Reviewerly/exHarmony huggingface dataset:
HLS Implementation of Quantum Shor’s Algorithm Using Matrix Pruning

Shor’s integer factoring algorithm served as a significant breakthrough in the field of quantum computing owing to its potential applications in modern cryptography. Simulating quantum algorithms in a way that offers a close imitation of quantum computers is required to provide some insight viably. This paper proposes an FPGA implementation of the Shor’s algorithm by employing High Level Synthesis (HLS). A detailed method of implementation including design of data-flow graphs, pruning matrices using Python scripts, writing a HLS compatible C++ code, and subsequent hardware synthesis is presented for an 8-qubit system. An estimated maximum frequency of 180 MHz has been achieved in this design.
Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Increasing scale of task in cloud network leads to problem in load balancing and its improvement in parameters. In this paper, we proposed a hybrid scheduling policy which is hybrid of both Particle Swarm Optimization (PSO) algorithm and actor-critic algorithm named as Hybrid Particle Swarm Optimization Actor Critic (HPSOAC) to solve this issue. This hybrid scheduling policy helps to each agent to improve an individual learning as well as learning through exchanging information among other agents. An experiment is carried out by the help of Python simulator with TensorFlow. Outcome shows that our proposed scheduling policy reduces 5.16% and 10.86% in energy consumption, reduces 7.13% and 10.04% in makespan time, and has marginally better resource utilization over Deep Q-network (DQN) and Q-learning based on Modified Particle Swarm Optimization (QMPSO) algorithm, respectively.
Multiple Object Detection and Segmentation for Remote Sensing Images

The Convolutional Neural Network (CNN) has been playing a significant role in processing remote sensing images and providing specific data from those images. The proposed system aims in processing the remote sensing images using CNN (AlexNet) through Unsupervised Learning algorithm and perform multiple object detection and segmentation. The object detection focus on detecting the required objects in the image and segmentation helps in representing those detected images more precisely using different segmentation processes. It is observed that the proposed system has delivered more effective output images with specific data present in the image. The proposed system has a huge scope in the field of Satellite Communication and with required update and advancement in technology the proposed system can be used for various applications.
Research on anomaly detection method combining distance correlation coefficient and autoencode

This study proposes a method of anomaly detection based on a combination of distance correlation coefficient-based feature selection algorithm and autoencoder. In this paper, we use the distance correlation coefficient to analyze the correlation of the original feature set, and divides the feature set into multiple feature subsets according to the correlation between features. The features within each feature subset are filtered by the constructed feature representativeness evaluation indexes to remove redundant features. Then, we built a convolutional denoising autoencoder to enhance the anomaly detection ability of the autoencoder in the time dimension. In the constructed autoencoder, a modular design approach is used to divide the encoder and decoder structures into encoding and decoding units, and the accuracy of fitting the network to the training data can be tuned by adjusting the number of these two units. Finally, the proposed method is validated with a turbofan engine. The results show that the proposed method outperforms other traditional methods in accuracy and has application value.
Content based Adaptive Image Demosaicing using Random Forest Algorithm.

Health care, forgery and engineering are just a few of the many industries that rely on the content of images. Mobile phone cameras use image sensors with Bayer patterning. It is necessary to use a demosaicing algorithm to extract the fullcolour image with requisite quality. Content-based adaptive demosaicing utilising random forest algorithm is proposed in this article, as it has the advantage of being easy to train and evaluate. Interaction curvature was used as the predictor. Interpolation techniques: linear, closest, cubic, rational v4 precede this section. For each pixel, 50 learning cycles are utilised, and all of this work is done using MATLAB software. Using random forest algorithms, ten pictures are used to calculate PSNR, SNR, SSIM, and MSSIM. All of the test photos were more efficient when using Random forest as a filter.
A medium-scale study of using machine learning fusion to improve TLE prediction precision without external information

This paper presents a machine learning (ML) approach that can improve orbit prediction precision and reduce uncertainties with a properly designed fusion strategy. The ML models are trained based on historical TLE sets and then ML-modifications are generated for the future states and uncertainty predictions. The ML-modification is first interpreted as a pseudo-measurement of the true orbit prediction error and then fused with the conventional orbital state and uncertainty propagation result. The regularized particle filter (PF) with progressive correction and systematic sampling is adopted for uncertainty propagation while using the Simplified General Perturbations #4 (SGP4) model. Comprehensive experiments are conducted and analyzed on over 100 resident space objects (RSOs) in different orbit types. The results demonstrate that the fusion strategy enables the ML approach to work with the advanced PF prediction method. The prediction precision can be significantly improved for the majority of the cases. The prediction accuracy is observed to be improved in some cases but without a clear pattern. The results and analysis also suggest that future studies of a monitoring system facilitating the fusion process would be helpful in practical applications.
Importance of Initialization in K-Means Clustering

Data clustering is a method of visualizing the data in such a way that enables the researcher to see similar patterns formed in the data and these lead to conclusions that can be helpful to interpret the data and could be further used for other research purposes. In this paper the focus would be on the initialization technique used and would present how an improper initialization of centroid could lead to bad or unfruitful results, not only this the complexity of the overall algorithm depends upon the type of initialization used. Thus, study compares various initialization techniques and their respective research work to come upon a study that would help the researcher to get an insight of the available techniques and thus choose the one suitable. This research would focus on the nature of the data presented and would see how different types of Datasets get affected by the choice of initialization. Along with this would also analyze the impact of repeating the K-Means Clustering Algorithm on the results.
Malicious Website Detection Based on URL Classification: A Comparative Analysis

Phishing has been one of the most frequent cyber threats in the recent decade, prompting an increase in anti-phishing research and the development of numerous solutions for detecting and preventing phishing assaults. This paper identifies the system’s vulnerabilities and adversaries’ tactics to deceive Internet users into trusting the malicious email or website and providing sensitive information and credentials. For this study, the relevant URL features are retrieved from the collected dataset that includes phishing and legitimate URLs of websites. The correlation among different features is studied that can help users to identify fake web URLs by scanning phishing specific properties. This paper also analyzes the performance outcome of the machine learning, ensemble, and deep learning techniques on the collected dataset. Each model’s performance is compared and measured, and random forest and gradient boosting with XGBoost are found to be the best optimal model for phishing binary classification problem in terms of accuracy (97.3%).
Online Education APP Information Supervision Based on Machine Learning Algorithms

The mobile Internet era has brought a new way of online learning on the mobile terminal, and various online education applications are emerging one after another. The benefits of online education are obvious, such as learning anytime and anywhere, massive network resources, rich and excellent teaching resources, etc., but today’s online education apps still have uneven quality, scarce research literature, and narrow educational sections. Students’ learning willpower is weak and other issues. Therefore, it is necessary to study online education APP information supervision based on machine learning algorithms. This article first discusses the concept of online education APP and expounds the application of machine learning algorithms, and then designs and develops a system for online education APP information supervision, and tests the performance of the system. The final test result shows that the system response time is basically maintained at about 23/ms, indicating that the system response speed is relatively fast; the system delay time is basically maintained at about 12/ms, it can be said that the delay time is very low, and it also shows that the system response speed is fast. At the same time, the running time of the system is about 45/m, which can save the memory occupied by the system itself, and supervise the user learning situation of online education while saving loss.
Personalizing Persuasive Principles to Improve Credibility

The role of credibility in persuasive systems is undoubted. It is therefore important that the determinants of credibility are clearly understood. Studies have asserted that personalizing persuasive principles, for instance, based on users’ personality, age, or gender is more effective in influencing behavior. However, there is little knowledge on how credibility determinants can be adapted to increase a system's credibility and system persuasiveness for specific users. This paper describes ongoing research that attempts to categorize credibility determinants based on their effectiveness for different users and contexts. The paper presents the research plan and findings from three initial studies.
Path Planning of Indoor Mobile Educational Robot Based on Improved Deep Reinforcement Learning

With the maturity of artificial intelligence and Internet of Things technology, the research on robots has also become one of the hotspots of artificial intelligence. Indoor mobile educational robots are an important part of machine intelligence. Research on the path of indoor mobile educational robots has become a key point in machine research. The purpose of this paper is to study the path planning of indoor mobile educational robots to improve deep reinforcement learning. This article first summarizes the research status of mobile educational robots at home and abroad. On this basis, the kinematics model of the indoor mobile educational robot is researched and analyzed. This article systematically elaborates the path planning based on the Actor-Critic algorithm and the deep reinforcement learning training model based on the minimum depth of field information. And use comparative analysis method, observation method and other research methods to carry out experimental research on the theme of this article. Research shows that the Actor-Critic algorithm proposed in this paper is shorter in path planning time and path distance than traditional algorithms.
Implementation of Vehicles Classification using Extreme Gradient Boost Algorithm

This paper presents the implementation of vehicles classification using Extreme Gradient Boost (XG Boost) Algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Generally, Classification based on different parameter such as hefty, classes, structures, extracting features, segmenting the images and semantic classification are being challenge to incorporate in Machine Learning. In order to overcome this barrier, XG boost algorithm has been implemented to achieve the high performance vehicle classification from the large scale surveillance dataset. The experimental results shows that the accuracy is improved in the vehicle classification with standard resolution image.
ResMintage: Leveraging Blockchain Technology to Share Hardware Resources

Data storage and protection are key concerns in today's world. Organizations have increased their network infrastructure to provide cloud storage and resource access to as many individuals as possible, which raises issues about the privacy of data stored or shared over a network. A centralized authority cannot manage such a vast data network. Cloud storage and other data infrastructure are becoming increasingly expensive as demand grows. Cases of data breaches and attacks have been recorded year after year, and the numbers continue to grow. Many firms, including LinkedIn and Facebook, have experienced massive data breaches in recent years. Another issue discovered was that idle hardware was not being used by many local computers. Although there are people and organizations with surplus local storage capacity, many organizations are looking for additional cloud storage. To address the aforementioned issues, this paper proposes a solution that uses Blockchain Technology to enable individuals or organizations to share hardware resources. Blockchain employs a peer-to-peer design. As Blockchain employs cryptographic techniques, it has the highest security factor of any system. This paper proposes a system in which users can share their local resources with users in need via a blockchain because there is a clear supply and demand requirement for resources. Users that share hardware resources will also be rewarded. There will be no need for any central authority to manage the data or the data kept with the shared resource with blockchain, preserving user privacy and security.
Pemanfaatan Bibliografi dan Terbitan Pemerintah di Perpustakaan sebagai Sumber Informasi

The library as a source of information must of course provide sources of information that can be utilized by users in meeting their information needs. Libraries must have collections that can support the needs of users by providing reference sources that can be used as sources of information. By providing secondary sources of library information such as bibliographies and government publications. Bibliography and Government Publications are one of the general reference sources that contain various important information that can be useful in information retrieval. The purpose of writing this article is to provide an overview related to the use of bibliography and government publications as a source of information, especially for users to meet information needs. Constraints in the use of Bibliography, namely the user does not know how to use the Bibliography. Meanwhile, obstacles in the use of government publications, namely human resources with educational background in Library Science are still minimal so that management is not optimal, lack of responsibility and initiative for library staff to manage government publications, has not been inputted to a computer which results in difficult retrieval processes and collections of publications are not yet available. neatly arranged. Besides that, so that the general public can take advantage of the services of general reference sources in the library as a reference in finding information.
Panoramic Video Quality Assessment Based on Spatial-Temporal Convolutional Neural Networks

The development of 5G technology and Ultra HD video provide the basis for panoramic video, namely virtual reality (VR). At present, the traditional VQA method is not effective on panoramic video. Therefore, it is crucial to design objective VQA models for the standardization of panoramic video industry. With the development of deep learning, excellent algorithms of VQA methods based on convolutional neural network have emerged. In this paper, we propose a full reference VQA model based on spatial-temporal 3D convolutional neural network, the feature extraction combined the time and spatial information. we verify and optimize the proposed VQA model based on VQA-ODV panoramic video database, its objective score has a higher correlation with subjective scores than that of traditional VQA methods.
Overview of Security Approaches Using Metamorphic Cryptography

Initially, researchers employed only one information security technique either cryptography or steganography to secure the communication. But later, researchers stress on the amalgamation of both cryptography and steganography, and this amalgamation is popularly known as metamorphic cryptography. Steganography can be classified on the basis of cover medium. This paper surveys the different metamorphic cryptography approaches which uses image as cover media for securing the data. This paper also covers general concepts of cryptography, steganography, classification of metamorphic cryptography, and evaluation parameters like PSNR, MSE.
Research on Deep Knowledge Tracing Model Integrating Graph Attention Network

The current mainstream knowledge tracking model is based on the neural network of deep learning, which has a certain improvement in performance. However, due to the difficulty of interpretability of the deep learning methods, and the previous literature did not involve the high-dimensional information between problems and knowledge points when their model used the answer record, there is a situation that the relevant information is not sufficiently extracted. In order to solve the above problems, a knowledge tracing model based on the graph attention network mechanism is proposed, which uses the graph attention network to reveal the potential graph structure between knowledge points in answer records, and aggregates the correlation degree through the attention mechanism, so that the input information of the model includes the relationship information between problems and knowledge points, which enhances the interpretability of the model and improves the prediction accuracy of the model. On the three commonly used public datasets, the proposed model can better reflect learners’ mastery of knowledge points.
A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database

Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie. Supplementary materials for this article are available online.
Opportunities and Challenges of Artificial Intelligence Education in Secondary Vocational Schools Under the Background of Emerging Engineering

In order to cope with the new generation of Scientific-technical revolution and industrial transformation, the construction of emerging engineering is in full swing. As the core technology of the new generation, artificial intelligence has had a disruptive impact on traditional industries and is an important part and module of the construction of emerging engineering. Secondary vocational schools are the cradle of cultivating applied technical talents and high-quality workers, the goals and content of talent training should closely follow the development trend of social technology and industry. However, artificial intelligence education in secondary vocational schools is still in the exploratory stage. Based on the development process of artificial intelligence education, this paper studies the opportunities and challenges of artificial intelligence education in secondary vocational schools under the background of emerging engineering, in order to provide a theoretical reference for the development of artificial intelligence education in secondary vocational schools in our country.
Low-Level Activity Patterns as Indicators of User Familiarity with Websites

Familiarity is a quality of user experience that has traditionally been difficult to define, capture, and quantify. Existing works on measuring familiarity with interactive systems have relied on surveys and self-reporting, which is obtrusive and prone to biases. Here, we propose a data-driven methodology to associate low-level activity patterns with familiarity. As a proof-of-concept, this methodology was tested on a website with 35,819 users over the course of 18 months, including 268 revisiting users who had reported their levels of familiarity with the platform. By using activity patterns as features of predictive models, we were able to successfully classify users with higher levels of familiarity with an accuracy of 82.7%. These results suggest that there is a relationship between user familiarity and activity patterns involving the exploration and use of navigational artefacts including breadcrumbs, navigation bars, and sidebar areas. This research opens up further opportunities for unobtrusively analysing the user experience on the Web.
Steganalysis of adaptive multi-rate speech streams with distributed representations of codewords

With the wide application of adaptive multi-rate (AMR) speech coder, steganography and steganalysis based on AMR coded speech streams have become a hot spot in the field of information hiding. Quantization Index Modulation (QIM)-based steganography is one of the most effective approaches to hide secret information into AMR coded speech streams with excellent imperceptibility and robustness. So far, accurate detection for QIM-based steganography in short-length or low-embedding-rate speech streams remains an open problem, though some approaches can complete detection in a short time. To address this challenge, we first analyze and verify the characteristics of QIM-based steganography, and present a novel and high time-efficient steganalysis model based on distributed representations. Specifically, a codeword embedding layer is introduced to capture distributed representations with a denser space; then we introduce a bidirectional Long Short-Term Memory (LSTM) layer and propose a gated attention mechanism to provide contextual distribution features with better generalization capabilities; finally, a Multi-Layer Perceptron (MLP) classifier is designed to distinguish normal or steganographic objects. The experimental results demonstrate that the proposed model can effectively detect QIM-based steganography in AMR speech streams and outperform the state-of-the-art ones.