Tuesday, December 01, 2020

Journal of Information and Communication Technology (JICT) Vol.20, No.1, January 2021

 
SARCASM DETECTION IN PERSIAN
Zahra Bokaee Nezhad & Mohammad Ali Deihimi 

 
Ahmad Hakiim Jamaluddin & Nor Idayu Mahat
 

 
SARCASM DETECTION IN PERSIAN
1Zahra Bokaee Nezhad & 2Mohammad Ali Deihimi
1Department of Computer Engineering, Zand University, Iran
2Department of Electronics Engineering, Bahonar University, Iran 
 
Corresponding author: zbokaee@gmail.comm.a.deihimi@gmail.com
 
ABSTRACT
 
Sarcasm is a form of communication where the individual states the opposite of what is implied. Therefore, detecting a sarcastic tone is somewhat complicated due to its ambiguous nature. On the other hand, identification of sarcasm is vital to various natural language processing tasks such as sentiment analysis and text summarisation. However, research on sarcasm detection in Persian is very limited. This paper investigated the sarcasm detection technique on Persian tweets by combining deep learning-based and machine learning-based approaches. Four sets of features that cover different types of sarcasm were proposed, namely deep polarity, sentiment, part of speech, and punctuation features. These features were utilised to classify the tweets as sarcastic and nonsarcastic. In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories. The study also used a new Persian proverb dictionary in the preparation step to enhance the accuracy of the proposed model. The performance of the model is analysed using several standard machine learning algorithms. The results of the experiment showed that the method outperformed the baseline method and reached an accuracy of 80.82%. The study also examined the importance of each proposed feature set and evaluated its added value to the classification.
 
Keywords: Sarcasm detection, natural language processing, machine learning, sentiment analysis, classification.
 
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PARAMETRIC FLATTEN-T SWISH: AN ADAPTIVE NONLINEAR ACTIVATION FUNCTION FOR DEEP LEARNING
1Hock Hung Chieng, 1Noorhaniza Wahid & 2Pauline Ong
1Faculty of Information Technology and Computer Science, Universiti Tun Hussein Onn Malaysia, Malaysia
2Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia
 
Corresponding author: hi160029@siswa.uthm.edu.my, nhaniza, ongp@uthm.edu.my
 
 
ABSTRACT
 
QActivation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community. However, ReLU contains several shortcomings that can result in inefficient training of the deep neural networks, these are: 1) the negative cancellation property of ReLU tends to treat negative inputs as unimportant information for the learning, resulting in performance degradation; 2) the inherent predefined nature of ReLU is unlikely to promote additional flexibility, expressivity, and robustness to the networks; 3) the mean activation of ReLU is highly positive and leads to bias shift effect in network layers; and 4) the multilinear structure of ReLU restricts the non-linear approximation power of the networks. To tackle these shortcomings, this paper introduced Parametric Flatten-T Swish (PFTS) as an alternative to ReLU. By taking ReLU as a baseline method, the experiments showed that PFTS improved classification accuracy on SVHN dataset by 0.31%, 0.98%, 2.16%, 17.72%, 1.35%, 0.97%, 39.99%, and 71.83% on DNN-3A, DNN-3B, DNN-4, DNN-5A, DNN-5B, DNN-5C, DNN-6, and DNN-7, respectively. Besides, PFTS also achieved the highest mean rank among the comparison methods. The proposed PFTS manifested higher non-linear approximation power during training and thereby improved the predictive performance of the networks.
 
Keywords: Activation function, deep learning, Flatten-T Swish, non-linearity, ReLU.
 
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DYNAMIC PROBABILITY SELECTION FOR FLOWER POLLINATION ALGORITHM BASED ON METROPOLISHASTINGS CRITERIA
1Kamal Zuhairi Zamli, 1,2Fakhrud Din, 1Abdullah Nasser, 3Nazirah Ramli & 3Noraini Mohamed
1Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Malaysia
2Department of Computer Science & IT, University of Malakand, Pakistan
3Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Pahang, Malaysia
 
Corresponding author: kamalz@ump.edu.my; fakhruddin@uom.edu.pk; abdullahnasser83@gmail.com.my, nazirahr, noraini_mohamed@uitm.edu.my
 
 
ABSTRACT
 
Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm that adopts its metaphor from the proliferation role of flowers in plants. Having only one parameter control (i.e. the switch probability, pa) to choose from the global search (i.e. exploration) and local search (i.e. exploitation) is the main strength of FPA as compared to other meta-heuristic algorithms. However, FPA still suffers from variability of its performance as there is no one size that fits all values for pa, depending on the characteristics of the optimisation function. This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. Adopting the problem of t-way test suite generation as the case study and with the comparative evaluation with the original FPA, FPA-MH gave promising results owing to its dynamic and adaptive selection of search operators based on the need of the current search.
 
Keywords: Dynamic probability selection, flower pollination algorithm, optimisation, t-way testing, data mining.
 
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AN INTELLIGENT SOFTWARE DEFINED NETWORKING CONTROLLER COMPONENT TO DETECT AND MITIGATE DENIAL OF SERVICE ATTACKS
Huseyin Polat & Onur Polat
Department of Computer Engineering, Gazi University, Turkey
 
Corresponding author: polath@gazi.edu.tr, onurpolat@gazi.edu.tr
 
 
ABSTRACT 
 
Despite many advantages of software defined networking (SDN) such as manageability, scalability, and performance, it has inherent security threats. In particular, denial of service (DoS) attacks are major threats to SDN. The controller’s processing and communication abilities are overwhelmed by DoS attacks. The capacity of the flow tables in the switching device is exhausted due to excess flows created by the controller because of malicious packets. DoS attacks on the controller cause the network performance to drop to a critical level. In this paper, a new SDN controller component was proposed to detect and mitigate DoS attacks in the SDN controller. POX layer three controller component was used for underlying a testbed for PacketIn messages. Any packet from the host was incremented to measure the rate of packet according to its device identification and its input port number. Considering the rate of packets received by the controller and threshold set, malicious packets could be detected and mitigated easily. A developed controller component was tested in a Mininet simulation environment with an hping3 tool to build artificial DoS attacks. Using the enhanced controller component, DoS packets were prevented from accessing the controller and thus, the data plane (switching devices) was prevented from being filled with unwanted flows.
 
Keywords: Security, DoS attack, decision making, software defined networking, POX controller.
 
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VALIDATION ASSESSMENTS ON RESAMPLING METHOD IN IMBALANCED BINARY CLASSIFICATION FOR LINEAR DISCRIMINANT ANALYSIS
1Ahmad Hakiim Jamaluddin & 2Nor Idayu Mahat
1Department of Mathematics, Universiti Putra Malaysia, Malaysia
2Centre for Testing, Measurement and Appraisal, Universiti Utara Malaysia, Malaysia
 
Corresponding author: ahmadhakiimjamaluddin@gmail.com, noridayu@uum.edu.my
 
 
ABSTRACT 
 
The curse of class imbalance affects the performance of many conventional classification algorithms including linear discriminant analysis (LDA). The data pre-processing approach through some resampling methods such as random oversampling (ROS) and random undersampling (RUS) is one of the treatments to alleviate such curse. Previous studies have attempted to address the effect of a resampling method on the performance of LDA. However, some studies contradicted with each other based on different performance measures as well as validation strategies. This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 two-group bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. The analysis on the location and dispersion statistics of the performance measures was further enlightened on: (i) the effect of a resampling method on the performance of LDA, and (ii) the enhancement in the learning fairness of LDA on objects regardless of sample size, hence reducing the effect of the curse of class imbalance.
 
Keywords: Linear discriminant analysis, pre-processing, resampling method, class imbalance, binary classification.
 
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All articles published in Journal of Information and Communication Technology (JICT) are licensed under a Creative Commons Attribution 4.0 International License.