Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-efficiency Enhancement using a Novel Dots Protocol based on Scheduler in WSNs Wildfire Application

“Forest Fires (FF)” have become far more frequent in current decades for several reasons, including people's activity and environmental disruption. In this study, we will discuss the structure of a “Wireless Sensor Network (WSN)” and a data-gathering technique for FF detection in actual environments. The energy efficiency is crucial due to the “Sensor Node (SN)” constrained processing capability and minimum power battery. Extensive study is required to address these concerns. The SNs must be placed as closely together as feasible so that the whole forest can be monitored. Such an implementation strategy produces a crowded network environment in which SNs' coverage could intersect. To achieve the coverage goal, the basic requirement is to activate all SNs following installation. Together all SNs inside these overlapping areas are left “ON” which will reduce the lifespan of the network. Some of the SNs were put into an “Active-State (AS)” whereas others are left in a “Sleep-State (SS)” strategy that may be used to prolong the network's lifespan and preserve its coverage. Our study proposes a new protocol called the “Dynamic Optimized Time Scheduler (DOTS)” based on clustering to deal with the network lifespan problem from two angles. At first, the “Enhanced Sleeping Scheduler (ESS)” method is used to keep a small group of SNs in AS to guard the target while the rest of the SNs in the SS. The next will be for SNs to occasionally share the data they've collected locally with one another. The primary use of this concept is to update receivers only after an update has taken place. We examine the “Energy Efficiency”, “Packet Delivery Ratio”, “Throughput”, and “Routing Overhead” of the proposed DOTS protocol in comparison to the “Clustering-based Compression-Aggregation (CCA)” and the “Tunicate Swarm Algorithm (TSA)” existing protocols in the WSN.
Effective Model of Detecting Driver’s Drowsiness

In recent years, accickents have increased as a result of drowsiness. To address this issue, this research study has developed a system that scans the driver’s face for signs of tiredness and sounds an alarm to recommend waking them up. In order to provide information that helps lower traffic accidents, the Drowsiness Detection machine learning model uses ML approaches to identify the state and mood of a driver. It is an action involving artificial intelligence. Systems can automatically develop themselves without being specifically created thanks to artificial intelligence (AI). The state of a driver can be determined via bioindicators, their actions while driving, and even their facial expressions. This research work presents a drowsiness detection model with EAR, MOR, and alert system. Additionally, machine learning methods like HAAR grounded waterfall classifier and OpenCV are used to evaluate the condition of the driver. Finally, this study concludes by listing the challenges that the current systems encounter and outlines the pertinent investigation possibilities.
Enhancement of Voltage Conversion Ratio using DC-DC Boost Converter with Coat Circuit

This article aims to develop a solar powered efficient DC-DC converter to charge electric vehicle. While implementing the DC sources for boost converter with coat circuit charger it becomes continuous power supply. In the boost converter with coat circuit, which raise the input voltage 3 time higher from the voltage of battery or PV cell. According to this method of boost, the voltage is boosted to three times higher than normal voltage rate which is stored in the battery for steady supply of voltage. The solar based dc-dc boost converter charger is effective for converting the low-level voltage to high-level voltage of a converter. Over voltage and current can be controlled and limited by coat circuit to be an efficient PV charger. The battery is connected between PV and converter to store energy from sunlight. By this of boost converter, the losses which occur is reduced and produce improved voltage conversion ratio. Improvising voltage conversion ratio using boost converter with coat circuit is premeditated using MATLAB and the simulation outputs have been verified with a model.
Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.
Advances in Microscopy Tech Offer Better Views

Microscopes have come a very long way since the 1600s when Henry Power, Robert Hooke, and Anton van Leeuwenhoek began publishing the first views of plant cells and bacteria. The major inventions of contrast, electron, and scanning tunneling microscopes didn’t arrive until the 20th century, and the men behind them all earned Nobel Prizes in physics for their efforts. Today, innovations in microscopy are coming at a fast and furious rate with new technologies providing first-time views and information about biological structures and activity, and opening up new avenues for disease therapies.
Energy-Efficient Routing Protocols for Wireless Sensor Networks

Wireless Sensor Networks (WSN) has been a topic of research since a very long time. The importance of WSN is of huge diversity as it is used in military services, environmental services, medicine, agriculture, etc. WSN is a composition of many sensor nodes that work together to send data from the source to the base station. The sensor nodes are placed systematically inside a network. The design of this network is of most importance as it deals with the reliability, efficiency, energy consumption, delay, and lifetime of the entire WSN. The longest lifespan of the system and lowering sensor node energy consumption are given top priority while developing a WSN. Various types of routing algorithms are used in the perspective of this. In this paper, the author has bifurcated the routing protocols into four main categories which are Multi-hop, Hierarchical, Cluster-based, and Long range. Apart from these, the author has also mentioned routing methods on Fault tolerance, Neutral network, Query based, and Swarm intelligence.
Development planning of complex equipment based on cumulative prospect theory

Due to the characteristics of complex equipment such as large investment, long development cycle, high-performance requirements, and fragility, the development cost is high. Which complex equipment should be given priority requires careful consideration. Considering the risk preference of decision-makers when making decisions, the cumulative prospect theory (CPT) is introduced into the development planning process of complex equipment, and a complex equipment development planning model based on the CPT is established. The objective probability is replaced by the subjective decision weight, and the cumulative prospect value of the scheme is used as the criterion for sorting schemes. Finally, the feasibility of CPT application in equipment development planning is verified by an illustrative case, and the sensitivity of model parameters has been analysed. The result shows that the proposed methods can well describe the incomplete rational behaviour of the decision-maker.
IoT Cloud Systems: A Survey

IoT has gained a massive prevalence in the last decade. Various businesses are leveraging IoT Applications for industrial and commercial use cases. IoT also presents use cases in research and academia. However, setting up IoT Systems is complex due to the distributed and multi-disciplinary nature of IoT Systems. As a direct consequence of this complexity, the entire service industry has emerged that assists users to deploy and manage IoT systems. This paper aims to survey some of the Cloud management systems that help simplify and shorten the deployment process of IoT Systems.
A Novel Framework for Fake News Detection using Double Layer BI-LSTM

Fake news is an information that has been carefully manipulated to mislead readers by using false facts and figures. Since the introduction of the Internet and social media, fake news has grown to be a significant problem. Identifying fake news has become an important area of research in Natural Language Processing (NLP). The key challenge is determining the veracity of news stories. There is an increasing difficulty in studying and designing a technological strategy to combat fake news without compromising speed and collaborative access to high-quality information. Despite the fact that various technologies have been developed to assist in the detection of false news, and despite significant breakthroughs, identifying fake news stays ineffective. In this research, a new framework has been proposed that utilizes Porter Stemmer, TF-IDF vectorizer for pre-processing and double layer Bi-LSTM for extracting the refined features to obtain better learning. In this model, initially, the summarized input vector is formed by concatenating the most relevant text attributes such as headlines, news for further process. The performance of the proposed model has been justified by evaluating its performance on three experimental datasets namely Kaggle fake_real_news, Liar and Politifact Fake_Real.
Applying Human Values Theory to Software Engineering Practice: Lessons and Implications

The study of human values in software engineering (SE) is increasingly recognised as a fundamental human-centric issue of SE decision making. However, values studies in SE still face a number of issues, including the difficulty of eliciting values in a systematic and structured way, the challenges of measuring and tracking values over time, and the lack of practice-based understanding of values among software practitioners. This paper aims to help address these issues by: 1) outlining a research framework that supports a systematic approach to values elicitation, analysis, and understanding; 2) introducing tools and techniques that help elicit and measure values during SE decision making processes in a systematic way; and 3) applying such tools to a month-long research sprint co-designed with an industry partner and conducted with 27 software practitioners. The case study builds on lessons from an earlier pilot (12 participants) and combines in-situ observations with the use of two values-informed tools: the Values Q-Sort (V-QS), and the Values-Retro. The V-QS adapts instruments from values research to the SE context, the Values-Retro adapts existing SE techniques to values theory. We distil implications for research and practice in ten lessons learned.
Performance Analysis of Device-to-Device Communication in Rural Areas

In long term evolution (LTE) cellular networks, the devices present in close proximity can communicate directly without going through the base station. This technology is known as device-to-device (D2D) communication. Higher spectral efficiency, low latency, enhanced throughput, etc. are some of the major features of D2D communication as compared to the conventional cellular networks. In this article, we have analyzed the performance of D2D communication for a rural area channel model. This requires using different path loss exponent and standard deviation compared to those for urban areas. We believe D2D communication is potentially useful in rural areas where cellular base stations are sparse. We measure throughput, delay and packet delivery ratio for a pair of mobiles in D2D mode. Our results show that D2D is promising in rural environment.
A comparative computational and experimental study of chaotic mixing of viscous fluids

The objective of this work is to develop techniques to predict the results of experiments involving chaotic dispersion of passive tracers in two-dimensional low Reynolds number flows. We present the design of a flow apparatus which allows the unobstructed observation of the entire flow region. Whenever possible we compare the experimental results with those of computations. Conventional tracking of the boundaries of the tracer is inefficient and works well only for low stretches (order 102 at most). However, most mixing experiments involve extremely large perturbations from steady flow since this is where the best mixing occurs. The best prediction of widespread mixing and large stretching (order 104–105) is provided by lineal stretching plots; surprisingly the technique also works for relatively low numbers of periods (as low as 2 or 3). The second best prediction is provided by a combination of low-order unstable manifolds – which indicate where the tracer goes, especially for short times – and the eigendirections of low-order hyperbolic periodic points – which indicate the alignment of striations in the flow. On the other hand, Poincaré sections provide only a gross picture of the spreading: they can be used primarily to detect what regions are inaccessible to the dye. Comparison of computations and experiments invariably reveals that bifurcations within islands have little impact in the mixing process.
Constrained Low-Rank Tensor Learning for Multi-View Subspace Clustering

This paper studies multi-view subspace clustering. Existing low-rank tensor learning methods employ t-SVD to explore the multi-view correlation information. However, the local structure in the view-specific feature spaces is ignored, and the self-representation coefficients and affinity matrix are often learned in two distinct processes. To solve the two problems, we propose constrained low-rank tensor learning (CLRTL). First, we adopt t-SVD to preserve the global low-rank structure and multiple views correlation. Meanwhile, a sparse congruency constraint using the $l_{2,1}$-norm is imposed on the coefficient matrix of each view to reveal the intra-view local structure. Furthermore, the affinity matrix is joined to the optimization framework to directly learn the correlation between samples. Last, we develop an efficient algorithm to deal with the proposed problem. Experimental results on the benchmark datasets verify the superiority of CLRTL.
Design and Development of Thickness Measuring System for Copper Wire

Numerous types of electrical wiring use copper as its electrical conductor. Electricity is produced, transmitted, and distributed via copper wire in telecommunications, electronic circuitry, and a variety of other electrical devices. Additionally, electrical connections are made from copper and its alloys. Copper wires are made using a specialized technique in industries, to make a wire, a rod of copper which is dragged, or forced through a die, which causes the rod to stretch and become thinner. To reduce the copper to the appropriate diameter for wire use, this is repeated numerous time using progressively smaller dies. The copper is annealed after being pulled. In Wires and Cables manufacturing industries, they are manufacturing the copper wires about 3,00,000 meters day to day. While manufacturing, after completing the process of cooling the heated copper wire, the copper wires are running over the roller. The workers used to find the thickness manually using the thickness measuring instruments such as screw gauge. It would take much time and also sometimes, they mistakenly note the thickness of wires while running in the roller or at the end of winding and also, workers are doing some manual errors. Hence, it is proposed to solve their hardship by the available technology for continuously measuring the thickness of copper wire using emf and light emitting method. This system will measure the various thickness of copper wires (0.1mm to 1mm) manufacturing in industries.
Miniaturized Triband Planar Monopole Antenna using Right Turned L-Shaped Stubs for Wireless Communications

Due to the increasing use of communication networks and wide deployment of various wireless technologies, the transmit and receive antennas are expected to be compact, provide multi service with good radiation and bandwidth characteristics. This paper deals with integrative operation of two such wireless technologies such as Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) which are considered to be the key technologies used in wireless communication and Networks. In addition to this, enabling Aeronautical Mobile Communication for Airport transportation is also achieved. To accomplish all the above mentioned communication requirements, the antenna design proposed in this paper is enabled with a Right turned L-shaped connection between the feedline and the lower portion of the radiator to improve the impedance and bandwidth characteristics. A $50 \Omega$ microstrip line is used to feed the radiating element. The length of the ground plane is reduced to achieve good bandwidth and resonance. The proposed antenna configuration resonates at 3.5 GHz (WiMAX), 4.75 GHz (Aeronautical Mobile Communication) and 5.5 GHz (WLAN) with a bandwidth of 400 MHz, 160 MHz and 200 MHz respectively. The foot print of the antenna is 35X12X1.6 mm3 providing an efficiency greater than 80% in all the three desired bands. The far field pattern of the proposed antenna is also obtained as per the application requirements. The entire design is simulated using Computer Simulation Technology (CST).
The Detection and Analysis of Fake News Using Machine Learning

Social media has become one of the hotbeds for the dissemination of fake news in the market. Although, the role of social media is very important for the campaign, broadcaster of any news, and trend formulation. However, it may be the reason for the dissemination of fake news and bring negative impactual results on society and individuals. Even some significant impacts on election campaigns, politics, trend settings, and marketing agencies can be executed using social media. So the detection of fake news is one of the most perpetual ways to set the right trend in the market. Traditional methods with manual filtering are not feasible for the detection of fake news effectively. Although, there are several techniques used to detect fake news such as data mining, natural language processing, social network analysis, and machine learning algorithms. In this paper, the focus is to detect false news using distinctive machine learning algorithms. The model is trained and tested of the data-set available freely as an open-source. The positive prediction rate is very high in the ROC curve indicates the prediction of fake news effectively. Our experiment indicates the high accuracy with the support vector machine classifier algorithm.
Virtual Sensors: Toward High-Resolution Air Pollution Monitoring using AI and IoT

The present article contributes a research vision for virtual sensing that combines Artificial Intelligence (AI) and Internet of Things (IoT) to increase the coverage of air quality information. Virtual sensors take advantage of correlations between different pollutants to estimate the concentrations of pollutants for which no affordable sensors are available. We cover key requirements and challenges, reflecting on the current state-of-the-art and identifying key research challenges. We also demonstrate the potential and feasibility of virtual sensing through experiments conducted with data from Helsinki, Finland, which show how standard $\text{PM}_{2.5}$ and temperature measurements can be used to provide reliable estimates of CO2 and black carbon concentrations. We also discuss potential applications that can benefit from the implementation of virtual air pollution sensors and establish a research roadmap for the path forward.
Global Identification Passport: A Unique Cloud based Passport Model

DDoS attacks pose a serious risk to cloud networks because of the money they might cost if they succeed. For this reason, the Security Research Group has been hard at work creating various detection methods to lessen the blow of any future cyberattacks. Cloud computing, often known as internet computing, is a model of delivering computing as a service, in which shared, elastically scalable and virtually hosted servers are made available to users anywhere there is an internet connection. Numerous companies provide cloud services online to meet the needs of various clientele. Since most cloud users lack their own computing capabilities, they must save their data, documents, and applications in the cloud in order to perform their work efficiently. The focus of this work is on the worldwide identification procedure utilising a digital passport in the cloud. There has been and will continue to be significant growth in cloud-based innovation and advancement in the coming years. Users can benefit greatly from cloud computing by delegating complex activities to remote data centers. Whether or not consumers can access the cloud’s services is determined by the terms of the Service Level Agreement (SLA) in place between the cloud’s suppliers and the users. By detecting when a service level agreement (SLA) has been broken, cloud service providers may boost customer confidence, decrease the likelihood of incurring costly fines, and boost their bottom line. This study makes two significant contributions to the fields of service provider selection for virtual machine (VM) migrations and SLA (service level agreement) violation detection in public cloud computing. The pros and cons of a passport-free international travel system were examined. This study has compared various research methodologies and frameworks for implementing new technologies. The present study is predicated on a Cloud Passport adoption model that was developed for previous work.
Low-power thermal tuning of SOI-CMOS photonic structures

Ring waveguide resonating structures with high quality factors are the key components servicing silicon photonic links. We demonstrate highly efficient spectral tunability of the microphotonic ring structures manufactured in commercial 130 nm SOI CMOS technology. Our rings are fitted with dedicated heaters and integrated with silicon micro-machined features. Optimized layout and structure of the devices result in their maximized thermal impedance and increased efficiency of the thermal tuning.