Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Emotion-Aware Networking Model for Enhanced User Experience in 5G networks

The emergence of 5G networks has considerably broadened the capabilities and features of mobile networks. A notable development in this area is the incorporation of emotional intelligence into mobile networks, leading to the proposal of the Emotion-aware Networking Paradigm (ENP) in this paper. ENP strives to equip mobile networks with the ability to identify and understand user emotions and adapt services accordingly. This paper introduces a model for implementing ENP in 5G mobile networks, called the Emotion-aware Networking Model (ENM), detailing its structure, functions, and main characteristics. This paper also explores the challenges and prospects related to ENM and offers examples of potential use cases. Additionally, this paper presents the Emotion-Aware Service and User Profile Management Function (EASUPMF) for ENM, designed to unleash ENM's potential by managing user profiles and delivering personalized emotional services based on emotion recognition. Several new Key Performance Indicators (KPIs) are also established to assess ENM's performance, such as Emotional Diversity, Emotional Alignment, and Emotional Intensity. Our research indicates that ENM could transform the mobile network sector and enhance user experiences by delivering tailored services grounded in user emotions.
Preventing the Elderly with Diabetes from the Risk of Falling using a Multilayer Perception Classifier

Elderly people are an important age group in society that must be constantly under control. Because of old age, these people suffer from various diseases such as blood sugar, blood pressure, and obesity. If the elderly are not treated and their diseases are not controlled by taking medicine, it will cause headaches, dizziness, and blurred vision, which will cause problems for them as a result of these symptoms. One of the problems they cause is physical and mental injury. Physical injuries in the elderly population are caused by falls. Therefore, providing methods to prevent them from falling can play an important role in their physical and mental health as well as in improving their quality of life. Machine learning algorithms can predict the condition of the elderly at any moment. If an elderly person has acute symptoms, the risk of falling can be prevented by turning on the smart sensors. In this study, we propose a method for predicting falls in elderly individuals with diabetes using a multilayer perception classifier. According to these results, elderly people with diabetes are more prone to falls because of symptoms caused by blood sugar, such as dizziness and motor nerve disorders. By designing an intelligent system that can predict the condition of the elderly based on various diseases, it is possible to prevent them from falling. In this study, various factors collected from this dataset were examined. By reviewing the data and confirming the opinion of a cardiovascular disease specialist, the elderly with diabetes had more symptoms, such as headache, dizziness, and blurred vision, than the rest of the elderly; therefore, these symptoms lead to a drop in blood pressure and fall. The results obtained from the implementation of the machine learning algorithm showed that machine learning could predict the risk of falling in elderly patients with diabetes with a high performance (accuracy=97%).
Detection of Truthful, Semi-Truthful, False and Other News with Arbitrary Topics Using BERT-Based Models

Easy and uncontrolled access to the Internet provokes the wide propagation of false information, which freely circulates in the Internet. Researchers usually solve the problem of fake news detection (FND) in the framework of a known topic and binary classification. In this paper we study possibilities of BERT-based models to detect fake news in news flow with unknown topics and four categories: true, semi-true, false and other. The object of consideration is the dataset CheckThat! Lab proposed for the conference CLEF-2022. The subjects of consideration are the models SBERT, RoBERTa, and mBERT. To improve the quality of classification we use two methods: the addition of a known dataset (LIAR), and the combination of several classes (true + semi-true, false + semi-true). The results outperform the existing achievements, although the state-of-the-art in the FND area is still far from practical applications.
Application of residual network in household waste classification

With the acceleration of urbanization, the amount of garbage has skyrocketed. This makes the burden of sorting waste by manual means heavier and heavier, resulting in a large amount of waste accumulation. In this context, the classification of household garbage gradually received attention. In order to improve the efficiency of garbage classification, we should deal with the increasingly complicated garbage classification work and make garbage classification intelligent and efficient. In this paper, convolutional neural network intelligent algorithm is used to solve the classification problem of organic and inorganic garbage in daily life. The accuracy can reach 90.34% only with low sequence training. Compared with existing commonly used garbage classification and recognition algorithms, the proposed garbage classification and recognition algorithm has better recognition performance and is more suitable for widespread application.
The Evolution of Women in Signal Processing and Science, Technology, Engineering, and Mathematics

When I began writing this 75th anniversary article celebrating women in signal processing (SP), I reread the 1998 editorial titled “Fifty Years of Signal Processing: 1948–1998” [1]. At that time, IEEE had more than 300,000 members in 150 nations, the world’s largest professional technical Society. Within the IEEE umbrella, there were 37 IEEE Societies and technical groups, and the IEEE Signal Processing Society (SPS) was the oldest among its many Societies.
Low-Boom Design Optimization for SST Canard-Wing-Fuselage Configuration

In this paper, the performance of Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA), which has been developed for reducing a number of function evaluations, is examined by using three analytical test problems. These test problems are also solved by a widely-used Multi-Objective Evolutionary Algorithm (MOEA), NSGA2, and two gradient-based methods, Sequential Quadratic Programming (SQP) and Dynamic Hill Climber (DHC) for comparison. ARMOGA is found to locate a Pareto front with a small number of function evaluations comparable to DHC. To utilize the present ARMOGA, an automated design system of low-boom Supersonic Transport (SST) configuration has been developed. To reduce the sonic boom for supersonic flight effectively with minimizing the drag, SST wing-fuselage configurations equipped with a canard are considered. The resulting system automatically generates unstructured grids around SST canard-wing-fuselage configuration.
Spectral shape metrics and structural collapse potential

This paper examines various parameters that provide a measure of spectral shape and studies how they relate to the potential of ground motion records to cause the collapse of a given structure. It is shown that when measuring the ground motion intensity by the spectral acceleration at the first‐mode period of the structure, Sa(T1), records causing collapse at low ground motion intensities typically have significantly different spectral shapes than those that do not cause collapse until much higher ground motion intensities. A spectral shape typical of damaging records is identified, and a metric for quantifying the spectral shape of a record called SaRatio is proposed and evaluated. SaRatio is defined as the ratio between Sa(T1) and the average spectral value over a period range. The ability of SaRatio to predict the collapse intensity, i.e. the minimum intensity at which a given ground motion causes the collapse of a given structure, is compared to other recently proposed spectral shape metrics including epsilon (ε), eta (η) and Np. The results demonstrate that SaRatio is typically a much better predictor of collapse intensity than other spectral shape metrics. Copyright © 2016 John Wiley & Sons, Ltd.
IP Service Core Network Architecture and Service Demonstration

With the vigorous development of new services, differentiated carrying requirements such as deterministic high throughput, deterministic delay, and deterministic jitter are proposed for IP carrying network. At the same time, owing to the dumb pipe characteristics of traditional IP networks, the price scissors between operators' traffic and revenue become more and more obvious. Hence, it is urgent for operators to develop differentiated carrying capacity of IP networks, reconstruct the network value chain, and achieve sustainable and healthy development of IP networks. Firstly, the concept and architecture of IP service core network are introduced, which can be smoothly evolved based on the existing network. Then the private network services are proposed under this architecture, to verify the private network services based on the IP service core network. The results show that the conceives of the IP service core network own high technical feasibility, clear advantages in service differentiation carrying, and great potential in business value.
Research on State Estimation of Distribution Networks Based on Multi-source Data Fusion Technology with PMU Measurement

With the introduction of phasor measurement units (PMUs), multi-source measurements with different time scales and accuracy coexist in distribution networks. The efficient utilization of multi-source measurements to accurately estimate the state of distribution network is an important prerequisite for operational decision-making. In this paper, a state estimation method based on multi-source measurement data was proposed for distribution networks. The time scale, synchronization and accuracy of multi-source measurements were considered. The integration of PMU data into state estimation model was discussed. The measurement functions were linearized by equivalent conversion of multi-source measurements. The data fusion strategy for different data types was presented to improve the estimation accuracy and shorten the estimation period. Case studies on an actual engineering example was performed to show the effectiveness of the proposed method. The improvements in network observability and estimation accuracy under different measurement conditions were verified.
Software Defined Network Load Balancing Based on Im-ACO

In order to improve the load balancing problem of software defined network (SDN), an improved joint algorithm based on the traditional ant colony algorithm and server load balancing algorithm is proposed. The algorithm uses a weighted least connection scheduling algorithm, the server with the least load is selected on the server side, and the improved ant colony algorithm (Im-ACO) is used to select the best path to the selected server. Theoretical analysis and experimental results show that the proposed joint algorithm can significantly improve network performance, higher network throughput and lower packet loss rate.
A Novel CNN-LSTM Model for Predicting the Railway Sector Stock Price in the Tehran Stock Exchange

As a country's financing arm, the capital market can play a crucial role in long-term projects and be responsible for large-scale financing. While the development of rail transport has exceptionally positive and significant effects on the country's macroeconomy, and investment in this sector leads to the prosperity of other economic sectors, the return on investment may be delayed or unclear. Consequently, this uncertainty diminishes the investment appeal in this field. Thus, predicting the price and future value of railway company stocks on the stock market is vital due to the need for the foresight to take safe investment measures and achieve sustainable financing. To this end, this paper investigated the use of deep learning methods to forecast the closing price of MAPNA and Toucaril stocks on the Tehran Stock Exchange. Stock prices were predicted using deep neural networks, including Long Short-Term Memory (LSTM), One-dimensional Convolutional Neural Networks (1D-CNN), and CNN-LSTM networks as a hybrid model. Finally, to evaluate the performance of the models, MAE, MSE, RMSE, MAPE, and R2 were used as evaluation criteria. The results indicate that deep learning models can forecast stock prices with accuracy. In this study, CNN-LSTM neural network produced the best results.
Electrical Conductivity Comparison Between Two Preparing Methods for Graphene/Copper Composite

Copper wires are the state-of-the-art materials which are widely used in almost every electric and electronic related field in civil lives or industry. One of the most active research topics consists of compounding carbon-based materials with copper, due to their performance advantage in density, electrical and mechanical properties. In this paper, graphene reinforced copper samples have been prepared in two different methods, namely metallurgy method and Chemical Vapour Deposition (CVD) method. Electrical conductivity measurements were performed on samples prepared via both methods. Technologies such as SEM was adopted in our analysis to Figure out the mechanism causes this difference from a microscope point of view. Analysis results indicate that, with properly controlled preparing parameters, graphene reinforced copper composite can achieve a distinctive improvement in terms of electrical conductivity over traditional copper material.
Content Features and Machine Learning Based Effective Fake News Detection

Machine learning is particularly crucial in the context of fraudulent messages in virtual entertainment due to the degree of biological and cultural concerns. Anyone can make a message that becomes a web phenomenon, whether it is real or phoney. Considering countries like India, the ideological organisations during general decisions actually act in this way by disseminating false information around the nation through online entertainment events. While some of the communications may be genuine, the majority are false. Spam communications and phoney messages carrying false information can propagate across online social networks. Several machine learning techniques are used by analysts to differentiate between spam communications and fake news. Customers are creating and sharing more data than ever before as a result of the continuous usage of virtual entertainment platforms, some of which are ludicrous and unrelated to the actual world. Computerizing the classification of text as false information could be challenging. Our study looks at a number of print characteristics that can be utilized to tell real text from phone. Based on these qualities, we train various machine learning algorithms using various training methodologies and assess their efficacy using four real-world datasets. According to an exploratory assessment, the student's technique in our suggested group is better than it was during her one-on-one tuition.
Short Circuit Fault Characteristics of Low Frequency AC Transmission System Based on Modular Multilevel Matrix Converter

Compared with power frequency AC transmission and DC transmission, low frequency AC transmission has its unique advantages, which is suitable for long distance transmission, large scale renewable generation and regulation of regional power grid. The fault characteristics affect the system design and operation, which is necessary to study. From the perspective of system topology and converter control, the characteristics of three-phase short circuit fault are analyzed and a simulation model is built on PSCAD for verification in this paper. After a fault occurs, it can be divided into two stages before and after the control responses. Before the control responses, the overvoltage is generated between the terminals of branch reactors and modular capacitors discharges. After the control responses, system characteristics are related to fault location and control strategy. The control and modulation of the converter can make the low frequency and power frequency systems interact with each other. This study can provide a reference for the parameters of equipment and converter control, as well as fault protection in low frequency systems.
Nonvolatile D- latch and flip-flop Designs based on new Memristor Technology

The fundamental components of practically all digital electronic systems are memory and sequential devices. If there is a sudden loss of data, such as with a power cutoff, instant data recovery is essential. To overcome this Nonvolatile memristors have been integrated into CMOS devices to satisfy the nonvolatile property requirements of sequential devices. The purpose of this paper is to describe how this approach can be used to improve the standard of nonvolatile D latches. A D latch proposed here uses only one memristor instead of the conventional design, many transmission gates and CMOS inverters are included. By designing our transistors with threshold loss, we eliminate the Weak effects. Simulations show that our suggested memristor-based D latch has 2.3X–3.6X faster switching between resistance states than the current designs. Causing the clock frequency to increase. Additionally, our approach enables us to choose from a considerably wider range of memristor threshold values. We create a nonvolatile master slave D flip-flop with a shorter delay than all existing designs and a smaller area using the suggested memristor-based D latch. Our designs have better They are a desirable option for data backup in practical applications due to their performance in terms of choosing the threshold voltage, area, and delay. Memristor, a Latch D, and master-slave Nonvolatile D flip-flop.
3D Building Reconstruction Using 2D GANs

There are many historical sites across the globe that hold a huge importance to mankind as it contains the history and culture of the past. But as buildings wither and lose their beauty over the course of time, we lose a precious piece of the heritage of mankind forever. With the growing emergence of new technologies and the internet, we are instead able to preserve these heritage sites through their reconstruction as 3D models using convolutional neural networks and simple 2D vision. We base our work mainly on a previous implementation of 3D shape reconstruction using 2D image GANs by Xingang Pan et al, where we mine for cues such as albedo, viewpoint, lighting and depth such that we can infer a 3D image from these factors. We believe our work is different from other works in that we use purely an 2D image to reconstruct its 3D form, unlike other related works such as Pixel2Mesh, which uses 3D Mesh Models for ground truths. Our work infers a 3D shape and model from simply one 2D image, which cuts costs and is much easier to implement as it does not require any other input other than the singular 2D image.
Interactive Derivation of Distributed Power Trading and Distribution Network Operation Considering Dynamic Network Tariffs

With the approaching transformation of China's energy structure to low-carbon under the "3060" target, the installed scale of distributed new energy will explode in the future, and the number and scale of distributed power peer-to-peer (P2P) transactions will continue to increase, and the distribution grid will face huge changes in terms of technology operation, pricing and cost recovery. Based on the additional short-term operating costs of P2P trading on the grid, this paper firstly establishes a P2P trading over-network tariff model with three parts: line occupation cost, voltage adjustment cost and network loss cost, and adopts continuous two-way auction to match the trading aggregation for P2P trading with small volume and scattered, high frequency and real-time characteristics, and then further designs a dynamic over-network tariff considering line utilization and incremental cost. The analysis shows that the proposed dynamic cross-network tariff can release stronger price signals to stimulate the tide distribution of P2P trading on the less utilized lines, while discouraging the higher utilized lines from generating more net tide increments to achieve the effect of "low promotion and high suppression" and realize the effective utilization of network assets.
Parking Lot Charging Pile Configuration Based on Monte Carlo Power Prediction

The construction of electric vehicle charging stations plays a very important role in the process of promoting the electrification of urban traffic, among which the prediction of electric vehicle charging demand is the premise and foundation of the planning and construction of charging stations. This paper first predicts the development of electric vehicles in Beijing; Based on the travel data of users in Beijing, the data are mined and analyzed, and the rules are reached by electric vehicles. Based on the parking data of EV users, the SOC distribution of users' parking was obtained. Monte Carlo method was used to simulate the arrival time and arrival condition of users, and then the charging process was simulated. After that, the charging decision model was established to obtain the spatial-temporal distribution curve of EV charging load. Finally, the charging pile and charging power capacity of the charging station are configured based on the regional charging demand and the spatial and temporal distribution curve of charging load.
The Role of Extraversion in Phishing Victimisation: A Systematic Literature Review

Over the last decade, phishing attacks have become preeminent and increasingly successful. Anti-phishing strategies focus on raising awareness and training users to identify risks. However, those strategies do not fully consider the psychological profile of each individual. We sustain that maximising potential victims’ resilience requires additional protection strategies to focus on individual personality traits.In this article, we concentrate on extraversion as a personality trait for which there is no consensus about its effect on susceptibility to phishing attacks. We implement a robust bibliographic analysis methodology and identify potentially relevant articles, which we screen and filter against inclusion and exclusion criteria. We report and analyse the findings of the 39 articles that fulfil all criteria and are deemed relevant to this research. Our analysis shows that, despite the positive correlation between extraversion and phishing susceptibility found in many studies, there is no consensus supported by a well-established psychological theory. Moreover, we identify a number of reasons justifying this lack of consensus, namely the use of non-representative samples, the non consideration of contextual factors, and the use of self-reported personality tests, which lead to limited reproducibility and data inconsistencies.
Fault diagnosis of pile machine based on improved artificial bee colony and K-means clustering algorithm

Aiming at the problem of pile machinery fault diagnosis, a fault diagnosis algorithm based on improved artificial bee colony and K-means clustering(IABC-K) is proposed. Firstly, the normalization method is used to preprocess the original fault data; Secondly, the artificial bee colony (ABC) algorithm is used to optimize the K-means clustering, which is affected by the initial clustering center, and cluster the characteristic parameters; Finally, the clustering and diagnosis results are analyzed. The results show that this method can effectively identify the fault type of pile driver and improve the efficiency of fault diagnosis of pile machinery.