Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Vehicles Taxonomy and Evaluation: Architectures, Protocols, and Issues

In order to improve safety and decrease accidents, VANET and Internet of Things were combined to create the concept of the Internet of Vehicles (IoV). In the IoV, a variety of communication channels are available, including vehicle-to-sensor communication because cars are thought of as powerful multisensory objects. The IoV’s topology is in fact not uniform and highly dynamic. The paper provides an in-depth analysis of IoV. Moreover, the analysis also addresses the factors that led to the introduction of IoV in vehicular network. The paper additionally elaborates on a comparison of the current architecture because it is not standardized.
Accident-Avoidance System using latest Sensing Systems

The most frequent causes of traffic accidents are exhaustion and drowsiness. The number of traffic accidents caused by sleepy and fatigued drivers rises every year. This suggested study introduces a computer - aided detection software system that will be useful in reducing the number of traffic accidents that are mostly the result of driver sleepiness. This surveillance system uses the DLib library and deep learning techniques to identify and forecast the outcomes. Additionally, this program has an alarm system to warn the user if they start to feel sleepy. A much-improved face tracking method that we utilized may increase the prediction’s accuracy. To identify facial traits, it employs 68 face references. The 68 landmark method is more efficient and efficient in distinguishing face features than the PERCLOS and Doppler cascade algorithms.
Improving Situational Awareness in Power Grids of Developing Countries: A Case Study of the Nigerian Grid

Electric power grids, especially in developing countries, are typically riddled with reliability issues, electricity theft, and vandalism exacerbated by minimal situational awareness available to operators. In this work, we propose a framework that optimizes situational awareness in power grid operations while integrating the need to prioritize economics as well. With the knowledge that limited economic resources could be a hindrance, the framework optimizes situational awareness by integrating budget over the specified time horizon. This hence provides a cost-efficient way towards a smarter and cyber-physical grid. The proposed framework is implemented by optimally placing phasor measurement units (PMUs) on the Nigerian 330KV, 28-bus transmission system, demonstrating the rolling horizon. Results show that situational awareness can be optimized with limited budget over a period of time, hence incentivizing power system stakeholders in developing countries.
Limitations of the Perceptual Deadband Approach for Haptic Data Compression

A perceptual adaptive sampling approach is used for haptic data reduction for a typical teleoperation application. The approach is based on the Weber’s law of perception and is widely named as the “perceptual deadband” approach. The approach transmits only those samples which are perceptually significant from the last perceived stimulus. Hence, the approach considers that the perception of the current stimulus depends on the last perceived force. In this work, we question this consideration and hypothesize that the perception of a change in stimulus is a function of the current and previous stimulus (irrespective of whether it is perceived or not). For the purpose, an extensive psychophysical experiment is designed where users are subjected to a stair-case force stimuli and are asked to respond to the change in stimuli. Perceived and non-perceived responses are recorded for several users. A machine learning classifier is trained to predict the label of the responses. Our findings suggest that the perception of the current force stimulus depends on the previous force stimuli, hence it validates our hypothesis that the assumption of the current structure of the perceptual deadband approach for haptic data reduction has limitations.
Digital Forensic Analysis of Ransomware Attacks on Industrial Control Systems: A Case Study in Factories

In the era of Industry 4.0, the Industrial Control Systems (ICS) are generally connected via a variety of network interfaces for real-time monitoring. As a result, ICS has become one of the main targets to be attacked by several threat actors by discovering the vulnerability in the system encrypting the data, and demanding ransom. Thus, protecting the network has become a major task for many organizations. Although several cybersecurity frameworks and best practices for ransomware are published, a research article case study on the ICS ransomware protection is rarely proposed. Thus, there is a need for actual case studies to increase cyber security experiences. Thus, this paper introduces three case studies based on qualitative research and a real case study. For the actual case study, the network diagram, event log, digital evidence, and digital forensic timeline are presented. For attack analysis, Microsoft ransomware attack framework and MITRE code are used since computer-controlling machines are Windows 10. Case analyses are compared for the understanding of cyber security vulnerabilities.
Optimal Utilization of Reactive Power Capabilities of Distributed Solar PV Inverter

To improve energy security and mitigate climate changes, renewable energy particularly solar energy is being significantly integrated with the power grid. Solar PV inverter, used for conversion of generated DC power into AC power has the ability to supply active and reactive power. Traditionally, only active power ability of solar inverter is being used. Although, the reactive power capability of the solar inverter can also be utilized for the maximization of benefits. In this study, the reactive power ability of the solar inverter has been used for minimizing power loss and enhancing the loadability limit of the distribution system. The loadability limit has been maximized by utilizing a new voltage-based stability indicator. New voltage-based stability indicator overcomes the computational complexity problem of unbalanced continuous power flow. Moreover, in the optimization, a Grey Wolf optimization algorithm has been used and the proposed methodology is applied and verified with IEEE-33 bus test system.
High Performance Computing Reinforcement Learning Framework for Power System Control

In this paper, the integration of a power system simulator and reinforcement learning (RL) tools and frameworks is presented. A proposed framework is easily applicable and can serve as a framework for further developing, training, and benchmarking RL algorithms on more complex tasks of power system control. The usage of standard RL frameworks enables a broad range of state-of-the-art algorithms to be implemented with high performance, scalability, and substantial code reuse. Also, the proposed framework design is suitable for scaling onto high-performance computing (HPC) clusters which significantly speeds up the computation. The IEEE 14-bus system is selected to show the simulation results of the proposed method.
Loan Repayment Prediction Using Logistic Regression Ensemble Learning With Machine Learning Algorithms

Lending activities are an important part of the credit activities of financial institutions and banks. This is an area that brings great potential for development as well as a sustainable source of profit for financial institutions and banks. However, lending to customers also brings high risks. Therefore, predicting the ability to repay on time and understanding the factors affecting the repayment ability of customers is extremely important and necessary, to help financial institutions and banks enhance their ability to pay debts. customers' ability to identify and pay debts on time, contributing to minimizing bad debts and enhancing credit risk management. In this study, Machine Learning models will be used: Proposing a method to combine Logistic Regression with Random Forest, Logistic Regression with K-Nearest Neighbor, Logistic Regression with Support Vector Machine, Logistic Regression with Artificial Neural Network, Logistic Regression with Long short-term memory and finally Logistic Regression with Decision Tree to predict customers' ability to repay on time and compare and evaluate the performance of Machine Learning models. As a result, the Logistic Regression with the Random Forest model ensemble is found as the optimal predictive model and it is expected that Fico Score and annual income significantly influence the forecast.
A data infrastructure for heterogeneous telemetry adaptation. Application to Netflow-based cryptojacking detection

The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures involved. These different infrastructures provide information sources that can be useful to detect cryptojacking, but with a wide variety of data formats and encodings. This paper describes the Semantic Data Aggregator (SDA), a normalization and aggregation system based on data modelling and low-latency processing of data streams that facilitates the integration of heterogeneous information sources. As a use case, the paper describes a Cryptomining Detection System (CDS) based on network traffic flows processed by a machine learning engine. The results show how the SDA is leveraged in this use case to obtain aggregated information that improves the performance of the CDS.
Emotion Classification in Thai music using Convolutional Neural Networks

Music is an integral aspect of human life. We use music for a variety of objectives, including enjoyment, treatment, and inspiration. This study is interested in evaluating song extraction utilizing natural language processing and deep learning techniques. To this end, we propose a framework for analyzing Thai music emotion based on the Hourglass which classify mood of music into 5 categories: Disapproval, frustration, love, optimism and remorse. First, we explain how to determine mood label for each song using BabelSenticNet corpus. We believe that using mood labels from BabelSenticNet is comparable to using humans to determine the mood of each song. In order to automate song classification without semantic knowledge, Fasttext was used to embed song lyrics and fed into Convolutional Neural Networks. Learning rates were adjust to find the best accuracy.
Revisiting Aggregation Sequentially with M-A-C’s and Detecting Functionality

A cryptographic tool known as the aggregate message authentication code (aggregate MAC) can condense the MAC’Stags from several messages into a single, condensed aggregate MAC’Stag. The sequentially aggregated MAC’s may additionally verify the (sequential) order of messages in addition to the correctness of multiple messages. In this study, we provide a novel sequentially aggregated MAC’s paradigm in which an aggregation kind of algorithm creates a sequential aggregating based just on any type of various sequences officially describe type of security in this paradigm, which uses separate MAC’Stags without a secret key. Additionally, without altering the MACs’ structure, we provide a general method of building consecutive aggregate MACs beginning from any MAC. By integrating the aggregation algorithm with other MAC’s methods currently present in the given type of networks, this property is helpful to increase the efficiency of the current networks. It also offer a sequential aggregate MAC’s with detecting capabilities by expanding the SAMAC’s findings (SAMD’S). It can define a message which is valid or invalid order for a particular type of message using the SAMD’S. We propose a general type of design of SAMD’S using random type of oracle model from which any MACs and non-adaptive group can be tested and their kind of protocols with formal type of security implementation proofs. We further define the type of security of SAMD’S.
Near-Infrared Observations of Extended Emission around PG 1545+210

Infrared images at H and Ks of the quasar PG 1545+210 clearly show both extended emission centered on the quasar and a companion galaxy. The magnitudes of the extended emission deduced from these observations depend strongly on the model adopted for the emission. Both an exponential disk model and a de Vaucouleurs model provide adequate fits to the extended emission. If it is assumed to follow an exponential disk galaxy, these measurements imply that the host galaxy has absolute magnitudes comparable with those of a Schechter L* galaxy. With this assumption for the intrinsic light distribution, the H magnitude and the Hubble Space Telescope limit on the V magnitude are inconsistent with color predictions for unreddened spiral galaxies at the redshift of the quasar. If the data are fitted to a de Vaucouleurs model, the extended emission dominates the total energy of the system and significantly exceeds that of an L* galaxy. The companion galaxy has colors consistent with its being at the redshift of the quasar, but its luminosity is at most a small part of that of the entire system. If the companion is at the redshift of PG 1545+210, its absolute magnitude is MH ~ -22 mag, significantly fainter than that of an L* galaxy.
Wireless Communication With Extreme Reliability and Low Latency: Tail, Risk and Scale

It is crucial to provide ultrareliable and low-latency communication (URLLC) for 5G wireless networks and above, this topic is now attracting a lot of interest in both business and academia. The fundamental requirement of URLLC is a change from conventional utility-based network design methods, where reliance on average numbers is no longer a choice but a requirement. Rather, there is a need for a comprehensive and systematic paradigm that considers factors like variability, packet size, network design, and topology. This research’s main objective is the first stage toward bridging this gap. After defining latency and dependability, we carefully explore potential URLLC enablers and the trade-offs they entail in order to go toward this objective. Then, we concentrate on a wide range of processes and approaches related to URLLC’s specifications and also how they apply to certain use cases. These findings offer clear guidance for the development of wireless networks with minimal latency and great dependability.
Applications of Deep Learning (DL) Techniques in Detecting Breast Cancer and Malignant Cells

According to the most recent statistics, the most common type of cancer globally is the breast carcinoma and kills close to 900,000 people annually. Early and accurate diagnosis of the illness can increase the likelihood of successful treatment and lower the mortality rate. In fact, an early diagnosis can help stop it from spreading and prevent the premature victims from getting it. Researchers who study cancer have a number of difficulties when attempting to differentiate between benign and malignant tumors as well as attempting to make judgments about benign and metastatic breast carcinoma. Examine the effectiveness of automated deep learning algorithms at identifying malignant cells in women’s breasts and cancer stage. This paper suggests applying deep learning algorithms to whole-slide pathology images in order to possibly increase diagnostic efficacy and accuracy. The convolutional neural networks (CNN), sparse auto encoders (SAE), and stacked sparse auto encoders are illustrations of techniques of deep learning were used in this research work. There are numerous public mammographic databases available. The methods discussed in this paper are put to the test using the mini-MIAS mammographic database. The stacked sparse auto encoder performs better, this method has to be tested in a clinical setting before being used. It has higher accuracy and precision as compared to CNN and SAE. Better diagnostic performance was achieved by several deep learning methods. Deep learning algorithms are used to better reliably identify tiny tumors while detecting breast cancer via a mammogram.
Behavioral Office Syndrome with IoT Devices

This paper presents a collection of computer use behavior prone to office syndrome by using pressure sensors in conjunction with IoT devices. IoT device was attached with a chair, and sensors were installed on the seat and the backrest. Another IoT set was linked with sensors installed under the mouse pad. The collected data of seven testers instructed to use computer and move freely revealed different behaviors. Some were sitting constantly with or without using backrest or touching mouse. Also a unique character like standing while using computer was found. To link with office syndrome estimation, we proposed a set of rules including thresholds for pressure values, time criteria and risk scoring. Using these criteria, the testers were found to have low and moderate risks of office syndrome.
A Comprehensive Steady-State Analysis for Modular Multi-Parallel Rectifiers (MMR) With Shared DC-Link

This article proposes a comprehensive steady-state analysis for a front-end Modular Multi-Parallel Rectifiers (MMR) system with a shared DC-link. A generalised mathematical representation for an n-number of parallel rectifiers is proposed. The MMR is analysed for both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). In addition, the CCM and DCM boundary of the MMR is identified mathematically. The impact of parasitic elements on the mode of operations is studied. In addition, the impact of different ASD scenarios on the mode of operation is studied. A 200 W Simulink model for the MMR is utilised to confirm the proposed steady-state mathematical modelling equations. A 200 W practical setup is implemented in a laboratory to validate the proposed mathematical equations.
The MDBUTMF Aberration Exposure Routine for Aberration Extirpating on Constant Sizableness Impulsive Noise (CSIN)

In aberration extirpating society, exceptionally in aberration exposure routine, the aberration exposure element is theoretical determined as aberration possible symbols. This scholar document investigates the efficiency of MDBUTMF aberration exposure routine, which is progressed from the SMF aberration exposure routine for achieving the weakness of the antecedent aberration exposure routine for extirpating the aberration on CSIN at entire aberration regularity. Each aberration element, which is scholarly determined from the aberration exposure element and its juxtaposition elements by the MDBUTMF aberration exposure routine, is associated with the pre-turning window-size for exposing whether the element is non-aberration and aberration. In this synthesized verification, bounteous aberration impressions, which are embodied of Lena and Pepper at entire aberration regularity, are operated for exposure the proficiency of the MDBUTMF aberration extirpating routine. The first augmentation of this scholar document is the optimal pre-turning window-size for maximum efficiency of the MDBUTMF aberration extirpating routine: aberration veracity, non-aberration veracity, miss rate and fault alarm. The later augmentation of this scholar document is the scholar proficiency of the MDBUTMF aberration extirpating routine, which is associated with the antecedent aberration extirpating routine for instant SMF, SF and AMF, in both objective measured index (PSNR) and subjective measured index (visualization).
Impact Analysis of Future Electric Vehicles Using Model of Real Distribution Feeders

Globally, the number of electric vehicles (EVs) continues to increase. This on-going trend poses challenges for power distribution systems. The inclusion of electric vehicle supply equipment (EVSE) should be compatible with a changing energy system, whose structure is becoming increasingly distributed. The impact of future EVs should be analyzed and considered in the system planning and upgrades. In this study, impact analysis of future EVs is conducted for a utility company of U.S. West Coast. Real data of distribution feeders is converted into the GridLAB-D model for running power flow analysis. An estimation of the additional loading of EVs in 2050 is provided by the utility company. Simple mitigation methods are tested to improve voltage profiles.
Phasor data correction and transmission system state estimation under Man-in-the-Middle attack

Cyberinfrastructure (e.g., sensors, actuators and the associated communication network) has become an integral part of our modern power grid. While these cyber technologies enhance situational awareness and operational efficiency, they also expose the physical system to cyber-attacks. In this paper, we consider the problem of transmission system state estimation based on measurements from a number of PMUs. In this context, a PMU data integrity attack, Man-in-the-Middle (MitM) attack that can potentially cause a severe impact on the grid is analyzed. Specifically, we propose a novel method based on an alternate expectation-maximization framework to mitigate the effects of these attacks on the state estimation process. Numerical tests are conducted on IEEE-14, 30 and 118 bus systems with different attack scenarios to validate the developed method. Unlike existing works, the proposed algorithm provides accurate state estimates without any prior knowledge of the location of the attack or the number of meters being attacked.
Quasi-Local Energy Distribution and Thermodynamics of Reissner—Nordström Black Hole Surrounded by Quintessence

We investigate quasi-local energy distribution and thermodynamics of the Reissner-Nordstrblack hole space-time surrounded by quintessence. We use the quasi-local energy distribution from Einstein energy-momentum complex. We plot the variation of the energies, temperature and heat capacity with the state parameter related to the quintessence ! q. We show that due to the presence of quintessence, the total energy of the outer region as well as the temperature and heat capacity decreases with the increase of the density of quintessence, while the total energy of the black hole region increases.
Design and Analysis of a Novel High-Speed Permanent Magnet Machine With Dual Semi-Cage Winding

This article proposes a novel high-speed permanent magnet synchronous machine (HSPMSM) topology. The characteristics of the proposed machine are: 1) The stator winding is divided into two parts and each part is connected by the end ring at one end, which is helpful to solve the existing shortage of overlong end length in traditional HSPMSM. 2) The stator of the machine is designed with closed slot, which can greatly weaken the eddy current loss and the cogging torque of the machine. 3) The machine is driven by two separate three phase converter, which can improve the fault tolerance. In this article, the topology, and the corresponding control system is analyzed firstly. After that the electromagnetic performance, including back electromotive force, cogging torque, torque, etc. are analyzed with finite element analysis (FEA). In addition, the losses, including the stator copper loss, the stator iron loss and the eddy current loss of the permanent magnet and sleeve of the rotor, are calculated by FEA. The mechanical strength and dynamic performance of rotor is also checked. Finally, a prototype was designed and manufactured. The experimental results the validity of the topology.
Seaport throughput forecasting by hybrid machine learning algorithm

Port throughput forecasting is not only a complicated problem but also a challenging task in seaport decision support system. This paper deals with novel hybrid model that utilizes discrete wavelet transform (DWT) and the deep learning method - long short-term memory (LSTM) network for port productivity forecasting. Based on the experimental results and related data analytics, it is found that the proposed hybrid method is superior to the five representative models in terms of prediction accuracy and efficiency; thus, advocating for the performance and robustness of the proposed hybrid strategy as well as the suitability in computer-based business decision.