Reviewerly/exHarmony huggingface dataset:
A Novel Ensemble Model for Breast Cancer Diagnosis

Breast cancer is one of the world's second-leading causes of death. One out of every eight women experiences this illness at some point in their lives. Patients with early detection of the disease have a higher likelihood of survival and have a better chance of recovery. There is a critical requirement to classify tumors for malignancy. This study investigates automatic breast cancer prediction using a variety of machine learning techniques. The most prevalent type of classification is binary (benign cancer versus malignant cancer). Machine learning (ML) techniques are now being employed to detect breast cancer. They have a high degree of classification accuracy and a wide range of diagnostic capabilities. This research work presents a novel ensemble model for binary classification of breast mass tumors on the WDBC dataset. The outcomes of seven distinct machine learning individual models (logistic regression, KNN, SVC, etc.) are compared to the hybrid ensemble model of the above seven techniques. The evaluation of the model is done in terms of numerous performance indicators, such as accuracy, precision and recall. Compared to stand-alone models, the results demonstrate that the ensemble model performs remarkably well, with an accuracy of 0.98%.
Machines and music: instrumental contributions to bluegrass

Abstract In the performance of bluegrass fiddle tunes, each repetition of the tune is generally played on a different instrument. I argue that the degree to which the instrument can influence the motivic material in improvised passages is beyond idiomaticism – where phrases might suit one instrument more than another – to the point where melodic pitch collections are shaped by the instrument itself. By combining post-human philosophies with music theories that emphasise instrument–player relationships, this essay shows how non-humans exercise agency in bluegrass improvisation. The resultant instrument-influenced passages contrast with each other, as each is played on a different instrument. This can help to signify formal structure within a performance, while the recurrence of particular instrument-influenced elements can be seen as a genre marker in bluegrass.
Higher Vocational English Online Course System Based on the Test Paper Formation Algorithm of the Distance Education System

At present, our country is actively promoting the informatization of education, using modern information technology to change the traditional education model, using modern information technology to change the traditional education model, thereby improving the quality of teaching. With the development of open education, the English distance education project has also opened a lifelong learning education system, which shows that the resources of the distance education system have been fully utilized. The quality of the whole people has improved, and the cost of education has also been reduced. This is a great project that benefits the country and the people. Modern information technology is a way of distance learning English. The main courses used in distance learning English are the course software required by modern distance learning training tools. Time constraints have brought huge challenges and opportunities to the education field. The purpose of this article is to study the online course system of English for higher vocational education based on the test paper generation algorithm of the distance education system. This article establishes a higher vocational English online course system based on the distance education system, and compares it based on some references and data from the Internet. In this paper, the distance education system and self-organizing test paper algorithm are analyzed, and corresponding optimization is carried out according to the use of this system. After a large number of investigations and experiments, comprehensive consideration of the cost and time of system implementation and the reliability of the test paper composition module have been optimized and analyzed. Corresponding practice is carried out to verify the reliability and authenticity of the self-organized test papers of the system, and the actual data is used to prove the practicality of the higher vocational English online course system based on the algorithm of distance education system test papers. Practice according to the data in this article It proves that the paper composition module in the higher vocational English online course system based on the distance education system paper composition algorithm in this article will accurately obtain the basic information of the candidates in order to accurately and accurately obtain the basic information of the candidates, and will choose a large number of difficult questions. Intermediate questions and give up difficult questions temporarily. In the data practice of this article, questions of medium difficulty accounted for 35% of the vertical score.
Report on the 1st workshop on argumentation knowledge graphs (ArgKG 2021) at AKBC 2021

The first workshop on Argumentation Knowledge Graphs (ArgKG) was held virtually at the Automated Knowledge Base Construction (AKBC 2021) conference on October 7, 2021. ArgKG @ AKBC 2021 brought together the Computational Argumentation and Knowledge Graphs communities, aiming to promote cross-pollination of ideas and encourage discussions and collaborations between the two communities. This paper describes the workshop and compiles several of its findings and insights. Date : 7 October, 2021. Website : https://argkg21.argmining.org.
Research on Teaching Reform Assisted by Online Teaching System of “Power Electronic Technology” under the New Engineering

Based on the new demand of national strategic development and the new situation of international competition, the construction of new engineering is to take the initiative to respond to the new round of scientific and technological revolution and industrial transformation, and support service innovation to drive development. “Power Electronic Technology” is a basic technical course for automation, electrical engineering and automation majors. It is widely used to realize the conversion and control of electric energy by power electronic devices. New engineering construction, as the background, based on the online teaching system to carry out all-round teaching reform, actively carry out online teaching and online learning, online teaching activities such as design of online teaching system, this paper proposed to carry out the project teaching method, the new trend of development of the micro-class teaching and follow the reform measures, such as for the “Power Electronic Technology” course teaching reform provides new methods and new ideas.
Knowledge Graph Completion via Bidirectional Translation and Interaction

Knowledge graphs have achieved great success for many artificial intelligence related downstream tasks. However, they are still far from being incomplete. In order to automatically predict missing facts, researchers proposed many knowledge graph completion methods. To address the issues that translation-based methods in handling the complex relations and symmetric relations of knowledge graph completion task, a new knowledge graph completion method via bidirectional translation and interaction called BI-TransE is proposed in this paper. First, BI-TransE embeds the entities and relations of triples into low dimensional vectors. Then, BI-TransE models the complex relations, such as 1-to-N, N-to-1, N-to-N, through the interaction between entities and relations, and models symmetric and inverse relations leveraging bidirectional translation score function. We evaluate our BI-TransE for knowledge graph link prediction task. Experimental results show that, BI-TransE can work well on complex relations and symmetric and inverse relation connectivity patterns, and achieves new state-of-the-art performance on four large-scale popular datasets.
Improved Actor Relation Graph Based on Motion Features for Group Activity Recognition

Group activity recognition is an important problem in video understanding, yet a challenging task because of more than one people in the same scene. Exploring relation between individuals and building the actor graph is an effective solution for recognizing group activity. However, how modeling the relationship among actors precisely is a crucial problem. Previous methods are limited in spatial features included actor appearance, distance between actors and other static information, which ignore the temporal dynamic information like the change of individual features between different frames which is important to infer group relationship. To address this issue, we manage to propose a novel group activity recognition network named Actor Motion Relation Graph (AMRG). It makes full use of the motion information of individual features over time. Specifically, we firstly utilize and adjust the motion excitation module to explore the change of individual features over time. Then, we embed the motion excitation module to the Actor Relation Graph (ARG) network to build the AMRG network to infer the relation matrix more precisely. Moreover, experimental results show that the proposed AMRG framework outperforms the state-of-the-art methods on the Collective Activity dataset.
Enhancing the Awareness of e-Mental Health Messages: The Effects of Narrative, Emoji, and Relevance

Computer-mediated[aut] Chan, Chi-Keung Alex communication[aut] Chan, Ka-Wai Kelly (CMC) via new digital media has been enriching the forms and modes of human communication. These fast-growing changes have also provided great opportunities for mental health professionals to deliver mental health messages and services more effectively and timely through these new digital media (e.g., mobile apps). Nevertheless, very few research studies have examined the various characteristics of an e-health message on mental health awareness, including the nature of an e-health message, use of emojis in a message, and relevance of a message. The present study conducted an online experimental study with a 2 (Nature of a message: narrative vs non-narrative) × 2 (Use of emojis: with emojis vs without) × 2 (Content of a message: relevance vs non-relevance) factorial design. This study recruited 169 university students with moderate mental health status screened by the 18-item Psychological Well-being Scale. After initial screening, the participants were randomly assigned to one of the eight conditions to read an e-mental health message with different combinations of message characteristics. After reading the e-mental health message, the level of mental health awareness (status, message credibility, and personalization) was measured. The results showed that only the main effect of using emojis in e-health messages was significant for mental health status but not for message awareness. The main effects of using narrative and relevant messages were not significant. All two-way interaction effects and the three-way interaction effect were insignificant. This study provides a better understanding of the use of emojis in e-mental health messages for raising awareness of mental health status. Furthermore, the effects among narrative, emoji, and relevance on the awareness of e-mental health messages need to be further investigated.
Educational design as an intelligent foundation and creative source of prospective engineers’ professional development

The innovative nature of engineering and technical education is focused on the digital productions dynamics and is largely determined by the quality of knowledge acquired at the university. Lifelong education, continuous renewal and the production of new knowledge based on available knowledge has become a necessary requirement for the individuals competitiveness in a modern society, where information and knowledge are the main resource and driving force. The subject of the study is substantiation of the educational project role as a result of professional education, which allows us to outline ways, techniques, methods that provide prospective specialists with an adequate understanding of the real world, successful adaptation to life in an information oversaturated environment and intellectual development in the digital world. The introduction of the educational project concept in the thesaurus of pedagogy allows us to strengthen the role of knowledge in professional and personal development. The paper establishes that the essence of educational design as a pedagogical phenomenon consists in the duality of educational and professional contexts; in the indication of the procedural manifestation in the project activity of prospective engineers professional and personal qualities; in the installation of an increase in experience of a value attitude to living knowledge when developing projects for creative transformation of the technosphere. The solutions given in the study are of a research nature and can be used in other similar Russian and foreign projects, as well as implemented when creating personalized e-learning systems and online courses in the dynamics of formation and development of educational design.
Creativity Processes Applied to the Innovation Management of a Digital Showroom for Cars

The COVID-19 pandemic has profoundly impacted the Brazilian service sector. With the health crisis, challenges and changes in the business models of car dealerships are also intensified. Creativity is discussed by several conceptual approaches. From the point of view of neuroscience, it is a response to the human need of solving problems. It is within this premise that the present work aims to enhance value proposition for a business model centered on automobile e-commerce. This purpose unfolds in an exploratory research on approaches, methodologies, and tools of creativity, as well as the application of Cristiano Alves's creative framework in this business model. The analysis of the results signals improvements in solutions for car e-commerce. With this, it is concluded that collective creativity is efficient in the discovery of potentially innovative ideas.
Saturated Reflection Detection for Reflection Removal Based on Convolutional Neural Network

Single image reflection removal is a technique that removes undesirable reflections, which occur due to glass, from images. Various methods of reflection removal have been proposed, but unfortunately, they usually fail to remove reflections particularly with very high pixel values. In this paper, we define these saturated reflections and their characteristics, as well as discuss and propose a removal system. The proposed system detects areas of saturated reflections based on our proposed model of convolutional neural networks and restores them by a conventional method of image estimation. In our experiments, the proposed system shows better peak-signal-to-noise ratio scores and perceptual quality than conventional methods of reflection removal.
Intuitive Robot Programming and Interaction Using RGB-D Perception and CNN-Based Objects Detection

Robots programming requires knowledge about a specific robot programming language and control interfaces. Also, re-programming the robot to perform a slightly different task requires domain-specific knowledge. These requirements limit the application of robots in small-scale flexible production plants where the robot should be re-programmed frequently. This paper presents a human-robot interface for programming a robotic arm with an RGB-D camera for pick-and-place tasks using natural language. We propose a novel architecture of the system that utilizes voice recognition, pointer, and CNN-based object detection modules to intuitively program robot motions. The created system allows the operator to program motion trajectories or choose an object for grasping using a visual pointer and voice commands.
An Enhanced Data Anonymization Approach for Privacy Preserving Data Publishing in Cloud Computing Based on Genetic Chimp Optimization

The data privacy. It is the biggest challenge in medical field to share and publish sensitive information about an individual to the cloud infrastructure. Therefore, it is essential to protect the patients’ information with high security and more data privacy. In this paper, a novel technique based on Mondrian based k-anonymization incorporated with Genetic-Chimp Optimization Algorithm is proposed to protect the privacy of the patients. The optimization algorithm employs average equivalence value and generalized information loss for the calculation of fitness value. Moreover, DNA-Genetic algorithm based encryption technique is also implemented after anonymization process to give extra protection to the anonymized database. The performance of the proposed privacy preservation technique is evaluated with respect to parameters such as information loss, privacy and utility. It can be observed that the proposed approach shows better results and it is efficient to preserve the privacy of medical databases when compared to other techniques.
An Efficient Low-Complexity Convolutional Neural Network Filter

Convolutional neural network (CNN) filters have achieved significant performance in video artifacts reduction. However, the high complexity of existing methods makes them difficult to be applied in actual usage. In this article, an efficient low-complexity CNN filter is proposed. We utilized depth separable convolution merged with the batch normalization as the backbone of our proposed CNN filter and presented a frame-level residual mapping (RM) to use one network to filter both intra- and intersamples. It is known that there will be an oversmoothing problem for the interframes if we directly use the filter trained with intrasamples. In this article, the proposed RM can effectively solve the oversmoothing problem. Besides, RM is flexible and can be combined with other learning-based filters. The experimental results show that our proposed method achieves a significant bjøntegaard-delta(BD)-rate reduction than H.265/high efficiency video coding. The experiments show that the proposed network achieves about 1.2% BD-rate reduction and 79.1% decrease in FLOPs than VR-CNN. Our performance is better with less complexity than the previous work. The measurement on H.266/versatile video coding and ablation studies also ensure the effectiveness of the proposed method.
Big Five Personality Prediction from Handwritten Character Features and Word ‘of’ Using Multi-label Classification

Handwriting is an expressive behavior and communicates one’s personality characteristics. In this work, we try to build a correlation between personality traits and handwriting features employing machine learning techniques. Quantitative analysis is done for correlating handwritten features and personality traits. Two datasets are created for the work, personality trait value dataset and handwriting feature value dataset. The Big Five personality assessment model along with BFI questionnaire are used to collect personality data. It is observed that in handwriting samples of a person, there is a consistency of certain features in respect of graphometric measurements which are both repeatable and reliable. We utilize the measurements related to four lowercase letters of English handwritten script, namely ‘a’, ‘g’, ‘n’ and ‘t’, and the features of the word ‘of’ as well. Personality trait prediction from handwriting features is formulated as a multi-label classification problem. Each of the five traits corresponds to a class in the classifier. Both non-binary and binary label values are examined by using three multi-label classifiers, Classifier Chain (CC), Binary Relevance (BR) and Label-Power set (LP). We employ KSTAR, K-nearest neighbor and Multi-layer Perceptron (MLP) as base classifiers. CC-KSTAR (94.1% with non-binary label values) and CC/LP-KSTAR (97.6% with binary label values) provide the best result considering train-test (67:33) as test option. CC-KNN (ED) (98.1% with non-binary label values) displays the best result considering cross validation as a test option.
NLP Applications for Big Data Analytics Within Healthcare

The significance of integrating Natural Language Processing (NLP) approaches in healthcare research has become more prominent in recent years, and it has had a transformational impact on the state-of-the-art. In healthcare, NLPs are developed as well as assessed on the basis of words, phrases, or record-level explanations based on patient reports such as side-effects of medications, Medicines for illnesses or semantic characteristics are prescribed (nullification, seriousness), etc. While some NLP projects take into account customer expectations at the level of an individual or a group, these projects are still in the minority. A special focus is placed on psychological wellness research, which is currently the subject of little research in healthcare NLP research networks but where NLP approaches are widely used. Although there have been significant advancements in healthcare NLP strategy improvement, we believe that in order for the profession to grow further, more emphasis should be placed on comprehensive evaluation. To help with this, we offer some helpful ideas, including one on a minor etiquette that may be used when announcing clinical NLP strategy improvement and assessment.
Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

The emergence of embedded and connected smart devices, systems, and technologies has given rise to the concept of smart cities in modern metropolises. They've made it possible to connect “anything” to the internet. As a result, the internet of vehicles (IoV) will play a critical role in newly constructed smart cities in the approaching internet of things age. The IoV has the ability to efficiently tackle a variety of traffic and road safety issues in order to prevent fatal crashes. Furthermore, ensuring quick, secure transmission and accurate recording of data is a particular problem in the IoV, particularly in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections. Furthermore, the authors qualitatively examined the suggested overall system performance and resiliency against popular security assaults. The proposed method solves the primary issues of vehicle-to-x (V2X) communications, such as centralization, lack of privacy, and security, according to computational experiments.
Dynamic DNN Nodes Drop-out Rate Analysis

The Dropout method is very useful when dealing with the overfitting problem that occurs during the training process of a data set whose data size is in the range thousands to tens of thousands. But the dropout rate in the current dropout method is fixed. This work aims to propose several methods that can dynamically adjust the dropout rate during training. Compared with the traditional dropout method, the method of dynamically adjusting the dropout rate can more effectively prevent the overfitting phenomenon, and ensure that the data is fully trained and improve the accuracy. The method can improve dropout performance and plays a more critical role in the latest federated learning and other fields.