Reviewerly/exHarmony huggingface dataset:
Application and optimization of wavelet threshold denoising algorithm in signal processing

Wavelet threshold denoising algorithm is a non parametric method. The image without changing its original form. Wavelet threshold denoising algorithm is also known as wavelet based denoising (WBD) or wavelet transform based denoising (wtbd). The application of wavelet in signal processing is to denoise the signal. It is used to reduce noise and improve the quality of digital image or video. This technology can reduce the background noise without affecting any other information in the image, and can be applied to video compression, image processing, medical imaging and other fields. Wavelet is a kind of mathematical function, which can be applied to image, audio signal and video sequence. Wavelet transform decomposes the image into its components (wavelet). Each part has a specific frequency content and scale factor, which allows reconstruction at any point on the time axis of the image sequence without losing resolution. Compared with other technologies, the main advantage of using this technology is that it will not change the original signal, but only remove the noise in it, so that you can clearly understand the content displayed in your application.
Target Recognition Method Based on Corner Feature

Aiming at the problem of the feature extraction and matching of ground targets, a fused corner feature extraction and the image matching algorithm is proposed. Firstly, Harris and the fast feature extraction methods are used to extract the feature points of multiple kinds of targets. Secondly, NCC image matching algorithm and surf image matching algorithm are used to match the target image. Finally, the target recognition algorithm based on corner feature extraction is analyzed to obtain a high matching rate. The simulation results show that the reasonable use of the improved image matching method can have a great impact on the result of target recognition.
Exploration of Application Security for Medical Electronic Health Card

This project is highly dependent on information security, where the technical system is organized following the requirements of information security level protection, and the unified security requirements online and offline. Based on the technology of QR code, a unified national identity for Internet medical and health services is constructed by means of security authentication and trans-mission. Medical institutions are the end-users of the electronic health card platform, which carry the task of connecting the medical system and the electronic health card system in our own departments or regions. To realize the specific application, it is necessary to build a secure docking platform within the electronic health card security system, formulate a secure application process, and standardize security management means.
Ethereum Gas Price Prediction Using Facebook Prophet Model

Ethereum is the most well-known and largest accessible, decentralized, and block chain technology system software, originally introduced in 2015. Unlike Bitcoin, which only permits for monetary exchange, Ethereum enables for the use of Smart Contracts, which are written in the Solidity programming language and executed on the Ethereum blockchain network. When these smart contracts are used to perform a task on the Ethereum blockchain network, the network imposes a transaction price known as gas fees. The prediction of gas prices was conducted in this study using the Facebook prophet model in python programming language with the aim to narrow the gap between the current unexpected gas fee price and the ability of predicting the future gas fee, based on past gas prices and current dataset gathered daily for eight (8) years between 2015 and 2022. The results of this study show a 365-day forecast of the price of ether using three assessment criteria, with results that are near to zero and a fit model.
Construction of Ontology Knowledge by Attribute Reduction and Rule Extraction of Three-Way Formal Concept Analysis

Three-way formal concept analyses are mathematical tools for applying three decision theories to formal conceptual analysis, with objects derived from three conceptual grids for data analysis and knowledge acquisition. FCA is identical with the ontology in that they use the formal method to describe the relationship between concepts, both emphasizing the need for a formal explanation of the model. This article introduces a methodological analysis of attribute reduction and rule extraction by three-way formal concept analysis. The purpose of building ontology is to build shared conceptual models of the real world that support knowledge-intensive domain applications. The paper presents construction of ontology knowledge by attribute reduction and rule extraction of three-way formal concept analysis.
Crypto Currency Cloud Mining

Cryptocurrency mining is a new cryptographic method of mining crypto currencies that makes use of a remote data center. Mining is advantageous, particularly in terms of financial support for a decentralized digital currency. A review of ten (10) past articles was done and brief investigation on crypto mining machines and how mining is carried out was conducted in this paper, in order to shed more light on the process of creating digital currency. It was concluding that crypto cloud mining based on blockchain technology is a modern creation. Because crypto mining is thought to be a novel method of creating digital currency, further progress on current mining techniques can be made in the future.
Aspect-Based Summarization: An Approach With Different Levels of Details to Explain Recommendations

Recommender systems have become crucial since they appeared, helping users make decisions. Commonly, recommendation algorithms use the historical interaction data between users and items to predict the users’ tastes and suggest new items. However, offering recommendations sometimes is insufficient for the user to make a decision. In this way, the recommendations’ explanation to support the decision-making process has been considered an essential property. The explanations of recommendations can be generated from different resources, such as unstructured data (e.g., users’ reviews), and presented in many ways, such as summarization. However, offering static explanations may not be helpful in several situations. For example, some users familiar with the content may be willing to receive explanations with fewer details than others who are not acquainted with the domain. In this context, we an approach propose to generate summaries with different levels of detail as post-hoc explanations. We used an aspect-based extractive summarization approach with hierarchical clustering of aspects to select sentences from users’ reviews. Then, this hierarchical structure is used to create explanations of recommended items with different lengths, depending on the user’s preferences. Our dynamic explanation system was evaluated against two state-of-art baselines, and the results are promising.
Improving Multilabel Text Classification with Stacking and Recurrent Neural Networks

Multilabel text classification can be defined as a mapping function that categorizes a text in natural language into one or more labels defined by the scope of a problem. In this work we propose an architecture of stacked classifiers for multilabel text classification. The proposed models use an LSTM recurrent neural network in the first stage of the stack and different multilabel classifiers in the second stage. We evaluated our proposal in two datasets well-known in the literature (TMDB and EUR-LEX Subject Matters), and the results showed that the proposed stack consistently outperforms the baselines.
Software Engineering Code Workshop Based on B-RRT<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mo>∗</mo> </mrow> </msup> </math>FND Algorithm for Deep Program Understanding Perspective

Developers will perform a lot of search behaviors when facing daily work tasks, searching for reusable code fragments, solutions to specific problems, algorithm designs, software documentation, and software tools from public repositories (including open source communities and forum blogs) or private repositories (internal software repositories, source code platforms, communities, etc.) to make full use of existing software development resources and experiences. This paper first takes a deep programmatic understanding view of the software development process. In this paper, we first define the software engineering code search task from the perspective of deep program understanding. Secondly, this paper summarizes two research paradigms of deep software engineering code search and composes the related research results. At the same time, this paper summarizes and organizes the common evaluation methods for software engineering code search tasks. Finally, the results of this paper are combined with an outlook on future research.
Quantum tomography with Gaussian noise

In this paper, we propose an estimation of quantum resources necessary for recovering a key using Known Plain Text Attack (KPA) model for SPARKLE family of LWC authenticated block ciphers - SCHWAEMM. The procedure is based on a general attack using Grover's search algorithm with encryption oracle over key space in superposition. The paper explains step by step how to evaluate the cost of each operation type in encryption oracle in terms of various quantum and reversible gates. The result of this paper is an implementation of the simplified version of this cipher using quantum computer and summary table which shows the depth of quantum circuit, the size of quantum register and how many gates of NCT family are required for implementing the ciphers and attacks on them.
Exploring and Materialising Bodily Experiences of Security through Lenses of Feminist HCI practices

In public spaces, such as urban areas and public transportation, people may experience feelings of insecurity, for example, regarding lack of security and fear of possible criminal intentions from others, which can lead to physical discomfort and (feelings of) unease. Starting from these challenges, we propose a one-day workshop aimed to explore bodily experiences of security. Together with the workshop participants, we will approach this research space from a feminist perspective, engaging with feminist issues, such as participation, advocacy, pluralism, and embodiment. Through innovative body-centered methods, we will foreground and explore individual and collective sensations of security, and materialize participants’ felt experiences and insights in the form of wearable prototypes. Through this workshop, we will reflect on individual and collective experiences of security through making, and elicit design implications for creating secure bodily experiences, which can be informative and inspirational for future research.
How Does Blockchain Company Circle Improve Transaction Efficiency and Stay Competitive

This research paper focuses on the analysis of a blockchain company Circle. Since Circle is a company that primarily focuses on peer-to-peer transactions, the paper is going to talk about how its Messaging Features and its product CENTRE help to improve transaction efficiency by solving the problems of trust, speed, and security on the foundation of blockchain technology. The potential impacts that the problems might cause and the specific ways to solve the problems are going to be discussed. After talking about transaction efficiency, the main part of the paper, the paper is going to summarize the work done by others relating to the Porter’s Five Forces Model, which is the theoretical foundation of the study, and an introduction about case study methodology. The paper is then going to shift its focus on one of Circle’s business strategies of creating USD Coin that can help retain the company’s competitiveness. And one of the use cases of Circle’s USD Coin service is going to be discussed. Finally, the paper is going to summarize the finding of the study, which is focusing on the substitute effect of the five forces model, and provide implications for other companies like Circle to maintain or increase its competitiveness based on the discovery of the study.
Information security with cryptography symmetric key encryption algorithms: a survey

In cryptography, data encryption turns ordinary information into an obfuscated code, and the information cannot be viewed without being decrypted. Thus, cryptography remains the main element of data protection. There are two kinds of cryptography, such as symmetric and asymmetric. There are many existing symmetric algorithms that are used to encrypt data, such as the Data Encryption Standard (DES), Triple Data Encryption Algorithm (TDEA), Enigmatic Diagonal Encryption Standard (EDES), Advanced Encryption Standard (AES), BLOWFISH, TWOFISH, Rivest Cipher 4 (RC4), Rivest Cipher 6 (RC6), which have been reviewed in this paper. It compares and tracks a summary of productive encryption calculations. The performance is compared with key size, space, time, security, and features of encryption methods.
A probabilistic-based approach for automatic identification and refactoring of software code smells

Programmers strive to design programs that are flexible, updateable, and maintainable. However, several factors such as lack of time, high costs, and workload lead to the creation of software with inadequacies known as anti-patterns. To identify and refactor software anti-patterns, many research studies have been conducted using machine learning. Even though some of the previous works were very accurate in identifying anti-patterns, a method that takes into account the relationships between different structures is still needed. Furthermore, a practical method is needed that is trained according to the characteristics of each program. This method should be able to identify anti-patterns and perform the necessary refactorings. This paper proposes a framework based on probabilistic graphical models for identifying and refactoring anti-patterns. A graphical model is created by extracting the class properties from the source code. As a final step, a Bayesian network is trained, which determines whether anti-patterns are present or not based on the characteristics of neighboring classes. To evaluate the proposed approach, the model is trained on six different anti-patterns and six different Java programs. The proposed model has identified these anti-patterns with a mean accuracy of 85.16 percent and a mean recall of 79%. Additionally, this model has been used to introduce several methods for refactoring, and it has been shown that these refactoring methods will ultimately create a system with less coupling and higher cohesion.
RETRACTED: A novel similarity metric for image filtering

The image quality investigation is a significant and challenging task. One of the principal issues is the presence of noise that influences the nature of image. Denoising assume an essential part in image quality examination. The nature of the image is dissected in view of different execution measurements. This work centers on the measurement of similarity for the filtered image and the estimation is made on different normal and clinical pictures. Denoising is done by different image filtering draws closer, also the result is great for different phases of image handling like segmentation and classification approaches. An enhanced Discrete Wavelet Transform (EDWT) is utilized for image filtering and the proposed similarity measurements in view of sine measurement among the noise and the input image for image denoising and examining the nature of image. Alongside the current comparability measurements, different measurements like Peak Signal to Noise Ratio, Normalized Absolute Error, and Structural similarity Index Measure are considered for the work. The investigation is tried on various images like brain MRI scan images from the cameraman, Barbara, vegetables, remote sensing images, natural image as well as BRATS dataset,. The simulation outcome shows that the EDWT based filtering yields an improved outcome for the performance measurements.
Sequential parametrized motion planning and its complexity

In this paper we develop a theory of sequential parametrized motion planning generalising the approach of parametrized motion planning, which was introduced recently in [3]. A sequential parametrized motion planning algorithm produced a motion of the system which is required to visit a prescribed sequence of states, in a certain order, at specified moments of time. The sequential parametrized algorithms are universal as the external conditions are not fixed in advance but rather constitute part of the input of the algorithm. In this article we give a detailed analysis of the sequential parametrized topological complexity of the Fadell - Neuwirth fibration. In the language of robotics, sections of the Fadell - Neuwirth fibration are algorithms for moving multiple robots avoiding collisions with other robots and with obstacles in the Euclidean space. In the last section of the paper we introduce the new notion of TC-generating function of a fibration, examine examples and raise some exciting general questions about its analytic properties.
An extension of binary cyclotomic sequences having order 2lt

Several reasonably cyclotomic sequences are constructed by cyclotomic classes having good pseudo-randomness property. In this paper, we derive the linear complexity of an extended binary cyclotomic sequences of order [Formula: see text] over finite field having period [Formula: see text]. Our result shows that these sequences have higher linear complexity, which can resist linear attack.
Design of Learning Environment for Undergraduate Comprehensive Literacy Education under Blended Learning Environment

Blended learning has become the dominant teaching approach in colleges and universities as they evolve. A good learning environment design can represent college and university teaching quality, improve undergraduates' literacy, and boost talent training. This paper introduces the data mining method of undergraduate comprehensive literacy education, discovers the association rules of the evaluation data, and introduces the undergraduate comprehensive literacy evaluation model and BP neural network model driven by theory and technology in a mixed learning environment, which promotes students' comprehensive literacy evaluation and builds a good learning environment. The results demonstrate that undergraduate classification prediction accuracy is similar by data mining, and most reach 99.58 percent. So, whether it is the training sample or the test sample, the prediction result of undergraduate comprehensive literacy is acceptable, which illustrates the validity of the data mining algorithm model and has strong application importance for developing a better learning environment.
An Intelligent Tracking System for Moving Objects in Dynamic Environments

Localization of suspicious moving objects in dynamic environments requires high accuracy mapping. A deep learning model is proposed to track crossing moving objects in the opposite direction. Moving objects locus measurements are computed from the space included in the boundaries of the images in the intersecting cameras. Object appearance is designated by the color and textural histograms in the intersecting camera views. The incorrect mapping of moving objects in a dynamic environment through synchronized localization can be considerably increased in complex areas. This is done due to the presence of unfit points that are triggered by moving targets. To face this problem, a robust model using the dynamic province rejection technique (DPR) is presented. We are proposing a novel model that incorporates a combination of the deep learning method and a tracking system that rejects dynamic areas which are not within the environment boundary of interest. The technique detects the dynamic points from sequential video images and partitions the current video image into super blocks and tags the border differences. In the last stage, dynamic areas are computed from dynamic points and superblock boundaries. Static regions are utilized to compute the positions to enhance the path computation precision of the model. Simulation results show that the introduced model has better performance than the state-of-the-art similar models in both the VID and MOVSD4 datasets and is higher than the state-of-the-art tracking systems with better speed performance. The experiments prove that the computed path error in the dynamic setting can be decreased by 81%.
Reasoning about Confidence in Goal Satisfaction

Goal models are commonly used requirements engineering artefacts that capture stakeholder requirements and their inter-relationships in a way that supports reasoning about their satisfaction, trade-off analysis, and decision making. However, when there is uncertainty in the data used as evidence to evaluate goal models, it is crucial to understand the confidence or trust level in such evaluations, as uncertainty may increase the risk of making premature or incorrect decisions. Different approaches have been proposed to tackle goal model uncertainty issues and risks. However, none of them considers simple quality measures of collected data as a starting point. In this paper, we propose a Data Quality Tagging and Propagation Mechanism to compute the confidence level of a goal’s satisfaction level based on the quality of input data sources. The paper uses the Goal-oriented Requirement Language (GRL), part of the User Requirements Notation (URN) standard, in examples, with an implementation of the proposed mechanism and a case study conducted in order to demonstrate and assess the approach. The availability of computed confidence levels as an additional piece of information enables decision makers to (i) modulate the satisfaction information returned by goal models and (ii) make better-informed decisions, including looking for higher-quality data when needed.
Quadrature Rules for the Fm-Transform Polynomial Components

The purpose of this paper is to reduce the complexity of computing the components of the integral Fm-transform, m≥0, whose analytic expressions include definite integrals. We propose to use nontrivial quadrature rules with nonuniformly distributed integration points instead of the widely used Newton–Cotes formulas. As the weight function that determines orthogonality, we choose the generating function of the fuzzy partition associated with the Fm-transform. Taking into account this fact and the fact of exact integration of orthogonal polynomials, we obtain exact analytic expressions for the denominators of the components of the Fm-transformation and their approximate analytic expressions, which include only elementary arithmetic operations. This allows us to effectively estimate the components of the Fm-transformation for 0≤m≤3. As a side result, we obtain a new method of numerical integration, which can be recommended not only for continuous functions, but also for strongly oscillating functions. The advantage of the proposed calculation method is shown by examples.
A Training Model of Wargaming Based on Imitation Learning and Deep Reinforcement Learning

This paper proposes an intelligent game confrontation model for a wargame based on imitation learning and deep reinforcement learning, given light of the supremacy of reinforcement learning for training in complex environments. In the context of simulating the battle between the red team and the blue team, a pre-training model based on expert empirical data is produced using imitation learning, and a TD3 algorithm based on the attention mechanism is further designed to build an experience pool using priority experience replay. Finally, the model is enhanced using the self-play approach to increase its training efficiency. Experiments conducted after training demonstrate that the model has a superior training impact, and the winning rate in simulation training is enhanced by 8% compared to the original model.