Reviewerly/exHarmony huggingface dataset:
The Analysis of Dual Axis Solar Tracking System Controllers Based on Adaptive Neural Fuzzy Inference System (ANFIS)

Artificial intelligence is commonly used in Photovoltaic (PV) control systems. Adaptive Neural Fuzzy Inference System (ANFIS) is one of the intelligent strategies that can be employed in the system controller. ANFIS technique shows high accuracy as it involved several processes which are the Fuzzy layer, Fuzzy Rule layer, Normalization layer, and Output Membership layer. The main objective of the proposed work is to model the dual-axis solar tracker using MATLAB software by utilizing the ANFIS technique, hence improving the performance of the solar system. The data used for training and testing are elevation angle and azimuth angle. 80% of the data is used for training and another 20% for testing in order to predict the solar radiation toward PV panels. A different set of input membership functions (MFs) is used in the system, which are Five MFs, Ten MFs, and Fifteen MFs. These MF are simulated to produce the best prediction of solar radiation. The results showaverage error gained for both training and testing data and minimum error indicates the accuracy of the predicted angle of dual axis solar tracker. In the finding, overall results show a good correlation between the actual and prediction value with 15 input MFs as it produced the lowest error value.
Quantum Computing Arrives

Quantum computing arrives. Preceded by Mādhava ofSangamagrāma and other Hindu mathematicians, around1,500 CE, and our previous work, we show at least 4 quantumproperties that are indeed universal in some number systems; herewe use them to enable quantum computing. Using the sets B={0,1},Bn, N, Z, Q, and Q*, one has +4 quantum properties that one cantrust, as archetypes of easy communication to friends and foes, toignoranti or cognoscenti, and for quantum consciousness. Therefore,this work applies to computer calculations, apps in cell phones,algebra, atoms, mathematics, physics, and the human mind.
Probability Distribution-Guided Adversarial Sample Attacks

In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer to attack the target model, and most AI models in real-world scenarios belong to black boxes, thus transferability becomes a key factor to measure the quality of adversarial samples. The traditional method relies on the decision boundary of the classifier and takes the boundary crossing as the only judgment metric without considering the probability distribution of the sample itself, which results in an irregular way of adding perturbations to the adversarial sample, an unclear path of generation, and a lack of transferability and interpretability. In the probabilistic generative model, after learning the probability distribution of the samples, a random term can be added to the sampling to gradually transform the noise into a new independent and identically distributed sample. Inspired by this idea, we believe that by removing the random term, the adversarial sample generation process can be regarded as static sampling of the probabilistic generative model, which guides the adversarial samples out of the original probability distribution and into the target probability distribution, and helps to improve transferability and interpretability. Therefore, we propose a Score Matching-Based Attack(SMBA) method to perform the adversarial sample attacks by manipulating the probability distribution of the samples, which can show good transferability in the face of different datasets and models, and give reasonable explanations from the perspective of mathematical theory and feature space. In conclusion, our research establishes a bridge between probabilistic generative models and adversarial samples, provides a new entry angle for the study of adversarial samples, and brings new thinking to AI security.
A Study on Customer Satisfaction Towards Amazon

The study focused on customer purchasing patterns in order to assess the level of satisfaction with Amazon's services. Consumer loyalty is commonly seen as the driving factor behind the link between an individual's relative's attitude and recurrent patronage. Customers may purchase anything online, including books, home goods, toys, hardware, and software. Furthermore, in just a few decades, the internet has grown in popularity with adult and young shoppers since it provides important benefits. Client loyalty is one of the most overused business clichés nowadays. To achieve these goals, a descriptive research approach was adopted. Customers may visit a website and shop while sitting in front of a computer. The internet's ability to collect information, provide a service, or purchase a product should be utilised by Amazon in order to expand its consumers and, ultimately, profit.
Lightweight Secure Message Transfer Protocol Based on ECC (Nist P-256) Internet of Things Equipped with Satellite Communications

With the expansion of Internet of Things (IoT) services and the use of satellite communications, according to the regional or continental extent of these services, the need for lightweight encryption has increased. In satellite communications, due to long distances, there are limitations in applying security, so heavy encryption algorithms such as RSA[1](#fn-0002) cannot be trusted for security. ECC[2](#fn-0003) using mathematical solutions and elliptic curve discrete logarithm problems (ECDLP[3](#fn-0004)) can be considered a lightweight algorithm in telecommunications. Here, we propose a new strategy for secure IOT data communication between a satellite link and a terrestrial link that uses the principles of ECC elliptic curve cryptography and the NIST P-256 standard for key agreement and encryption for transmitting messages over the satellite communication platform.
Spectrum of Adjacency Matrix of Graphs in Cryptography

Abstract Having access to an elliptic curve with a specified number of points over a specific finite field isa prerequisite for many contemporary cryptography systems. The isogenies, which are surjectivemorphisms, play a significant role as specific mappings between these curves. Isogeny volcanoesare graphs with elliptic curves as their vertices and l-isogenies as their edges. Two identical ellipticcurves over Fq share the same trace t and cardinality. David Kohel examines the relationshipsbetweenisogenies of degreel and curves in Ellt(Fq ), the collection of curves defined over Fq withtrace t, in his thesis. More precisely, he describes the structure of the graph of l-isogenies definedon Ellt(Fq ).There is a relation between Isogeny graphs and orders in OK and uses modular poly-nomials to find the conductor of End(E). The connected components of this graphs are isogenyvolcanoes and it is possible to travel through these structures using modular polynomials, evenwithout knowing the cardinality of the curve.By using this information computation of the l-adicvaluation of the trace t, for l|g possible and hence obtain some information on the cardinality of thecurve. Isogeny graphs of supersingular elliptic curves plays a major role in cryptography.Isogenybased cryptography, studies cryptosystems whose security is based on the difficulty of finding apath in isogeny graphs of supersingular elliptic curves. This study investigates the use of graphadjacency matrices in maintaining the security of isogeny based key exchange protocols.
Dy-MIL: Dynamic Multiple-Instance Learning Framework for Video Anomaly Detection

Abstract Anomaly detection is an extremely challenging task in the field of visual understanding because it involves identifying events that deviate significantly from normal patterns. One of the primary reasons for the difficulty of this task is the diversity and complexity of anomalous events. Therefore, it is impossible for us to collect all types of anomalies and label them. In recent work, weakly supervised methods becomes one of the optimal solutions for anomaly detection. Thus, in this paper, we focus on weakly supervised learning and propose a dynamic multiple-instance learning framework for video anomaly detection, which develops a dynamic ranking method combined the k-max-selection scheme to enlarge the inter-class distance between anomalous and normal instances by only using video-level labels. Experimental results demonstrate that our framework achieves superior improvements on three benchmark datasets, including the ShanghaiTech dataset, UCF Crime dataset and NUT dataset.
Application of inverse modelling for prediction of the surface water chemistry of the ekulu river in enugu, southeastern nigeria

Surface water chemistry evolution of Ekulu River was conducted by inverse geochemical modelling using the PHREEQC computer program. In this study, samples were collected from the Ekulu river at 100m intervals, and were taken to the laboratory for major anions and cations, heavy metal and bacterial content analyses. Inverse geochemical modelling results reveal that the river evolved as a result of dissolution of siderite, quartz, gypsum, galena, sphalerite, pyrolusite and precipitation of goethite and pyrite. The Piper diagram indicates Calcium-Chloride water type, with high Ca2+ + Mg+ and Cl- + SO42 contents, which is typical of water originated from gypsum dissolution and mine drainage. The results of the analyses show that the water cannot be recommended for drinking because the E. coli and coliform contents are higher than the permissible limits. The water chemistry of Ekulu river is as a result of secondary minerals dissolution and mine drainage from Onyeama coal mine whereas the microbial content resulted from pollution from recent faeces.&#x0D; &#x0D;
Employing Podcast Technology in Developing English Pronunciation: A Quasi-Experimental Study

This study looked at the impact of podcast technology on the development of English listening skills. It used a mixed method (qualitative and quantitative methods). A quasi-experimental technique with a post-course test was adopted. Two teachers were interviewed in a semi-structured format. The sample for the study was 52 fifth-grade students at a private school in Amman, Jordan. They were divided into two classes (26 students for the experimental class and 26 for the control class). The experimental class was taught listening via teacher and podcast, while the control class was taught listening via teacher alone. To collect data, a multiple-choice test, survey, and interview were carried out. To analyze data, SPSS was used to analyze quantitative data, and Braun and Clark's thematic approach was used to analyze qualitative data. The findings of the study were that (i) the effect of employing podcast technology on improving listening skills is obvious and (ii) the superiority of the experimental class over the control class is large. (iii) the possibility of using podcast technology in the development of listening skills, increasing the student's bank of vocabulary, and improving their pronunciation.
A survey of Face detection and Recognition system

In recent days, our world has entered the age of advanced digital systems, which means humans has to interact with technology in all scopes of live, this leads too as a result to another main concept, security. There are many security technologies that was developed to keep user information save. for example, there are several technologies used to let just the accurate user to log into devices like fingerprint, face recognition, or any another type of these systems. These technologies are in continuous development and are still suffering from accuracy, so several studies were performed to get the best accuracy for any of these systems. Computer vision was developed to give the devices the ability to see and recognize objects in an image or a video scene. It is a science that aims to build an intelligent application that are able to understand the content inside images as humans can do. To create a system like this, first step is to make Data acquisition, this task is done by cameras which gives us a sequence of frames when we record a video. After that, this data is analyzed and studied to extract important features from it. This feature will allow us to re- construct the description of the outer world in a manner that can understood by computer system. Face recognition systems are used on a wide range in mobile applications, banks, and military sites, police stations to make a recognition of the criminals, even in covid19 situations the face recognition was used too. Our proposed method aims to increase the accuracy a face recognition system using deep learning models, the system proposed is able to work on any platform compiled with python which is the application was used in our study. The model performed gives a high accuracy with a very speed result.
Lightweight image super-resolution reconstruction based on inverted residual attention network

In recent years, methods based on deep convolutional neural networks have made great progress in the field of image super-resolution reconstruction. However, mainstream approaches generally establish many network layers, leading to high computational costs and memory usage that are unsuitable for resource-limited edge devices. To alleviate this issue, a lightweight inverted residual attention network (IRAN) is proposed to obtain better super-resolution reconstruction performance with lower parameters and computation. The main structure of the IRAN consists of a series of inverted residual attention groups (IRAGs), which are mainly composed of several inverted residual attention blocks (IRABs). IRAB effectively reduces the network parameters and computation while extracting depth features by introducing the inverted residual structure and uses the simple channel attention mechanism to learn the important channel feature information. In addition, an enhanced spatial attention mechanism is introduced at the beginning and end of IRAG to further improve the reconstruction performance of the network. The experimental results show that compared with the mainstream lightweight networks, not only the peak signal-to-noise ratio and structural similarity of image quality metrics are better but also the parameters and the computational effort of the network are lower.
A Study on Design Method of Smart Device for Industrial Disaster Detection and Index Derivation for Performance Evaluation

There are various ICT technologies continuously being developed to reduce damage by industrial accidents. And research is being conducted to minimize damage in case of industrial accidents by utilizing sensors, IoT, big data, machine learning and artificial intelligence. In this paper, we propose a design method for a smart device capable of multilateral communication between devices and smart repeater in the communication shaded Areas such as closed areas of industrial sites, mountains, oceans, and coal mines. The proposed device collects worker’s information such as worker location and movement speed, and environmental information such as terrain, wind direction, temperature, and humidity, and secures a safe distance between workers to warn in case of a dangerous situation and is designed to be attached to a helmet. For this, we proposed functional requirements for smart devices and design methods for implementing each requirement using sensors and modules in smart device. And we derived evaluation items for performance evaluation of the smart device and proposed an evaluation environment for performance evaluation in mountainous area.
Eligibility Verification based on Immutable Personal Information without Revealing the Owner’s Identity

When an individual needs to prove eligibility, it is sufficient to know whether or not s/he meets the eligibility, but any existing method inevitably exposes the identity of the owner or unnecessary additional information in the process of providing personal information. In this paper, among the immutable items of personal information such as gender, date of birth, and place of birth, we propose a method in which the owner provides only essential item(s) to the eligibility verifier with each iterm marked on one option among multiple choices. In this way, the eligibility verifier can access the combination of items stored in the blockchain with the consent of the information owner, and can safely store the access history by requesting recording in the blockchain again. In the proposed method, the user does not worry about his/her identity being revealed or his/her personal information being overly exposed, and the eligibility verifier can check only necessary items and search later records without separately storing the records.
Pemanfaatan Media Sosial Bagi Volume Penjualan Produk Skincare Dan Kosmetik Derr Cosmetics

Technology that is increasingly becoming more developed and advanced makes people's activities easier. Internet technology that is increasingly sophisticated gives birth to social media that at this time cannot be separated from our lives. Social media has a positive impact on society, especially for those who have businesses. Social media is currently widely used by business people to market the products they sell. Derr Cosmetics is one of the sales businesses in the field of skincare and cosmetics located in Palangka Raya, Central Kalimantan which participates in utilizing social media to market its products. By utilizing social media in marketing its products, Derr Cosmetics managed to increase its sales volume to be larger and have many customers.
Development of model predictive controller in avoidance design of Unmanned Aerial Vehicle (UAV)

This research was accomplished by employing already developed mathematical model of a 6DoF UAV in free space through the motion of the 6DoF aircraft (unmanned aerial vehicle) determined by coordinate systems which allow an aircraft’s position and orientation in space to be kept tracked. The discrete method of the model developed for simulation in Simulink was realized with a suitable transfer function. Then, an artificial neural network model predictive adaptive controller that can handle the nonlinearities associated with the UAV was developed based on state space technique and the model predictive controller (MPC) utilizes a neural network model to envisage future plant (aircraft) responses to potential control signals. The developed model predictive controller network was successfully trained offline using Feed-forward Back-propagation algorithm with speed and position as inputs since the underlying objective of this work is to improve the speed and position in order to advance the safety collision distance of the UAV because these parameters are mostly considered in the avoidance maneuver performance. Also, the results reveal that the speed of the generated UAV is approximately the input speed of 75MS–1 during the time the UAV is affecting maneuvering. Significance of the result is that the proposed algorithm is capable of generating collision-free trajectory for different waypoint cases.
What's New Online

The aim of the study was to compare the issues surrounding persistent as opposed to transient food insecurity among US veterans.
Text Interpreter &amp; Converter

Abstract: With the exponential growth in data, it is essential to analyze it. The data is a valuable source ofinformation and knowledge that should be effectivelyinterpreted to be helpful. The data should be interpreted in different forms based on the user requirements to get precise information and knowledge. Nowadays smartphones are the most commonly used electronic device. A smartphone is notonly a communication device, it is also a powerful computing device. So it is possible to apply translation, text extraction, summary, and much moretechniques, which require much computational work. This paper presents an application to analyze the text from documents on smartphones. However, it is challenging to interpret the documents onsmartphones. The proposed application converts the documents or images to searchable and editable digital text, and further, it can be used to analyse them into different forms. The objective of this application is four-fold 1) To recognize text from documents orimages by using optical Character Recognition 2) Summarization of the text 3) Translate the extracted text to different languages 4) Generation of speech from the text by using a text-to-speech algorithm.
Credit Card Fraud Detection using Python &amp; Machine Learning Algorithms

Abstract: Browsing and many other online sites have increased the digital payment modes through which risk of frauds during transactions got increased. It is necessary to have a look on fraud transactions so that the customers does not pay for what they haven’t done. Such complications may be intercept with Data mining through Machine Learning. It aims to display the customization of a data set by applying machine learning with Credit Card Fraud Detection. The CCFD complications comprise of analyzing previous transactions through credit card along the data of the unauthorized users. These models are then applied to analyze whether the new transaction is authorized or not. In this project, we have concentrated on examining and pre-refining the data sets in addition to the deployment of numerous inconsistency observation methods such as Logical Regression, Random Forest, Decision tree, XG Boost on Credit Card Transaction data.
Learning Resources Acquisition, Management, and Utilization Practices of select IPED and Non-IPED Schools Divisions in Mimaropa Region: Basis for the Proposed Learning Resource Service Model

Abstract: The study determined the practices of select IPEd and Non-IPEd Schools Divisions in terms of learning resources acquisition, management, and utilization. It also aimed to identify the extent of practices, its compliance with the DepEd guidelines, the significant differences between the IPEd and Non-IPEd schools divisions’ extent of practices and compliance with the DepEd guidelines, as well as the gaps and some major issues encountered in terms of learning resources acquisition, management, and utilization.
CNN LSTM Hybrid Approach for Sentiment Analysis

Abstract: In recent years, one of the most popular study subjects has been sentiment analysis. It is employed to ascertain the text's actual intention. It is primarily interested in the processing and analysis of natural language data. The development of technology and the phenomenal rise of social media have produced a vast volume of confusing textual information. It's critical to examine the feelings that underlie such writings. Sentiment analysis reveals the core of irrational beliefs kept in enormous volumes of text. The primary objective is to get the computer to comprehend the backdrop of the data so that it may be divided into material that is good or bad. (i) Several machine learning models, including Naive Bayes, XGboost, Random Forest, LGB Machine, etc., are trained in this study. (ii) The implementation of the deep learning model Bi-LSTM, whose accuracy has showed promise. (iii) Bidirectional Encoder Representations from Transformers (BERT), a pre-trained language model that used an external Bi-LSTM model, was implemented. Then, a new approach of CNN-LSTM hybrid model is applied to IMDb dataset which performed better than all the models.