Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions Unveiled: Analyzing Public Sentiment on IoT and AI Integration in Revolutionizing Social Interactions

The IoT and AI are transforming social connections and communication. The article investigates how this symbiotic relationship impacts Twitter sentiment. Using the Twitter Sentiment Dataset," this study analyzes how IoT and AI are changing linked experiences. Analyzing Twitter sentiment on this historic merger shows positive and negative sentiments. Social media allows people to express their thoughts and feelings. This evolving digital environment reflects society and reveals technology adoption sentiment. The merging of IoT with AI is a key technological achievement. Tweets provide a real-time snapshot of worldwide dialogues and reactions to understand public opinion on integration. This study examines the complicated sentiment landscape using the "Twitter Sentiment Dataset," which captures IoT and AI sentiments. Social media sentiment analysis has been used to study public reactions to technological advances. Several studies show that sentiment analysis can predict technological adoption, examine attitudes and concerns, uncover issues, influence legislation, and address ethical issues. Research also emphasizes sentiment polarization's enthusiasm-realism balance and how sentiment predicts technical impact. The research used the "Twitter Sentiment Dataset," with over 1.6 million tweets. Lowercase, punctuation, and stopwords were removed from the dataset. After that, NLP algorithms like sentiment analysis examined the text. The training and test sets were split, and TF- IDF vectorization retrieved features. This research tested sentiment analysis machine learning models (Bernoulli Naive Bayes, Linear Support Vector Classification, and Logistic Regression). Later, the best model was stored. The main measure of sentiment analysis model accuracy was accuracy. Logistic regression outperformed with 83% accuracy. The models accurately captured IoT and AI integration tweets, including joy and concerns. Sentiment analysis can disclose society's feelings, making IoT-AI integration promising. This study reveals that algorithms and human sentiment drive technology. Technology- emotion interactions affect public opinion, innovation ethics, and policy. The study shows that societal emotion is essential to technical advancement and lays the framework for human-valued IoT and AI.
The Surface and Atmospheric Radiation Budget and Aerosol Forcing With a New Formulation for Ocean Surface Albedo

The ocean surface is the most ubiquitous boundary condition for shortwave (SW). To fully exploit the increased accuracy of Clouds and Earth’s Radiant Energy System (CERES) (Wielicki et al. 1996) top of atmosphere (TOA) SW (when corrected for the effect of the earth’s annulus), we require an improved a priori ocean albedo. Uncertainties in the computed surface and atmospheric radiation budget (SARB) and aerosol forcing due to ocean albedo (~0.005) are now as large as those due to observational error in TOA albedo (~0.005). We compute ocean albedo with a coupled model (evolved from Jin and Stamnes 1994), explicitly accounting for radiative processes in both sea and air, and using measured wind speeds and aerosol optical depth (AOD). Look up tables (LUTs) are used to specify the ocean spectral albedo in appropriate bands of a highly modified Fu and Liou (1993) radiative transfer code. Tests of the modified code over land are described in Charlock et al. 2001. The new ocean albedo LUTs include an empirical, wind-speed-dependent adjustment for foam. Figure 1 shows that at low values of cosSZA (solar zenith angle), the sea albedo increases from the visible to the near infrared; the opposite is noted at high values of cosSZA. The impact of the sea on TOA albedo is several times larger than the aerosol. This is critical because when aerosol forcing is inferred with satellite data, uncertainties in ocean albedo translate directly into uncertainties in the aerosol forcing. Satellites can infer aerosol forcing to TOA broadband SW by two routes (Figure 2). In the first route, a narrow band satellite radiometric observation and an assumed value for ocean spectral reflection can be used to retrieve the spectral AOD; the aerosol forcing to broadband is then computed with various assumptions over the spectrum. In the second route, broadband satellite data is used to retrieve the reflected SW at TOA, and aerosol forcing is produced by differencing this observation with a calculation assuming pristine (aerosol-free) sky over the sea. By either route, the optical properties of the ocean that one assumes are crucial; if they are wrong, the inferred TOA aerosol forcing is also wrong. How well do we know ocean albedo?
Time Domain Analysis of Joint Broadband Radar and Single Carrier Communication in Frequency Division Multiplexing

Radar imaging like airborne synthetic aperture radar (airborne SAR) requires more and more image resolution and therefor high system bandwidth is necessary. Additionally an integrated communication link for friend-foe identification is helpful for the system user. This publication describes therefor the time domain analysis of a joint broadband radar and single carrier communication in frequency division multiplexing (FDM) mode by using an FMCW radar with 2 GHz bandwidth combined with a QPSK communication with a data rate of 0.2 Mbit/s. This includes the successful elaboration of the advantages of this approach over orthogonal frequency division multiplexing (OFDM) regarding the peak to average power ratio (PAPR) and the complementary cumulative distribution function (CCDF). The maximal PAPR of the proposed system is only 6 dB in the simulation approximately and about 6.2 dB in the measurement after high power amplification.
Impact of Social Media Marketing Towards Purchase Intention of Green Cosmetics in Indonesia

This research aims to find out the effect of social media and green brand knowledge on its purchase intention through attitude as intervening variable. The method used in this research is quantitative method, by using nonprobability sampling - purposive sampling technique and multiple regression analysis. Researchers spread questionnaires to 211 respondents and use path analysis to determine the influence between variables. The results of this research are significantly: social media affect attitude, green brand knowledge affect attitude, attitude doesn't affect green cosmetics purchase intention, social media doesn't affect green cosmetics purchase intention, green brand knowledge affects green cosmetics purchase intention, social media doesn't affect green cosmetics purchase intention through attitude, and green brand knowledge doesn't affect green cosmetics purchase intention through attitude.
Novel Intrusion Detection Algorithm of Information System based on RBM-KSVM

To improve the performance of traditional models, this paper proposes the novel intrusion detection algorithm of the information system based on RBM-KSVM which belongs to the machine learning based method. As network security has received attention from various institutions, research related to intrusion detection has also received attention from the academic community, the existing methods always suffer from the low accuracy. In this designed model, the information system data features are firstly studied. For this study, the representation is selected as virtual, while the characteristics are selected as the mixed data neighborhood space. Secondly, RBM can convert input to output and then reconstruct the input from the output as an unsupervised generative model that reconstructs original data. This will optimize the parameter of the detection model. In the experiment, the performance regarding kernel and accuracy are both tested. Through the results, it is evident that the proposed model is efficient.
Unveiling the Depths: A Comprehensive Analysis of Natural Language Processing and Generative Adversarial Neural Networks for Text Generation Models in Deep Learning

Deep Learning comes under Machine Learning that accomplishes more power and flexibility by learning to present different concepts or relations of real world to simpler concepts. We use Deep learning fundaments in this paper because it has massive amount of data that helps in innovations. We include these neural networks of deep learning because it comes with a high accuracy rate with lower computations. Natural Processing Language (NLP) and Generative Adversarial Network (GAN) are the methods that individually contribute to the text generation method. Although these are two different technologies giving the output for some common motive where Text generation plays a very important role in smart translations and dialogue systems. This review paper presents a model centered around text generation. This is done because combinedly we want to present what can be different approaches to look at a model like this. To solve the problem of unnecessarily used large texts, unsatisfactory feedback, NLP is used for text generation, GANN is used for text generation model, image generation etc. Finally, this is done to reduce time complexities, speed, efficiency in process because this is noticed that learning for a problem plays a vital role in education to enhance features.
Model of a central pattern generator using a spiking neural network, included different types of neurons

The performance of various locomotor acts in animals is ensured by the central pattern generators (CPG). The CPG is an efficient system that is capable of independent generation of activity patterns, while the operation of the CPG can be corrected due to sensory feedback. Such properties of CPG are successfully used in robotics. In this paper, we propose a half-central CPG model using Izhikevich neurons. We selected some parameters for CPG neurons, thereby obtaining membrane properties that are close to those of real brain cell membranes that were found in natural neuronal networks. The pacemaker neuron is responsible for generating the rhythm in the network, it is able to generate spikes with a frequency range of 0.3–7.7 Hz, depending on the input signal, which affects the speed of the locomotor cycle. The model is implemented in the Arduino software environment, and in the future, it can be interfaced with a robotic installation that performs rhythmic locomotor patterns.
On Sampling and Bandwidth Influences of Distributed Radar Sensors on Automotive SAR Applications

Methods for high-resolution environmental mapping based on chirp sequence radar sensor networks are presented in this paper. The radar sensor network is mounted on a vehicle, allowing synthetic aperture radar (SAR) processing to be performed in non-stationary operation. For this purpose, raw data from all sensors in the radar sensor network is processed and combined into a common map. Based on measurements, it is shown that the coherent and joint SAR processing of incoherent-networked radar sensors has significant advantages. On the one hand, it is shown that this improves the imaging quality extraordinary. On the other hand, it is shown that the joint processing of all incoherently connected radar sensors in the network allows a significantly higher vehicle speed without violating the Nyquist criterion for spatial sampling and causing artifacts. Finally, a qualitative measurement-based bandwidth evaluation is performed to investigate the influence of range separability on SAR imaging.
Analysis and Simulation of a Coherent FMCW Lidar-Photonic Radar Combined Sensor System for Large Aperture Phased Array MIMO

A sensor system combining light detection and ranging (lidar) and radio detection and ranging (radar) using a large aperture phased array and multiple input multiple output (MIMO) approach is presented. Optical frequency modulated continuous wave (FMCW) local oscillator (LO) distribution over fiber allows to operate several photonic radar transceiver (TRX) frontends coherently. This enables large apertures and increased angular resolution in MIMO imaging radars. Reusing the FMCW LO signal for optical sensing could be beneficial for coherent large aperture phased array MIMO lidar system with low additional cost. Within this paper such a lidar-radar combined sensor system (LiRaCSS) is proposed and analysed. The results of the analysis are validated by means of the proposed hardware model, using Lumerical Interconnect. Finally, it should be noted that all components of LiRaCSS can be monolithically integrated into a single silicon based electronic photonic integrated circuit (EPIC).
Driving the Future: Using TAM and UTAUT to Understand Public Acceptance of Fully Autonomous Vehicles

The implementation of fully autonomous vehicles (FAVs) has been proposed to yield societal and environmental benefits. However, there are uncertainties regarding the factors influencing the acceptance of FAVs in developing countries which has been understudied. Therefore, this study aims to examine the factors affecting public acceptance of the adoption of FAVs, applying the Technology Acceptance Model (TAM) and Unified Theory and Acceptance Technology (UTAUT) conceptual frameworks. Data was collected through an online survey (n = 593). The results reveal a moderate predictive ability of both TAM and UTAUT in explaining behavior intention to use FAVs. The most significant predictors of FAVs adoption behavior are perceived usefulness and social influence. This study also identified the perceived ease of use, attitude toward behavior, hedonic motivation, performance expectancy, and effort expectancy as significant predictors. Conversely, facilitating condition is found to exert minimal influence on FAVs acceptance. Finally, none of the demographic factors (age, gender, education, occupations, and residencies) serves as a significant moderator in the relationship between the main factors of TAM and UTAUT. Our findings may help researchers and practitioners to enhance the acceptability of FAV adoption.
Additive Manufacturing Technologies for Wireless Anti-Counterfeiting Solutions

Additive manufacturing processes, both bi-dimensional as well as three-dimensional ones, provide unparalleled degrees of freedom with respect to formative and subtractive approaches. This feature, together with a high level of customization, can be exploited for the design of innovative radio frequency solutions, including devices for identification and sensing. An interesting application is represented by the use of additive manufacturing for realizing an additional level of security implemented at physical layer.
VU Research Portal Does Accessibility to Higher Education matter? Choice Behavior of High School Graduates in the Netherlands

This paper identifies pivotal factors behind individual decision-making in the transition from high school to post-secondary education in the Netherlands. We apply a multinomial logit framework to individual data on post-secondary education choices. Specifically, our modeling approach accommodates two types of effects that have not received ample attention in the literature. First, we include information regarding the geographical accessibility of the higher education system. Second, we allow the individual observations to be correlated within schools in order to account for localized social interactions. Our results confirm the paramount influence of the student’s track record and talent. The results, however, also show that geographical proximity to universities or professional colleges significantly increases the probability of high school leavers continuing their education at the post-secondary level. JEL
Low Frequency Noise Analysis in AlGaN/GaN HEMTs

In this work, the low frequency noise characteristic of short channel AlGaN/GaN HEMT devices (Lg= 0.2 um) are shown. Low Frequency noise (1/f noise) analysis helps in estimation of the material and device quality, the trap/defect concentration and the reliability prediction of the GaN HEMTs. The drain current noise variation at different temperatures and different Al compositions have been shown. The Hooge mobility fluctuation model (HMF) has been used to investigate the noise performance of the device. The results are calibrated with recent experimentally measured data. The noise performance improves for lower Al composition and higher temperature. Hence a threshold should be maintained for the Al composition and operating temperature to get high drain current as well as low 1/f noise.
A Framework for Providing Structured Invoice Document using Optimized Bert Enabled Deep Convolutional Neural Network Classifier

Given the context of the COVID-19 pandemic, the efficient processing and management of COVID-19-related invoice documents have become crucial. These documents may contain critical information related to healthcare, supplies, and services provided during the pandemic. Ensuring accurate and efficient handling of such documents is essential for healthcare organizations, government agencies, and businesses involved in pandemic response. To address these challenges and streamline the invoice processing workflow, the framework known as Quash Hunt Optimization with Bidirectional Encoder Representations from Transformers-enabled Deep Convolutional Neural Network (QHtO-BERT enabled Deep CNN) is proposed that leverages the power of BERT, to capture semantic information from invoice text. Google Optical Character Recognition (OCR) is an advanced technology developed by Google, enabling the retrieval of text from images or scanned documents. The process of text annotation using Named Entity Recognition (NER) involves the task of recognizing and categorizing named entities existing within a provided text. The BERT-based approach significantly improves semantic understanding, while the optimized DCNN ensures feature extraction and classification tasks. The model's accuracy is elevated by harnessing the Hunting and Escaping characteristics within the QHtO algorithm. The performance of the QHtO-BERT-enabled Deep CNN method concerning accuracy, sensitivity, and, specificity stands at 94.7%, 95.92%, and 95.02%, respectively.
Digital Twin Model for Smart Maintenance of Data Center Facilities : Study Literature Review

Digital twin is a technology that supports the implementation of digital transformation. The digital twin was developed to support decision making in various sectors. The digital twin concept is almost 20 years old and continues to evolve for use cases for the technology in new industries. Maintenance is one of the important points of the product life cycle as the origin of the digital twin. The data center facility is one of the supporters of digital transformation which must be able to guarantee the availability of its services. The availability of data center services is affected by the chosen maintenance strategy. This paper will discuss data centers and maintenance strategies as well as definitions, characteristics to examples of implementing digital twins, especially in the case of implementing intelligent maintenance. It is hoped that with the description of this paper, more specific research opportunities related to optimizing the application of digital twins for maintenance specifically used in data center facilities are expected.
Scytalopus petrophilus (Rock Tapaculo): a new species from Minas Gerais, Brazil

We describe a new species of Scytalopus tapaculo from the southern section of the Espinhaco Range and other points in Minas Gerais, Brazil. Scytalopus petrophilus sp. nov., Rock Tapaculo, occupies a variety of habitats between about 900 and 2,100 m elevation, from open, rocky shrub associations (campos rupestres) high in the mountains to taller forest in steep‐ walled valleys to second‐growth of semi‐deciduous woodland. Its morphology, vocalizations, and preliminary genetic profile place it solidly within the S. novacapitalis (Brasilia Tapaculo) complex, joining that species, S. pachecoi (Planalto Tapaculo), and the newly described S. diamantinensis (Diamantina Tapaculo). S. petrophilus is phenotypically diagnosed from its allospecies by combinations of morphological and vocal characters. Key-WoRdS: Scytalopus, tapaculo, new species, holotype, type‐locality, nomenclature, synonymy, Espinhaco. genetic profile (Mata et al. 2009). Raposo et al. (2006), followed by Raposo and Kirwan (2008), identified this form as S. speluncae (Mouse‐colored Tapaculo) but Mau‐ ricio et al. (2010), in a different interpretation of the same objective pieces of evidence, showed that the new species cannot be assigned to Scytalopus speluncae (Menetries). Furthermore, Mata et al. (2009) showed that S. speluncae and the unnamed taxon are not even sisters. The purpose of this paper is the formal designation of this taxon as: Scytalopus petrophilus sp. nov.
Deep Bispectral Analysis of Conversational Speech Towards Emotional Climate Recognition

Peers' conversational speech plays a significant role in shaping the emotional climate (EC) during interactions. Machine-based recognition of EC provides insights into the emotional perception of conversations by both peers and external observers. In this paper, we propose DeepBispec, a novel approach for EC recognition using deep bispectral analysis. DeepBispec applies windowed bispectral analysis to the 1D conversational speech signal. By capturing higher-order spectral correlations, the bispectrum magnifies the nonlinear characteristics present in speech signals. The estimated $\mathbf{2D}$ -bispectrum magnitude contours, representing these interactions, are transformed into colored images and fed into a convolutional neural network (CNN). The CNN learns deep features from the bispectrum magnitude contours, enabling it to predict the valence (V) and arousal (A) labels associated with the EC. Evaluating DeepBispec on the K- EmoCon dataset using 10-fold cross-validation, we achieve an accuracy of 0.789 (A)/0.771 (V), an F1 score of 0.850 (A)/0.836 (V), and an area under the curve (AUC) of 0.812 (A)/0.788 (V). These results surpass existing benchmarks, demonstrating the effectiveness of bispectrum in capturing nonlinear characteristics and improving EC recognition. DeepBispec introduces an innovative approach to analyzing conversational speech for enhanced EC recognition. By leveraging deep bispectral analysis and CNN, it uncovers the higher-order spectral correlations and nonlinear dynamics of speech signals. This contributes to a deeper understanding of emotional dynamics in conversations and provides valuable insights into EC perception.
An Efficient High-Gain DC-DC Converter for Solar Photovoltaic Applications

This paper presents a new high-power DC-DC converter designed specifically for use with photovoltaic systems. The proposed converter stands out because of its innovative features, such as its reduced number of components and its single switch operation that results in efficient and cost-effective operation. This converter is frequently called the Slim Boost converter to reflect its compact size and refined design. Modifying the duty cycle allows for an increased voltage gain and a noninverted output voltage in the converter. This allows the converter to function silently and efficiently in Continuous Conduction Mode (CCM) for extended periods of time, as do the few active components it contains. The converter's efficiency of up to 95% is exceptional compared to other current converters, and it is reached during CCM operation. The suggested converter has many benefits that make it a great option for use in solar systems and other forms of renewable energy generation. In this research, we explore the inner workings of the suggested converter, explaining its component design, computing its voltage gain, and comparing and contrasting its performance with that of other converters. In addition, simulation results verify the suggested converter's operation by comparing it to the performance of other, more well-known converters.