Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of Noisy Malay Text: State of Art, Challenges and Future Work

Sentiment analysis (SA) is a study where people’s opinions and emotions are automatically extracted in the form of sentiments from the natural language text. In social media monitoring, it is very useful because it allows user to gain an overall picture of the extensive public opinion behind many topics. Most works on SA are for the English text. Only a few works focus on the Malay language. Currently, a review on SA for the Malay language only focus on the SA approaches and the dataset. Some major issues such as the pre-processing techniques used to normalize the noisy text, the most employed performance measures for Malay SA, and the challenges for Malay SA has not been reviewed. Malaysians tend not to fully follow any abbreviations rules when writing on social media. Thus, a lot of noisy text can be found in social media sites like Facebook and Twitter which create some issues to SA process. Hence, the aim of this study is to investigate the state of the art, challenges and future works of SA for Malay social media text. This study provides a review on various approaches, datasets, performance measures, and pre-processing techniques used in the previous works on SA of the Malay text. More than 700 articles from journals and conference proceedings have been identified using the search keywords, however, only 17 relevant articles published from year 2013 to 2018 were reviewed. The findings from this review focus on three commonly used SA approaches which are lexicon-based, machine learning, and hybrid.
Dual-Polarized Perpendicularly Fed Balanced Feed Antenna With High Polarization Purity

The design and analysis of a high-performance dual-polarized perpendicularly fed microstrip patch antenna are presented. The planar balanced feeding technique has been evolved to a highly isolated nonplanar method for exciting a dual-polarized microstrip patch antenna. Reducing the number of parallel dielectric and ground plane layers improves the isolation between polarization and increases the antenna scan capability. The simulation and measurement results of the proposed antenna are presented. The proposed antenna possesses high-performance electromagnetic characteristics. Input isolation of better than 52 dB between orthogonal polarizations in the entire frequency band of 2.7–2.9 GHz and cross-polarization level of better than $-$32 dB have been achieved in the measured results.
A Submodularity-based Clustering Algorithm for the Information Bottleneck and Privacy Funnel

For the relevant data $S$ that nests in the observation $X$, the information bottleneck (IB) aims to encode $X$ into $\hat {X}$ in order to maximize the extracted useful information $I(S;\hat {X})$ with the minimum coding rate $I(X;\hat {X})$. For the dual privacy tunnel (PF) problem where $S$ denotes the sensitive$/\mathrm {p}\mathrm {r}\mathrm {i}\mathrm {v}\mathrm {a}\mathrm {t}\mathrm {e}\wedge $ data, the goal is to minimize the privacy leakage $I(S;X)$ while maintain a certain level of utility $I(X;\hat {X})$. For both problems, we propose an efficient iterative agglomerative clustering algorithm based on the minimization of the difference of submodular functions (IAC-MDSF). It starts with the original alphabet $\hat {\mathcal {X}}:= \mathcal {X}$ and iteratively merges the elements in the current alphabet $ \hat {\mathcal {X}}$ that optimizes the Lagrangian function $I(S;\hat {X})-\lambda I(X;X)$. We prove that the best merge in each iteration of IAC-MDSF can be searched efficiently over all subsets of $\hat {\mathcal {X}}$ by the existing MDSF algorithms. By varying the value of the Lagrangian multiplier $\lambda $, we obtain the experimental results on a heart disease data set in terms of the Pareto frontier: $I(S;\hat {X}) \mathrm {v}\mathrm {s}. -I(X;\hat {X})$. We show that our IAC-MDSF algorithm outperforms the existing iterative pairwise merge approaches for both PF and IB and is computationally much less complex.
Ecuadorian mass media on Twitter during the 30-S

Internet and the massive deployment of mobile and wireless communication systems and devices are generating new digital environments that are drastically changing the way in which social ideas and debates are produced, accessed and disseminated. Online social networks are becoming the virtual public spheres by which the social and political events of this network society are being debated. Given this new digital and technological context, many researchers are questioning the processes of dissemination of the information generated into these virtual social structures and the validity of the traditional models of media influence in these environments. In this paper, we present the results of an empirical analysis of the dissemination of information on Twitter produced by two large Ecuadorian newspapers: “El Universo” (private newspaper) and “El Telégrafo” (public newspaper) during events of police insubordination on 30th September 2010. The results show that, given the restrictions imposed that day by the Ecuadorian government on the news broadcast from the private media, Twitter became on a space of diffusion and access of citizens to alternative views. In addition, it was observed that the information emitted during the 30-S by these newspapers, presented a structure of diffusion in multiple stages and multiple directions.
Analysis of Maximal Topologies and Their DoFs in Topological Interference Management

Topological interference management (TIM) can obtain degrees of freedom (DoF) gains with no channel state information at the transmitters (CSIT) except topological information of network in the interference channel. It was shown that TIM achieves symmetric DoF 1/2 when internal conflict does not exist among messages <xref ref-type="bibr" rid="ref7">[7]</xref>. However, it is difficult to assure whether a specific topology can achieve symmetric DoF 1/2 without scrutinizing internal conflict. It is also hard to derive a specific topology directly from the conventional condition for symmetric DoF 1/2. Even except for the topology achieving symmetric DoF 1/2, topology achieving specific DoF less than 1/2 is not well known. With these problems in mind, we propose a method to derive all maximal topologies directly in TIM, named as alliance construction in <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-user interference channel. That is, it is proved that a topology is maximal if and only if it is derived from alliance construction. Further we translate a topology design by alliance construction in the alignment-conflict graph into topology matrix and propose conditions for maximal topology matrix (MTM). Moreover, we propose a generalized alliance construction that derives topologies achieving DoF <inline-formula> <tex-math notation="LaTeX">$1/n$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">$n\geq 3$ </tex-math></inline-formula> by generalizing sub-alliances. A topology matrix can also be used to analyze topologies with DoF <inline-formula> <tex-math notation="LaTeX">$1/n$ </tex-math></inline-formula>.
GeSi EAM-Based 160 Gb/s Nyquist Half-Cycle Subcarrier Modulation Transmission

We demonstrate the transmission of beyond-100 Gb/s Nyquist half-cycle 16-quadrature-amplitude modulation (16-QAM) subcarrier modulation (SCM) signals with a compact silicon-based GeSi electro-absorption modulator. In combination with nonlinear compensation, the measured bit-error rate (BER) after 2 km standard single-mode fiber (SSMF) transmission is below the 20% hard-decision forward error-correction (HD-FEC) threshold of $1.5\times 10^{\mathbf {-2}}$ for single-lane 160 Gb/s operation. The presented modulator shows great potential for high-capacity and low-power-consumption silicon-photonic transceivers for data-center interconnects.
Performance Evaluation of Linear Precoding mmWave Multi-User MIMO Systems with NYUSIM Channel Simulator

In this paper, we evaluate the performance of the digital beamsteering (DBS) precoder in multi-user multiple-input multiple-output (MIMO) systems operating at mmWave frequencies. This evaluation is done using a 3-D mmWave statistical channel model developed based on outdoor extensive measurement in New York city. The NYUSIM channel simulator generates 3-D realistic statistical temporal and spatial parameters. The DBS performance is evaluated in terms of capacity achieved as a function of the number of users and compared with two classical precoders, namely, zero forcing (ZF) and conjugate beamforming (CB). The base station is equipped with three different antenna array configurations: uniform linear array, uniform square array and uniform rectangular array.Simulation results show that DBS can be realistically considered as an attractive precoder for mmWave multi-user MIMO system with tens of antennas at the base station; trading-off performance, low implementation complexity and less overhead.
Fog offloading: Review, Research Opportunity and Challenges

Fog computing is growing from its infancy to become the new age technology transition, it has not only aligned with the principle of cloud computing but it also supports and works in tandem with all the services and features of the cloud. The new age technology demands are reduced latency, memory footprint, efficient execution, scalability, constrained power, and distributed computing. The fog has been able to fulfill all the above technology demand. This paper presents the most recent innovation in computation offloading in fog environment and its integration with IoT by highlighting the benefits and challenges in implementation. An extensively surveyed fog computing and computation offloading have been presented in detail with the study of their application. Finally the research challenges in implementing a heterogeneous computation offloading architecture and future research directions are discussed.
A Model for Evaluation Smart City Readiness using Structural Equation Modelling: a Citizen's Perspective

Smart City is the use of ICT-based governance and cloud is now a trend throughout the world. Various increasingly complex city problems are expected to become increasingly more efficient by applying the concept of smart cities in city governance. However, in its implementation, many crucial problems significantly affect the success of the implementation of smart cities, including the lack of competent human resources, ICT policies and governance that have not been efficient, lack of government commitment and the lack of community participation. This study aims to model smart city readiness factors in community perspectives. The method in this study is quantitative descriptive research using a combination of the Technology Readiness Index model, the Technology Acceptance Model and the Delone McLean Model. Data collection is done by conducting surveys and interviews with 200 citizens. Data analysis using Structural Equation Modeling with the help of AMOS 23.0 software. The results showed that there were a significant relationship and relevance between the variables in the model used in the TRI, TAM and Delone McLean models. This study contributes to the relationship modelling of the factors that determine the success of applying the smart city concept. This research provides recommendations to the government and stakeholders to pay more attention to the crucial factors in the success of developing smart cities.
An optimal feature selection based classification model for disease diagnosis in cloud environment

In the Cloud computing platform, several technologies are practiced to manage the huge amount of information and also afford the comfort of routine. In this state, every cloud-based application plays an extensive role in the real-time applications. To avail efficient facilities to the patients, this paper presents an optimal feature selection based data classification model particularly for cloud platform. The presented model is based on two stages namely genetic algorithm based feature selection (GA-FS) and neural network (NN) based data classification. The presented GA-NN model is applied to diagnose the diseases and its various stages. The experimentations have been directed by the benchmark dataset and the real-time medicinal data that is gathered from numerous medical organizations. The simulation outcomes demonstrate that the efficiency of the GA-NN method beats the prevailing methods to predict diseases.
Formal Verfication of Security Protocol Using Spin Tool

Nowadays, security is a big issue related with the network. In order to send sensitive information through the network, we require a mechanism to ensure the information is sent to the intended receiver. Various methods is discussed which is used for verification and model checking method is implemented to construct a model using promela which verifies the properties that satisfies with a cryptographic protocol. This paper involves a brief survey on different methods available for the verification of security protocols and suggests a better method based on qualitative analysis on the survey. Also, we give a verification analysis of NSPK protocol using model checking tool Spin.
Agent Localization for WSN based on Variable Step-Size ILMS and DLMS

In this paper, a new localization approach is proposed based on the concept of Diffusion LMS (DLMS) to mitigate node and link failures in Incremental LMS (ILMS). It takes advantage of the known locations of powerful anchor nodes to estimate the location of tremendous unknown agents/nodes in Wireless Sensor Network (WSN). In addition, new techniques are developed according to the idea of Variable Step-Size (VSS) for ILMS and DLMS algorithms to extremely enhance the performance and convergence rate. Furthermore, the theoretical performance in terms of Mean Square Deviation (MSD) is derived and evaluated for DLMS and VSS-DLMS Algorithms. Simulation results show that the proposed VSS-ILMS and VSS-DLMS algorithms outperform the existing approach. From the Mean Square Error (MSE) point of view, they also have advantage of faster convergence in compare to the traditional ILMS and DLMS algorithms.
Polarity Detection Using Digital Media

People now use digital mediums for expressing their opinion on any matter. This interaction caused a tremendous increase in the amount of data generated online. Automatic sentiment extraction provides an effective method for analysing public opinion on any topic. This paper proposed an approach for summarising large documents and for automatically extracting sentiments. Twitter and news feeds are the 2 primary sources of data used in this paper . In this work Latent Semantic Analyser extracted the mostly discussed topics and are visualised using a word cloud. Valence Aware Dictionary and Sentiment Reasoner is the lexicon and rule based sentiment analysis tool used to extract sentiments from collected data. Valence Aware Dictionary and Sentiment Reasoner extracts the polarity expressed on text and the results are visualised for better analysis. The topics selected for data collection are Kerala flood, Election 2019 and Sabrimala issue.
DRAP: Decision Tree and Random Forest Based Classification Model to Predict Diabetes

This paper proposes a method called "DRAP" for detecting Diabetes. DRAP uses recorded data of the existing patients and classifies the unknown instances into one of the two classes - yes or no (whether diabetic or not). We have used a hybrid of decision tree and random forest algorithms to construct DRAP.Features considered for the constructing DRAP are glucose level, blood pressure, insulin, body mass index, age, etc. The proposed algorithm is evaluated on real-life data set. Our seminal results show the accuracy of 72% for Decision Tree and 76.5% Random Forest, respectively.
Classification of Rheumatoid Arthritis using Machine Learning Algorithms

Rheumatoid Arthritis (RA) is a persistent provocative ailment that effects and decimates the joints of wrist, finger, and feet. If left untreated, one can lose their ability to lead a normal life. RA is the most typical fiery joint inflammation, influencing around 1–2% of the total populace. Throughout the years, soft computing played an important part in helping ailment analysis in doctor’s decision process. The main aim of this study is to investigate the possibility of applying machine learning techniques to the analysis of RA characteristics. As a preliminary work, a credible database has been identified to be used for this research. The database has outputs of array temperature values from thermal imaging for the joints of hand. Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. In this preliminary work, algorithms such as bagging, J48 and AdaBoost classifiers were trained and tested using the assessment criteria such as accuracy, specificity and sensitivity using Weka tool. From the preliminary finding of this paper, it can be concluded that bagging has better classification accuracy over the others for rheumatoid arthritis dataset.
A Deep Model for Multi-Focus Image Fusion Based on Gradients and Connected Regions

In this paper, we propose a novel unsupervised model for multi-focus image fusion based on gradients and connected regions, termed as GCF. To overcome the stumbling block of vanishing gradients in applying deep networks for multi-focus image fusion, we design the Mask-Net which can directly generate a binary mask. Thus, there is no need for hand-crafted feature extraction or fusion rules. Based on the fact that objects within the depth-of-field (DOF) have shaper appearance, i.e., larger gradients, we use the gradient relation map obtained from source images to narrow the solution domain and speed up convergence. Then, the constraint of connected region numbers is conductive to finding the more accurate binary mask. With the consistency verification strategy, the final mask can be obtained by adapting the initial binary mask to generate the fused result. Therefore, the proposed method is an unsupervised model without the need of the ground-truth data. Both qualitative and quantitative experiments are conducted on the publicly available Lytro dataset. The results show that GCF can outperform the state-of-the-art in both visual perception and objective metrics.
The Development of Methods for Detecting Melon Maturity Level Based on Fruit Skin Texture Using the Histogram of Oriented Gradients and the Support Vector Machine

Melon (Cucumis melo L.) has economic value and promising prospects in the marketing aspect. Export data shows that melons are in the fifth place in fruit commodities foreign exchange earner. The increasing production and demand for melons have not been balanced with the optimization of harvesting and postharvest handling. The similarity of melon skin texture between mature, undercooked and immature for people who have difficulty detecting melons from various fruit skin levels to be different from one assessor with the other assessor. From these problems, research was conducted to develop a method that can detect the maturity level of melons based on fruit skin texture. The purpose of this research is to find out whether the method for detecting the maturity level of melons can be standardized and what method has the best accuracy in detecting melon maturity levels based on fruit skin texture. The data in this study using the 450 images obtained from melon farmers. The results of this research show that the best method for detecting melon maturity levels in this study is a combination of methods (mean filtering matrix [5×5] + gamma contrast stretching 2.0) + (mean adaptive threshold) + (histogram of oriented gradients) + (support vector machine) with an accuracy of 78.67%.
Design and Development of a Low-Cost High-G Centrifuge System (Cyclone)

High-G Centrifuge Systems provide a safe and controllable environment for G motion fields required for fighters’ pilots training. Lacking an operational Human Centrifuge System in Australia, the Institute for Intelligent Systems Research and Innovation (IISRI) at Deakin University has designed and built a low-cost High-G Centrifuge System, named Cyclone. Cyclone has the capability of generating a maximum sustained acceleration of 9G with an onset rate of 5G/sec. The system has an effective arm length of over 5m, which is actuated by a capable gear-motor. This paper addresses the main characteristics of Cyclone including modular design, self-balanced structure and rotating main drive. Moreover, the design and development of the system are discussed and the experimental results of two test scenarios will be presented. The developed system offers the highest level of operational and occupational safety to be rated as a human-capable system. The human-rating of Cyclone is currently investigated.
Trajectory Tracking Control of a Mobile Robot with Model Predictive Controller and Observer

This paper proposes the use of a model predictive controller with state observer to solve the trajectory tracking and kinematic model identification problem in mobile robotics field. Since it can be shown that observer and model predictive controller dynamics are independent from each other; model predictive controller based on a local linear-time-invariant model is designed by minimizing a finite horizon quadratic cost function and observer dynamics is designed by using a pole-placement technique. Performance of the proposed model predictive controller and observer pair was compared against that of a regular model predictive controller and it has been shown that the proposed controller-observer pair has better trajectory tracking performance.
Face Anti-spoofing using Hybrid Residual Learning Framework

Face spoofing attacks have received significant attention because of criminals who are developing different techniques such as warped photos, cut photos, 3D masks, etc. to easily fool the face recognition systems. In order to improve the security measures of biometric systems, deep learning models offer powerful solutions; but to attain the benefits of multilayer features remains a significant challenge. To alleviate this limitation, this paper presents a hybrid framework to build the feature representation by fusing ResNet with more discriminative power. First, two variants of the residual learning framework are selected as deep feature extractors to extract informative features. Second, the fullyconnected layers are used as separated feature descriptors. Third, PCA based Canonical correlation analysis (CCA) is proposed as a feature fusion strategy to combine relevant information and to improve the features’ discrimination capacity. Finally, the support vector machine (SVM) is used to construct the final representation of facial features. Experimental results show that our proposed framework achieves a state-of-the-art performance without finetuning, data augmentation or coding strategy on benchmark databases, namely the MSU mobile face spoof database and the CASIA face anti-spoofing database.
Low Voltage Dual Beam Capacitive Switches For Reconfigurable Microsystems

In this work, the modeling and simulation of a temperature tolerant switch design with improved RF (Radio Frequency) performance is proposed. Simulations are done in HFSS (High Frequency Structure Simulator) and Coventor Ware. Switch is fabricated on a quartz substrate. The simulated value for pull-in voltage is obtained as 27.8 V. RF performance parameters for a switch include insertion loss, return loss and isolation. The concepts of impedance matching and inductive tuning of a temperature tolerant switch are investigated which shows an improvement in RF characteristics of switch in Ka band ie. return loss improved to −45 dB from −15 dB, and isolation improved to −65 dB from −55 dB . Impedance matching involves two switches separated by a high impedance transmission line of a specific length, which is optimized to 600 μm to get minimum return loss. It gives 0.45 μm beam deflection at 500 K.