Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Monitoring System of Home-scale Internet of Things (IoT)

During the Covid-19 pandemic, there was an increase in the use of electrical energy on a household scale. This study developed a home-scale power monitoring system to monitor electrical power at a household scale. This research was conducted to facilitate access to home power monitoring via Android based on the Internet of Things using the Thinger.IO web browser and Telegram Messenger using the Bot feature. This power monitoring system develops using the ACS712 sensor as a current reader and the ZMPT101B as a voltage reader, using an ESP32 microcontroller with a WiFi module that can be connected online. The results of this study are that the accuracy of the ZMPT101B voltage sensor is excellent, with an accuracy value of 99.845%. The ACS712 current sensor has the highest difference value with a reference of 0.32 A and 0.04 A for the lowest difference value. The data logger system on Thinger.IO works well in recording data. The Bot Feature on the Telegram application can work well even though it has an average delay of up to 4.2 seconds, depending on the speed of the internet network.
WZ frame reconstruction algorithm based on side information improved in distributed video coding

Distributed Video Coding (DVC) is an encoding mode that uses decoder-side information to reduce encoder complexity. The coding efficiency of DVC depends critically on the quality of side information generation and the accuracy of correlated noise modeling. Based on the transformation domain Wyner-Ziv (TDWZ) coding, block matching and optical flow method are used to generate initial side information frames, and a WZ reconstruction algorithm based on side information improvement and a method to improve the accuracy of correlated noise modeling are proposed by gradually updating the noise residual coefficient. Experimental results show that for WZ frames with GOP size 2, the proposed scheme can significantly improve the coding efficiency compared with TDWZ and DISCOVER while reducing the complexity of the decoding end. The system's rate distortion (RD) performance has also been improved.
Design and Performance Study of a Large-Scale Brushless Doubly Fed Reluctance Generator

The brushless doubly fed reluctance generator (BDFRG) is considered a viable generator alternative to be used in large variable speed wind turbines due to its various benefits including partial rating converter, absence of rotor winding and brushless operation. A literature review shows that there is still a lack of research a design procedure to make this machine widely used in such applications. This paper focuses on the design of a large scale BDFRG by estimating the initial parameters from a small-scale machine. Some fundamental issues concerning the BDFRG design are addressed. The performance of a 1.5 MW design is examined through finite element analysis (FEA) employing Ansys Maxwell software.
Data Analytics Methodology Application in Marketing

Data Analytics is the process of examining data sets to discover and interpret meaningful patterns in the data. Data Analytics is used in business industries to enable organizations to make more informed business decisions. This paper illustrates the step-by-step implementation of the MIDANO data analytics methodology in a business process that handles a large amount of data from the Marketing and Sales area of an Educational Center, to perform data analytics tasks to enhance customer attraction strategies and improve sales. A qualitative evaluation of the methodology is also carried out to identify possibilities for improvement in the methodology. The result shows the usefulness of the methodology offering opportunities for improvement in the management of the large amount of information, which was mostly found in flat files (Excel) from where filters were used to generate Marketing reports without considering historical information.
A Review : Classification and Detection Of Plants Diseases Using Machine Learning And Soft Computing Techniques

This work contains an overview of leaf diseases discovery with numerous image dispensation techniques. Digital image handing out is a reckless, consistent and accurate technique for different algorithms can likewise be used aimed at disease detection, identifications and classifications of foliar diseases of plants. This the article boons the systems used through various authors for identification diseases such as clustering methods, basic colour imaging investigation methods, classifiers and artificial neural network designed for classifications of ailments. The foremost motivation of our work is on investigation of dissimilar foliar disease discovery techniques and as well offers an outline of various image handing out methods.
A Loop Closure Detection Method Based On CNN For Extracting Global And Local Features

Loop closure detection (LCD) is an important module of visual SLAM (Simultaneous Localization and Mapping), which can be regarded as an image retrieval task. The correct LCD can effectively reduce the cumulative errors during visual SLAM localization and obtain a globally consistent map. The currently common approach to LCD is based on a bag-of-words (BoW) and traditional manually designed features. However, The BoW-based method ignores the spatial location information and semantic information of visual features, and image aliasing will affect the detection accuracy of the algorithm. Meanwhile, the artificially designed features are very sensitive to light changes. This paper proposes a LCD algorithm based on convolutional neural networks (CNN) to extract global and local features. Extensive experiments on six publicly available datasets have shown that the method provides high recall with 100% accuracy in most of the evaluated datasets relative to traditional algorithms and some deep learning-based methods.
Video Streaming OTT Platforms: A Comparative Study of their Streaming Infrastructure with Strategies Implemented

Streaming platforms revolutionized the media and entertainment industry, which led to the cord-cutting phe-nomenon. This phenomenon led to user preference towards adopting streaming platforms compared to wired devices as one of the most popular ones for television. Streaming happens to be a service within over-the-top (OTT) video services. Implementation is of the essence as a main driving force for an OTT video service as its strategy. Several such businesses have failed, while a strategy implemented by a particular service eventually leads to an increase in the consumer base. This paper attempts to study strategies used by popular digital streaming platforms to capture viewership in India. Competitive analysis of these streaming platforms is done based on the factors that influence over-the-top (OTT) adoption by customers. Furthermore, we review the streaming infrastructure of the three OTT platforms and how their technical infrastructure benefits the positioning of the streaming platforms.
OXYGEN ABUNDANCES IN METAL POOR SUBGIANT STARS FROM THE O I TRIPLET

This paper examines the oxygen abundance of 24 main-sequence turn-off and subgiant stars through the permitted O I triplet near λ7774 {Å}. In agreement with prior investigators, we find that the O abundance from the triplet is about 0.53 dex higher than is typically found from other means such as the [O I] λ6300 {Å} line or the UV and IR bands from OH. Our average [O/Fe] is +0.79 ± 0.29 (1 σ), with the uncertainties in the individual methods being dominated by systematic errors in our incomplete understanding of the formation of these high excitation lines under non-LTE conditions. Several methods of resolving the on-going discrepancy are discussed in detail. We find that the problem is not fixed by simply changing the model atmosphere parameters, and that the best methods seem to require a detailed non-LTE analysis based on both the structure of the oxygen atom and the source function within and above the photosphere. The trend of increasing O with decreasing Fe is briefly explored and is found to be qualitatively consistent with other works.
CampusX: An IoT-Powered Real-Time Monitoring System for University Campuses

There is an ever-urgent need for accessing real-time crowdedness and airflow information for indoor study spaces in universities, for example, to control COVID-19 transmission risk. Even before the pandemic, many students spent valuable time finding suitable study areas with proper lighting, low noise, and ample seating. This paper presents a pilot system, CampusX, which aims to provide students with useful real-time information about study spaces on campus. Our system collects and analyzes environmental data before presenting them to students as useful information. This helps them to select the most suitable study spaces. The main components of this system include a sensor platform, data collection and processing pipelines, networking, and an interactive web-application.
Optimal VNF Scheduling for Minimizing Duration of QoS Degradation

Network services provisioning with Service Function Chaining (SFC) consists of various controls such as Virtual Network Function (VNF) placement and traffic flow routing, executed dynamically. These controls take time to execute from start to completion (reconfiguration delay), and the reconfiguration delay varies depending on the type of control. When control is executed with a long reconfiguration delay to eliminate Quality of Service (QoS) degradation, it will take longer to complete, resulting in continued degradation. To optimize the network performance, a control method needs to consider the difference in the reconfiguration delay. However, most of existing works do not assume the difference, and aim to optimize QoS only at the completion of controls. In this paper, we assume different reconfiguration delays for each control in SFC provisioning, and propose a control scheduling that optimizes QoS during control execution on the basis of them. We first propose a network model in which the reconfiguration delay of each type of control is different and formulate the scheduling problem of network controls that minimizes the duration of QoS degradation. Then we propose a control scheduling method to solve the problem. Our method develops a stochastic search on the basis of the load degree of VNF instances to obtain a sub-optimal solution with low computational complexity. We also provide a packet-level simulation to verify the performance of our method when QoS degrades due to traffic demand rapidly increasing. Simulation results show that our method reduces the delay degradation and its duration compared with the control scheduling method that optimizes performance at the completion of controls.
The performance estimation and functions of anti-virus programs using modern security model

False antiviruses, today they are one of the methods of Internet fraud, which are seriously gaining popularity. Its essence is to force the user to pay for a program that removes the most dangerous viruses from the computer and protects the user's computer from various threats in the future. And if the program offered to buy is not a “fake”, everything will be fine. But in addition to matter and energy, another element plays a huge role in human life - information. It is a variety of information, news, news, knowledge, skills. Computers focused on storing and transferring information and the computer revolution took place. In this paper, the performance estimation and functions of anti-virus programs using modern security model. A suite of antivirus applications from Microsoft designed to combat various viruses, spyware, root kits, and Trojans. A copy of Windows that works only on computers with this software (Vista, Windows 7) has been verified. Antivirus Microsoft Security Essentials replaced Windows Live OneCare. Despite the adoption of laws in many countries to fight computer crimes and the development of special software tools for protection against viruses, the number of new software viruses continues to grow. This requires a personal computer user to know about the nature of viruses, how to attack and protect against viruses.
Comparison Of Unsupervised Machine Learning Algorithm F or Dimensionality Reduction

The dimensionality of a dataset is the sum of all its variables, traits, and features. The objective is to minimize the number of columns used to depict this axis. Machine learning (ML) is a data-driven technique in which computers learn without human intervention by analyzing large amounts of historical data. Incredible uses of ML may be found in a variety of fields. Supervised learning, unsupervised learning, and reinforcement learning are the three primary learning challenges in ML. The authors of this work provide a thorough comparison of several approaches to Dimensionality Reduction. Training a model using an unlabeled dataset is known as unsupervised learning. The model picks up knowledge independently by studying the characteristics of the training dataset. The model then uses these learned characteristics to draw inferences about the test data. Clustering, k- means, agglomerative, principal component analysis, and Fuzzy C-means are just few of the many unsupervised learning methodologies and algorithms available. When using support vector machines with a radial basis function kernel for disease diagnosis based on gene or protein expression data, we present empirical comparison results comparing PCA to other dimension reduction (DR) methods (i.e. K-means Clustering Algorithm, Principal Component Analysis (PCA), Agglomerative, Apriori Algorithm, Fuzzy C-means). Among all this algorithms PCA gives the best results for the gene and protein dataset in terms of accuracy, cross validation rates and computing time. Our research shows that principle component analysis (PCA) not only outperforms other DR methods in terms of classification accuracy, but is also significantly more efficient since it does not need the optimization of a tuning parameter. The results lead us to believe that principal component analysis (PCA) might be a useful DR method for developing a trustworthy and unbiased classifier for gene or protein expression data.
Secure Communication between Arduinos using Controller Area Network(CAN) Bus

Present-day vehicles have numerous Electronic Control Units (ECUs) and they communicate with each other over a network known as the Controller Area Network(CAN) bus. In this way, the CAN bus is a fundamental component of intra-vehicle communication. The CAN bus was designed without focusing on communication security and in this way it is vulnerable to many cyber attacks. As the vehicles are always connected to the Internet, the CAN bus is remotely accessible and could be hacked. To secure the communication between ECUs and defend against these cyber attacks, we apply a Hash Message Authentication Code(HMAC) to automotive data and demonstrate the CAN bus communication between two ECUs using Arduino UNO and MCP2515 CAN bus module.
The starburst – active galactic nuclei connection on nuclear scales in near by Seyfert galaxies

Abstract Several works have shown that there is an empirical correlation between the star formation rate and the luminosity of the active galactic nucleus (and thus the black hole accretion rate, ṀBH) of Seyfert galaxies. This suggests a physical relation between the gas forming stars on kpc scales and the gas on sub-pc scales that is feeding the black hole. Simulations predict this relation and also that the correlation should be more prominent on smaller physical scales. We have compiled high angular resolution (0.4–0.8″) mid-infrared spectroscopy obtained with T-ReCS, VISIR, and Michelle of 29 Seyferts. We use the 11.3 μm PAH feature to probe the star formation activity in the inner ~65 pc, and its relation with the ṀBH on these physical scales.
Towards a Domain-Specific Language for Provisioning Multiple Cloud Testing Environments for Mobile Applications

Towards a Domain-Specific LanguagTowards a Domain-Specific Languag Provisioning testing environments for mobile applications is one of the most significant challenges within the software industry. Due to this high complexity that exists when provisioning test environments within the multiple available cloud platforms, it is necessary to make a significant investment in human resources, like time and effort for the implementation and execution of testing. There is additional complexity: testing software in a single environment is no longer sufficient. Today's mobile industry is constantly growing, and execution environments tend to be always different; the hardware configuration is usually different and sometimes exceeds the software barrier. It is challenging to execute testing on each of the existing devices, as this requires a long task of human intervention. Today some platforms provide testing services in different environments. However, not all providers have the complete set of environments that one would like to have, and specific knowledge is mandatory for using each available tool. It is a task that requires expertise and time. This work seeks to mitigate the impact on time and the learning curve through a high-level tool developed using a model-oriented approach, thus reducing the time needed for setting up each required platform for organizations. As a solution, we propose a Domain-Specific Language for provisioning multiple cloud testing environments for mobile applications. The configuration of the environment is done with the Domain Specific Language to make the usage easier by the final user. The necessary code is generated through transformations to set up an environment on cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). This usage of this platform results in fewer code lines written and less time learning about the specific knowledge for each platform.
NIV-Detector: An Automated Approach for Detecting Next- Intent Security Vulnerability in Android Applications

In Android inter-process communication, any component can start another public component using an Intent messaging object nevertheless the components belong to different processes or applications. Besides, the private components should be protected and only be accessible by the same process. However, the malicious application can breach access and directly starts private components from another process, causing Next-Intent Vulnerability (NIV). The leading cause of NIV comes from lunching unsafe Nested Intent sent by the malicious application. In this paper, we propose a new approach and implement its tool to automatically inspect NIV code smells. We integrate our tool, named NIV-Detector, with Android Studio as a plugin to be available during development time. We use NIV-Detector to inspect 100 Android GitHub projects. As a result, we successfully confirmed ten vulnerable projects with 14 NIV smells.
Arbitrarily Varying Wiretap Channels With Non-Causal Side Information at the Jammer

Secure communication in a potentially hostile environment is becoming more and more critical. The Arbitrarily Varying Wiretap Channel (AVWC) provides information-theoretical bounds on how much information can be exchanged even in the presence of an active attacker. If the active attacker has non-causal side information, situations in which a legitimate communication system has been hacked can be modeled. We investigate the AVWC with non-causal side information at the jammer for the case that there exists a best channel to the eavesdropper. Non-causal side information means that the transmitted codeword is known to an active adversary before it is transmitted. By considering the maximum error criterion, we also allow messages to be known at the jammer before the corresponding codeword is transmitted. A single-letter formula for the Common Randomness (CR)-assisted secrecy capacity is derived. Additionally, we provide a formula for the CR-assisted secrecy capacity for the cases where the channel to the eavesdropper is strongly degraded, strongly noisier, or strongly less capable with respect to the main channel. Furthermore, we compare our results to the CR-assisted secrecy capacity for the cases of maximum error criterion but without non-causal side information at the jammer (blind adversary), maximum error criterion with non-causal side information of the messages at the jammer (semi-blind adversary), and the case of average error criterion without non-causal side information at the jammer (blind adversary).
Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated.
Classifiers Based on Machine Learning for Detection of Myocardial Infarction

Myocardial infarction (MI), also known as a heart attack, occurs when major blood vessels supplying blood to the heart become blocked, resulting in the death of myocardial cells. If MI is not detected early and the patient is not treated, it can result in a cardiac arrest, which can lead to death. The most commonly used medical treatments include the use of drugs or the performance of surgeries, which can be very costly for patients because healthcare can be expensive. As a result, it is critical to diagnose myocardial infarction at the appropriate time. The availability of large medical datasets in recent years has opened up new avenues for early detection of this disease. Wearable devices that accurately measure ECG and have built-in machine learning models have the potential to save millions of lives. While the new technique produces impressive results, its accuracy is lower when compared to traditional machine learning techniques used for MI detection. In this paper, a few traditional machine learning classifiers like Support Vector Machines, K Nearest Neighbors, Logistic Regression, and Random Forest are used to improve efficiency over other conventional classifier models. When compared to other classifiers, the results presented in this paper confirmed that, despite having the shortest processing time, logistic regression has a low percentage of specificity, accuracy, and sensitivity. Similarly, Linear SVM has the same processing time as KNN, but KNN has a 3% higher accuracy, implying that the proposed model can be applied to clinical datasets and patients successfully testified.
Detection and Localization of DDoS Attack During Inter-Slice Handover in 5G Network Slicing

Network slicing plays a crucial role in supporting Fifth Generation (5G) mobile network, which is designed to efficiently accommodate a diverse range of services with varying service level requirements. In this work, our efforts are largely aimed at exposing security flaws in 5G network slicing from a Distributed Denial of Service (DDoS) attack perspective. Time consuming authentication process during the inter-slice handover procedure is exploited to launch a DDoS attack. To address this issue, we offer novel attack detection and localization algorithms. We have compared results for various combinations of average waiting time and average switching rate to detect the attack and localize compromised user equipments. As per experimentation results, our approach resulted in an accuracy of 91% for detecting an attack and 96% for identifying compromised users.