Reviewerly/exHarmony huggingface dataset:
Bivariate Double Density Discrete Wavelet for Enhanced Image Denoising

Image denoising is of paramount importance in image processing. In this paper, we propose a new design technique for the design of Double density Discrete Wavelet Transform (DD DWT) AND DD CWT filter bank structure. These filter banks satisfy the perfect reconstruction as well as alias free properties of the DWT. Next, we utilized this filter bank structure in image denoising. Our denoising scheme is based on utilizing the interscale correlation/interscale dependence between wavelet coefficients of a DD DWT of the noisy image. This is known as the Bivariate Shrinkage scheme. More precisely, we update DD DWT of the noisy image at a certain scale, according to their correlations with the next coarser scale. The Maximum Likelihood Estimation are used for this update. Comparisons have been made with classical denoising schemes that threshold the DD DWT coefficient as well as denoising schemes employing Complex Wavelet Transform (CWT) filter banks. Illustrative examples are given to show the superiority of the proposed Bivariate DD DWT technique over current literature techniques.
A study on the Application of Computer Music in Mass Music Tutoring

Abstract Computer music occupies an important place in the field of musical art, a highly expressive and creative genre that opens up a whole new path for innovation in music production. With the continuous improvement of people’s living standard nowadays, more and more people begin to pay attention to the improvement of spiritual needs, and mass music culture is loved and recognized by people in this environment, and has been vigorously promoted. For the implementation of mass music tutoring, the application of computer music technology has an important role to play, not only to break the limitations of traditional culture, but also to enhance the musical ability and music literacy of the masses. This paper makes an in-depth study on this, firstly, it elaborates the concept of computer music, introduces the specific composition of computer music system, and finally discusses the application ways of computer music in the process of mass music tutoring with the actual situation.
POLYNOMIALS WITH A RESTRICTED RANGE AND CURVES WITH MANY POINTS

In this paper, we introduce novel properties of restricted range polynomials compared to those developed by L. Rédei in ([1]). We showcase a new method for determining their exponent set and leverage this to construct curves over finite fields with a multitude of rational points.
Load Aware Hotspot Selection for SLA Improvement in Cloud Computing and Protect Environment by Reduction In CO<sub>2</sub> Emissions

Abstract Internet of Things (IoT) is a leading concept that envisions everyday objects around us as a part of internet. In order to accomplish this attribution, cloud computing provides a pathway to deliver all the promises with IoT enabled devices. The outbreak of COVID-19 coronavirus, namely SARS-CoV-2, acts as feather to the cap for the growth of Cloud users. With the increasing traffic of applications on cloud computing infrastructure and the explosion in data center sizes, QoS along with energy efficiency to protect environment, reducing CO 2 emissions is need of the hour. This strategy is typically achieved using Three Layer upper Threshold (TLTHR) policy to analyze and perform VM consolidation. The proposed model controls number of migrations by placement of virtual machines, based on VMs and their utilization capacity on host. The efficacy of the proposed technique is exhibited by comparing it with other baseline algorithms using computer based simulation. Hence better QoS and energy efficiency has been obtained than other classical models.
Application based Android as a Development of German Learning Media at Level A1 and its Effectiveness

This research aims to develop German learning application, namely Deutschlernen which can be downloaded on Playstore. In addition, this research also aims to know the validity, practicality, and effectiveness of German Language Learning Media based on Android’s Deutschlernen. The research method used in this application is a research and developing method ADDIE Model with 5 Steps like analysis, design, development, implementation and Evaluation to improve the language ability to guess an available answer through pictures, vocabulary, expressions, dialogue, grammar, practice questions, quizzes and puzzles that use German. All German learning materials for beginners will be packaged in an application (Deutschlernen) which can be accessed and downloaded by using Playstore in a smartphone, so that this research can be used not only for German language students, but also for the general public who wants to learn German. Through the educational game Deutschlernen, German learners will be enabled to improve their vocabularies, to memorize some useful expressions, to understand the rule of German grammar by mastering German language learning material at level A1 help the students to improve their German language skills quickly and easily. Based on the result of the research, it can be known that the Deutschlernen Android application contains level A1 German learning materials, featuring grammar, vocabulary (Wortschatz) and useful phrases (Redemittel) and is a very flexible learning medium because it can be accessed anywhere via the Playstore.
Traffic Prediction with Graph Neural Network: A Survey

Traffic prediction plays an important role in intelligent transportation systems. Accurate traffic forecasting can make for better traffic management and alleviate traffic problems, such as traffic congestion and traffic pollution. Graph data structure can well express the topology structure of traffic network, so graph model has more development space in the field of traffic prediction. The main purpose of this paper is to provide a comprehensive survey for the graph neural network in the field of traffic prediction. First, the graph model framework was divided into four categories, namely graph convolution networks, graph attention networks, graph auto-encoders and graph generative networks. Then, related literatures are introduced around the four frames. Finally, suggestions on the future development direction of the graph neural network are given.
Types of Phishing

In the past few years, phishing has developed into a global danger that comes in many different shapes and sizes. When attackers mix phishing techniques with additional attack approaches, they create a unique sort of phishing attack. This “pseudo-phishing” has become more sophisticated in recent years, and it is now targeting a larger audience. This chapter will go through many forms of phishing assaults, such as social engineering-based phishing, DNS-based phishing, and content injection phishing and others. Every phishing assault has a different method of identifying victims, but the goal is the same in every case. As much as companies try, phishing attempts are often successful because they target the weakest links in an organization, which are its employees. According to KnowBe4, phishing is the primary method behind 90 percent of cyberattacks, hacks, and data breaches.
Survey on machine learning applied to medical image analysis

This paper presents a selective survey on recent advances in machine learning applied to medical imaging. It aims to highlight both innovations that increase the performance of the models and methods that ensure certainty, interpretability and robustness of the trained models. The paper focuses particularly on new concepts such as attention modules that allow to gather specific features considering global context. Its second main focus is given to domain adaptation methods to enhance model robustness to distribution shifts. Finally, we discuss uncertainty estimation and interpretability methods to evaluate confidence in a trained model.
Logical Operations in Complex Multivalued Hierarchical Logic with One Real and Two Imaginary Logical Structures

A complex multivalued hierarchical logic is studied, in which three propositional logics are integrated - one real and two imaginary. They have non-congruent binary states, arranged in descending order of degree of true, obeying the axioms of Boolean algebra and lattice. Analytical dependences are obtained, describing the behavior of the separate states of the complex logic depending on the operations on them - disjunction, conjunction, equivalence and implication. The possible directions for research and application of this class of logical structures are outlined.
Challenges to Parental Involvement in Children’s ESL Learning

This paper highlights the findings from the research question, namely: “How do the participating parents work with their children at home?” This is a qualitative study involving seven participating parents of struggling readers of ESL in a Malaysian primary classroom. Interviews were conducted to obtain information from the participants and data was inductively analysed encompassing a co-construction of meaning between the participants’ expressions and researcher’s own interpretation. Two major themes emerged from the data namely involvement; and challenges. In this paper, the results of one of the main themes developed, that is challenges will be discussed. This paper focuses on three types of challenges reported by the participating parents of this study which are “parents and family factors”, “school and teacher factors” and “child factors”. The findings of this study suggest some implications for the system surrounding struggling readers in the Malaysian context including strengthening home-school relationship and establishing educational program for parents.
Postal Automation System in Gurmukhi Script using Deep Learning

Nowadays in the era of automation, the postal automation system is one of the major research areas. Developing a postal automation system for a nation like India is much troublesome than other nations because of India’s multi-script and multi-lingual behavior. This proposed work will be helpful in the postal automation of district names of Punjab (state) written in Gurmukhi script, which is the official language of the state in North India. For this, a holistic approach i.e. a segmentation-free technique has been used with the help of Convolutional Neural Network (CNN) and Deep learning (DL). For the purpose of recognition, a database of 22[Formula: see text]000 images (samples) which are handwritten in Gurmukhi script for all the 22 districts of Punjab is prepared. Each sample is written two times by 500 different writers generating 1000 samples for each district name. Two CNN models are proposed which are named as ConvNetGuru and ConvNetGuruMod for the purpose of recognition. Maximum validation accuracy achieved by ConvNetGuru is 90% and ConvNetGuruMod is 98%.
Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information

Line segment matching is essential for industrial applications such as scene reconstruction, pattern recognition, and VSLAM. To achieve good performance under the scene with illumination changes, we propose a line segment matching method fusing local gradient order and non-local structure information. This method begins with intensity histogram multiple averaging being utilized for adaptive partitioning. After that, the line support region is divided into several sub-regions, and the whole image is divided into a few intervals. Then the sub-regions are encoded by local gradient order, and the intervals are encoded by non-local structure information of the relationship between the sampled points and the anchor points. Finally, two histograms of the encoded vectors are, respectively, normalized and cascaded. The proposed method was tested on the public datasets and compared with previous methods, which are the line-junction-line (LJL), the mean-standard deviation line descriptor (MSLD) and the line-point invariant (LPI). Experiments show that our approach has better performance than the representative methods in various scenes. Therefore, a tentative conclusion can be drawn that this method is robust and suitable for various illumination changes scenes.
School Financial Management based on Accountability and Transparency

This study was conducted with the aim of analyzing school financial management based on accountability and transparency in educational organizations, especially State Vocational Schools in Pekanbaru City. Data collection was carried out on 9 State Vocational Schools in Pekanbaru City, with the number of respondents consisting of principals, treasurers, teachers and members of school committees. The approach used in this study is an exploratory research approach. For the analysis of data and information obtained in research activities used quantitative descriptive techniques. Based on the level of technology readiness as an indicator that shows how ready or mature a technology can be applied and adopted by users/prospective users, it can be on a scale of 3. So that the results of this study can explore financial management in education units with an in-depth analysis of the principles of accountability and transparency in management. finance at the State Vocational School of Pekanbaru City, Riau Province, Indonesia. In general, the results of this study found that financial management at SMK Negeri in Pekanbaru City from the analysis of accountability and transparency has been carried out well, but has not been maximized so that it needs to be improved. This is because some schools are still based on rules and technical guidelines and do not yet have their own innovation/creativity that can support transparency and accountability in financial management. The main contribution of this research is recommendations to improve accountability and transparency of school financial management. The recommendation is the need for innovation and creativity in school financial management so that school financial management becomes flexible. In addition, there is a need for regulations governing the management of school finances, especially funds sourced from school committees.
Support Vector Machine Classifier for Prediction of Breast Malignancy using Wisconsin Breast Cancer Dataset

Cancer is the world's second largest cause of death.In 2018, 9.6 million people died from cancer.In any medical sickness, breast cancer is one of the most delicate and endemic diseases.This is one of the primary causes of female death in the world.Breast cancer kills one out of every eleven women around the world."Early detection equals improved odds of survival," says a well-known cancer adage.As a result, early detection is essential for successfully preventing breast cancer and lowering morality.Breast Cancer is a type of cancer that affects one of the most significant issues that humanity has faced in recent decades has been diagnosis and prediction.Cancer detection that is accurate can save millions of lives.Effective technologies for diagnosing malignant breasts aid healthcare providers in diagnosing and treating patients in a fast and accurate manner.Experiments were carried out in this study to categorize breast cancer as benign or malignant using the Wisconsin Diagnosis Breast Cancer (WDBC) database.Support Vector Machine is a supervised learning technique (SVM).The SVM classifier's classification performance is evaluated.Experiments demonstrate that the SVM model has a fantastic performance, with a classification accuracy of 96.09 percent on the testing subset.
Visual analysis of global research trends in social bots based on bibliometrics

Purpose In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots. Design/methodology/approach Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics. Findings The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots. Originality/value Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336 .
Causal Event Extraction using Iterated Dilated Convolutions with Semantic Convolutional Filters

Causal Event Extraction (CEE) is a joint extraction task of events and causality, which can help text understanding, event prediction and so on. Recent research has achieved state-of-the-art performance in various Natural Language Processing (NLP) tasks by combining pre-trained models with neural networks. However, ambiguity of event description and long-distance dependence of event causality result in the low accuracy of extractors. In this paper, we propose a model to incorporate in-domain knowledge by taking frequent expression of event causality into account, and use iterated dilated convolutions to expand the perception field of event causality. External causal knowledge is modeled as frequent n-grams with different length, which is used as convolution filters during kernel initialization, enhancing the ability of model to capture the boundary of event description. To obtain long-distance dependence of event causality, we use iterated dilated convolutions to aggregate context from the entire sentence. Experimental results show that our method significantly outperform the baselines with faster convergence speed.
A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers

Data center, as an important infrastructure of cloud computing, is experiencing rapid growth in both quantity and scale, which causes the high energy consumption and severe environmental problems restricting the development of data centers. Task scheduling can significantly improve the energy efficiency in cloud computing and alleviate the constrain of the high stress on environment. But efficient task scheduling in heterogeneous cloud environment is rather challenging because of the dynamic and complicated environment of data centers. In this paper, we propose a new meta-heuristic task scheduling algorithm called WACOA combining the whale optimization algorithm with the ant colony algorithm, which uses pheromones to collect part excellent solutions from historical information to schedule tasks. Experiments show that WACOA is superior to the whale optimization algorithm and ant colony algorithm. WACOA can reduce energy consumption and improve the performance on task scheduling.
Design of a Confidentiality Model Using Semantic-Based Information Segmentation (SBIS) and Scattered Storage in Cloud Computing

Though cloud computing offers several essential advantages such as cost cutdowns, accessibility, and expandability there arise issues related to the safety of information due to the missing of controls over the storage and administration of the outsourced information which still retards several users from transferring to the cloud environment. There is a diverse confidentiality safeguarding scheme based on the pre-planned encoding of outsourced information. The information encoding provides vigorous safety but the expenses in obstructing the effectiveness of the services and feature restrictions are employed over the encoded information onto the cloud environment. Since effectiveness and features are significant merits of cloud computing particularly in SaaS it is essential in designing a confidentiality safeguarding scheme that is dependent on dividing the information and based on the scattered storage provided by the progressively standard schemes of multi-clouds.
Improved probability hypothesis density filter for multi‐target tracking of non‐cooperative bistatic radar

Non-cooperative bistatic radar refers to the passive bistatic radar using a non-cooperative radar as the illuminator of opportunity. Limited by the non-cooperation and bistatic configuration, multi-target tracking of the non-cooperative bistatic radar is confronted with challenges of low detection probability and high clutter. Based on the sequential Monte Carlo implementation of probability hypothesis density (PHD) filter, this study proposes an improved PHD (I-PHD) filter. Similar to the tracking gate in data association, whether each candidate target is detected at each time is determined and recorded, and the sequential probability ratio test distinguishes a real target and false target based on this record. To distinguish miss detection and target death, a new survival probability dependent on target position is defined, and the survival probability of a specific target drops rapidly when it is located near the boundary and moves outwards. To label targets and extract their states, a new method of track identification and state estimation is proposed. The simulation results illustrate that the proposed I-PHD filter can effectively track multiple targets of non-cooperative bistatic radar under low detection probability and high clutter and has superior performance than competing methods.
MigSGX

Recently, containers are widely used to process big data in clouds. To prevent information leakage from containers, applications in containers can protect sensitive information using enclaves provided by Intel SGX. The memory of enclaves is encrypted by a CPU using its internal keys. However, the execution of SGX applications cannot be continued after the container running those applications is migrated. This is because enclave memory cannot be correctly decrypted at the destination host. This paper proposes MigSGX for enabling the continuous execution of SGX applications after container migration. Since the states of enclaves cannot be directly accessed from the outside, MigSGX securely invokes each enclave and makes it dump and load its state. Atthe dump time, each enclave re-encrypts its state using a CPU-independent key to protect sensitive information. For space- and time-efficiency, MigSGX saves and restores a large amount of enclave memory in a pipelined manner. We have implemented MigSGX in the Intel SGX SDK and CRIU and showed that pipelining could improve migration performance by up to 52%. The memory necessary for migration was reduced only to 0.15%.