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Rice Diseases Recognition Using Transfer Learning from Pre-trained CNN Model
(2023-01-01)
Hamhongsa, Wittawat
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Wiangsripanawan, Rungrat
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Thorncharoensri, Pairat
This research aims at applying the well-known pre-trained convolutional neural network (CNN) image classification algorithms such as InceptionV3, Xception, ResNetV2, InceptionResNetV2, and DenseNet to classify the five diseases of rice's leaves in Thailand. Data sets used are from 3 sources: UCI database, Rice Leaf Disease Image Samples dataset and images collected by authors in Thailand between 2018–2020. Our initial experimental result shows that using the pre-trained CNN models to classify the rice leaves disease seems to be possible with a greater number of images required. Therefore, the image data augmentation technique is used to add the number of images into the dataset. The experimental result with data augmentation shows that it could increase rice disease classification efficiency up to 16.533% (especially for the ResNet50V2 model).
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The Comparison of Password Composition Policies Among US, German, and Thailand Samples
(2023-01-01)
Lomchan, Jenjira
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Wiangsripanawan, Rungrat
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Shahandashti, Siamak F.
The study by Mayer, Kirchner, and Volkamer published at SOUPS 2017 showed that the password composition policy (PCP) strength of both the US and German websites was not influenced by the security but by the usability features of the websites. Surprisingly, the PCP strength of the banking website category was the lowest, whereas the government website was the highest. Our aim in conducting the first study is to find whether 78 Thai frequently used websites in 2018 would yield the same surprising results. Our finding showed an opposite perspective, the highest PCP strength was from the banking websites, followed by university and government websites, respectively. Two more security features were added to our study: 2FA and HTTPS. Although some German websites employing 2FA allowed lower PCPs for better usability, Thai websites with 2FA did not loosen the password requirements. Also, employing HTTPS did not impact the PCP strength. The study with Thai websites was reinvestigated in 2021, two years after the Personal Data Protection Act (PDPA) was announced. The result showed that the median PCP strength of all Thailand samples had grown from 26.6 in 2018 to 31.0 in 2021. The banking websites still retained the highest PCP strength. A significant change appeared on the government websites, increasing from 29.9 to 40.4. In summary, the security features such as the size of services, and values of assets which play no part in both the US and German PCPs were heavily concerned by Thai websites. Government and university websites in Germany and USA gave much higher PCP strength than those in Thailand. The Thai government's PCP strength sharply increased in 2021 due to the privacy law. Nevertheless, it was still lower than the results in Germany and USA in 2016. Therefore, the criteria influencing PCP vary depending on the country.
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Thai word safe segmentation with bounding extension for data indexing in search engine
(2019-01-01)
Klahan, Achariya
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Pannoi, Sukunya
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Uewichitrapochana, Prapeepat
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Wiangsripanawan, Rungrat
Word segmentation ambiguity in Thai language affects data indexing process by creating the inverted index relatively to the segmentation results. This phenomenon leads to unreasonable search result. This article proposes Thai word Safe segmentation algorithm using dictionary to solve this problem so that all different terms in an ambiguous part of the sentence are queryable. Next, it shows the bounding extension to improve Safe segmentation performance. It also compares several off-the-shelf implementations of the trie data structure which -we believe- is the best data structure for dictionary-based Thai word segmentation and compares the efficiency of serializable libraries for de-serializing trie in the analyzer’s initial state. Finally, it evaluates the Safe segmentation with several implementations called Safe Analyzer. The experimental results also show that the linked-list Trie and Protostuff library give the outstanding results. The Safe segmentation can definitely solve the ambiguity problem but still it could not solve the misspell within text accurately.
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HapticPoints: The extended PassPoints graphical password
(2019-01-01)
Ratchasan, Trust
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Wiangsripanawan, Rungrat
The most common issue of alphanumeric passwords is users normally create weak passwords for the reason that strong passwords are difficult to recognise and memorise. Graphical password authentication system is one of the approaches to address the issues of alphanumeric passwords memorability. Wiedenbeck et al. propose PassPoints in which a password is a sequence of any 5 to 8 user-selected click points on a system-assigned image. Nevertheless, PassPoints still faces the problem of predictable click points and shoulder surfing attack. In this paper, we propose an alternative graphical password system on smartphones called HapticPoints. By adding haptic feedback to PassPoints as additional decoy click points, the aforementioned problems can be prevented without needing users to do any additional memory task. We also conduct a user study to evaluate and compare the usability of HapticPoints and PassPoints.
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A study of graphical password usability on smartphones in a week
(2018-01-01)
Ratchasan, Trust
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Wiangsripanawan, Rungrat
Smartphone technology nowadays is very progressive. With a powerful camera, high-speed connectivity, touchscreen interface, millions of different applications development and also a biometrics authentication system such as fingerprint scanner, iris scanner and even face scanner but whenever biometrics authentication system accidentally failed a smartphone still uses text passwords, PINs or Android's Unlock Pattern as a secondary authentication system but users typically create week text passwords since strong text passwords are difficult to recall and memorise. PINs and Android's Unlock Pattern are easier to recall and memorise but also having the guessablity problem. Graphical password system is one of the approach which propose of solving the memorability problem. In this paper we implemented and optimised the selected existing graphical password systems to smartphones and conducted an experiment to study and compare a usability of graphical password to the current secondary authentication system on smartphones in a week.
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Edited audio detection using ensemble learning
(2015-02-27)
Suwan, Takdanai
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Jaiyen, Saichon
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Wiangsripanawan, Rungrat
Detecting edited audios is the challenging problem that can help forensic scientists to separate genuine, unedited recording from edited recordings. This paper proposes the technique for detecting edited audios using Ensemble Learning. This problem can be considered as a two-class classification problem which audio data are classified into two classes including edited and unedited audios. The performance of the proposed model is compared with the performance from the Support Vector Machine, Naïve Bayes, Radial Basis Function Neural Network, and Probabilistic Neural Networks. The experimental results demonstrate that the proposed model is the most appropriated method for detecting the edited audios.
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Protecting peer-to-peer-based massively multiplayer online games
(2015-01-01)
Susilo, Willy
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Chow, Yang Wai
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Wiangsripanawan, Rungrat
The rapidly growing popularity of massively multiplayer online games (MMOGs) has given rise to an increase in the number of players worldwide. MMOGs enable many players to interact together through a shared sense of presence created by the game environment. The peer-to-peer (P2P) network topology overcomes communication bottleneck problems associated with centralised client/server systems. However, many architectures are proposed in an ad hoc manner and enhancing the security of such systems is an elusive research problem. In this paper, we address this important issue by making the following contributions. Firstly, we formalise the notion of P2P-based MMOGs and demonstrate that existing P2P-based MMOG architectures can be unified using our model. To our knowledge, this is the first time that this has been done in the literature. Secondly, we use our model to develop a real-time cheating detection mechanism to identify cheating players, which can be used to expose several MMOG cheating strategies. In addition, we propose a new reputation-based system as well as a blacklisting system for P2P-based MMOGs to enhance the cheating detection process. Finally, we discuss the concept of protecting MMOGs against cheating using automated programmes.
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Towards formalizing a reputation system for cheating detection in peer-to-peer-based massively multiplayer online games
(2012-01-01)
Susilo, Willy
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Chow, Yang Wai
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Wiangsripanawan, Rungrat
The rapidly growing popularity of Massively Multiplayer Online Games (MMOGs) has given rise to an increase in the number of players world wide. MMOGs enable many players interact together through a shared sense of presence created by the game. The Peer-to-Peer (P2P) network topology overcomes communication bottleneck problems associated with centralized client/server systems. However, many architectures are proposed in an ad hoc manner and enhancing the security of such systems is an elusive research problem. In this paper, we address this important issue by making the following contributions. Firstly, we formalize the notion of P2P-based MMOGs and demonstrate that existing P2P-based MMOG architectures can be unified using our model. To our knowledge, this is the first time that this has been done in the literature. Secondly, we use our model to develop a real-time cheating detection mechanism to identify cheating players, which can be used to expose several MMOG cheating strategies. Finally, we propose a new reputation based system for P2P-based MMOGs to enhance the cheating detection process. © 2012 Springer-Verlag.

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