Reviewerly/exHarmony huggingface dataset:
Rabin-P Encryption Scheme Analysis on MQTT

This paper presents a Rabin-P Encryption Scheme Analysis on Message Queuing Telemetry Transport (MQTT), where MQTT is one of the OASIS standard messaging protocols used for the Internet of Things (IoT). MQTT is very popular since it has a light footprint on energy consumption and computation. MQTT payloads are not secure by default unless all the links are encrypted by Secure Sockets Layer (SSL)/Transport Layer Security (TLS). However, all clients and brokers must install certificates to use SSL/TLS. Another way, the payloads can be encrypted with an encryption scheme to provide end-to-end without having to conFigure all the client and broker. Therefore, an encryption scheme known as Rabin-p is introduced to MQTT payloads. In this work, a microprocessor selected Raspberry Pi 4. Then, an analysis is conducted on MQTT with the Rabin-p scheme to analyze the encryption and decryption runtime of the Rabin-p encryption scheme on MQTT and the energy usage of the Rabin-p encryption scheme on MQTT. Although asymmetric encryption provides higher security, it requires an expensive calculation that leads to higher energy usage. However, Rabin-p offers a lower processing algorithm. A conclusion can be made that using Rabin-p in MQTT can help improve the payload security since Rabin-p offers a complex algorithm which is it harder to decrypt the messages when communicating between devices. Rabin-p can also be integrated with MQTT Mosquitto since it uses minimal energy.
Evaluation of K-fold value in breast cancer diagnosis technique using SVM and bio-inspired optimization algorithm (JA-ABC5)

Breast cancer is a fatal condition that kills thousands of people annually and is becoming more common. Lowering the mortality rate linked to breast cancer requires early detection. On the other hand, screening tests like mammography, ultrasound, and MRI that rely on human interpretation run the risk of overdiagnosis or underdiagnosis. Classification techniques can be used to improve the accuracy of breast cancer diagnosis to get around this limitation. The purpose of this study is to determine how K-fold cross validation affects breast cancer classification performance. The K-fold value is crucial in determining the right value to use in order to speed up evaluation and guarantee consistency in the analysis. The study looks at how breast cancer identification accuracy is impacted by the K-fold value. The accuracy of the algorithmic performance estimation depends on K. Finding the right K value is crucial because a higher value of K produces an estimate that is more accurate but also costs more to compute. For practical classification performance analysis, a K-fold value of K5 is advised based on the Wisconsin dataset results. With an accuracy rate of 98.49% and an average completion time of 2677.823 seconds, this value showed superior robustness and completion time. This study emphasises the need for and value of K-fold cross-validation in enhancing the classification accuracy of breast cancer.
OUTCOME OF LEARNING DRAMA IN LITERATURE CLASSROOM

It is to be sensed and realized in the field of Education, on the importance of productivity in learning. To inculcate and impart the process of productivity in learning literature as an art of study has become the need of the hour. It is essential to bring the students close to get a better grip over literature as well as language. Generally speaking, literature has been considered as a subject which either the professors or students read and appreciate the works of it through different genres like poetry. Drama, fiction, etc. for the aesthetic pleasure that too mostly from the perspective of the established critics or interpreters point view of the text. It will not be an exaggeration if we present the reality of approach, as of borrowed opinion without the enterprise of the individual experiences or opinion moreover, it cannot be denied in the circle of learned fraternity that this approach of learning literature would enhance the learners’ skills and helps them in generating their thought process relevant to the topic. It is possible only by means of Learner-Centric method. The activities with which they are engaged will result in their achievement. It will rather help them to fix their career goal clearly. If this process of performing through dramas, folks, etc. has been encouraged in live classroom, then their learning outcome will also soar high to reach the zenith of experiencing literature there by to attain their goal. Thus involving the students as the performers of eminence work of art has not been encouraged rather failed to seek role in the class room activity, doubts apart, it will result in more mundane and monotonous learning of literature Instead if learning of literature entertains in bringing out the perception of students performing literary work on the stage, based on their ability in it, it will definitely help us to identify the real literary programmer among the students authentically.
A Novel Target Value Standardization Method Based on Cumulative Distribution Functions for Training Artificial Neural Networks

Function approximation by artificial neural networks (ANNs) are often carried out via a collocation grid approach. However, for certain combinations of grids and functions, the training data for the relevant ANN can be extremely skewed and hence affect training efficiency, thereby necessitating standardization or normalization techniques. The choice of collocation grids often allows uniform training features but not targets. In this paper, we contribute a comparison between methods of target standardization - the relatively common min-max, z score, normalized log methods and the uncommon empirical cumulative distribution function (CDF) method - in terms of the resulting approximation capabilities of the ANNs using the standardized target values. We demonstrate that the empirical CDF is a general and robust standardization method that allows for efficient training and good approximations insituations of extreme target skewness. Novel modifications using a mix of true CDF and empirical CDF in standardizing targets are used to successfully reduce biases arising from the empirical CDF standardization method.
Development of an Artificial Intelligence (AI) Based Visual Counting System for the Food Industry

Artificial Intelligence (AI) has revolutionized various industries, including the food industry. One of the critical tasks in the food industry is counting the products during processing, packaging, and transportation. Manual counting is tedious, time-consuming, and prone to errors, which can lead to significant losses. The development of an AI-based visual counting system for the food industry aims to automate the counting process, reduce errors, and improve efficiency. This paper discusses the development of an AI-based visual counting system for the food industry, including the challenges, advantages, and potential applications. The proposed system utilizes deep learning algorithms to analyze digital images of food products and provide accurate counts. The system’s effectiveness was evaluated through various experiments, and the results indicate that it can significantly improve the accuracy and efficiency of visual counting in the food industry.
Video Object Detection From the Large Video Frames Using Inception Networks

Finding video content that can be utilised to spark the interest of a viewer is the purpose of the procedure that is being described here. In video frames, background occlusion or a dynamic backdrop shift in foreground regions can also present difficulties; similarly, fuzzy moving targets and fastmoving objects might present difficulties. In other words, the model strives to be as comprehensive as possible in terms of its optimization. The proposed approach makes use of an InceptionNet to successfully accomplish the goal of recognising significant items in dynamic films by employing a benchmark video dataset. This should allow the method to fulfil its intended purpose. The information concerning the temporal, geographical, and local limitations on the scene is collected by this network. When it comes to discovering important items in films, the suggested strategy is put up against established ways that employ benchmark datasets as a measuring stick. The results of this comparison will help determine whether method is superior. According to the findings of the experiments, the proposed method, which makes use of a deep learning model, performs substantially better than the state-of-the-art saliency models that have been employed in the past. This is indicated by the fact that the methodology makes use of a deep learning model.
Assessing the Importance of Browser Fingerprint Attributes towards User Profiling through Clustering Algorithms

Browser fingerprint is often linked to privacy as it is a method to gather data about the configuration of the browser to identify a user. The browser’s configurations which are also known as attributes are the key to making the user to be identified. Web browsers explicitly disclose information about the host system to websites by making that information available to them, such as attributes like the screen resolution, local time, or OS version. Since each browser has its different attributes values and that will make each of them unique and so it is important to understand well about the attributes. In this research, the purpose is to discover the dominant attributes which are highly identifiable via the clustering algorithm. Experiment results showed that if the attribute is unique, it will be hard to cluster into groups. This can be proved by using a clustering algorithm on WEKA platform.
A Preliminary Study on Learners’ Personal Traits for Modelling Learner Profiles in ITS: A Sensor-free Approach

Intelligent tutoring systems (ITS) are computer-based learning systems that use intelligent technologies to provide adaptive learning paths and contents to learners by considering their characteristics and needs. The learner model, which is a representation of different learner’s characteristics, plays a key role in this adaptation. Research on educational psychology showed that emotion and learning have a hidden mutual relationship. If a learning process involves both cognitive and affective dimensions, then learning is like to be more usable, socially appropriate, acceptable and memorable, ultimately leading to significant improvement in the learner’s performance and the learning experience. However, traditional ITS ignore emotional aspects in learner modelling mostly; instead, they focus on learners’ cognition and motivation to maximise the knowledge to be achieved. The characteristics related to cognition are not well defined and primarily sparse when modelling a learner profile tailored to the learning domains. This study attempts to investigate the most common and relevant personal traits involved in the learning process based on the domains of learning. It also explores the most adopted techniques to identify such traits from log files of learners’ interactions with ITS without using physical and physiological sensors (i.e., camera, EEG) to model characteristics learner profiles for ITS aiming to improve its adaptiveness and leverage personalised learning.
Enhanced Blockchain Scalability for IoT-based Smart Devices -A Generic Model Development

Blockchain become well known as there is third-party contribution while doing the exchanges or transactions. Blockchain is the innovation for utilizing digital forms of money. It draws in the consideration of specialists and academicians, along with various highlights of Blockchain it is having the significant issue of versatility and scalability which can be classified into throughput, cost, limit, and systems networking. Scalability influences due to some different components like block size and block interval time which likewise may lessen the security. The system may get powerless against various attacks on the off chance that we indiscriminately change the scalability. To address these challenges, this paper con-ducts an intensive analysis of blockchain scalability challenges in the IoT context and subsequently proposes a generic model to enhance scalability. The investigation covers restrictions on different blockchain stages and how system parameters such as throughput, latency, block size, and network influence a peer-topper system’s effectiveness, security, and energy consumption.
Enhanced Assistive Technology on Audio-Visual Speech Recognition for the Hearing Impaired

People who have difficulty hearing can use speech recognition software to communicate differently. The task is audio-visual speech recognition for better lip-reading comprehension. Audio speech recognition is the process of turning spoken words into text. The neural network model is trained using the Librispeech dataset. The input sound signal creates sound frames with a stride of 10 milliseconds and a window size of 20-25 milliseconds. It uses audio as the input, and feature extraction extracts information from features. A visual speech recognition system automatically recognizes spoken words by observing how the speaker moves their lips. The suggestion considers body language to understand the communicator's spoken words, increasing interpretation accuracy by 5.05%.
How the Metaverse Can Leverage the Business World

The metaverse is a shared, virtual space where users can interact with each other and digital objects in real time. It has the potential to revolutionize the way we do business by providing a new platform for conducting transactions, collaborating on projects, and connecting with customers. Metaverse can benefit businesses by providing a new platform for virtual events. The metaverse also has the potential to revolutionize the way businesses collaborate and work with each other. Overall, the metaverse has the potential to be a powerful tool for businesses, providing a new platform for conducting transactions, collaborating on projects, and connecting with customers. More research is needed to provide reliable data for decision-making by managers, so based on these research gaps, this study aims to assess the challenges and opportunities, thus building a benchmark on how the metaverse can leverage the business world.
Fusion model with attention mechanism for carbon-neutral sports competitions

Introduction In sports competitions, using energy-saving and emission-reduction measures is an important means to achieve the carbon neutrality goal. Methods In this paper, we propose an attention mechanism-based convolutional neural network (CNN) combined with the gated recurrent unit (GRU) for carbon neutral energy saving and emission reduction prediction model in sports competitions, CNN is a feedforward neural network whose input is a two-dimensional matrix. The main feature of CNN is that it can handle multi-channel input data, and the use of GRU can make the model structure simple and largely reduce The use of GRU can make the model structure simple, which largely reduces the hardware computational power and time cost and also better solves the long dependency problem in RNN networks. CNN-GRU extracts the data for features and then optimized by the attention mechanism. Results The model collects real-time carbon emissions data from sports events, including game times, lighting usage, air conditioning emissions and other carbon emissions data, and uses deep learning algorithms to predict and compare carbon emissions from sports competition. Discussion In identifying energy saving and emission reduction measures conducive to the realization of the goal of carbon neutral sports events, the model has a certain reference value for realizing energy saving and emission reduction in sports competitions under carbon neutrality goals.
Fractal Slope-Based Seismic Wave Detection Method

ABSTRACT Automatic P-wave arrival detection is the first task in an earthquake early warning systems. This study proposes a novel detection method for this based on a fractal slope (FS). We improved the calculation method of the fractal dimension to increase the calculation speed and proposed a continuous algorithm. Furthermore, we applied FS in conjunction with the short-term average over the long-term average (STA/LTA), named STA/LTA + FS. We designed orthogonal experiments with different parameters and selected a total of 40,020 sets of seismic waves from the Japanese dataset to test the best parameters. A total of 45,302 sets of seismic waves from the STanford EArthquake dataset and the Chinese dataset were selected to test the generality of the proposed method. The results show that the mean error in detection time of the proposed method is +0.042 s for different datasets. In addition, STA/LTA + FS is robust over a wide range of signal-to-noise ratio, epicentral distance, and magnitude, with the percentage of timing errors below 0.5 s higher than 95%.
Quantum computing for a profusion of postman problem variants

Abstract In this paper we study the viability of solving the Chinese Postman Problem, a graph routing optimization problem, and many of its variants on a quantum annealing device. Routing problem variants considered include graph type, directionally varying weights, number of parties involved in routing, among others. We put emphasis on the explanation of how to convert such problems into quadratic unconstrained binary optimization (QUBO) problems. QUBO is one of two equivalent natural paradigms for quantum annealing devices, the other being the Ising Model. We also expand upon a previously discovered algorithm for solving the Chinese Postman Problem on a closed undirected graph to decrease the number of constraints and variables used in the problem. Optimal annealing parameter settings and constraint weight values are discussed based on results from implementation on the D-Wave 2000Q and Advantage. Results from classical, purely quantum, and hybrid algorithms are compared.
Automatic Idiom Identification Model for Amharic Language

Idiomatic expressions are important natural parts of all languages and prominent parts of our daily speech. Idioms cannot be interpreted from the words that they are formed with directly and people may not understand the meaning. From past literature, it was noted that idiom affects Natural Language Processing research like machine translation, semantic analysis, and sentiment analysis. Other languages like English, Chinese, and Indian idioms are recognized through different methods in different research. As there is no standard method and research to identify Amharic idioms, this study is aimed to build a model to identify idioms for the Amharic language using a supervised machine learning approach. The study used 800 labeled expressions for training and 200 expressions for testing from Amharic idiom books “የአማ ረኛ ፈሊጦች” and different Amharic documents. To measure the performance of the model, we used accuracy, precision, recall, and F-score. Finally, a 97.5% accuracy result was achieved from the testing dataset showing a promising result. The study contributes to the information systems discourse about improving the awareness and knowledge of researchers on Amharic idioms.
Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations

Abstract In order to achieve more efficient and optimised resource scheduling, this research carried out a multi‐objective task resource allocation method for low‐voltage station edge computing based on hierarchical Bayesian adaptive sparsity. Based on hierarchical Bayesian adaptive sparsity, the multi‐objective task resource allocation technical framework for edge computing in low‐voltage stations is established, which is composed of end pipe edge cloud; After collecting real‐time operation data of power distribution equipment, substation terminals, transmission terminals, etc. in the architecture end, it is transmitted to the data middle platform and service middle platform of the Internet of Things management platform in the cloud through the edge Internet of Things agent; Set and solve the constraint conditions, and build a multi type flexible load hierarchical optimal allocation model; The abnormal area topology identification sub module of multi‐objective task resource of low‐voltage station area edge computing is used to identify the abnormal area topology of the current low‐voltage station area; Taking it as input, the multi‐objective task resources of edge computing are allocated, and the multi‐objective task resources allocation method of edge computing in low pressure platform area is realized under the differential evolution algorithm. The experimental results show that the proposed method has good convergence effect, strong distribution ability, relatively gentle increase in energy consumption, and the calculated results are basically consistent with the actual values, with good effectiveness.
A single camera unit-based three-dimensional surface imaging technique

This paper introduces a simple three-dimensional (3D) stereoscopic method using a single unit of an imaging device consisting of a charge-coupled device (CCD) and a zoom lens. Unlike conventional stereoscopy, which requires a pair of imaging devices, 3D surface imaging is achieved by 3D image reconstruction of two images obtained from two different camera positions by scanning. The experiments were performed by obtaining two images of the measurement target in two different ways: (1) by moving the object while the imaging device is stationary, and (2) by moving the imaging device while the object is stationary. Conventional stereoscopy is limited by disparity errors in 3D image reconstruction because a pair of imaging devices is not ideally identical and alignment errors are always present in the imaging system setup. The proposed method significantly reduced the disparity error in 3D image reconstruction, and the calibration process of the imaging system became simple and convenient. The proposed imaging system showed a disparity error of 0.26 in the camera pixel.