Reviewerly/exHarmony huggingface dataset:
A Study of Application of Data Mining Algorithms In Healthcare Industry

Data mining is a relatively new area of computer science that brings the concept of artificial intelligence, data structures, statistics, and database together.It is a high demand area because many organizations and businesses can benefit from it.There is no doubt that it is a great idea to use data mining tools in the healthcare systems to improve decisions made by the experts in this field.It has been seen that certain groups of people living in certain areas of the world with certain food diets carry certain diseases more than some other groups of people.It will be a great contribution to the healthcare system to know what the root of this type of problems is.The most critical challenge in this contribution is the issue of data management and utilization.Data mining algorithms can be used to find useful patterns in the patients' statistical data to find the associations among certain disease and possible causes of them.Thus, health care organizations are finding value as well as strategic applications to mining patient data, in general, and community data, in particular.While significant gains can be obtained and have been noted at the organizational level of analysis, much attention has been given to the individual, where the focal points have centered on privacy and security of patient data.While the privacy debate is a salient issue, data mining (DM) offers broader community-based gains that enable and improve healthcare forecasting, analyses, and visualization.This paper will study a few cases where data mining has been used to mine patient data to make decisions about patients and investigates how data mining can be beneficial in the context of healthcare system.
Information extraction from Visually Rich Documents using graph convolutional network

Visually rich documents, such as forms, invoices, receipts, and ID cards, are ubiquitous in daily business and life. Various methods have been used to convey such diverse information, including text, layout, font size, or text position. Combining these elements in information extraction can improve the result performance. However, previous works have not effectively utilized the cooperation between these rich information sources. Text detection and recognition have been performed without semantic supervision (e.g., entity name annotation), and text information extraction has been performed using only serialized plain text, ignoring rich visual information. This paper presents a method for extracting information from such documents, which integrates textual, non-spatial, and spatial visual features. The method consists of two main steps and uses three deep neural networks. The first step, Text Reading, employs two CNN models (Lightweight DB and C-PREN) for OCR tasks, based on the state-of-the-art models DB and PREN, with two improvements. These improvements include reducing noise by removing the SE block of DB and integrating both context and position features in PREN. The second step, Text Information Extraction, uses a graph convolutional network, RGCN, for name entity recognition. Experiments on self-collected and two public datasets have demonstrated that our method improves the performance of the original models and outperforms other state-of-the-art methods.
VEER: enhancing the interpretability of model-based optimizations

Many software systems can be tuned for multiple objectives (e.g., faster runtime, less required memory, less network traffic or energy consumption, etc.). Such systems can suffer from “disagreement” where different models have different (or even opposite) insights and tactics on how to optimize a system. For configuration problems, we show that (a) model disagreement is rampant; yet (b) prior to this paper, it has barely been explored. We aim at helping practitioners and researchers better solve multi-objective configuration optimization problems, by resolving model disagreement. We propose a dimension reduction method called VEER that builds a useful one-dimensional approximation to the original N-objective space. Traditional model-based optimizers use Pareto search to locate Pareto-optimal solutions to a multi-objective problem, which is computationally heavy on large-scale systems. VEER builds a surrogate that can replace the Pareto sorting step after deployment. Compared to the prior state-of-the-art, for 11 configurable systems, VEER significantly reduces disagreement and execution time, without compromising the optimization performance in most cases. For our largest problem (with tens of thousands of possible configurations), optimizing with VEER finds as good or better optimizations with zero model disagreements, three orders of magnitude faster. When employing model-based optimizers for multi-objective optimization, we recommend to apply VEER, which not only improves the execution time, but also resolves the potential model disagreement problem.
Supplementary Figure from AACR Project GENIE: 100,000 Cases and Beyond

Supplementary Figure from AACR Project GENIE: 100,000 Cases and Beyond
Artificial Intelligence-Based Breast Nodule Segmentation Using Multi-Scale Images and Convolutional Network

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge.As a result, the diagnostic performance is significantly biased.To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice.This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process.In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network.Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process.By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies.The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.
In-depth Recommendation Model Based on Self-Attention Factorization

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company.Traditional recommender systems use Factorization Machines for rating predictions and each feature is selected with the same weight.Thus, there are problems with inaccurate ratings and limited data representation.This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems.This model uses Convolutional Neural Networks to extract features from user and item reviews.The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error.The model was experimentally evaluated using six classes of dataset.We compared MSE, NDCG and time for several real datasets.The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.
STUDY OF THE LEVEL AND STRUCTURE OF MOTIVATION FOR STUDIES OF BACHELOR STUDENTS IN THE DIRECTIONS OF PREPARATION "MANAGEMENT", "ECONOMY"

Practical psychologists and theorists usually consider motivation as an extremely complex controller of behavior and moderator of human activity, a multidimensional, heterogeneous complex of "drivers" interconnected in a non-linear way. The aim of the study was to assess the quality of the motivational aspect of the education process through an analysis of the dynamics of the development of the motivational complex of students during the entire time of study at the university. The study used a classification of learning motives, dividing motives into cognitive ones, formed directly by educational activities, and external, social motives that lie outside of learning. As a research method, we used the method modified by the authors for studying the motives of A. A. Rean and V. S. Yurkevich, adapted for students in higher education. The article describes a series of techniques for developing interest in learning among university students. The results of measuring the level of motivation and the structure of the motivational complex are described and demonstrated. Ideas have been put forward that the level and structure of motivation to study can serve as an indicator of the quality of education. Suggestions for managing the motivational complex are given.
Ends and Means of Knowledge Management: Tacit Knowledge to Explicit Knowledge Conversion

The author interprets conversion of tacit knowledge into explicit knowledge as means-end of knowledge management. The subject of the article is tacit knowledge owned by an employee. The purpose is to reveal the reason for the conversion of non-formalized knowledge of employees to formalized knowledge of a firm. For the knowledge-based production, it is not necessary to formalize all knowledge. The conversion of non-formalized knowledge of employees to formalized knowledge of the organization is one of the functional goals of the firm owner, necessary to obtain stable profit without the direct participation of employees, as well as an end in itself for knowledge managers. The study can serve as a starting point for continuing scientific research in the field of tacit knowledge and as theoretical basis for the development of practical measures for using the knowledge of employees with due respect to their legitimate interests and rights.
New results in SAT – cryptanalysis of the AES

Many significant computational problems can be converted to SAT problems. Although these problems are often encoded by formulas with hundreds of thousands of clauses and propositional variables such formulas can sometimes be effectively solved by contemporary SAT solvers. Such solvers can be successfully used for cryptanalysis of symmetric ciphers. In this case, operations of the cipher are written in the form of Boolean formulas. Then, together with randomly selected bits of the plaintext and the key, it is transformed into a set of clauses corresponding to the encryption of the given algorithm. The ciphertext obtained using SAT solvers is used to create a set of decryption clauses. Using the SAT solver again, we go to the cryptanalysis of the cipher with the selected open text, looking for the bits of the encryption key. In this article, we examine how SAT techniques behave to the AES cipher, the current standard for symmetric encryption. We also compare the results obtained in this case by several SAT solvers.
A Probe into the Hybrid Teaching Mode of Advanced Mathematics Based on "Adaptive Learning"

Advanced Mathematics, as an important basic subject, is a basic compulsory course to all engineering majors. In order to improve the teaching effect of "Higher Mathematics" and cultivate personalized innovative talents, this paper discusses the blended teaching mode based on "adaptive learning". On the basis of using modern information technology to improve "teaching and learning environment" and "teaching and learning methods", this paper studies the customization of personalized learning content and learning objectives, and realizes the transformation from "teaching-oriented" to "learning-oriented" in higher mathematics, so as to give full play to students' subjective initiative as possible. Finally, by comparing the blended teaching with the traditional teaching, it is found that the blended teaching based on "adaptive learning" greatly improves the teaching effect and can be widely used in practical teaching.
Research and Practice on Detection of Abnormal Campus Accounts Based on User Sign-in Logs of Business Systems

In recent years, information security has been getting more concerned, and timely detection of abnormal campus accounts has great significance to the information security of colleges and universities. This paper proposes an abnormal campus accounts detection method using a machine learning model to detect abnormal campus accounts that have suspicious activities, and uses the quantity and the percentage of user sign-in logs of different campus business systems to detect accounts that have unusual access behaviors. This paper uses user sign-in logs of campus business systems for 9 consecutive months and the known abnormal campus accounts as data sources, and generates samples based on the proposed method to train the machine learning models, then verifies the detection effect of the trained machine learning models. The results show that the proposed method achieves a detection rate of up to 75% with a false-positive rate of 0.26% in identifying abnormal campus accounts.
Research on the training mode of innovative and entrepreneurial talents in higher vocational colleges based on the integration of production and education under the normal state of epidemic prevention and control

At present, the entrepreneurship education of college students in my country still has the following pain points, such as the in-depth integration between production and teaching, the lack of effective integration of various educational resources to create a complete education security system and innovation and entrepreneurship system, the lack of a relatively complete talent training curriculum system, lack of Measures of the integration of production and education to comprehensively cultivate the ability of innovation and entrepreneurship of college students, etc. This paper proposes that by combining the characteristics of higher vocational colleges, deepening the integration of production and education, promoting innovation and entrepreneurship, and building a talent training model for innovation and entrepreneurship, the education chain, talent chain, industry chain and innovation chain can be organically connected. Practice has proved that all parties involved have achieved a win-win situation of co-construction and sharing.
Review Study of Image De-Noising on Digital Image Processing and Applications

This paper reviews several studies of image de-noising on digital image processing and applications. Noisy images contain different noise that exist either due to environment or electronic interferences. Ergo, de-noising is crucial to eliminate the noise that disturb data collecting process. The impact of de-noising on image processing can result for accurate and precise data collected from the image. Additionally, de-noising process required several crucial steps that help to enhance knowledge on digital image and its application. Hence, study and understanding de-noising can improve multiple aspect such as image quality, data sensitivity and specificity, accuracy of the collected data, and increase the percentage of each parameter.
Research on Mixed Teaching Mode Based on Excellent Resource Sharing Courses

With the constant infiltration of the concept of education and teaching reform and the proposal of new curriculum teaching standards, more and more teachers have increased their attention to the mixed teaching model, and have expanded and extended the teaching content by taking high-quality resource sharing courses as an important carrier of effective teaching methods.Through the application of high-quality teaching resources, we can help students expand their learning horizons and enrich their subject knowledge reserves, practically implement various relevant teaching policies, and change the traditional single teaching method, in order to bring students a better sense of knowledge learning experience, and achieve the ultimate teaching purpose of enhancing students' core literacy.
Mathematical Statistics and Big Data Analysis on Intelligent Teaching Evaluation Model

The advent of the era of big data has had a profound impact on teaching evaluation.With the help of statistical knowledge, this paper starts with data preprocessing such as trimmed mean and score standardization, and further discusses the application of big data analysis, data consistency and correlation, reliability and validity in teaching evaluation.Based on mathematical statistics and big data analysis, a systematic intelligent teaching evaluation model is constructed.
Research on College English O2O Classroom Teaching Evaluation Model under the Environment of Internet + Education Ecology

The latest College English Teaching Guide (2020) provides corresponding guidance for both teaching methods and teaching evaluation: College English should vigorously promote the deep integration of modern information technology and curriculum teaching, and College English courses should use information technology to establish normal monitoring and feedback mechanism of basic teaching data, and promote course evaluation under new teaching modes such as online and offline blended teaching.Therefore, it is necessary to study the O2O classroom teaching evaluation model of college English under the environment of Internet + Education ecology.The scientific evaluation index system can not only objectively evaluate the current teaching situation, find the advantages and disadvantages in the current situation, but also provide a scientific and systematic reference basis for the new round of teaching practice.
We All Make Mistakes, But We’re Not All Human: The Influence of Voice-based Assistant’s Mistake on Anthropomorphism: An Abstract

In 2021, at least 132 million individuals in the USA used voice-based assistants in some form or the other (eMarketer, 2022). Alexa, Cortana, Siri, Bixby, and Google Assistant are some of the most prominent voice-based assistants that are integrated into the everyday lives of most individuals. While these voice-based assistants perform many important tasks in their day-to-day lives, they are also known to make frequent mistakes (Charlton, 2019). For example, they often make mistakes in processing human commands and ask users to repeat their questions multiple times. How do consumers react to such mistakes? Research on voice-based assistants (VA) is still nascent and has only recently started exploring their influence and impact in a marketing context (Moriuchi, 2019; Dellaert, 2020; Tassiello et al., 2021; Whang & Im, 2021). To the best of our knowledge, no research in marketing has studied the influence of VA’s mistakes on anthropomorphism perceptions. According to Foehr and Germelmann (2020, p. 182), voice-based assistants can be conceptualized as “technologically based on artificial intelligence systems that react to consumers’ voices and are able to adapt to individual consumer habits and needs over time, they possess some degree of autonomy, and they consist of a software component and a hardware component.” The current research proposes a framework to categorize VA’s mistakes and empirically tests how these mistakes influence user perceptions. In particular, this research aims to understand the effects of three different types of VA mistakes resulting from incorrect output from a VA on perceptions of anthropomorphism. Grounded in SEEK theory of anthropomorphism, the current research demonstrates the positive effect of VA failures on anthropomorphism perceptions. Furthermore, based on descriptive gender stereotypes, the current research also illustrates the existence of gender stereotypes that users hold for VA. Particularly, the current research demonstrates that gender cues such as the voice of the VA (male vs. female) enhance the effect of mistakes on anthropomorphism perceptions. The proposed research has two main theoretical contributions. This research contributes to the anthropomorphism literature by empirically demonstrating that a mistake by VA lead to higher anthropomorphism. Additionally, this research proposes the ‘suggestive’ gender of the VA influences anthropomorphism perceptions. By doing so, the current research also contributes to the literature on descriptive norm stereotypes and illustrates the exitance of gender stereotypes among VA technology.
Evaluation of Tweet Sentiments Using NLP

With the changing behavior of different types of social networking sites, such as Instagram, Twitter, SnapChat, etc. the amount of data shared by people, or users of a particular social site, is rapidly expanding. Every day, around million and billion pieces of data are uploaded. This is because a certain website has millions of users. These folks want to offer their thoughts and opinions on any topic they want. We hope to deduce the feelings behind these posts in this study. It is difficult to collect and analyze people’s reactions to purchasing a product, using public services, and so on. Sentiment analysis is a frequent debate preparation work that seeks to uncover the sentiments that underpin opinions in various texts. In recent years, sentiment analysis researchers have focused on assessing opinions on a variety of topics, including movies, commercial products, and everyday societal challenges. Twitter is one of the most popular micro-blogs where customers may express themselves. Opinion research using Twitter data has gotten a lot of attention in the previous decade. The two methodologies for assessing feelings from the text are first one is the knowledge base approach, and the second one is machine learning based approach. In this work, we use a Machine Learning based technique to evaluate tweets on electrical devices, such as smartphones and laptops. It is feasible to determine the influence of domain information on sentiment categorization by performing sentiment analysis in each domain. A novel feature vector for categorizing of tweets as positive or negative, as well as extracting people’s opinions on items has shown. Paper presents an analysis of popular methodologies for opinion mining, such as machine learning and lexicon based approaches, as well as assessment measures.
Practical Implications of Dequantization on Machine Learning Algorithms: A Survey

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.