Miracle-dz/UniFAR-FTUs huggingface dataset:
SiGe and CMOS Technology for State-of-the-Art Millimeter-Wave Transceivers

Innovation and evolution are paramount where the demand for wideband, data-intensive connectivity is ever-increasing, and the only constant is change. Standards that define the operation of next-generation mobile networks are moving away from the traditional radio frequency (RF) spectrum and into millimeter-wave (mm-wave) bands. The physical layer (PHY) for IEEE 802.11ad Wi-Fi and 802.11ay WLANs dictates operation in the unlicensed 60 GHz band. 5G New Radio (NR) applications utilize selected bands from 26 to 39 GHz. Additionally, newly developing Fourth Industrial Revolution (4IR) applications depend on 5G NR as an enabling technology. Satellite communications and wireless backhaul will occur in E-band between 70–86 GHz. Increasing demands from the market cause designers to push boundaries, and the development of standards guide technological advances. Perhaps the most substantial improvements are observed in integrated circuit technology. This article details the major Si processes, namely Complementary Metal Oxide Semiconductor (CMOS) and SiGe Bipolar CMOS (BiCMOS), and their foray into the wireless transceiver space traditionally dominated by GaAs. CMOS and BiCMOS have become popular in many communities because of their low fabrication cost and excellent digital integration capabilities. RF performance has matured to where Si is a serious competitor for tried-and-tested III-V technologies. Some extreme environments and sophisticated applications still favor GaAs and GaN, however. GaAs, for example, can yield unparalleled output power and excellent noise figure performance, albeit at a higher cost and increased design and manufacturing complexity due to the multi-chip nature of these circuits.
A Review on Textual Question Answering with Information Retrieval and Deep Learning Aspect

Text-based Question Answering (QA) is an essential task in natural language processing that aims to provide relevant and accurate answers to users’ queries. Traditional approaches to QA relied on rule-based systems and hand-crafted features. However, recent advancements in information retrieval and deep learning have shown promising results in improving the performance of QA systems. The combination of IR and deep learning techniques has led to significant improvements in QA performance. Hybrid models, such as the bi-encoder and tri-encoder architectures, have been proposed to leverage the strengths of both IR and deep learning models. Additionally, pre-training techniques, such as BERT and RoBERTa, have been shown to improve the performance of QA systems by providing pre-trained contextualized word embeddings. This study provides a brief overview of the QA system and its different domains and subdomains. This study also provides review of different proposed models and different datasets available for the task and evaluate their performance using some performance metrics. A comparison between various techniques are employed using the obtained result.
Fake News Detection using Hybrid BiLSTM-TCN Model with Attention Mechanism

In the past decade, social media has seen a tremendous surge in popularity, but it has also come with its fair share of problems. One of the most significant concerns is the proliferation of disinformation, or false and misleading information spread by individuals and organizations for the purpose of promoting their own ideologies and products. To combat this rise of disinformation, several text detection methods based on artificial intelligence and machine learning have been developed. This study presents a novel solution using a combination of Bi-LSTM, TCN, and attention mechanism. The proposed model leverages Glove Embedding for word representation, Bi-LSTM for capturing sequential text dependencies, attention mechanism for identifying important text elements, and TCN for deep feature extraction. The efficacy of the proposed model was assessed through empirical trials utilizing several models on the publicly available ISOT dataset. The results of these trials were highly impressive, demonstrating success of this research endeavor.
Sentiment Polarity Categorization of Product Reviews using Twitter Data

Sentiment analysis, commonly referred to as opinion mining, reveals the attitudes and feelings of consumers about specific goods or services. The sentiment polarity classification, which identifies whether a review is favourable, negative, or neutral, is the fundamental issue with sentiment analysis. There are still some study gaps, as some studies only investigate the positive, neutral, and negative sentiment classes; none of these studies considered more than three classes; also, none of these studies considered the individual and combined effects of the sentiment polarity aspects. No prior method took into account the verb, adverb, adjective, and their combinations, as well as the five sentiment classes and three sentiment polarity traits. This study, provides a method for categorizing online reviews of Instant Videos based on their sentiment. Proposed study makes use of a substantial data set of 500,000 internet reviews. This review-level categorization process Adjective, verb, and two polarity traits are taken into account additionally as well as their pairings with various senses.
Pothole Detection using Roboflow Convolutional Neural Networks

India has a vast network of roads which connects the various cities and villages, due to which its maintenance becomes a challenging task and also many accidents takes place because of the damaged roads and also the accidents are increasing every year because of the increase in the number of potholes. As the inspection of these roads for damages is done manually, a significant amount of time is consumed, cost increases as it is a labor intensive process and also the possibility of human error increases. Also, as the road network is very large, it is not feasible for the Authority travel to all the places for inspection. In order to solve this problem and to increase the efficiency we can use various Image Processing techniques (such as CNN, YOLOv5) in order to find the damages and then comparing the same to find the technique with maximum accuracy. This data can further be sent to the government for repairing the roads and this helps in reducing accidents and further ensuring the safety of the citizens especially during rainy season. By using these techniques, the problem of damaged roads can be solved and also a lot of resources can be saved by repairing the roads in the right time and also by reducing the labor cost.
Mixed Multinomial Probit Model Accommodating Flexible Covariance Structure and Random Taste Variation: An Application to Commute Mode Choice Behavior

is paper developed a mixed multinomial probit (MMNP) model with alternative error specification and random coefficients (for both generic variables and personal attributes) to accommodate flexible covariance structure and taste variation. e MMNP model can be efficiently estimated with analytic approximations of multivariate normal cumulative distribution functions, which avoid defects of simulation-based integration in the mixed multinomial logit (MMNL) model. e integral dimension of the MMNLmodel increases as random coefficients increase, but it only depends on the number of available alternatives in theMMNP model. Simulation experiments and empirical analysis of Shanghai commuters’ mode choice behavior were undertaken to examine the performance of MMNP models. Both simulation results and empirical results show that MMNP models can well accommodate flexible covariance structures and taste variation reflected through random coefficients being associated with both generic and personal variables. Empirical results indicate that the MMNP model performs better than traditional discrete choice models, such as the multinomial logit, the cross-nested logit, MMNL, and multinomial probit models. Random coefficients of “invehicle time of car” and “number of companions” indicate taste heterogeneity and the identifiability of random coefficients associated with both generic and personal attributes. Pairwise positive correlations between car/taxi, bus/metro, and bus/bus and metro are to be expected. However, the positive correlation between the car and metro modes may be unique to the Chinese city, Shanghai, because of the developed metro system. Unequal error variances reflect heterogeneities in unspecified factors in commute modes’ utilities. e MMNP model will offer an alternative efficient way to accommodate taste heterogeneity and flexible error covariance structure in discrete choice models. Compared with the MMNL model, the MMNP model can accommodate more random coefficients without increasing computational complexity.
Detecting COVID-19 Vaccine Stance and Symptom Reporting from Tweets using Contextual Embeddings

Determining sentiments of the public with regard to COVID-19 vaccines is crucial for nations to efficiently carry out vaccination drives and spread awareness. Hence, it is a field requiring accurate analysis and captures the interest of many researchers. Microblogs from social media websites such as Twitter sometimes contain colloquial expressions or terminology difficult to interpret making the task a challenging one. In this paper, we propose a method for multi-label text classification for the track of”Information Retrieval from Microblogs during Disasters (IRMiDis)” presented by the”Forum of Information Retrieval Evaluation” in 2022, related to vaccine sentiment among the public and reporting of someone experiencing COVID-19 symptoms. The following methodologies have been utilised: (i) Word2Vec and (ii) BERT, which uses contextual embedding rather than the fixed embedding used by conventional natural language models. For Task 1, the overall F1 score and Accuracy are 0.503 and 0.529, respectively, placing us fourth among all the teams, while for Task 2, they are 0.740 and 0.790, placing us second among all the teams who submitted their work. Our code is openly accessible through GitHub. 1 © 2022 Copyright for this paper by its authors.
Spatiotemporal Attention Networks for Traffic Demand Prediction

Travel demand, which include taxi, bus, and bike demand, forecasting the travel demand is an important part of intelligent city intelligent transportation system. Accurate prediction models can help cities pre allocate resources to meet travel demand, reduce energy waste. Travel demand prediction can be summarized as spatiotemporal sequence prediction. For a long time, in the field of spatiotemporal sequence prediction, most of them will emphasize the effective capture and modeling of nonlinear and complex spatiotemporal dependency, which can effectively improve the accuracy of spatiotemporal sequence prediction. At the same time, the demand for multi-step prediction is also increasing. Long-time high-precision prediction can effectively improve the auxiliary role of the model for decision-making. To address these issues, we propose a Spatiotemporal Attention Network (STATTN) with a novel spatiotemporal attention mechanism that capture dependency in time-dimension and spatial-dimension at the same time which is spatiotemporal dependency. In order to learn high-quality representation of spatial points in spatiotemporal sequence units, we adopt dilated temporal 1d convolutional neural networks which has ability to learn representation from data through back propagation. To alleviate the error propagation, we use the generate-style decoder which can generate the output without iteration steps. Through extensive experiments on two prediction tasks, we demonstrate the advantages of STATTN in short-term and long-term prediction scenarios.
A New Feature for Prediction of Significant Earthquakes by Artificial Neural Networks

Earthquakes happen continuously, but those that are of higher magnitude are more important to detect, because their detection can be directly used for early warning, evacuation and preventing loss of life. However, at present there is no scientific method that can predict earthquakes a reasonable amount of time (at least a few days) in advance. Earthquakes are very unpredictable, and their nucleation process is not completely understood, and local parameters play an important role, making it difficult to build a generalized model that applies to all earthquakes in different fault lines in different locations. In this study, I have applied machine learning techniques to fundamental parameters from data published by Kandilli Earthquake Research Center and US Geological Survey, as well as derived parameters proposed by other researchers and compared the results to those calculated with a new spatial feature that considers the spatial clustering of earthquakes in three dimensions before a major shock. The results show that the spatial feature may improve accuracy in some cases and demonstrate how accuracy is improved by the new feature.
Prediction of Pneumonia using Hybrid Deep CNN and ML Classifiers

Pneumonia is a disease that is caused in the lungs and result in many deaths. This is a type of infection, where the air sacs(alveoli) of the lungs will be filled with fluid or pus. The fluid formed inside the lungs is the result of a mixture of bacteria, viruses, fungi, or chemical irritants. The lungs swell due to this fluid. This can happen in one or both lungs. This infection can be seen most commonly in people above 60 years and children under 5 years old. Usage of antibiotics cures pneumonia Chest X-Rays plays a major role in detecting the pneumonia. Early analysis utilizing Computer-Aided Diagnosis (CAD) prevents pneumonia. Several Deep Learning (DL) approaches have been developed to detect pneumonia using CAD systems. A hybrid Convolutional Neural Network (CNN) model is proposed based on Machine Learning (ML) classifiers. This mechanism detects if a person has pneumonia or not. This mechanism is attached to a user interface where people can use and know whether they have pneumonia or not.
Modeling Method for Analysis of the Current Density Distribution and IR Drop in Traces of the Printed Circuit Board

The proposed modeling method is based on the vertex-centered finite volume method. The vertex-centered strategy is used to generate control volumes after analyzing the pixel information of the trace layout, particularly for identifying the solutions at the boundary region. By applying Gauss's Divergence Theorem and Taylor series truncation with 2nd order precision, the integration form of the continuity equation is approximately simplified to a linear relationship between the electric potentials of cell vertices. After the electric potential distribution is identified, the current density distribution can be further derived using finite difference approximation. The 45° inclined linear boundary can be implicitly reconstructed through modeling. Based on the modeling method, a test solver was programed in MATLAB. To verify the method's feasibility, a trace layout was simulated using both the test solver and COMSOL Multiphysics.
Forecasting of Nifty 50 Index Volatility Using Seasonal ARIMA Model

Traders who are at the beginner stage are more likely to make mistakes and suffer losses in the volatile market. The study focuses to develop an autoregressive integrated moving average model for forecasting the Nifty 50 index based on the historical dataset by using the advanced technique of time series analysis. The train-testing approach of machine learning and Akaike Information Criterion (AIC) has been used on the monthly data of eight years for the selection of the autoregressive model. Using the auto-correlation functions (ACF & PACF) and the seasonality component in the dataset, the seasonal ARIMA model of (0,1,1)(0,1,2)12 has been selected to forecast the Nifty 50 index values. Based on this selected model, values have been predicted and compared with the actual values for validation purposes. The results show that the model could be utilized to forecast the Nifty 50 index. The model can be helpful for a researcher, and the forecasted values will help a trader to protect from severe losses in his beginner stage.
Deterministic Annealing for Stochastic Variational Inference

Stochastic variational inference (SVI) maps posterior inference in latent variable models to nonconvex stochastic optimization. While they enable approximate posterior inference for many otherwise intractable models, variational inference methods suffer from local optima. We introduce deterministic annealing for SVI to overcome this issue. We introduce a temperature parameter that deterministically deforms the objective, and then reduce this parameter over the course of the optimization. Initially it encourages high entropy variational distributions, which we find eases convergence to better optima. We test our method with Latent Dirichlet Allocation on three large corpora. Compared to SVI, we show improved predictive likelihoods on held-out data.
Geometry of Sets and Measures in Euclidean Spaces: Rectifiability and analytic capacity in the complex plane

Analytic capacity and removable sets In this chapter we shall discuss a classical problem in complex analysis and its relations to the rectifiability of sets in the complex plane C . The problem is the following: which compact sets E ⊃ C are removable for bounded analytic functions in the following sense? (19.1) If U is an open set in C containing E and f : U\E → C is a bounded analytic function, then f has an analytic extension to U . This problem has been studied for almost a century, but a geometric characterization of such removable sets is still lacking. We shall prove some partial results and discuss some other results and conjectures. For many different function classes a complete solution has been given in terms of Hausdorff measures or capacities. For example, if the boundedness is replaced by the Holder continuity with exponent α, 0 E is that H 1+α ( E ) = 0, see Exercise 4, Dolzenko [1] and Uy [2], and for the corresponding question for harmonic functions Carleson [1]. Kral [1] proved that for the analytic BMO functions the removable sets E are characterized by the condition H 1 ( E ) = 0. The problem (19.1) is more delicate, because the metric size is not the only thing that matters; the rectifiability structure also seems to be essential as we shall see. Ahlfors [1] introduced a set function γ, called analytic capacity , whose null-sets are exactly the removable sets of (19.1).
Building Usage Classification Using a Transformer-based Multimodal Deep Learning Method

Building usage classification is of great significance for urban planning and city digital twinning applications. So far, however, the problem of mixed building use has not been addressed, and detailed categories cannot be assigned to individual buildings. This paper employs a state-of-the-art Transformer-based multimodal deep learning method to extract and fuse image features from satellite images with textual features of point-of-interest (POI) data. The derived features along with the relationship between the two types of data are utilized for the classification task. A custom dataset prepared for the city of Wuhan, China, with eight land-use categories has been classified yielding a microf1-score of 80.7%. Results show that the proposed method can effectively improve the classification results, achieving 5.6% higher accuracy as compared to the results based upon a single data source.
A Comprehensive Survey on Role of Fog Computing and Need for Semantic Technology in Modern Computing Platform

Fog computing can be considered as one of the trending paradigms, as it has the potential of bringing the end devices closer to the cloud. Fog plays a vital role in bridging the gap between the end user and cloud with low latency, which in turn helps in the better realization of Intelligent IoT. The emergence of IoT applications has paved the way for Semantic Interoperability (SI). Instead of storing raw data, SI attempts to annotate these data with semantic information that ensures smart management of huge volumes of data generated by IoT. Nevertheless, such semantic reasoning in the cloud can be computationally expensive as it deals with enormous amount of incoming data. So, distributing the procedure of semantic reasoning among a hierarchy of distributed fog nodes than in a centralized cloud will gives the better result. This research study investigates how semantics work in fog computing
Object Recognition based on Deep Learning Algorithms using Embedded IoT with Interactive Interface

Object detection is one of the most popular applications of machine learning in the modern era. With the growth of IOT in recent times, dedicated devices offering real-time object detection have seen overwhelming demand and applications in many sectors e.g. security, healthcare, workplace etc. Different algorithms and approaches have been implemented and studied in terms of object detection. To refer to a few the YOLO family, RCNN family, SSD etc. This research study compares the performance of YOLO and Faster RCNN based on a custom dataset containing different objects and items. The IOU of each data point (image) is calculated and compared. YOLO performs better for a small margin. In this research study, a language learning interactive model is demonstrated based on object detection(YOLO), NLP, python, flask and IoT (Raspberry Pi.) The web application is running on is divided into two parts, learning and practice. The learning part has a raspberry pi device which has a camera module that captures real-time footage, recognizes the object and reads out its name in the language the user is learning. The practice has the same setup, with the application asking the user to show a particular object. Points are awarded for correct guesses, making the learning process more interactive and involving.
Human Emotion Recognition by Audio Signals using MLP Classifier

The capacity to identify emotions from speech has drawn increasing attention throughout time. Speech emotion recognition is an emerging topic in the domain of human computer interaction It can be used for a wide range of things, such as communicating with robots, banking, contact centres, car dashboards, video games, and more. The process of accurately determining a human’s emotion from their speech is called speech emotion recognition. It enhances computer and human communication. Despite the challenge of annotating audio and the subjective nature of emotions, SER makes it possible to forecast a person’s emotional state. The benchmark RAVDESS dataset is used in this study’s analysis of speech emotion. We attempted to locate underlying emotions in speech by examining the acoustic properties of recorded audio data.