Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Artificial Intelligence to Monitor the Evolution of Opinion Leaders’ Sentiments: Case Study on Global Warming

Emotional and sentiment analysis of social media content is essential for smart city analytics. In the past few years, researchers have relied on online content’s sentiment analysis to capture public opinion about current events. Despite their merits, the existing solutions take a retrospective coarse-grained approach that analyses millions of social media posts to study public opinion about past events (e.g., presidential elections). Such models give late insights, which makes it difficult to intervene or adjust strategies based on the evolution of public opinion over time. Moreover, such approaches lack efficiency and scalability since they require the analysis of millions of posts to obtain accurate results. In this work, we address those limitations by proposing a framework for the real-time monitoring of the evolution of public opinion over time. To ensure efficiency and scalability, we focus on the analysis of high impact social media content generated by opinion leaders and their followers. To build our framework, we leveraged our opinion leaders’ identification algorithm, along with text mining and text classification techniques, to capture and analyze the evolution of the sentiments and emotions of 34 opinion leaders concerning the topic of global warming. The results obtained are very promising and open the door to advanced social media analytics to monitor public opinion in real-time.
Spatial Attention for Autonomous Decision-making in Highway Scene

Automated decision making is still a significant challenge to realize fully autonomous driving. A common method that encoding surrounding vehicles in a grid map is used to describe observation space for decision making algorithm. It preserves vehicles spatial characteristics. But commonly in human driving, distinct position and speed surrounding vehicles contribute differently to make decision. We introduce a spatial attention module to calculate weights for each vehicle and integrate the attention mechanism into Deep Q network to make decision actions. The agent, ego vehicle, is trained in a simulated highway environment. Simulation results show the proposed method can get significant performance gains compared with other deep reinforcement learning methods by using two kinds of metrics.
SecONet: A Security Framework for a Photonic Network-on-Chip

Photonic networks are already commercially available at the board-level, and many fabrication facilities can fabricate optical networks and integrate them with traditional silicon-based SoCs. Almost all the research in on-chip photonics has been in the areas of performance enhancement and static power reduction. However, before the large-scale adoption of such technologies, it is necessary to solve security problems. As opposed to electrical NoCs, optical NoCs are shared to a much larger extent, and are significantly more sensitive to the latencies of cryptographic operations. Hence, it is necessary to design a novel protocol for securing such networks. We propose a novel, secure, and efficient optical network in this paper (SecONet) that is immune to eavesdropping, spoofing, replay, and message-removal attacks. Using a combination of speculative execution and pre-computation, we reduce the performance overhead of 39.53% with a conventional implementation to 14.2% for a suite of Splash2 and Parsec benchmarks. The additional area overhead of our proposed hardware is modest: 1.6%.
Prediction of Emotions from the Audio Speech Signals using MFCC, MEL and Chroma

Emotion recognition from audio signals is a recent research topic in Human-Computer Interaction. A speech emotion recognition (SER) system is developed to detect emotion from the audio speech signals. SER is created by extracting various audio features and combining them differently to create feature vectors. The efficiency of emotion recognition system depends on type of features extracted and classifier used for detection of emotions. The feature vector contains important audio features like MFCC, MEL, and Chroma. Feature vectors are created using two combination of features. Several machine learning paradigms were used for the emotion classification task. Their performances are compared using confusion metrics. The model compares the performances on change in feature vector. The proposed model searches for the best classification algorithm, which has considerable improvement in emotion prediction.
COMAR: Classification of Compromised versus Maliciously Registered Domains

Miscreants abuse thousands of domain names every day by launching large-scale attacks such as phishing or malware campaigns. While some domains are solely registered for malicious purposes, others are benign but get compromised and misused to serve malicious content. Existing methods for their detection can either predict malicious domains at the time of registration or identify indicators of an ongoing malicious activity conflating maliciously registered and compromised domains into common blacklists. Since the mitigation actions for these two types domains are different, we propose COMAR, an approach to differentiate between compromised and maliciously registered domains, complementary to previously proposed domain reputation systems. We start the paper with a thorough analysis of the domain life cycle to determine the relationship between each step and define its associated features. COMAR uses a set of 38 features costly to evade. We evaluate COMAR using phishing and malware blacklists and show that it can achieve high accuracy (97% accuracy with a 2.5% false-positive rate) without using any privileged or non-publicly available data, which makes it suitable for the use by any organization. We plan to deploy COMAR at two domain registry operators of the European country-code TLDs and set up an early notification system to facilitate the remediation of blacklisted domains.
Application of Dynamic System for Tropical Fruit Supply Chain

There are some factors and variables that have a strong relationship in a supply chain system. This also applies to consumer goods derived from agricultural products. Tropical fruits as agricultural products have a large number of consumers in North Sumatra. The number of sales of tropical fruit is strongly influenced by selling price. The flow of this product from farm to consumer involves supplier, consumer, and producer supply chain subsystem. This study begins by expanding the main model of the tropical fruit's supply chain and come across the matter variables. Anylogic software is being used for the method analysis and applied by modeling and simulation of the tropical fruit supply chain. The results indicate that the supply chain of tropical fruit in North Sumatra is cross-sectoral due to it is relative to some consecutive relation.
Autonomous Emergency Steering Using Deep Reinforcement Learning For Advanced Driver Assistance System

Although automobile technology has continuously evolved in recent years, there are still many traffic accidents all over the world. In order to reduce the number of traffic accidents, we focused on developing an emergency collision avoidance system. In this work, our goal is to propose a smart controller which can calculate the trajectory in order to avoid vehicle collision in the emergency situation while preventing the potential secondary collision caused by the change of trajectory in order to avoid the first collision. We consider two emergency driving scenarios as a first step towards this goal and propose a solution using deep reinforcement learning algorithm. We present detailed results of training and validation in the simulated environment. Further, we also validate the proposed solution in an actual vehicle and show that the trained controller successfully avoided collisions in case of an emergency situation without any human interaction.
Automatic Device Model Parameter Extractions via Hybrid Intelligent Methodology

We report an advanced hybrid intelligent methodology for device model parameter extractions combining multiobjective evolutionary algorithms, numerical optimization methods, and unsupervised learning neural networks on a unified optimization framework. The results between experimentally measured data and the calculation from industrial standard compact models are accurate, stable and convergent rapidly for all I-V curves. Verifications from diodes, bipolar transistors, MOSFETs, FinFETs, to nanowire MOSFETs confirm the robustness of the developed prototype, where the extraction is within 5% of accuracy.
A Study of the Change in the Temperature ofMaximum Density of Water and Aqueous Solutionsas a function of Pressure

The aim of this research is to study the shift in the temperature of maximum density of water and aqueous solutions as a function of pressure. One of the many anomalous properties of water is that it passes through a maximum in density in the liquid state. In order to accurately measure the temperature of maximum density (Tmd), convective flow is monitored in a rectangular container containing the fluid. A temperature gradient is held across the chamber and it is cooled and heated in a quasi-steady state manner. A double cell convection pattern forms in the vicinity of the density maximum. This double cell is tracked by monitoring the temperature at selected points in the fluid. The change in temperature of maximum density due to concentration and applied pressure can be investigated using this technique. At a pressure of one atmosphere, this density maximum occurs in pure water at a temperature of 3.98 C. It is known that the temperature of maximum density decreases as the pressure increases; for pure water this occurs at a rate of 1 C per 50 bar. Experimentally the shift in the temperature of maximum density of aqueous solutions is tracked over the pressure range 1 to 100 bar. It is found that the temperature of maximum density drops as the pressure rises for all solutes studied, but that the rate of decrease changes depending on the nature of the solute. For ionic salts, the rate of decrease is steeper than that for pure water, whereas for monohydric alcohols the rate of decrease is less that that for pure water. These divergent trends become more apparent as solute concentrations increase. The behaviour of the temperature of maximum density is modelled on both macroscopic and microscopic levels. A simple macroscopic model is proposed by combining state functions for water with those of solutes. This approach predicts that the rate of decrease of the temperature of maximum density for ideal (noninteracting) mixtures as a function of pressure is less than for pure water (but not as pronounced as the change observed in the alcohol solutions). Microscopic modelling at the molecular level is done using Monte Carlo methods. Non-ideal mixtures are studied by introducing molecules whose interactions with water are either stronger or weaker than the water-water interactions. In all cases it is found that the rate of change of the temperature of maximum density as a function of pressure lessens compared to the rate for pure water. The models thus help in understanding some, but not all, of the experimental observations.
Performance Modeling of Silicon Carbide Photoconductive Switches for High-Power and High-Frequency Applications

In this article, we focus on the physical modeling of the nonlinear operation of intrinsic photoconductive semiconductor switches (PCSS) based on 4H-SiC using coupled electrical and optical simulations to provide performance bounds of the switch as a function of material and geometry parameters, as well as applied bias. We also conduct a full design-space exploration to identify the optimal operating and design conditions to maximize the compound metric <inline-formula> <tex-math notation="LaTeX">$f_{\mathrm {op}} P_{\mathrm {out}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$f_{\mathrm {op}}$ </tex-math></inline-formula> is the maximum operating frequency, and <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula> is the maximum output power the switch can provide. We quantify that a 10-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> long and 5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> thick 4H-SiC PCSS can deliver output power density greater than 2W/mm at 150 GHz when triggered by a 0.325-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> laser with intensity of 3 kW/cm<sup>2</sup>. The output power density can be significantly enhanced by increasing the optical generation rate as well as by using thicker SiC to improve its absorption characteristics. A brief discussion of signal distortion and electrostatic screening effects at high optical bias is included. Finally, we present an analytic model of charge cloud propagation and the frequency of operation based on the physics, material parameters, and geometry of the PCSS. The model accurately captures <inline-formula> <tex-math notation="LaTeX">$f_{\mathrm {op}}$ </tex-math></inline-formula> of 4H-SiC PCSS over a broad range of laser spot size, device length, and electrical bias applied at the contacts.
Cuffless Blood Pressure Measurement Using a Microwave Near-Field Self-Injection-Locked Wrist Pulse Sensor

A new cuffless methodology of measuring the blood pressures (BPs) of human subjects is designed using a microwave near-field self-injection-locked (NFSIL) wrist pulse sensor. The NFSIL wrist pulse sensor is primarily composed of a self-oscillating complementary split-ring resonator and an amplitude-based demodulator. It generates a concentrated electric field in the near-field region for wrist pulse waveform detection. The reflective pulse transit time is extracted from this measured wrist pulse waveform and substituted into the BP computation algorithm to estimate the systolic and diastolic BPs (DBPs) of the subjects being tested. In this article, four calibration factors are adopted in the BP computation formulas to improve the accuracy of the calculated systolic BPs. In addition, the calibration procedures of the proposed NFSIL BP sensor are clearly summarized and experimentally verified. The BPs of a young healthy subject are measured using the NFSIL BP sensor at five designated times for eight days continuously. Compared with the BP measured by the commercial sphygmomanometer, the mean difference and standard deviation of the systolic and DBPs of the subject are 0.26 ± 3.67 and 0.56 ± 6.99 mmHg, respectively. In addition, to verify the generalizability, ten test subjects aged from 23 to 48 years are recruited to measure BPs for five days continuously. The measured results in this study demonstrate the effectiveness of BP measurement using the proposed NFSIL BP sensor. Due to the advantages of having a simple system architecture, compact sensor size, low cost, and high sensitivity, the proposed NFSIL BP sensor has great potential for development as a commercial cuffless BP sensor for overnight BP monitoring.
One-step Local Feature Extraction using CNN

We propose a one-step Local Feature Extraction Network framework to solve the sparse feature matching problem. In our network, we use raw camera data and the Structure from Motion (SfM) algorithm to restore the corresponding relationships of the different feature map. Our network combines the detector and descriptor as one step to build an end-to-end Local Feature Extraction network. At the same time, the whole process is differentiable and we train our network by the loss of feature map. Finally, we train our network on indoor datasets and prove its accuracy and rapidity advantage over other methods.
High-Resolution Chipless Tag RF Sensor

In this article, a new coupled structure based on microwave planar resonators is proposed to demonstrate the capability of ultrahigh quality factor performance using a chipless passive resonator. The platform is based on two passive split-ring resonators (SRRs), one as a reader, and the other one as the tag. The reader resonator is designed to operate at 2.6-GHz resonance frequency and is coupled to an active feedback loop with a microwave amplifier to compensate for the resonator’s loss and increase the loaded quality factor of the response. The bandwidth of the feedback amplifier is modified such that the amplifier’s gain is higher at the resonance frequency of the tag than that of the reader by adding a capacitor between emitter and collector of the bipolar junction transistor (BJT) amplifier. The tag is designed at 1.6 GHz and is located at a 2.5-mm vertical distance from the reader. As a result, the overall quality factor of about 75 000 is achieved for the tag performing the sensing. The presented technique provides a great practical solution for highly sensitive, noninvasive, and real-time sensing applications. The proposed sensing tag is integrated with a microfluidic chip to highlight its capability for small volume sensing and lab-on-a-chip applications. The sensitivity potential of the design is verified by detecting the concentration of acetone in deionized water. The average sensitivity of the presented sensor is more than 35 kHz/(1% of acetone concentration variation), which is offering extremely high sensitivity of the structure considering the very small volume of the exposed material under the test and the distance between the sensor and the sample.
Design and Analysis of Square Hole based MEMS Piezoelectric Energy Harvester using PiezoMUMPs Process

This paper presents an efficient way to improve the performance of the MEMS piezoelectric energy harvester by incorporating square shaped through hole to the classical cantilever configuration. The design constraints w.r. t PiezoMUMPs fabrication process for this particular square hole based topology have been discussed. The harvester characteristics such as voltage, power, resonant frequency, stress and strain profile has been studied for different configurations. In this work, the resonant frequency has been improved by decreasing the stiffness of the harvester without increasing the mass of the beam. It is observed that not only resonant frequency reduces but also the voltage and power of the piezoelectric energy harvester increases due to the effect of hole. The square hole based piezo harvester produces 1.1 $\mu \mathrm{W}$ whereas the without hole based harvester produces only 131 nW for the overall harvester dimension of $(1250 \mu \mathrm{m}\times 250\mu \mathrm{m})$.
Extraction of Fetal ECG in a Non-invasive Way and Estimation of Fetal Heart Rate

Fetal electrocardiogram (FECG) extraction in a non-invasive way has a vital role in medical diagnosis during pregnancy. Though there has been significant progress in adult electrocardiography, the analysis of fetal ECG is still in its rudiments. The existing methods for fetal ECG analysis are the invasive ones that cause severe infection to the uterine area being crucial for both mother and prenate yielding the necessity of non-invasive methods. In this paper, a non-invasive method is proposed to extract FECG from maternal abdominal ECG (AECG) which starts with the data collection followed by the pre-processing of the data, maternal QRS detection from the pre-processed data and finally fetal ECG extraction & fetal heart rate (FHR) estimation from the data after maternal QRS is properly removed. This method showed very good accuracy when compared to many previous works as mentioned in the paper.
Unveiling SSL/TLS MITM Hosts in the Wild

The SSL man-in-the-middle (MITM) attack uses forged X.509 certificates to impersonate each endpoint and intercept encrypted connections between clients and servers. Prior researches have revealed the prevalence of MITM attacks from SSL proxies, firewall, anti-virus, adware and malware. In this work, we design and implement a novel method to discovery suspicious SSL MITM hosts in the wild. Our results show that hosts using untrusted certificates are vulnerable to MITM attacks. We find 322,831 forged certificates, most of which are generated by TLS proxies and corporate firewalls such as Lancom System, Fortinet, Technicolor, Ubiquiti, and so on. The main reason of these forged certificates disclosed in public maybe origin from default configurations. At the end, we clarify the limitations of our method and give suggestions to defend against SSL MITM attacks.
Analysis of the Given Law Accuracy of a Mine Skip Lifting Unit Movement Using a Vector-Controlled Electric Drive System

Based on the analysis of the development mining and coal industries trends, including mine lifting units, the expediency of introducing new modern electric drive systems capable of ensuring high accuracy of working out the given movement law of the lifting vessels is determined. Based on the model of the cargo lifting installation electromechanical system, researches of the level of working out a given speed accuracy at the point of loaded skip entry into the unloading curves with variations of the load value and the degree of balancing are made. The analysis of the obtained research results is performed and the feasibility of using electric drive systems with induction vector-driven electric motors with existing mine lifting installations is substantiated.
Bayesian networks and Support Vector Classifier in damage risk assessment of RC prefabricated building structures in mining areas

The development of methods for capturing and collecting information with regard to the issues of Structural Health Monitoring and Building Maintenance Management allows for more accurate description of multifactorial processes in the whole life cycle of building structures. This includes the problem of damage risk assessment, which is a complex issue, consisting of a number of factors resulting from the applied structural, technological and material solutions, quality of maintenance or variability of originally assumed operating conditions. Additionally, this problem is getting significantly more complicated when it is necessary to take into account external influences from changing environmental impacts. This is the case with existing building developments located in areas subjected to damaging effects of underground mining in the form of continuous ground deformation and mining tremors. In the long run, the external environmental effects resulting from the decommissioning of the mines should also be taken into account. These are qualitatively similar to continuous deformations of the terrain, but as a rule, they significantly cover much larger area which was not considered to be affected by negative mining impacts during mining operations. From the social and economic points of view, not only the prediction of the extent and intensity of damage, but also the diagnosis of its causes are important. The Bayesian inference framework offers a possibility of creating a universal model for assessing the risk of damage to existing building structures. However, in the case of complex processes, this structure is not known and its search is a basic methodological problem. In the conducted research presented in this article, the Bayesian network structure was extracted using selected score-based learning methods. Currently, the adopted approach is the subject of intensive research, especially in the field of medical science, biology and genetics. Therefore, implementation of such methodology is an innovation in the interdisciplinary field of civil engineering and mining. However, due to the fact that this methodology is still being developed, it needs to be verified. For this purpose, the SVC (Support Vector Classifier) method was used. The effectiveness of the adopted methodology was finally confirmed. This was done by comparing the results obtained for the selected Bayesian network and Support Vector Machine classifier. This formed the basis for formulating conclusions on the applied methodology and on the influence of specific factors on the modelled damage risk process in the group of existing RC prefabricated buildings structures.