Reviewerly/exHarmony huggingface dataset:
High-performance and balanced parallel graph coloring on multicore platforms

Abstract Graph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the same color. The number of colors used corresponds to the number of parallel steps in a real-world end-application. Therefore, the total runtime of the graph coloring kernel adds to the overall parallel overhead of the real-world end-application, whereas the number of the vertices of each color class determines the number of the independent concurrent tasks of each parallel step, thus affecting the amount of parallelism and hardware resource utilization in the execution of the real-world end-application. In this work, we propose a high-performance graph coloring algorithm, named ColorTM, that leverages Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices. ColorTM detects and resolves coloring inconsistencies between adjacent vertices with an eager approach to minimize data access costs, and implements a speculative synchronization scheme to minimize synchronization costs and increase parallelism. We extend our proposed algorithmic design to propose a balanced graph coloring algorithm, named BalColorTM, with which all color classes include almost the same number of vertices to achieve high parallelism and resource utilization in the execution of the real-world end-applications. We evaluate ColorTM and BalColorTM using a wide variety of large real-world graphs with diverse characteristics. ColorTM and BalColorTM improve performance by 12.98 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> and 1.78 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> on average using 56 parallel threads compared to prior state-of-the-art approaches. Moreover, we study the impact of our proposed graph coloring algorithmic designs on a popular end-application, i.e., Community Detection, and demonstrate the ColorTM and BalColorTM can provide high-performance improvements in real-world end-applications across various input data given.
Encrypted Malware Detection Methodology without Decryption using Deep Learning based Approaches

Abstract The proportion of encrypted web traffic is rapidly increasing. The widespread use of encrypted traffic on the Internet provides safe and secure communication between users and servers. However, cybercriminals are also employing encrypted traffic to conceal their malicious activities. Malware detection in encrypted traffic is an arduous task for security professionals due to the complex nature of encrypted traffic on the web. Traditional approaches need to decrypt the content of network traffic, check it for threats, re-encrypt the network traffic content, and send it to the server. This solution compromises the integrity of encryption and the user’s privacy and safety. DL is currently the most effective AI technology that can reduce the manual determination of feature sets to improve classification accuracy. This paper proposes a methodology by using DL algorithms for malware detection in encrypted traffic without decryption. With this architecture, the user’s privacy is maintained, and the efficiency of detecting threats in encrypted traffic is enhanced. Three deep learning techniques, namely, multilayer perceptron (MLP), 1D convolutional neural network (1-D CNN), and long short-term memory (LSTM), are tested on the CTU-13 malware dataset, which comprises flow based features of network traffic. Experimental results show that MLP based approach outperforms 1-D CNN, LSTM, and other existing approaches.
How Much Entanglement Do Quantum Optimization Algorithms Require?

Many classical optimization problems can be mapped to finding the ground states of diagonal Ising Hamiltonians, for which variational quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) provide heuristic methods. Because the solutions of such classical optimization problems are necessarily product states, it is unclear how entanglement affects their performance. An Adaptive Derivative-Assembled Problem-Tailored (ADAPT) variation of QAOA improves the convergence rate by allowing entangling operations in the mixer layers whereas it requires fewer CNOT gates in the entire circuit. In this work, we study the entanglement generated during the execution of ADAPT-QAOA. Through simulations of the weighted Max-Cut problem, we show that ADAPT-QAOA exhibits substantial flexibility in entangling and disentangling qubits. By incrementally restricting this flexibility, we find that a larger amount of entanglement entropy at earlier stages coincides with faster convergence at later stages. In contrast, while the standard QAOA quickly generates entanglement within a few layers, it cannot remove excess entanglement efficiently. Our results demonstrate that the role of entanglement in quantum optimization is subtle and provide guidance for building favorable features into quantum optimization algorithms.
Deep Learning Technique Based Character and Speech-to-Text Recognition for Interactive Online Writing with Virtual Pen and Eraser

ZeroTouch is the new emergence of sensing technologies as a touch-free interaction of air writing. The main objective of this paper is to use the pen with motion mode of interaction for the efficient online gesture. The virtual pen is detected in webcam with different contour detection is made along with eraser functionalities. But it is made much easier with OpenCV technologies. To display the text from speech in clear format a whiteboard function is added using Tkgui toolkit. A faster region-based convolution neural network technique is proposed for image text detection and then the detected image is converted to text and saved in different image formats.
Low Resource Style Transfer via Domain Adaptive Meta Learning

Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of non-parallel data to guide transferring different text styles. (ii) colossal performance degradation when fine-tuning the model in new domains. In this work, we propose DAML-ATM (Domain Adaptive Meta-Learning with Adversarial Transfer Model), which consists of two parts: DAML and ATM. DAML is a domain adaptive meta-learning approach to learn general knowledge in multiple heterogeneous source domains, capable of adapting to new unseen domains with a small amount of data. Moreover, we propose a new unsupervised TST approach Adversarial Transfer Model (ATM), composed of a sequence-to-sequence pre-trained language model and uses adversarial style training for better content preservation and style transfer. Results on multi-domain datasets demonstrate that our approach generalizes well on unseen low-resource domains, achieving state-of-the-art results against ten strong baselines.
Poor Accuracy in Determining Erratic User Behavior in Social Media Networks Using KNN Algorithm Comparing SVM Algorithm

The main aim of this research is to determine the erratic user behaviour over social media using machine learning classifiers by comparing Novel K-Nearest Neighbour algorithm and Support Vector Machine algorithm. Classification is performed using K-Nearest Neighbour with sample size (N=10) and Support Vector Machine sample size (N=10), and results were compared based on the accuracy of both algorithms. The KNN is used to determine the accuracy of erratic user behaviour with the help of social media network reviews with twitter data. The accuracy achieved for KNN is (95.30%) and SVM is (92.67%). The statistical significance between K-Nearest Neighbour &amp; Support Vector Machine is (p=0.0094) where (p&lt;0.05).K-Nearest Neighbour algorithm helps in determining with more accuracy in erratic user behaviour over social media networks, and here KNN algorithm shows better accuracy than SVM algorithm.
Automatic Bug Tracking System Using Text Analysis and Machine Learning Predictions

Every day, many bugs are raised, which are not fully resolved, and a large number of developers are using open sources or third-party resources, which leads to security issues. Bug-triage is the upcoming automated bug report system to assign respective security teams for an ample rate of bug reports submitted from different IDEs within the organization (on-premises). Furthermore, by predicting the appropriate team (who can resolve the bug) in an organization, the bugs can be assigned once it is tracked. With this, cost and time can be saved in tracking and assigning the bugs. In this paper, we are implementing an Automatic bug tracking system (ABTS) to assign the team for the reported bug using the Text analysis for bug labeling and classification machine learning algorithm for predicting developer.
Classification Model for Tumor Detection in Breast Cancer Patients Using K-Neighborhood and Decision Tree

Breast cancer is a malignancy affecting many women worldwide and is associated with a high fatality rate. The paper’s main objective is to detect the breast cancer tumor using a K-neighborhood and compare it with Decision Tree classification to evaluate accuracy using the Machine Learning technique. The k-neighborhood algorithm is applied to 10 images from a dataset of more than 300. For the same, the accuracy values are evaluated. It consists of breast cancer images in the research study of K-neighborhood machines and a decision tree with 20 sample sizes. Based on the statistical analysis, the significance value for calculating accuracy was p&lt;0.05. Breast cancer detection is performed using a K-neighborhood, which means 87.5% and 80.5% in the Decision Tree classification. The performance of the K-neighborhood is considerably improved than the Decision Tree classification in terms of accuracy.
Development of Virtual Painting Method using OpenCV Library with Finger Gesture on Online Learning Platform

With the pandemic situation that has occurred for the last two years to date, educators and students carry out many learning activities online. Learning activities are carried out using virtual meeting media, the concept of meetings and discussion processes that are carried out virtually with existing digital communication devices. From the problems, it was found that students were likelier to be less active when learning theory than practice, which made it difficult for educators to find various media that would be given to students to support online learning activities to be more interactive. This study discusses the creation of a system that can be a medium for delivering helpful material to improve the quality of interaction between educators and students. A virtual painter is one of the media to support the online interactive learning process, where educators can complete the material presented with a clearer picture that educators provide to students. Virtual painters are used to tracking finger pattern movements where the user moves his hand as needed, namely drawing and release, which educators can use to deliver more interactive material to students in theoretical and practical learning. The system design method used in building the virtual painting is Rapid Application Development (RAD) which is carried out through 8 stages, starting from Requirements Analysis to Operation and Maintenance. Next, the diagram design uses UML notation and the phyton with OpenCV coding process. After the virtual painter had been made, an evaluation was conducted and resulted in a positive impression of all six scales: attractiveness, efficiency, clarity, precision, stimulation, and novelty.
Correlation Analysis of Voting Regression and Decision Tree Algorithm to Predict House Price with Improved Accuracy Rate

The primary goal of this study is to use efficient machine learning algorithms to anticipate better house prices, typically inflated. Materials and Methods: : This study will study the differences between near-accurate price prediction utilizing Novel Voting Regression (Group 2) and Decision Tree methods (Group 1). The sample size used to carry out this research was N=10 for each group studied. Clincle was used to calculate the sample size. The pre-test analysis was maintained at 80%. G-power is used to calculate the sample size. Statistical analysis yielded a significance value of 0.001. Results: : The accuracy of the Novel Voting Regression Algorithm for house price prediction is 82.94%, which is greater than the Decision Tree Algorithm’s 72.54%. The Independent Sample T-test has a statistical significance of 0.584. Conclusion: : As a result, it can be stated that the Novel Voting Regression technique can produce results that are almost as accurate as of the Decision Tree technique.
The Functional and Storage Risks Associated to the Size Estimation of Parallel Computing Applications

The software size estimation is the basic and essential process in the Software Development Life Cycle (SDLC) for budgeting, developing and delivering in the scheduled manner. The modern computer software like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Data Analytics are highly dynamic, distributed and parallel processing. To identify the size of this versatile environment is highly difficult and risk based. A small fault may cause negative consequences and also suspension of the project. This article gives a study on the functional, social, economic, political, organizational and nonfunctional risks associated to the parallel computing applications.
Predicting Customer Behaviour Using a Self-Organizing Map and Comparing with Random Forest

The main of this paper is to analyse the performance of customer behaviour in online shopping by using precision value.A novel Self-organizing map with Random Forest was iterated at different times for predicting accuracy percentage of customer behaviour. The result proved that the Novel Self-organizing map got significant results with 96% accuracy compared to Random Forest with 93% accuracy. Self-organizing mapis a simple and most effective algorithm to build fast machine learning models. The self-organizing map helps in predicting with more accuracy the percentage of customer behaviour.
Pengenalan dan Pelatihan Sistem Pengarsipan Elektronik di Kantor Kelurahan Limbungan Kecamatan Rumbai Timur Kota Pekanbaru

This service activity aims to provide understanding and training to Administrative Employees of Limbungan Village Office. A common problem with partners is that they do not have adequate skills in managing archives electronically. This service activity can improve the understanding and skills of the Administration employees in Limbungan Village Office. The outputs produced according to this activity plan are in the form of scientific articles. The method of implementing the activity is counseling. Submission of material is carried out by lecturers who are members of the team proposing community service activities. Based on the results of the pretest and posttest, it can be concluded that there is an increase in the participants' understanding of electronic archives and their management using a system, where this system will also assist in the retrieval of these archives.
Analisis Malware Ahmyth pada Platform Android Menggunakan Metode Reverse Engineering

Android is the most widely used operating system in the world with a percentage of 74.82% in the Android operating system market share. This fact makes malicious software (malware) developers make mobile phone users with the Android operating system the main target for malware attacks. Attackers can change the application code by entering malicious code, repackaging the application, and publishing the application on the android application market. This study aims to find out how the malware that has been inserted into the android application works by using dynamic analysis and extracting the dangerous licenses used by the ahmyth malware by using reverse engineering techniques. The results of the analysis show that the ahmyth malware will run its service after the device restarts and waits for commands from the C&amp;C server to perform certain actions on the infected device.
A model of addapting in Romanian language of a verbal phonemic fluency test (The Controlled Oral Word Association Test -COWAT)

Verbal fluency is an indicator of good cognitive functioning. The Controlled Oral Word Association Test (COWAT) is part of the examination tools in neuropsychology. For English there are stratified standards by age and background (Tombaugh, Kozak, &amp; Rees, 1999). The phonemic combinations used are F-A-S, C-T-L or P-R-W. These combinations are not specific to the Romanian language. This study proposes a way to identify phonemes that could be specific to the Romanian language for an adapted version of the COWAT test.
Efficient Cloud Environment for Secure User Authentication to Prevent Cyber Attacks Using DES Over AES

The proposed work is aimed at providing an efficient cloud environment for secured cloud-based users to improve user authentication and storage security to prevent cyber-attacks based on the Data Encryption Standard algorithm over the Advanced Encryption Standard algorithm. A total of 160 data samples were collected for both the Data Encryption Standard algorithm and the Advanced Encryption Standard algorithm, with a G power of 80%, a threshold of 0.05%, a confidence interval of 95%, mean and standard deviation. size of the training and test datasets is 70% and 30% respectively. The Data Encryption Standard Accuracy is 94% and the Decryption Time value is (66 ns), while the Advanced Encryption Standard Accuracy is 80% and the Decryption Time value is (72 ns), with a significance value of 0.003 (p 0.05). Encryption Standard achieved significantly better authentication than the Advanced Encryption Standard for the identification of lack of security due to cyber-attacks.
Penulisan Surat Dinas dan Pengelolaan Arsip Sekolah bagi Tenaga Kependidikan Se-Cabdin Wilayah VII Provinsi Sumatera Barat

The purpose of community service activities is to improve the ability of educational staff in making official letters and managing school archives. The target of this activity is the education staff of SMA/SMK/SLB di Dinas Pendidikan Cabang Wilayah VII Provinsi Sumatera Barat. In this activity, problems will be completely solved related to the making of official letters and the management of school archives to realize school accountability. The approach used in implementing community service programs is Participatory Rural Appraisal (PRA) which emphasizes partner participation in all activities, from planning, implementation, and evaluation. The output target in the form of a process is an increase in the ability of the target audience in understanding the concept and process of making official letters and managing school archives. The results of the training and mentoring activities carried out are expected to create school archives that are systematically organized so that the school and the education office can access the archives easily and quickly.
Different Approaches on Security, Privacy and Efficient Sharing of Electronic Health Records Using Blockchain Technology

Electronic health records (EHRs) are both important and sensitive since they store crucial data that is routinely shared across various parties, such as clinics, pharmacies, and medical practices. These health files require increased safety and confidentiality to prevent leakage or misuse by a third party. There have been instances where a security breach in a patient’s electronic medical records has occurred. Blockchain technology may be useful in providing privacy for these documents. The fundamental goal of the work mentioned in this article is to improve the security, privacy, management, and efficient sharing of medical records. In this study, we will present a complete assessment of several strategies for safeguarding EMR privacy using blockchain technology.
PERANCANGAN SISTEM LOGIN MENGGUNAKAN PERANGKAT NODE MCU DAN RFID

Abstract At one of xyz educational institutions in Tasikmalaya, there is a system, it can be used by users and it can freely access the system as long as they know the address of the site. The problem is with free access of user to the system, then there is a possibility for users who do not have access rights to the system will try to log in and to break into the system. For the irresponsible this will be manipulation or theft of a data. Regarding to that problems, the purpose of this research is to anticipate system login access by designing and building a prototype security system using NodeMcu and RFID technology. The method used in this research is qualitative with the development of agile models, namely Kanban models, while for design using UML models. The final result of this research is a prototype of a login system using NodeMcu and RFID technology as a security alternative to a system on web.
Unique Approach of Movie Rating Prediction Using Support Vector Machine Algorithm and K-Nearest Neighbors Algorithm

The aim is to predict movie rating using Support Vector Machine Algorithm and K-Nearest Neighbors Algorithm. An aggregate of 392 examples were gathered from film datasets and these datasets were taken fromkaggle dataset. Two experimental calculations were performed, one with K-Nearest Neighbors algorithm and another with Novel Support Vector Machine algorithm. Sample size of N=5 is taken for both algorithms. The computation processes were executed and verified for exactness. SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Novel Support Vector Machine calculation was applied and it had accomplished mean accuracy of 91.8% when compared with mean accuracy of 53% of K-Nearest Neighbors algorithm. The outcomes were obtained with a significance value of 0.03 (p&lt;0.05). A unique approach model is affirmed to have higher exactness than K-Nearest Neighbors calculation.
Copy Move Forgery Detection Using Novel Quadsort Moth Flame Light Gradient Boosting Machine

A severe problem in modern information systems is Digital media tampering along with fake information.Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel.Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments.However, high complexity affects the developed methods.Presently, it is complicated to resolve the issue of the speed-accuracy trade-off.For tackling these challenges, this article put forward a quick and effective Copy-Move Forgery Detection (CMFD) system utilizing a novel Quad-sort Moth Flame (QMF) Light Gradient Boosting Machine (QMF-Light GBM).Utilizing Borel Transform (BT)-based Wiener Filter (BWF) and resizing, the input images are initially pre-processed by eliminating the noise in the proposed system.After that, by utilizing the Orientation Preserving Simple Linear Iterative Clustering (OPSLIC), the pre-processed images, partitioned into a number of grids, are segmented.Next, as of the segmented images, the significant features are extracted along with the feature's distance is calculated and matched with the input images.Next, utilizing the Union Topological Measure of Pattern Diversity (UTMOPD) method, the false positive matches that took place throughout the matching process are eliminated.After that, utilizing the QMF-Light GBM visualization, the visualization of forged in conjunction with non-forged images is performed.The extensive experiments revealed that concerning detection accuracy, the proposed system could be extremely precise when contrasted to some top-notch approaches.