Miracle-dz/UniFAR-FTUs huggingface dataset:
A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification

Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to present an effective sarcasm identification framework on social media data by pursuing the paradigms of neural language models and deep neural networks. To represent text documents, we introduce inverse gravity moment based term weighted word embedding model with trigrams. In this way, critical words/terms have higher values by keeping the word-ordering information. In our model, we present a three-layer stacked bidirectional long short-term memory architecture to identify sarcastic text documents. For the evaluation task, the presented framework has been evaluated on three-sarcasm identification corpus. In the empirical analysis, three neural language models (i.e., word2vec, fastText and GloVe), two unsupervised term weighting functions (i.e., term-frequency, and TF-IDF) and eight supervised term weighting functions (i.e., odds ratio, relevance frequency, balanced distributional concentration, inverse question frequency-question frequency-inverse category frequency, short text weighting, inverse gravity moment, regularized entropy and inverse false negative-true positive-inverse category frequency) have been evaluated. For sarcasm identification task, the presented model yields promising results with a classification accuracy of 95.30%.
Complex Network Analysis of Women's Singles Tennis Matches

This paper presents an analysis of a tennis network based on professional women's tennis matches from the WTA Tour in the period 2010–2020. After the creation of the dataset, a weighted network was constructed using Python and visualized in Gephi tool. A detailed analysis was carried out using social network theory and specific metrics. Universal principles of complex networks such as scale-free degree distribution, clustering, small-world have been examined. Results agree with the works from the literature and show that this tennis network carries real-world network characteristics.
Modification of Wireless Reverse Charging Scheme with Bundling Optimization Issues

This paper attempts to modify the Internet Reverse Charging (IRC) model by adding a bundling optimization model by considering the Cobb-Douglas utility function to manage homogeneous consumer satisfaction and by using the end-to-end delay QoS attribute. The model formed is expected to achieve maximum revenue for the ISP to maximize ISP profits by minimizing internet usage costs for customers and by providing the best quality information services. Previous research focused only on reverse charging scheme without offering bundling strategy to attract the consumers. In fact, the consumer' choices would be the first priority to be taken in designing the good model. Then, modified model is designed based on the usage-based pricing scheme, that charges the amount of internet access by its usage. The modified model is intended to be designed to show that the improvement in previous model with no use of bundling schemes can reduce the profit the ISP. The step taken for designing is then to be formulated into The Mixed Integer Nonlinear Programming (MINLP) problem by using LINGO 13.0 software. The model is then implemented using bandwidth usage data collected in one of the local server. This issue is divided into two cases, namely the case of $\alpha$ (base price) as a parameter and $\beta$ (quality premium) as parameters and variables with sub-case $PQ_{ij}$ (cost changes due to QoS changes) decreases in the usage based pricing scheme. The modification results show that maximum revenue for the ISP is obtained in Case 1 ($\alpha$ and $\beta$, as parameters) and Case 2 ($\alpha$ as parameter and $\beta$ variable), which are divided into 2 sub-cases with the same objective value obtained. It means that by setting $\alpha$ and $\beta$ to be varied when $PQ_{ij}$ decreases, are able to maximize the total cost.
Bayesian Network Approach to Modelling Nitrogen Utilization Efficiency of Dairy Cows

Losses of nitrogen (N) from dairy cattle farming system cause environmental pollution and impact human health. A number of statistical models for predicting manure N excretion from lactating dairy cows have been developed based on regression analysis. In this study, we proposed a Bayesian network-based approach to modelling relationships among factors influencing manure N excretion of lactating dairy cows using a dataset collated from total diet digestibility studies undertaken at Agri-Food and Biosciences Institute in Northern Ireland. The preliminary results indicate that Bayesian network model can be used to capture relationships among factors that influence N utilization efficiency and can be used to establish causal influence among predictors. These may provide an effective tool for optimizing the management of feed N resource for current dairy production and developing strategies to reduce N excretion in dairy production systems.
Hydration Study of Proteins in Solution by Microwave Dielectric Analysis

We propose a method for evaluating protein hydration which enables one to evaluate the amount of restrained water and distinguish between “weakly” and “strongly” restrained water on proteins in an aqueous solution using microwave dielectric measurement. Measurements were taken with a precision microwave network analyzer and a thermostated glass cell at 20.0 ± 0.01 °C with an open-end flat-surface coaxial probe. Examined proteins were cytochrome c, myoglobin, ovalbumin, bovine serum albumin, and hemoglobin. The present method is based on the Wagner equation and the Hanai equation for emulsion analysis and assumes that there is “weakly” restrained water on proteins which has a simple Debye-type relaxation in the gigahertz region and that the dielectric constants of hydrated solutes at high-frequency limits are given by the electron polarizations of atom groups. This analysis enabled us to characterize more precisely the hydration state of proteins in water.
Maintenance logistics management: a new Lean/REX model to activate the human potential

Maintenance logistics is a generic approach to manage maintenance activities and its resources. Even though this new framework remains promising, its managerial aspect is still in need to focus more on the practical perspective. In this paper, we present a literature review of main maintenance management models based on maintenance policies/ strategies discussion to conclude the necessity to build a new sustainable model activating the human factor potential. Therefore we propose a Lean/REX model leading to a new practical standards conception inspired by the lean culture and experience feedback to seek new improvement opportunities in maintenance logistics.
Improved observer design for heat equation with constant measurement delay via Legendre polynomials

In this paper, we present improved results on observer design for 1D heat equation. We first introduce an observer under delayed spatially point measurements that leads to an estimation error with time-delay. Inspired by recent developments in the area of delayed ODEs, we suggest augmented Lyapunov functionals based on the Legendre polynomials. Then, sufficient exponential stability conditions are derived in the form of linear matrix inequalities (LMIs) that are parameterized by the degree of the polynomials. Finally, a numerical example illustrates the efficiency of the results that allow to enlarge the value of delay preserving the stability by more than 20%.
CNTFET Technology for RF Applications: Review and Future Perspective

RF CNTFETs are one of the most promising devices for surpassing incumbent RF-CMOS technology in the near future. Experimental proof of concept that outperformed Si CMOS at the 130 nm technology has already been achieved with a vast potential for improvements. This review compiles and compares the different CNT integration technologies, the achieved RF results as well as demonstrated RF circuits. Moreover, it suggests approaches to enhance the RF performance of CNTFETs further to allow more profound CNTFET based systems e.g., on flexible substrates, highly dense 3D stacks, heterogeneously combined with incumbent technologies or an all-CNT system on a chip.
Rapid determination of calcium, magnesium, sodium and potassium in milk by flame atomic spectrometry after microwave oven digestion

A rapid method for determining Ca, Mg, Na and K in skim, semi-skim and whole milk was studied. These elements can be accurately determined in milk by atomic absorption (Ca and Mg) and atomic emission (Na and K) spectrometry after digestion of the sample with HNO3 in domestic and analytical microwave ovens in sealed poly(tetrafluoroethylene) reactors. Microwave-oven mineralization allowed accurate determination as tested against certified milks. The results were also in good agreement with those obtained after precipitation with trichloroacetic acid in certified and real liquid milk samples. Low recoveries for Ca in whole milk were found, owing to matrix effects, and the method of standard additions was necessary to avoid them. Mean recoveries determined for one of the milk reference materials studied were 99.3, 100, 100.5 and 100.3% for Ca, Mg, Na and K, respectively. The method gave relative standard deviations of 1–2% for the four elements. The limits of detection were 63, 15, 18 and 72 µg l–1 for Ca, Mg, Na and K, respectively. The method is very practical for routine laboratory analyses of relatively large numbers of samples.
Comparative Analysis of DDoS Detection Techniques Based on Machine Learning in OpenFlow Network

Software Defined Network (SDN) allows the separation of a control layer and data forwarding at two different layers. However, centralized control systems in SDN is vulnerable to attacks namely distributed denial of service (DDoS). Therefore, it is necessary for developing a solution based on reactive applications that can identify, detect, as well as mitigate the attacks comprehensively. In this paper, an application has been built based on machine learning methods including, Support Vector Machine (SVM) using Linear and Radial Basis Function kernel, K-Nearest Neighbor (KNN), Decision Tree (DTC), Random Forest (RFC), Multi-Layer Perceptron (MLP), and Gaussian Naïve Bayes (GNB). The paper also proposed a new scheme of DDOS dataset in SDN by gathering considerably static data form using the port statistic. SVM became the most efficient method for identifying DDoS attack successfully proved by the accuracy, precision, and recall approximately 100% which could be considered as the primary algorithm for detecting DDoS. In term of the promptness, KNN had the slowest rate for the whole process, while the fastest was depicted by GNB.
MOBDroid: An Intelligent Malware Detection System for Improved Data Security in Mobile Cloud Computing Environments

We propose an intelligent malware detection system (MOBDroid) that aims to protect the end-user’s mobile device (MD) in mobile cloud computing (MCC) environment. MOBDroid utilizes the Android Operating System (OS) permission-based security system. The APK files of 28,306 benign and malicious applications (apps) collected from the AndroZoo and RmvDroid malware repositories were used in the system development process. The apps were decompiled in order to extract their manifest files and construct a dataset comprising the permissions requested by each of the apps. We identified some unique permissions that could be used to distinguish between malicious and benign apps and performed a series of experiments using a machine learning (ML) model; the model drew on the ML.net library and was implemented in C#. net. In the experiments conducted, we obtained classification accuracy of 96.89%, a detection rate of 98.65%, and false negative rate of 1.35%. The results indicate that our model compares very favorably to other models reported in the extant literature.
Improving the Stability of Mesoporous MCM-41 Silica via Thicker More Highly Condensed Pore Walls

Thicker-walled mesoporous MCM-41 silicas with improved thermal and hydrothermal stability were prepared via secondary crystallization (in which calcined MCM-41 was used as silica source) or by using long crystallization times. Unusually, the thicker pore walls of the as-synthesized (surfactant containing) forms of these Si−MCM-41 materials are primarily made up of fully condensed silica units. Q4/Q3 ratios as high as 3.4 or 4.5 are obtained for two-step (48 h each) crystallization or 96 h crystallization at 150 °C, respectively. A diffusion-controlled mechanism in which silicate units and surfactant molecules are continually added onto the “seed” crystals (during secondary recrystallization) or to the growing surfactant/silicate aggregate (for long crystallization time) is proposed to explain the pore wall thickening and higher silica condensation.
Optimization of SV-kNNC using Silhouette Coefficient and LMKNN for Stock Price Prediction

A broker often finds it difficult to decide to buy or sell shares. Due to the large amount of stock data and errors in analyzing stock indicators, it can cause the broker to suffer losses. Predicting stock prices with high accuracy becomes onerous. Although unnecessary variables have been excluded but there are still outliers. It is still difficult to attain the most optimal class value at the data clustering stage. The objective of this research is to propose a model to refine the accuracy of stock prediction and to produce the best class at the data clustering stage. The proposed model is named SV-kNNC - Silhouette Coefficient - SOM (Self Organizing Map) - LMKNN (Local Mean-based K Nearest Neighbor). First, the best class value is obtained using the Silhouette Coefficient, then the data is weighted, finally the stock data is classified using the LMKNN to secure the prediction results. In the testing section, SV-kNNC - SOM + Silhouette Coefficient - LMKNN is compared against SV-kNNC - SOM - KNN (K Nearest Neighbor). Confusion Matrix is used during the test to get the predictive accuracy value. It can be seen from the results of the prediction accuracy of BBCA's shares is 86.99% with the best K is 3, ASII company is 78.30% with the best K is 3, TLKM company is 85.10% with the best K is 5, BBRI company is 84.73% with the best K is 6, PGAS company is 71.36% with the best K is 5.
Performance Analysis of Stochastic Model Predictive Control with Direct and Indirect Feedback

Stochastic model predictive control (SMPC) approximates the solution to constrained stochastic optimal control problems by solving a simplified problem repeatedly over a reduced prediction horizon. This paper demonstrates and discusses significant open challenges for current SMPC methods in terms of their closed-loop performance and conservatism regarding constraint satisfaction. In particular, we compare two forms of formulating chance constraints in SMPC. First, we consider a direct feedback formulation, which corresponds to the typical implementation of SMPC. Direct feedback formulates chance constraints for the predicted state distribution conditioned on the current measured state at each time step during the receding horizon control. Indirect feedback, in contrast, formulates constraints by introducing a suitable nominal state, which allows to enforce chance constraints on the closed loop. In numerical examples, we demonstrate that direct feedback, i.e. the typical form of SMPC, can result in significant conservatism, allowing almost no constraint violations. This results in significantly reduced performance, which we show can be alleviated with indirect feedback formulations. In addition, we prove that indirect feedback can recover the unconstrained optimal solution given by LQR control whenever it is feasible also for the constrained optimal control problem.
Dynamic Quantized Leader-follower Consensus under Denial-of-Service Attacks

This paper investigates the multi-agent leader- follower consensus problem under Denial-of-Service (DoS) attacks with data rate constraints. In our analysis, we first derive a lower bound on the data rate for the leader-follower consensus problem, which can guarantee the quantizers of the leader and followers do not overflow at any time, even in the presence of DoS attacks. The main contribution of the paper is the characterization of the trade-off between the tolerable DoS attack levels for leader-follower consensus and the required data rates for the quantizers during the communication attempts among the agents. To mitigate the influence of DoS attacks, we employ dynamic quantization mechanism with scale-up and scale-down capabilities. This paper also reveals the another trade-off between the data rate for the leader state quantization and the one for the follower state quantization. This makes saving the overall communication load and maintaining the resilience possible.
Nonisothermal Crystallization Behaviors of Flame-Retardant Copolyester/Montmorillonite Nanocomposites

In order to obtain poly(ethylene terephthalate) (PET) engineering plastics with good flame retardancy, heat resistance, and mechanical properties, a novel phosphorus-containing copolyester (PET-co-DDP)/organo-montmorillonite (OMMT 1%) nanocomposite (PET-co-DDP/OMMT) was prepared by in situ intercalating polymerization. Nonisothermal crystallization kinetics and nanoscale morphology of this composite have been investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and differential scanning calorimetry (DSC). Based on the results of the nonisothermal crystallization kinetics, the flame-retardant copolyester PET-co-DDP has a lower crystallization rate than pure PET, while PET-co-DDP/OMMT nanocomposite has a higher crystallization rate than pure PET. Based on the Augis and Bennett method, the activation energies for nonisothermal crystallization of pure PET, PET-co-DDP, and PET-co-DDP/OMMT nanocomposite were evaluated as 101, 138, and 76 kJ mol−1, respectively. All the evidence shows that PET-co-DDP strongly influences the crystallization behavior because of its irregular chain structure, while the addition of nanoscale OMMT to this copolymer can significantly enhance the crystallization rate owing to its remarkable nucleating effect. An understanding of the above crystalline behaviors will be beneficial in preparing PET engineering plastics with good overall comprehensive performance.
Effects of ligand planarity on the interaction of polypyridyl Ru(II) complexes with DNA

Two new polypyridyl ligands containing substituent Br at different positions in the phenyl ring, OBIP {OBIP = 2-(2-bromophenyl)imidazo[4,5-f]-1,10-phenanthroline}, PBIP {PBIP = 2-(4-bromophenyl)imidazo[4,5-f]-1,10-phenanthroline} and their Ru(II) complexes, [Ru(dmp)2(OBIP)]2+1 and [Ru(dmp)2(PBIP)]2+2 (dmp = 2,9-dimethyl-1,10-phenanthroline), have been synthesized and characterized. The binding of the two complexes to calf thymus DNA (CT DNA) has been investigated by spectrophotometric methods, viscosity measurements, as well as equilibrium dialysis and circular dichroism spectroscopy. Theoretical calculations for the two complexes were also carried out applying the density functional theory (DFT) method. The structure of complex 1 has been determined by single-crystal X-ray diffraction techniques. The imidazo[4,5-f]-1,10-phenanthroline moiety is not coplanar with the 2-bromophenyl ring, having a dihedral angle of 48.6° in the OBIP ligand. The twisted conformation has been further confirmed by theoretical calculations, in which this dihedral angle is 48.2°. The theoretical calculations also suggest that the PBIP ligand in complex 2 is essentially planar (dihedral angle is 0.4°). The experimental results show that while complex 1 binds to CT DNA via a semi-intercalative mode, complex 2 strongly binds to CT DNA through intercalation. Complex 2 is thus a much better candidate as an enantioselective binder to CT DNA than complex 1. Some experimental regularities or trends have been reasonably explained by the theoretical results. These suggest that the planarity of the intercalated ligand has significant effects on the spectral properties and the DNA-binding behavior of the complexes, and that the DFT method can be used effectively to explain and predict some regularities or trends in the interaction of polypyridyl Ru(II) complexes with DNA.
Implementation and Performance Evaluation for DSRC-Based Vehicular Communication System

Traffic accidents and congestion are the most common on-road problems that might be anywhere and anytime. Recently, to deal with such problems, well-coordinated wireless communication system between vehicles, road side units (RSU), drones, etc., namely vehicle-to-everything (V2X) communication system, has been considered as a key technology enabling on-road human safety and convenience. One of the well-known building blocks of the V2X communication system is Wi-Fi based Dedicated Short Range Communication (DSRC) which performs stably, owing to a long year of study, simplicity and capability of distributed operation. In spite of promising opportunities brought by DSRC, its elaborate performance evaluation must be done under real-world scenarios, in advance to the actual use, since its performance is directly related to human and vehicle safety. In this context, this article presents field test results of DSRC-based V2X communication system we implemented. As a result of the line-of-sight (LoS) test, the distance represented by 90% or more of packet reception rate (PRR) was 720 meters at 5 dBm, and 1,035 meters at 11 dBm. In Non-LoS (NLOS) shadowing test, the distance represented by 90% or more of PRR was 175 meters at 5 dBm, and 520 meters at 11 dBm. And in NLoS intersection test, the distance represented by 90% or more of PRR was 320 meters at 5 dBm, and 515 meters at 11 dBm. Meanwhile, the distance for PRR 90% was up to 520 meters at urban environment, 1,219 meters on highway, and 1,700 meters inside a tunnel.
Millimeter Wave Carbon Nanotube Based Flip Chip Coplanar Interconnects

In this work, we investigate the performances of vertically aligned carbon nanotube (VACNT) arrays for high frequency interconnects. The frequency range of this study is the W band (75 GHz to 110 GHz). The type of interconnect is coplanar, meaning the interconnect can connect two subparts supporting a coplanar waveguide (CPW) mode. Starting from the promising results of a previously measured device [1] we suggest a way to improve the behavior of VACNT arrays interconnects. We perform full-wave modeling of a new structure that will be fabricated and tested and we compare different modelling approaches for the design of such interconnects.