1 model, reducing 79.1% of the number of learnable weight parameters and 48.3% of the number of FLOPs while achieving 0.8% increase in top-1 accuracy.
Synthesizing Imperative Programs from Examples
Sunbeom So, Tae-Hyoung Choi, Jun Jung, Hakjoo Oh
http://doi.org/10.5626/JOK.2017.44.9.986
In this paper, we present a method for synthesizing imperative programs from input-output examples. Given (1) a set of input-output examples, (2) an incomplete program, and (3) variables and integer constants to be used, the synthesizer outputs a complete program that satisfies all of the given examples. The basic synthesis algorithm enumerates all possible candidate programs until the solution program is found (enumerative search). However, it is too slow for practical use due to the huge search space. To accelerate the search speed, our approach uses code optimization and avoids unnecessary search for the programs that are syntactically different but semantically equivalent. We have evaluated our synthesis algorithm on 20 introductory programming problems, and the results show that our method improves the speed of the basic algorithm by 10x on average.
Efficient Attribute Based Digital Signature that Minimizes Operations on Secure Hardware
Jungjoon Yoon, Jeonghyuk Lee, Jihye Kim, Hyunok Oh
An attribute based signature system is a cryptographic system where users produce signatures based on some predicate of attributes, using keys issued by one or more attribute authorities. If a private key is leaked during signature generation, the signature can be forged. Therefore, signing operation computations should be performed using secure hardware, which is called tamper resistant hardware in this paper. However, since tamper resistant hardware does not provide high performance, it cannot perform many operations requiring attribute based signatures in a short time frame. This paper proposes a new attribute based signature system using high performance general hardware and low performance tamper resistant hardware. The proposed signature scheme consists of two signature schemes within a existing attribute based signature scheme and a digital signature scheme. In the proposed scheme, although the attribute based signature is performed in insecure environments, the digital signature scheme using tamper resistant hardware guarantees the security of the signature scheme. The proposed scheme improves the performance by 11 times compared to the traditional attribute based signature scheme on a system using only tamper resistant hardware.
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