Reviewerly/exHarmony huggingface dataset:
Implementasi Algoritma Noekeon Untuk Mengamankan File Audio

Along with the development of an increasingly advanced era, and supported by the development of very rapid information technology, especially in producing audio files, it can now be done with smartphone devices. With the application on smartphones with devices to make audio both for private and public. Audio files need to be secured with better security, in order to anticipate if the account is far from being in the hands of unauthorized people, one way to secure audio files is by using cryptographic techniques, with this technique, unauthorized people cannot see the information contained in the audio. the. One of the algorithms that can be used to secure audio files is the NOEKEON algorithm, Noekeon is a repeating block cipher with a block length and key length of 128 bits each, which consists of a simple round transformation application that repeats itself, followed by an output transformation. Noekeon has 16 rounds (Nr) iterations in each round four transformations are carried out, namely, Theta, shift offset consisting of two transformations Pi1 and Pi2 and gamma The advantages of the Noekeon algorithm do not limit the keys used or use these keys to exploit into cipher text and avalanche effects obtained meet the strict Avalanche criterion. If there is an error in one block cipher it cannot affect the other blocks
Audit Sistem Informasi Absensi Pada MI Miftahul Ulum Menggunakan Cobit 4.1

This study aims to determine the extent to which the company implement IT governance. The focus of this research is on the COBIT 4.1 domain, namely PO1, PO4, PO9, DS5, DS9, DS12, and ME. The method is based on COBIT 4.1, this research uses several stages, namely literature review, data collection, and data processing at MI Miftahul Ulum. The results of data processing are used to find weaknesses in each domain. Weaknesses or problems compared to the ideal conditions that have been set for the COBIT 4.1 audit framework at each level, the problem encountered at MI Miftahul Ulum is that there are still problems with the attendance system, problems often occur such as not being read by the system even though they have made attendance which results in reduced attendance. teacher appraisals and salaries. The results of the calculation of the largest maturity level in the DS12 domain with a maturity level value of 3.89, must be increased again, while the smallest value in the PO1 domain with a maturity level value of 3.15 must be repaired again so that the attendance system is as expected.
Analisa Kualitas Aplikasi Performance Simanis dengan Metode End User Computing Satisfaction (EUCS)

A process to find and find out the extent of an information system implementation activity from the point of view of an information system evaluation. It aims to determine the level of user satisfaction in using a system. This study was conducted to measure the quality of the Simanis application using the End User Computing Satisfaction (EUCS) method, as well as using questionnaires to respondents and IPA Importance Performance Analysis. The results of the application quality level still need to be evaluated because the GAP level of 5 dimensions, namely content (content), accuracy (accuracy), appearance (format), user satisfaction (easy to use), and timeliness (timeliness) is still far from user expectations. The results of the user assessment of the Simanis performance application, no attributes found in quadrant 1. This indicates that the priority level for changes in some features does not exist, meaning that the features already provide the needs of the users. Suggestions in this study, application developers must continue to meet the performance expectations of the Simanis application in the 5 dimensions that have been assessed.
Performance of ECC Algorithm Using Game Theory for Aadhaar Card

In the Aadhaar Card security is a crucial aspect. Hence it should be kept as private as possible, we will require some data-handling strategies. This document would be useful for data security. The advantage of this technique is in terms of text encryption and decryption security. Asymmetric algorithms like RSA (Rivest, Adi Shamir, and Leonard Adleman) and ECC (Elliptic Curve Cryptography) are used. The RSA algorithm is used to create a pair of keys: one is a public key and another one is a private key. In both smart cards and Aadhaar cards, the RSA algorithm is the most secure algorithm for data protection. The performance of the ECC algorithm with GT (Game Theory) Methods is examined in this research paper. Combining ECC and GT approaches improves the speed and security of this paper. The GT-ECC algorithm&#x0027;s performance is recommended to be improved by increasing the Speed, Avalanche Effect, Speed, power consumption and Throughput. The experimental findings of the GT -ECC technique show improved performance. There is also a detailed mathematical justification for the GT -ECC algorithm.
Integrated Security Systems using Big Data

Big data is the domain that processes large datasets from various online sources. Big data is not suitable for processing small datasets. This domain works very efficiently on large datasets. By using a single system it is not possible to run the large datasets, so it is important to run the large datasets by using multiple datasets. Many applications generate large data day by day. Applications such as e-commerce, networking, banking and tours, travels, etc. Security is most widely used by many applications to secure big data. Many companies are processing sensitive and secure data to prevent attacks on data. Various threats attack such data and hold these data at high risk. To overcome this new integrated security is required to secure and provide privacy for the large data. Previously, many existing approaches are applied to this big data, but these are not capable of handling the big data with security. The proposed approach is developed by unique security system with map-reduce algorithms. Map-reduce is the technique that uses parallel processing algorithms for the efficient processing of data.
Quantitative Analysis and Research on the Multicast Algorithm of Distributed Blockchain-oriented e-commerce System in Village Revitalization

Quantitative analysis and the research on the multicast algorithm of the distributed blockchain-oriented e-commerce system in village revitalization is conducted in the paper. With the expansion of the application field of edge computing, its characteristics such as wide distribution, complex environment and large number have great disadvantages for its own security. Due to the lack of guarantee of equipment security and network security, the embedded chip data storage in edge computing itself will not have its own encryption mechanism. As a means of access edge computing permission control, digital certificate can control the access scope, operation level and data permission, and provide a more secure computing and storage environment. With these core ideas, the efficient models are then demonstrated. And the applications to the village revitalization is also studied. Through the experimental testing, the performance is then validated.
MPPT and ANN based Solar Tracking System using Photo Diode Sun Sensor

For solar energy generated by High Concentration Photovoltaic and Concentrating Solar Power technologies, precise tracking systems are needed. Artificial Neural Network (ANN) and Maximum Power Point Tracking (MPPT) based Closed-loop systems utilise data supplied by a sun sensor, which takes up direct and diffuse sunlight. Increased measurement noise at the sensor's output due to diffuse sunlight reduces the maximum attainable gains in the closed-loop controller, resulting in decreased tracking performance. A four-quadrant photodiode solar sensor may be improved by using infrared and linear polarising optical filters. Regardless of the kind of controller used in the solar tracker, these filters limit the influence of diffuse radiation and increase the effectiveness of sun tracking systems. This paper presents tracking experiments including solar trackers under both indoor and outdoor situations, using various methods, such as Maximum Power Point Tracking (MPPT), as well as artificial neural networks (ANN).
Enhancing Unpaired Underwater Images With Cycle Consistent Network

Visual perception of underwater imagery is affected greatly due to poor visibility, scattering, absorption and refraction of light despite using high-end expensive cameras. The optical hindrances create a non linear distortion that affects the performance of tasks involving object detection, object tracking, classification and object segmentation.Underwater exploration is carried out by Autonomous Underwater Vehicles (AUV) and Underwater robots. Several tasks that require visually perceptive imagery for performing marine trash identification, monitoring the species and their migration, inspection of the health of coral reefs, tracking the cables of submarines and ship wreckage, seabed mapping. This can also be used for enhancing underwater images of user pictures. Marine life is under an increasing threat from human activities which can be monitored using the proposed idea.The paper successfully implements automatic color enhancement, dehazing, and contrast adjustment using deep learning techniques that perform image-to-image translations. The setup uses Unsupervised learning approach on unpaired dataset and Cycle Consistent Generative Adversarial Network. The paper provides comparison of the Unsupervised learning approach with the Classical Image processing approach that uses color correction adaptatively and the application of dehazing theory.
Trust in Cloud: Perspective from Access Control Models and Machine Learning-A Detailed Study

The number of cloud users and the cloud service providers are in increasing trend. With the concepts that allow the users to login to various services with a single identity, it is mandatory to check the trustworthiness of the users as well the service providers. So it is obvious any access made should be trusted at both the ends in the cloud environment. When a study is made on the trust evaluation models, it is observed that most of the models are dependent on the past behavior of the entity concerned. This depicts the suitability of the machine learning model in evaluating trust in the trust based access control models. To have a clear understanding a study is made on the various works in three contexts. First, Access control models employed in the cloud location, RBAC (Role Based Access Control), ABAC (Attribute Based Access Control) and in detail Trust based access control model. Second the usage of machine learning models in the context of addressing various security issues in the cloud computing and in the context of usage of machine learning models in access control models. Third various Trust evaluation techniques that are in practice in the cloud environment is also studied. The inferences made from the studies are also discussed.
Convolutional Neural Network for Speaker Recognition Embedding with Biometric System

Growing popularity of biometrics has led to concerns regarding the misuse and privacy of biometric data stored centrally. As a result of noise and variation between classes, biometric verification systems face challenges. Using an inter-modal biometric verification system, fingerprints and voice modalities can both be used. The System uses multiple biometric templates to combine the two modalities. Using finger print and voice data together effectively helps to minimize privacy concerns since the minutiae of a finger print are hidden among the artificial features derived from the speaker’s spoken utterance. This research work utilize Gaussian mixture models in conjunction with Universal Background Models (UBM). A pipeline for improving speaker recognition and Convolutional Neural Networks to learn speaker characteristics are also included. When compared with previous results for voice verification, the system database was used the accuracy of biometric identification is increasing overtime. On the whole, biometric identification can provide a secure identity management system for the global citizen that is fully independent. A majority of private employers also require biometric identification systems for access control to workplaces or time clocks that can't be tampered with. Student attendance is taken automatically through the proposed system.
Building Tamil Text Dataset on LGBTQIA and Offensive Language Detection using Multilingual BERT

Lesbian, Gay, Bisexual, Transgender and Queer or Questioning (LGBTQ) community worldwide undergo depression, mental health problems and develop suicidal thoughts, due to non-acceptance by the society. This primarily stems from the lack of awareness and connection with the LGBTQ community. This paper aims to collect tweets and classifies them as offensive or non-offensive using manual and automated methods. The tweets are in Tamil (a classical Indian language), which is limited in terms of data and research. The presented dataset is annotated by two annotators and validated by a Cohen’s kappa metric score of 0.97. A finetuned Multilingual BERT (mBERT) Transformer model along with baseline models of Naive Bayes and Long Short-Term Memory detect offensive and non-offensive texts using the benchmark dataset, with the mBERT model performing the best, scoring a F-score of 93% for Non-Offensive class and 70% for Offensive class We also discuss preliminary findings from the dataset, which can be investigated further for specific research directions in the future.
Hope Speech Detection in Tamil and English Language

Hope endows the power required to conquer life’s challenges. Since the COVID-19 pandemic, hope speech detection has gained momentum in the natural language processing field due to the need for positive reinforcement on the internet. Hope speech detection identifies texts in social media comments that evoke positive feelings in people. This paper aims to identify hope speech in YouTube comments. To discern the hope speech as distinct from the YouTube comments, various machine learning and deep learning algorithms (Support Vector Machines, Logistic Regression, Convolutional Neural Networks + Bidirectional long-short term memory (Bi-LSTM), Multinomial Naive Bayes (MNB), Ensemble models) have been used. These YouTube comments have been created as a part of the task "EACL-2021: Hope Speech Detection for Equality, Diversity, and Inclusion". Based on the best configurations that showed the highest results in trials for the shared tasks, weighted F1-scores of 0.608 for Tamil and 0.911 for English were achieved.
Comprehensive Data Collection and Feedback Algorithm for Smart Music Online Guidance based on Immersive Virtual Media Environment

Comprehensive data collection and the feedback algorithm for smart music online guidance based on immersive virtual media environment is studied in the paper. The construction of a music smart classroom based on core literacy requires schools, teachers and education departments to pay attention to the subject of music, and secondly, to continuously improve teachers' information technology level, hence, this paper proposes the novel data collection and feedback algorithm for smart music online guidance. From the analysis of general technical factors, training quality and VR characteristics are external variables that affect the user immersion experience to analyze the impact of perceived ease of use and self-efficacy on user immersion experience. Furthermore, the signal data processing algorithm is designed for the efficient analysis of the signal collected. Through the experiment, the performance is validated.
Breast Cancer Classification Using Ensemble of Machine Learning Boosting Algorithms

Breast Cancer (BC) is one of the numerous typical diseases worldwide, occurring in 22.9% of all cancers in women and causing 13.7% of cancer deaths. The BC prognosis is highly demanded to increase the survival rate of the patient suffering from BC. Throughout this paper, an automated decision-making pipeline for BC detection has been proposed, incorporating Machine Learning (ML) algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), XGBoost (XGB), AdaBoost (AdB), and preprocessing such as Outlier Rejection (OR) and Attribute Selection (AS). A weighted ensemble of ML models has been recommended in the introduced pipeline. The experiments are trained and evaluated using Breast Cancer Wisconsin’s public dataset from UCI Repository, employing five-fold cross-validation. The best possible accuracy obtained from the proposed framework is 97.0%, with the utilization of seventeen features out of a total of thirty. The observed results conclude that a weighted ensemble of AdB and XGB in conjunction with OR and AS as a preprocessing can successfully enhance the BC detection outcomes with a significantly short execution time of 2.10 seconds.
Generative Inpainting of High-resolution Images: Redefined with Real-ESRGAN

Embracing generative models to the inpainting realm has resulted in its overwhelming acceptability as an image editing technique. However, these generative inpainting techniques tend to be cumbersome in view of the large memory footprint and resources consumed during the operation. This accounts for why images above 2k resolution are far from being considered as input for inpainting operations. In the present era of super-resolution, where human eyes are accustomed to viewing images beyond 6k and 8k, inpainting algorithms were not at par with the resolution benchmark. This paper proposes a high-resolution inpainting algorithm that addresses these concerns. To improve the computational overhead, the proposed architecture follows a hierarchically scaled 2-stage generative inpainting network. The proposed framework showcases a novel approach of injecting a blind Super Resolution Generative Adversarial Network network in an inpainting scenario. The resolution lost in the first phase is regained by the embedded pre-trained Real-ESRGAN model employed in the final phase. Qualitative and quantitative evaluation of the proposed method was performed using various datasets. Results obtained on comparing with the state-of-the-art inpainting methods were promising and ought to be a big leap towards super-resolution inpainting methods.
Network Culture-Oriented Monitoring of Colleges based on Word Vector and Data Stream Clustering with New Development Concept

Network culture-oriented monitoring of colleges based on word vector and data stream clustering with new development concept is studied in the paper. Internet public opinion is a product of the Internet age, and it is a concentrated reflection of college students' views on things. It is not only related to students' own cognitive characteristics, but also closely related to the characteristics of the network communication. Therefore, in the process of network public opinion management, it is necessary to first analyze the mechanism of negative network public opinion generation and the evolutionary way of dissemination of college students. Hence, in the designed model, the word vector and data stream clustering are combined. According to the characteristics of product text data, there is no contextual timing information in the elements such as product name and attribute, then, this research study consider using the vector data mining as the way to detect the inner structure of the data. In further, the network data monitoring algorithms are combined to make the comprehensive modelling.
Energy Efficiency in Cloud Computing using Hybrid Algorithm

With the rise of cloud computing, the high-energy consumption of data centers has become a serious concern. The cloud is an energy-intensive technology, with data centers housing hundreds of hosts. Energy usage is quite high 24 hours a day, seven days a week. As a result, there is a need to investigate various ways to minimize cloud energy consumption and to develop an algorithm to reduce cloud energy consumption. This work presents a hybrid method for energy efficiency in cloud computing that uses an artificial bee colony and a gravitational search algorithm. The proposed hybrid algorithm shows better energy efficiency compared to existing methodologies. Our proposed methodology consumes 15% lower energy than ABC algorithm and 27% lower than Gravitational search method in cloud computing. On other hand, number of active host and has been reduced w.r.t no. of Virtual Machine.
Research on Intelligent Information Collection of Business Negotiation System based on Speech Rate Change Recognition Algorithm

This paper conducts the research on intelligent information collection of business negotiation based on speech rate change recognition algorithm. The overall impact of speech speed on pitch is that the pitch decreases at a slow speed, which is mainly reflected in the lowering of the lower line of the tonal range, which is a more important factor leading to the expansion of the tonal range. A fast speech rate leads to a narrowing of the tonal range, and a narrow tonal range will inevitably lead to a decrease in the degree of distinction between tones and the convergence of the four tones. With this prior information, the designed model is selected to be the Naive Guassian model to construct the efficient algorithm of the model. The negotiation system is selected to be the testing scenario. Through the testing on the different aspects, the performance is validated.
Performance Assessment of Simulink Based Speech Radio Band Extension Technique on Elderly People

The main drawback of conventional communication networks is the limitation of the frequency band. Proper speech frequency for telecommunication systems spans from 3 to 3.4 Khz and is popularly known as In-Band (IB). The IB does have a significant impact on communication apprehension and interpretation. It has a more significant impact on senior adults over 60. The Telephony Speech Spectrum Enhancement (TSSE) Technique, when used in conjunction with the IB speech signal, tries to improve the IB speech signal's performance and perception by transmitting erroneous audio frequency information as additional data. On The receiver end, these omitted voice spectral characteristics sent as extra data help build an improved Wide Band (WB) signal. This work simultaneously builds the transmitter and the receiver with Simulink and investigates the potential of TSSE to boost the perception of telephonic conversation among older adults. During this investigation, the effectiveness of the TSSE approach is analysed by contrasting it with a Conventional Telephony Signal Enhancement Technique (CTSET). Subjective as well as quantitative tests analysed and validate the improved performance in the grade and perception of IB signal for the older adult population.
O-RDHF: Optimized Reversible Data Hiding Framework for Media Information Security

Reversible Data Hiding (RDH) techniques are currently being explored for various concerns in addition to security vulnerabilities of information networks. The area has become actively emerging from a research viewpoint to meet the security requirements for traditional content distribution and sharing networks. Therefore, the study attempts to design and model an efficient, optimized framework for reversible data hiding in video steganography-oriented applications. The prime motive behind this is to achieve better reversibility and yet a balanced tradeoff between embedding capacity (EC) and visual perception (VP) of the embedded frames for H.264/AVC. It also emphasizes on compression performance of the H.264/AVC bitstream to comply and make it resilient with bandwidth constraints during transit. The core objective of this research motive is to attain optimum performance modelling for Peak Signal to Noise Ratio (PSNR), EC, and computing time while accomplishing good reversibility for H.264/AVC bitstream. However, the performance metric for the optimized RDH framework considers i) Video size, ii) No. of original bits, iii) Number of compressed bits, iv) Compression Ratio (CR), v) PSNR, and vi) Average Encoding Time (ET).