Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Algorithms for Unbalanced Dataset Promotion Prediction for Employees

The practice of giving and accepting promotions is one of the most important in human resources. Management can keep morale and productivity high with the use of a merit-based, open promotion system inside the company. The prospect of a promotion is one of several extraneous reasons that drives many employees. maintaining the worker engaged and committed to the organization is crucial to maintaining his current productivity levels intact. It's a vital tool for motivating workers and monitoring their output. The promotion process takes into account a wide range of factors, including an employee's seniority, performance, competencies, age, awards, training score, and organizational devotion. Workers' promotion needs will be evaluated using Machine Learning methods such Support Vector Machine, Artificial Neural Network, and Random Forest, and a prediction strategy will be explored based on the findings. Accuracy, precision, and recall all improved by 1%, and the f1-score reached a perfect 98% when the ROS technique was combined with the Random Forest algorithm. Human resource professionals and business leaders might use the findings of this study to make more accurate predictions about who and when promotions will occur.
Family Defects Detection Method of Power Equipment Based on Data Fusion

Addressing the challenges and substantial maintenance expenses associated with detecting defects in power equipment families during grid operations, this study introduces a novel method for identifying these defects. This method leverages a multidimensional approach to data fusion. Initially, it involves processing various sources of data and constructing a recursive graph based on structured time series data. This step helps in aligning both structured and unstructured data within a uniform descriptive framework. Subsequently, the method involves extracting features from the recursive graph and existing image data by utilizing a convolutional neural network, resulting in distinct feature vectors. These vectors are then intelligently combined and merged using adaptive weighting. This technique focuses on defect detection in power equipment families, enabling enhanced perception of the state of the equipment through the integrated analysis of multi-modal data features.
Design of an Anti-High Overload Wide Beamwidth Antenna

This paper presents a topological structure in which two inverted F -shaped units are placed back to back. Two inverted F -shaped elements are cascaded through an inverted feeding network with a phase difference of 180°, which realizes the coverage of antenna energy in a wider angular domain. The anti-overload energy of the antenna is enhanced by adopting metal structural parts, threaded installation and foaming assembly process. The wide-beam antenna designed based on this topology can achieve VSWR<2.0 in 2.20 - 2.38 GHz (relative bandwidth is 7.8%). At the central frequency $(f=2.28$ GHz), the 3 dB beam width of the wide-beam antenna reaches 254° (phi = 0 plane), and the 3 dB beam width at the other orthogonal plane also reaches 115° $(\text{phi}=90^{\circ})$, and the gain of the antenna is greater than 0 dBi (phi = 0°) in the range of theta angle of 256°.
vStream IT: Video Streaming for Resource Constrained IoTs - An Optimal Control Approach

In the smart industrial era, with the rise of AR/VR technologies, dense deployments of IoTs such as ‘head mounted displays’ and ‘smart glasses’ are expected to become pervasive. For such dense IoT client deployments, edge-server based video streaming framework can improve the network performance such as low latency, and high and stable throughput. Such network enhancements can be utilized to improve the performance of end-to-end video (application) streaming by appropriately designing Adaptive Bit Rate (ABR) algorithm that also takes into account the resource constrained nature of the hosting IoTs. In this paper, we develop one such IoT friendly deterministic optimal control based ABR streaming algorithm. The exponential complexity of underlying state space is tackled by exploiting the underlying problem structure to yield an optimal and computationally-simple ABR algorithm namely, Video Streaming for IoTs (vStreamIT), which also use minimal storage. The efficacy of the proposed vStreamIT algorithm is evaluated against the state of the art and is shown to exhibit improved streaming performance in terms of bit-rate, while consuming less buffer space (a critical storage constraint for IoTs).
Research on Power Wireless Sensor Network Protocol Based on Ambient Backscatter Communication

With the development of new power systems, the number of sensing nodes has sharply increased, resulting in increased maintenance costs and tight spectrum resources. Ambient backscatter communication is powered by environmental energy harvesting, which is battery free, low-power, and does not require additional spectrum resources. In order to adapt to energy harvesting wireless sensor networks, this paper proposes a group-polling medium access control protocol, which combines probability polling with non orthogonal multiple access. The protocol divides the communication process into two stages: detection and transmission. By reducing the size of data packets in the contention stage, the waste of time slots caused by collisions is reduced. By increasing the amount of data in the non-contention stage, the data transmission rate per unit time is increased. The simulate results show that this group-polling protocol can effectively improve the system throughput and provide a low-power protocol solution for power-aware systems.
Channel Pruning-Based Lightweight YOLOv5 for Pedestrian Object Detection

Channel Pruning (CP) and lightweighting of neural network models has an important place in the field of object detection. In this paper, we introduce a global channel pruning with sparse nature and use this pruning method to achieve model lightweighting in YOLOv5, so as to achieve more lightweight pedestrian object detection while ensuring detection accuracy. We conducted extensive experiments on a large public dataset, CrowdHuman. The results show that the lightened YOLOv5n and YOLOv5m are 72%/40.1% and 80.2%/50% in mAP50 and mAP50-95, respectively, while parameters and GFLOPs are reduced by about 49% and 58%, respectively. The lighter model helps to achieve pedestrian target detection on resource-constrained smart devices.
A Hierarchical Framework for Dynamic Global Path Planning

This article presents a hierarchical global path planning algorithm designed for mobile robots. The global path planning problem is divided into three distinct components: waypoint generation, reference path generation, and path optimization. In the waypoint generation phase, we allocate the computation of environmental changes and map updates within each frame point cloud based on dynamically updated visibility graph. We then derive waypoints by identifying visibility edge vertices. Next, a smooth reference path is obtained through B-spline fitting optimization, delineating the convex driving region within the environment for further optimization within homotopy classes. To mitigate collision issues arising from B-spline fitting, we formulate the path optimization problem in the Frenet coordinate system as a quadratic programming task. This approach yields a globally collision-free path with high computational efficiency. Simulation experiments have been conducted to validate the effectiveness of the algorithm proposed in this paper.
Comparison of the Performance of Chinese Text Classification Based on Bert Augmented Model

With the continuous generation of large amounts of Chinese text data such as social media, news, comments, etc., text classification technology has become increasingly important. Future development trends may include designing more in-depth models, combined with pre-trained models such as BERT or GPT, to improve classification accuracy. At the same time, multilingual and cross-domain text classification will also become a research hotspot to adapt to the needs of different languages and industries. With the continuous advancement of technology, Chinese text classification is expected to play a greater role in information filtering, public opinion analysis, smart medical and other fields. BERT is a powerful pre-trained model capable of capturing rich semantic information. Combining it with other neural networks can integrate BERT's context understanding capabilities and the characteristics of other networks to obtain a richer and more expressive feature representation. This article will use various recurrent neural networks and convolutional neural networks combined with the BERT model to observe the performance capabilities and applicability of different combined models.
Heart Sound Classification Based on MFCC Feature Extraction and Long-Short Term Neural Networks

The classification of heart sounds is of utmost importance in promptly identifying cardiovascular disorders, particularly in small primary healthcare clinics. Although significant advancements have been achieved in the classification of heart sounds recently, most of these developments rely on traditional segmented attributes and classifiers with limited depth. These traditional approaches to representing and classifying acoustic signals must be revised to capture the nuances of heart adequately sounds. They often face challenges in delivering accurate results due to the cardiac environment's complex and variable acoustic conditions. This study suggests an enhanced Mel-Frequency Cepstrum Coefficient (MFCC) feature-based technique for classifying heart sounds and a Long-Short Term Memory neural network (LSTM). The neural network receives MFCC-based features to perform feature learning, followed by the classification task. The experiment's findings show that the suggested technique performs effectively over a range of tolerance windows.
An Energy Storage Configuration Method for New Energy Power Station Balancing Consumption and Economy

New energy power stations will face problems such as random and complex occurrence of different scenarios, cross-coupling of time series, long solving time of traditional multi-objective optimization algorithm, slow convergence speed, and easy to fall into local solutions when allocating energy storage in consideration of promoting consumption and actively supporting scenario requirements. Based on this, this paper proposed a new energy storage configuration method suitable for multiple scenarios. Utilize the output data of new energy power stations, day-ahead power forecast data and grid frequency data. Extract typical working condition curve of energy storage demand. Build the optimized configuration model of energy storage. An improved multi-objective particle swarm optimization algorithm is proposed. Realize the optimal allocation of energy storage in new energy power stations. Finally, the effectiveness and practicability of the proposed method are verified by the simulation analysis of the actual new energy power station.
UWB Circularly Polarized Slot Antenna for Multiple Communication Systems

This paper presents a novel broadband circularly polarized antenna (CPA) for multiple communication systems. The proposed antenna achieves a wideband characteristic with the help of a modified ground plane with a circle loop feeding structure. The circle loop feeding structure is used to achieve circular polarization. Additionally, circular slot ground is modified by loading a four-truncated corner and incorporating the I-shaped strip as perturbation elements. In order to validate the performance of the antenna design, an antenna prototype is printed, manufactured, and tested. The proposed antenna is compact with a small size of 40 mm × 40 mm × 1.6 mm. The antenna has a good impedance matching over a frequency band of 1.9 to 9 GHz, with an axial ratio below 3 dB in the bands 3.03-3.09 GHz, 4.8-4.9 GHz and 5.6-5.91 GHz. The simulated realized gain is 5.1 dB at 5.8 GHz and 2.25 dB at 2.2 GHz. The measured results show that the proposed antenna is a good candidate for portable UWB applications.
Mobile Application Security Risk Score: A sensitive user input-based approach

This research paper introduces a Hierarchical Weighted Risk Scoring Model specifically designed to assess the risk levels of mobile applications based on user inputs. Through an extensive review of literature on risk score calculation models and term sensitivity identification techniques, this study categorizes terms based on their sensitivity, particularly in relation to sensitive user inputs that may potentially lead to data leaks. The sensitivity of user terms are defined based on the guidelines from PIPEDA. By integrating these terms, along with test outcomes and weights, the model accurately calculates risk scores. The resulting risk assessments provide users with valuable insights, empowering them to make informed decisions and effectively manage risks associated with mobile application usage. This research contributes to the field by offering a user-centric framework that combines various risk score calculation models and term sensitivity identification techniques, tailored specifically for mobile applications and addressing the potential risks arising from sensitive user inputs.
BBR vs. BBRv2: A Performance Evaluation

Google's new congestion control algorithm, BBR, has seen wide adoption. However, there are concerns about its unfairness to legacy congestion control algorithms and the high retransmissions experienced under lossy conditions. In response, Google designed BBRv2 to address these concerns. However, the performance of BBRv2 with respect to BBR or Cubic has not been studied systematically. This paper presents a fine-grained performance study of BBRv2 under a variety of RTTs, band-widths, buffer sizes, and loss conditions, in both LAN and WAN environments. We construct a decision tree to determine whether BBR or BBRv2 performs better under different conditions. We observe that BBRv2‘s goodput is significantly low compared to BBR's under induced loss and bursty losses, and the effect is magnified under large BDP. Our results suggest that BBRv2 trades performance for better fairness under losses. To bridge this gap, we investigate the workings of BBRv2 and find that BBRv2 employs a long-term upper bound on sending rate that is not robust to losses. This upper bound is continually decremented in the presence of persistent losses, thereby depressing goodput. We show that by aligning BBRv2‘s upper bound with its maximum bandwidth estimation, BBRv2‘s performance can be greatly improved while maintaining its fairness.
Integration of SAP and Intelligent Robotic Process Automation

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance.
A New Triple-Stacked Step-Up Converter for High-Voltage Gain

This paper presents a novel three-stage stacked dc-dc boost converter. It can be used in applications where a high boost is required between the output and input voltage. The converter can be used to replace many conventional cascaded converters, but it will have only one active switch. The main dc values expressions for the converter are derived from the charge balance and volt-second balance equations. An analysis is then performed to study inductor currents, capacitive voltages, and the ripple effects. The design formulas and the semiconductor stresses are then provided based on them. A prototype is theoretically designed, and the operation of the converter is confirmed by simulation. Finally, a PCB prototype is proposed, the new topology is practically built, and experimental signals and measurements are acquired.
Artificial Intelligence-Based Modeling of Microwave GaN HEMT Power Dissipation

The GaN HEMT power dissipation is modeled as function of the drain source voltage, the gate source voltage, the drain quiescent bias voltage and the gate quiescent bias voltage in pulsed mode. The developed model is based on Artificial Neural Networks (ANN) technique. The neural network is trained with two different algorithms: the genetic and the Levenberg-Marquard algorithms. High ANN model accuracy is obtained with the Levenberg-Marquard algorithm. An optimization procedure is carried out in the purpose to determine the optimal numbers of quiescent bias voltages used in the network training. The obtained optimal data are used to train the ANN. The comparison between the device output power versus the drain source voltage curves obtained from the ANN model and those simulated from analytical equations, has shown a weak model error and validates the proposed methodology.
Temporal Fusion Transformer for Multi Horizon Bitcoin Price Forecasting

Bitcoin is one of the cryptocurrencies that have a large number of holders and a high transaction volume. Its price has experienced major fluctuation since its inception, resulting in relatively high price volatility. This has been a challenge for researchers over the past decade in forecasting its future price. In this study, we aim to address this problem by utilizing a neural network model called the Temporal Fusion Transformer (TFT). The TFT model uses the concept of multi-horizon forecasting to learn from historical data and predict future prices based on multiple time steps. Due to the numerous factors influencing Bitcoin's price, our study incorporates additional predictive data, such as Twitter sentiment, trends, and seasonality. The proposed method is trained using the TFT model with historical Bitcoin data from 2014 until 2020, then we performed transfer learning by using different past covariates as inputs. As a result, the proposed method in this study demonstrated the best performance, with a MAE of 0.05, a RMSE of 0.07, a MAPE of 5.92%, and a quantile loss of 0.03, surpassing other approaches. This makes it the state-of-the-art solution for highly volatile forecasting using a neural network model.
IoT based Realtime Pregnancy Monitoring System

The healthcare landscape has undergone a significant transformation through the integration of the Internet of Things (IoT), paving the way for inno-vative solutions across various applications, including pregnancy monitoring. This study presents a system for continuous monitoring that uses the Internet of Things to track the vital signs of both mothers and fetuses. The proposed system makes use of several sensors, such as an FHR, MUC, and ECG sensor, all of them connected to a microcomputer unit. After being analyzed by the MCU and wirelessly transmit-ted to a web-based server, the sensor data is present-ed on an interactive user interface by the website cli-ent. The GUI provides medical professionals with a comprehensive study of being pregnant, including current data and past patterns that can be used to identify potential problems and take prompt action. Because of its affordability, scalability, and user-friendliness, the system can be implemented in both wealthy and poor countries. Overall, by offering im-proved accuracy, reliability, and accessibility to pa-tients as well as healthcare professionals, the recom-mended method represents a significant advancement in pregnancy monitoring. Pregnancy monitoring could be revolutionized by this Internet of Things-based device, which could improve medical results for pregnant women globally.
A Low-Profile, Broadband Metasurface Antenna Loaded with Nonuniform Structure

In order to meet the user's demand for miniaturization and high communication quality of mobile terminal equipment, a design method of low profile, broadband metasurface antenna is proposed. In this design, more current loops are introduced into the inverted-F multi-branch excitation structure through probe feeding to generate more excitation modes synchronously. At the same time, a nonuniform metasurface parasitic structure is introduced above and below the inverted-F excitation structure, which can control the resonant frequency and realize broadband radiation, while ensuring the miniaturization and low profile of the antenna. The simulation results show that the -6 dB bandwidth of the antenna is $\mathbf{7.01}\sim \mathbf{8.46}\ \mathbf{GHz}$, and the electrical size of the antenna is $\mathbf{0.308}\boldsymbol{\lambda}_{\mathbf{0}} \times \mathbf{0.308} \boldsymbol{\lambda}_\mathbf{0} \times \mathbf{0.040} \boldsymbol{\lambda}_\mathbf{0}$.
A W-band Cavity Backed Slot Antenna Based on Multi-Layer Silicon Platelets

In this paper, a wideband multi-mode cavity backed slot antenna (CBSA) is presented. It is composed of four layer silicon platelets, which are radiation slot layer, resonant cavity layer, feeding slot layer, and feeding waveguide layer. Two higher cavity modes $(\text{TM}_{130},\ \text{TM}_{310})$ are utilized in this antenna, leading to a bandwidth over 11 %. As an example, a w-band CBSA is designed. Simulation results show its work bandwidth $(\vert \mathrm{S}_{11}\vert \leq-10\text{dB})$ is from 83.9 GHz to 93.7 GHz, and the peak gain is 13.2 dB ~13.4dB.
Optimizing Energy Consumption in Smart Homes: A Comprehensive Review of Demand Side Management Strategies

this paper delves into the important of demand side management (DSM) in optimizing energy consumption in smart distribution systems. The quest for energy efficiency in buildings, whether residential or commercial, has accelerated recently due to growing integration of smart grid technologies and a shared commitment to sustainability. The creation of novel methods for demand-side management (DSM), which is essential for maximizing energy use and grid stability, is a key component of this push. The most current developments highlighted in this abstract, which captures the state of the art in an ever-evolving discipline. There is a noticeable movement in the demand response (DR) space towards real-time utility-customer communication. A new age of flexible and dynamic energy use is being ushered in by automated demand response systems, which allow buildings to dynamically change their energy consumption based on price signals and grid circumstances. DSM may change load profiles and lower demand for peak loads by empowering customers and vendors to make knowledgeable decisions about energy consumption. This paper offers a thorough examination of demand response initiatives, energy efficiency laws, and DSM strategies, with an emphasis on improving DSM for household energy use. Along with highlighting the shortcomings of current energy indicators, the research investigates the barriers and untapped possibilities for increasing energy efficiency. An overview of many DSM processes and approaches provided in this work, together with information on their objectives, constraints, and possible directions for further study.