Reviewerly/exHarmony huggingface dataset:
Building a smart dynamic kernel with compact support based on deep neural network for efficient X-ray image denoising

Gaussian filtering is a successful computer operation vision to reduce noise and calculate the gradient intensity change of an image. However, it’s well known that in scale space context, the Gaussian kernel has some drawbacks, loss of information caused by the unavoidable Gaussian truncation and the prohibitive processing time due to the mask size. To give a solution to both problems, a new kernel family with compact support and its separable version were presented in the literature. The theoretical study of these kernels shows that the new family kernel is parameterised by a scale parameter and generated in such a way that fine scale structures are successively suppressed when the scale parameter is increased. Moreover, the scale parameter is increased, the image is blurred and details and border are removed. All these disadvantages are related to the static nature of these kernels. In this paper, we propose a smart kernel based on deep neural networks (dnn) to create a dynamic kernel with compact support called DSKCS. The parameter involved in the filtering process is calculated in real time and supervised by deep neural networks. The filter is continuously variable to detect, clean and avoid noisy areas of the image. Extensive experiments show that the proposed kernel can improve the classic kernel and presents a solution for its limitations related to its static nature. Furthermore, different metrics calculated illustrate our approach efficiency. As stated in the filtering performance, which reveals the highest PSNR and NCC with the metrics results (PSNR = 32.18, NCC = 0.95). Also, we recorded more than 0.89 for area under curves of the classification results using DBN-DNN technique.
BIG DATA SERVICE CAPABILITY OF ENTERPRISE SCALE DATA MIDDLE PLATFORM

With the rapid development of technologies such as big data, cloud computing, Internet of things and artificial intelligence, electric power enterprises are faced with the strategic deployment of digital transformation. By upgrading IT architecture, power grid companies build the data middle platform, and separate the data operation and product technology from its front platform. Through service reusing and data sharing, the business capability can be shared, which providing technical support for power enterprises to serve a variety of complex business scenarios. This paper focuses on the architecture design of data middle platform in power grid companies, and analyzes its service capability. In addition, this paper sorts out the capability output framework of big data middle platform, and analyzes the data service requirements and application modellings for data middle platform in different business scenarios.
A Brief Introduction to RSA Encryption

RSA encryption is commonly used in the modern world now and we will make a brief introduction on this particular encryption method in this paper. We will focus on how RSA encryption works and why this encryption method is reliable.
Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. Filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers to deal with this issue. Most of the existing researches and review papers dealing with feature selection in single-label data. While, recently multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining, and bioinformatics. Moreover, researchers have recently focused on applying swarm intelligence methods in selecting prominent features of multi-label data. To the best of our knowledge, there is no review paper that reviews swarm intelligence-based methods for multi-label feature selection. Thus, in this paper, we provide a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for multi-label classification tasks. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures, and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works, and at the end of this survey, some challenges, issues, and open problems of this field are introduced to be considered by researchers in the future.
A Proposed hash algorithm to use for blockchain base transaction flow system

Blockchain technology introduces a new approach to storing information, implementing tasks and functions, and building trust between participating nodes.Although blockchain technology has received extensive attention in various application contexts in recent years, the issue of privacy and security remains the primary focus of discussions of the blockchain.The use of hash algorithms can provide secure blockchain integration, and many hash algorithms offer solutions to data integrity and security problems within the context of blockchain technology.However, they are also subject to problems related to time, lack of resources, and memory usage.In this research, an algorithm is proposed to generate a hash based on chaos theory (1D and 2D) logistic maps and the new Merkle-Damgård construction.Hash outputs are tested in terms of time, complexity, and collision.The proposed algorithm is evaluated according to Jaccard similarity and various coefficient measurements, and it was found that the similarity between the inputs and the outputs does not exceed 0.1932 percent.All outcomes indicated successful performance.The proposed algorithm was implemented on a blockchainbased transaction flow system, consumed fewer resources than other hash algorithms (such as SHA1, SHA2, and MD5), and requires mere milliseconds to implement.
Instagram as a Media to Foster EFL Students’ English Writing Skill

The rapid development of technology encourages educators to create interesting learning methods that are attractive to students. Using Instagram as the learning medium in teaching, this research aimed to find out students’ writing skills improvement and their perception after being taught using Instagram. This research used a mixed-method design to analyze the students’ writing improvement and perception of the use of Instagram. To collect data, the researcher carried out a pretest and posttest of one class pre-experiment with 25 students and a semi-structured interview of ten participants who got the significant posttest writing improvement. Statistical findings revealed the students’ result of the posttest was higher than pretest, the data shows that the Significance (2-tailed) value is smaller than 0.05. It means statistically there is a significant difference between students’ pretest and posttest results. This was supported by the semi-structured interview on which the result shows the students’ perceptions of the use of Instagram as a learning medium were mostly positive. The findings of this research are relevant to the development of English Language Teaching, particularly to create interesting learning strategies that are attractive to students.
Research on the Ranked Searchable Encryption Scheme Based on an Access Tree in IoTs

With the continuous development of the Internet of things (IoTs), data security and privacy protection in the IoTs are becoming increasingly important. Aiming at the hugeness, redundancy, and heterogeneity of data in the IoTs, this paper proposes a ranked searchable encryption scheme based on an access tree. First, this solution introduces parameters such as the word position and word span into the calculation of the relevance score of keywords to build a more accurate document index. Secondly, this solution builds a semantic relationship graph based on mutual information to expand the query semantics, effectively improving the accuracy and recall rate during retrieval. Thirdly, the solution uses an access tree control structure to control user authority and realizes fine-grained access management to data by data owners in the IoTs. Finally, the safety analysis of this scheme and the efficiency comparison with other existing schemes are given.
Human Activity Classification Using Deep Convolutional Neural Network

The human activity classification is an identification of person’s actions from an image or video frames captured by a camera. This helps in tracking person movements which are useful for various purposes. The human activity classification is a challenging task due to factors like complex background captured in the image, occlusion, and camera view angle. Therefore, there is a need for development of a robust human activity classification system. In this paper, a classification model based on deep convolutional neural network is proposed which extracts coordinates of human joints and parts affinity field from an image for predicting human pose and classification of activity. The model is designed to carry out classification of six major activities, and they are raising hand, pick-up an item, squats, namaste greeting, on-phone, and pointing a gun. These activities represent a class of suspicious and non-suspicious activities. The activity “namaste greeting” is included considering the COVID-19 issue. The proposed deep convolutional neural network comprises of two branches for joints coordinates estimation and parts affinity field estimation. The model is trained on MPII human pose dataset and tested on live videos captured from a webcam to evaluate its performance. The model gives highest training accuracy of 99.7 and 99.2% test accuracy.
Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor

The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals.
Dynamic Review-based Recommenders

Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of text. In the present work we leverage the known power of reviews to enhance rating predictions in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end. Moreover, our representations are time-interval aware and thus yield a continuous-time representation of the dynamics. We provide experiments on real-world datasets and show that our methodology is able to outperform several state-of-the-art models. Source code for all models can be found at [1].
A Context-Aware and Immersive Puzzle Game using Machine Learning and Big Data Analysis

Recent years, video games have become one of the main forms of entertainment for people of all ages, in which millions of members publicly show their screenshots while playing games or share their experience of playing games [4]. Puzzle game is a popular game genre among various video games, it challenges players to find the correct solution by providing them with different logic/conceptual problems. However, designing a good puzzle game is not an easy task [5]. This paper designs a puzzle game for players of all age ranges with proper difficulty level, various puzzle mechanics and attractive background setting stories. We applied our games to different players to test play and conducted a qualitative evaluation of the approach. The results show that the pace of puzzle games affects play experience a lot and the difficulty level of the puzzles affects players' feelings to the game.
Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization (AEMO) is discussed.Firstly, reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied, a distance measure and an adaptive penalty function are used to address this scenario.Secondly, AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition, the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed, the trial solutions with poor quality will gradually be forgotten.Therefore, the number of trial solutions is decreased, and the convergence of the algorithm is accelerated.Finally, a numerical simulation is conducted, and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.
A 3D FPGA Track Segment Seeding Engine Based on the Tiny Triplet Finder

the data item to be found in a pattern recognition problem contains two free parameters, and if the constraint connecting the measurements and the two free parameters has an approximate shift invariant property, the Tiny Triplet Finder can be used.
DYNAMICS AND IMPACT OF DIGITAL FOOTPRINT ON PROJECT SUCCESS

The digital footprint of the project is its integral characteristic, reflecting both the “official” information on the project, unnecessary and any mention of the project including social networks and other Internet resources. An entropy model for assessing the success of a project is proposed. The model covers the content (semantic part), its environment, and the dynamics of implementation. The increase in informational entropy (uncertainty) of the project cannot be estimated as the sum of the increase in entropy associated with each element of the digital footprint. The main reason for this is the synergy inherent in the digital footprint. Separately insignificant elements of a digital footprint of a project of a negative nature, with their significant number and periodic appearance, cause a “snowball” effect, which leads to an increase and exaggeration of small destructive effects of individual digital footprints. Therefore, it makes no sense to consider the increase in information entropy for each element of the digital footprint of the project, but it is necessary to consider the impact of each new element of the digital footprint on the information entropy of the project. Each element of an active digital footprint is formed on time and meets the necessary requirements. The organized formation of a digital footprint promotes order in documentation, timely awareness of risks, the formation of the required level of demand for a project product, and a proactive assessment of success. The digital footprint should also help attract new stakeholders to create a favorable project image and promote the project’s product. The fulfillment of this set of conditions ensures a decrease in entropy and, as a result, the success of the project. Conversely, if active and passive digital traces of a project increase entropy, then the likelihood of project success is reduced.
LEARNING CHANNELS AND KNOWLEDGE MANAGEMENT TOOLS AND PRACTICES IN VOCATIONAL TRAINING

Objective: This research aims to identify the channels of learning in a group of academics from the Pedagogy course.&#x0D; Methodological procedures: To conduct this study, an exploratory research was carried out in the case study format with data collection through a questionnaire with 20 questions that were answered by all students in the class, corresponding to 18 participants.&#x0D; Results: The results showed that most students are synesthetic and learn better if the strategy adopted by the teacher involves dynamic interactions and practical activities.&#x0D; Benefits: In this way, it is understood that the tools and practices of Knowledge Management have a lot to offer teachers in view of their demands in the classroom, as well as academics can benefit when their learning channels are considered in conducting the teaching and learning process, which contributes to the formation of competent professionals who perform the profession with excellence.
Moodle as e-learning system for esp class

The aim of the paper is to examine the correlation of the knowledge gained by students in online and offline classes, consequently, the overall performance of e-learning, as well as the effectiveness of the certain language skills among the main four (listening, speaking, reading, writing) by virtue of students’ feedback and objective experimental results using statistical methods data processing. The paper demonstrates the results of the e-learning experimental research conducted during the academic year 2020/21, including the remote learning period due to the Covid19 pandemic situation in the spring semester, at the State Agrarian and Engineering University in Podilya, Kamianets-Podilskyi, Ukraine. Online study support for ESP was personally created as a four-credit course due to ECTS in the Moodle Learning Management System (LMS) with the application of Big Blue Button. The research sample is represented by 100 students majoring in veterinary medicine and enrolled in the mandatory subject of English for Specific Purposes. The group of students was enrolled in the lessons based on their bachelor curriculum. The subjects, referred to as experimental and control groups, accordingly, both wrote the primary test, at the beginning of the course, and the final one at the end. For pre-test content, the online standard test for B1 level was applied. The post-test was created by the educators, who were involved in the experiment. Due to the results procession and test analysis, it is admitted that the students of the experimental group showed slightly worse results in listening comprehension section, while having the same indicators concerning writing skills, and they had essentially worse outcomes in speaking and reading comprehension if compared to their peers from the control group. Mann-Whitney and Wilcoxon non-parametrical pair tests were selected to check the statistical hypotheses, due to the character of input data.
Development of a Smartphone-Based Virtual Reality Platform for Radiation Oncology Patient Education

Navigating cancer care is a major life stressor and negative determinant of cancer patient quality of life and potentially patient satisfaction. Patient education has been shown to ameliorate these negative outcomes, but this has proven difficult in the field of radiation oncology. One obstacle for effective patient education in radiation oncology is the inherent difficulty of explaining a visual spatial treatment paradigm using traditional verbal or written teaching aids. However, existing visual patient education platforms are lacking in key areas including fidelity to patient experience, cost, scalability and generalizability. In that context, the present study presents a cheap, immersive, realistic, and scalable smartphone-based virtual reality interface where patients can learn about and visually experience their treatment from anywhere using almost any modern smartphone.We utilized a Ricoh Theta V 360-degree camera to capture immersive spherical still images and videos of various parts of the patient experience, including simulation, Klarity mask-making, and the treatment vault. Images and videos were uploaded and processed in the Google environment (Google Photos and YouTube, respectively). The smartphone-based virtual reality experience was accomplished using various IOS and android devices attached to inexpensive Google Cardboard-compatible headsets. Separately, the VR experience was tested on an Oculus Quest 2 device as well.Images and videos were quick and easy to obtain (initial proof-of-concept was accomplished within 3 hours), and the approach was tractable for even those with limited audiovisual experience. The immersive images and videos were subjectively tested and validated by multiple investigators on the Oculus Platform as well as the Google Cardboard Platform using both IOS and Android devices as reliably representing the chosen aspect of treatment. The overall cost required to add the VR equipment to any existing smartphone was under $15 using commercially-available components.The present study describes a VR-based radiation oncology patient education experience that is inexpensive and modular to nearly any institution or clinical scenario. The already nominal cost can potentially be driven lower by 3D printing or similar customization. Moreover, patients can engage the simulation from anywhere with minimal assistance needed. The present proof-of-concept study has the potential to increase patient quality of life, treatment compliance and satisfaction all while reducing stress. Furthermore, the current global pandemic underscores the need for remote education resources both now and in the future. To these ends, this approach is currently being prospectively validated for impact on patient knowledge, satisfaction, stress and quality of life endpoints.