Reviewerly/exHarmony huggingface dataset:
Finite-Function-Encoding Quantum States

We introduce finite-function-encoding (FFE) states which encode arbitrary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>-valued logic functions, i.e., multivariate functions over the ring of integers modulo <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>, and investigate some of their structural properties. We also point out some differences between polynomial and non-polynomial function encoding states: The former can be associated to graphical objects, that we dub tensor-edge hypergraphs (TEH), which are a generalization of hypergraphs with a tensor attached to each hyperedge encoding the coefficients of the different monomials. To complete the framework, we also introduce a notion of finite-function-encoding Pauli (FP) operators, which correspond to elements of what is known as the generalized symmetric group in mathematics. First, using this machinery, we study the stabilizer group associated to FFE states and observe how qudit hypergraph states introduced in Ref. \cite{2017PhRvA..95e2340S} admit stabilizers of a particularly simpler form. Afterwards, we investigate the classification of FFE states under local unitaries (LU), and, after showing the complexity of this problem, we focus on the case of bipartite states and especially on the classification under local FP operations (LFP). We find all LU and LFP classes for two qutrits and two ququarts and study several other special classes, pointing out the relation between maximally entangled FFE states and complex Butson-type Hadamard matrices. Our investigation showcases also the relation between the properties of FFE states, especially their LU classification, and the theory of finite rings over the integers.
Sentiment Analysis on Social Distancing and Physical Distancing on Twitter Social Media using Recurrent Neural Network (RNN) Algorithm

The government is seeking preventive steps to reduce the risk of the spread of Covid-19, one of which is social restrictions that have become popular with social distancing and physical distancing. One way to assess whether the steps taken by the government regarding social and physical distancing are accepted or not by the community is by conducting sentiment analysis. The process of sentiment analysis is carried out using a variant of the Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM). In this study, the results obtained from the sentiment analysis, where the public response to social distancing and physical distancing has more positive sentiments than negative sentiments. To measure the accuracy level of sentiment analysis using the Recurrent Neural Network (RNN) algorithm and evaluation of the modeling is done using confusion matrix where the results obtained for the training dataset are 89% accuracy, 89% recall, 89% precision, and 89% F1 Score. Meanwhile, for the test dataset, an accuracy of 80% was obtained, a recall of 79%, a precision of 81%, and an F1 score of 80%.
A Solution for Vehicle Attributes Recognition and Cross-dataset Annotation

Vehicle type recognition is a momentous application of target detection in intelligent transportation system, meanwhile, it is also one in the midst of the research hotspots of scholars at home and abroad at present. Convolutional neural networks (CNNs) model has been shown to achieve some impressive performance in several image processing fields, such as vehicle detection and type recognition. However, those vehicle detection methods available now are short of the capacity to identify the vehicle type, and the related datasets for vehicle type classification lack the annotation information of attributes such as location. Thus, we propose an improved Faster R-CNN based vehicle detection and type recognition method that consists of two parts: vehicle detection and type recognition and automatic annotation of attributes across datasets. This method first uses BIT-Vehicle dataset to train the model used for vehicle detection and type recognition, then migrates the model to the surveillance-nature data part of the CompCars dataset for labeling test, and indirectly obtains the labels of attributes. The vehicle recognition ability of the target detection model combined with ZF-Net, VGG-16 and ResNet-101 convolutional neural networks on BIT-Vehicle dataset and CompCars dataset are compared. The experimental results indicate that the scheme proposed not only has high recognition accuracy in BIT-Vehicle dataset, but also has reliable labeling results in the surveillance-nature data part of CompCars dataset.
CONCEPT DRIFT IN DATA STREAM CLASSIFICATION USING ENSEMBLE METHODS: TYPES, METHODS AND CHALLENGES

Ensemble Methods grows along with Machine Learning and Computational Intelligence domain proves to be effective and versatile. It helps in reducing variance and improves accuracy. It effectively addresses many Machine Learning challenges such as data stream classification, class Imbalance in datasets and concept drift occurrence in non-stationary environments. Data stream refers to rapidly generated heterogeneous data in a continuous way. One of the key challenges considered in learning from data streams is the detection of concept drift, i.e., changes in data distribution underlying data streams, observed over time. Such changes in incoming data deteriorate the accuracy of the classifier since classifier has been learned over past data instances that are stable. Thus detection of concept drift is important task. The real life examples of drift are spam detection, credit card fraud detection, and weather predictions. This paper provides an overview of ensemble methods, data stream classification, concept drift, its types and drift detection methods and concludes with the related works in detecting concept drift that occurs during data stream classification.Keywords: Ensemble, Classifier, Data Streams, Class Imbalance, Concept drift
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models.

When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment. Massively pretrained language models like BERT can encode not only just the words in a document but also the context around the words along with them. This begs the questions, Does a pretrain language model also automatically encode sentiment information about each word? and Can it be used to infer polarity towards different aspects?. In this work we try to answer this question by showing that training a comparison of a contextual embedding from BERT and a generic word embedding can be used to infer sentiment. We also show that if we finetune a subset of weights the model built on comparison of BERT and generic word embedding, it can get state of the art results for Polarity Detection in Aspect Based Sentiment Classification datasets.
Research on Development of Financial Fraud Application Tool Based on Adaptive Entropy Projection Clustering Algorithm for Financial Report Text Data Mining

The problem of financial fraud in listed companies in China has occurred from time to time, seriously jeopardizing the interests of the financial reporting information demand side and disrupting the normal operation of the capital market. The effective supervision of listed companies' financial reports is an effective means to achieve financial fraud control. However, due to the huge amount of corporate financial report data, it is urgent to develop financial fraud application tools suitable for financial report text data mining. Therefore, this paper constructs an adaptive entropy projection clustering model, and uses the adaptive entropy projection clustering algorithm to develop and design financial reporting text data mining and forecasting financial fraud application tools, in order to identify financial fraud enterprises to a large extent. Users of financial information can make better use of the financial information disclosed by listed companies, and make correct decisions, and promote the sound development of the capital market.
Ontology-based Data Management Tool for Studying Radon Concentration

Interpreting data for solving information needs demands an understanding of the semantics of row data.In this paper, an ontology-driven management of data of the presence of radon gas in soil is presented.The main contribution of this research relies on the formal definition of the semantic of the presence of radon in the dimensions of space and time.As a result, a dynamic data view generator for the observation of radon measurements was deployed without actual software programming but with the proper adjustment and instantiation of the ontology ViewOnto, a web ontology which was created for the formal description of data views.An experimental assessment of the proposal was carried out with data obtained from stone aggregate mines and civil constructions in Tungurahua.The usability of the resulting data management system was successfully validated with the criteria of experts on usability and researchers with experience in radon measurement.
Enhancement for graph operations in relational database for criminal intelligence domain

The aim of the research was to improve the performance of graph operations in relational database for semi-structured data. First of all, this required us to select a model that allows storing semi-structured data with relations. For this purpose, we selected one of the existing solutions, which stores semi-structured data in a model built using JSON as a native type and adapted it to our requirements. Secondly, we created an algorithm based on common table expression with recursion with the extension of JSON to facilitate more complex graph analysis of data. We compared proposed solutions with Neo4j, chosen as a representative of graph databases. The results show that we obtained an improvement in execution performance in some cases. Although we focused our use cases on criminal intelligence domain, the research output can be applied to every domain with semi-structured data.
Research on the Development of Financial Fraud Application Tool Based on Machine Learning of Financial Report Text Data Mining

China's capital market development has made great achievements, but the development has also produced a number of issues of great concern, which is more serious, and is the existence of a large number of financial reporting fraud problems of listed companies. Because the securities market does not have the ability to identify the authenticity of the financial report text data, and the financial fraud information has a certain survival soil. To this end, this paper constructs a machine learning model and data mining algorithm, and designs a financial report text data mining forecasting financial fraud application tool development method based on machine learning. After typical sample data verification, the application tool can effectively interpret and identify financial report text. The data has a good reference value for improving the early warning, discovery and prevention capabilities of financial fraud in China's capital market.
Pemanfaatan Data Mining dalam Penentuan Rekomendasi Mustahik (Penerima Zakat)

This study applies data mining in determining recommendations for mustahik. The application is carried out using a classification method with an artificial neural network algorithm where the attributes used are age and type of work of the head of the family, the condition and ownership of the residence, the place of sewage, family monthly income, number of dependents, and diet. Tests are carried out using a combination of values ​​between learning rate, epoch, k-fold, and hidden layer neurons. Based on the test results from the classification process, it is found that the artificial neural network algorithm has the highest accuracy when the number of hidden layer neurons is six, the learning rate is one, the fold is seven, and the number of epochs is 200, which is 92.09%. The test results are then displayed on the Mustahik information system page.
A Systematic Mapping Study of Behavior Driven Development

Behavior Driven Development (BDD) is an agile development method commonly known as an extension of Test Driven Development (TDD). It aims to improve the fulfillment of customer requirements in soft ...
Cultivation and Improvement of Students’ English Reading Ability in Higher Vocational Colleges

With the effective implementation of quality education, the teaching reform is accelerating, and the cultivation of comprehensive literacy abilities of vocational college students has become the focus of teaching. Therefore, vocational English teaching pays more attention to improve students’ reading ability. Based on the actual teaching practice, this article analyzes the current situation of English reading learning for vocational students and discusses countermeasures of improving students’ English reading ability, hoping to give certain enlightenment on promoting the overall quality of vocational English teaching.
Blogging as a Tool to Organize the Profession-oriented Independent Work of Future Teachers

The relevance of the study in question is caused by the growth of students' independent work and the need to train future teachers so that they can organize this work more effectively using modern technologies.One of the optimal information and communication technologies for organizing students' independent work is blogging.Therefore, this paper explores the attitude of future teachers towards blogging, its potential forms of use in organizing students' independent work.The key research method to study this problem, in our case, has been the survey of Vocational Training students.It helped us to determine what future teachers think about blogging as a teaching tool, what skills they have and do not have for this, and also the problems that a teacher may face when organizing students' independent work with blogging.This paper discusses the main features of blogging as a teaching tool.The criteria for assessing the results of student blogging, the stages of student blogging following the authors' methodology, are presented.Moreover, the article shows the results of experimental work where the Vocational Training students have been writing their blogs in Russian for over a month.In the research, it was found that student-bloggers lack independent thinking, while the majority of participants would prefer to use blogging in their future teaching activities.The results of our research conclude that the use of blogging to organize students' independent work facilitates the development of the required competencies such as professional language competence.Furthermore, it is proven that the experience and skills of blogging provide more opportunities for educators in their job and career.
Studi Eksploratif Dampak Pandemi COVID-19 TerhadapProses Pembelajaran Online Pada SMP N 48 Konawe selatan

The purpose of this research is to identifiti get the information obsteles in the proses online theaching and learning home because of covid pandemic 19. Use of cases this study qualitative used to again information resulting from the pandemic covid 19 of the learning proses in SMP N 48 Konawe Selatan. For the purpose of secrecy respondens given the initials A1,A2,A3, A4,A5,A6. Interview semi structured done and a list of questions are theachers and parents in SMP Negeri 48 Konawe Selatan. The result of this research is that there a number of problem for  student, theacher, and parents in learning activities online namely technologi mastery is week, the edition of the cost of the quota the internet, the existence of extra work in older people in accompanying the child learn, communication and socialition on among stutednts theacher and parents be diminished and the hours that nenjadi not limited for theacers because they have to communicate and coordinate whit parents other theachers and principals.
Fiction Title Semantic and Grammatical Features

The study aims at considering the title as a dicteme of a special kind, representing the dictemic name of the text, with its special semantic and structural features. The article analyzes the XVII-XX centuries British fiction titles structural and semantic features and their role in the problem of linking the title with the text content. Such organic aspect of the title complex structural-semantic unity problem as the translation adequacy and equivalence problem is studied in the article as well. Scientific novelty of the research is the consideration of a large form fiction title based on the conceptual apparatus of the dictemic text structure theory. The attained results have shown that the title requires the macro context of the entire work for its complete implementation since the author’s intention in the title is disclosed retrospectively. In different centuries, the fiction authors used different means of expressing their attitudes and ideas in their masterpieces’ titles.
ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.
MAIN APPROACHES AND IDEAS REGARDING PATRIOTIC EDUCATION OF SENIOR PRESCHOOL CHILDREN IN INTERACTION OF PARENTS AND EDUCATORS

The article defines the approach to studying the research problems. The essence of hermeneutic, systemic activity; personality-oriented approaches in the context of patriotic upbringing of senior preschool children is characterized. The significance of these approaches in interaction of parents and educators as factor of patriotic upbringing of senior preschool children is revealed. The works of scientists who studied these approaches are considered. It has been determined that thanks to the hermeneutic approach, the teacher learns to apply effective methods and forms of interaction with parents and children, based on an understanding of the tasks of patrioti ceducation, can help overcome existing prejudices regarding patriotism. The hermeneutic approach makes it possible to determine the meanings of a number of concepts, such as “interaction”, “patriotic education”, “patriotism”, etc. The importance of a systematic approach is that it will help all subjects to build an effective system of interaction, determines the focus of the teacher and parents on ensuring optimal functioning of those systems in which children are included in order to organize patriotic education. The activity approach focuses on the use of various techniques and methods of interaction between educators and parents for the patriotic education of senior preschoo lchildren; helps the active, not passive assimilation of knowledge about patriotism. The personality-oriented approach is associated with the teacher's aspirations to promote the development of the child's individuality, the manifestation of his subjective qualities. Thanks to the personality-oriented approach, it is possible to more purposefully and effectively build classes and educational activities of patriotic content. &nbsp;&nbsp
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning

Commonsense question answering (QA) requires to mine the clues in the context to reason the answer to a question, and is a central task in natural language processing. Despite the advances of current pre-trained models, e.g. BERT, they often learn artifactual causality between the clues in context and the question because of similar but artifactual clues or highly frequent question-clue pairs in training data. To solve this issue, we propose a novel DynamIc Self-sUperviSed Erasure (DISUSE) which adaptively erases redundant and artifactual clues in the context and questions to learn and establish the correct corresponding pair relations between the questions and their clues. Specifically, DISUSE contains an \textit{erasure sampler} and a \textit{supervisor}. The erasure sampler estimates the correlation scores between all clues and the question in an attention manner, and then erases each clue (object in image or word in question and context) according to the probability which inversely depends on its correlation score. In this way, the redundant and artifactual clues to the current question are removed, while necessary and important clues are preserved. Then the supervisor evaluates current erasure performance by inspecting whether the erased sample and its corresponding vanilla sample have consistent answer prediction distribution, and supervises the KL divergence between these two answer prediction distributions to progressively improve erasure quality in a self-supervised manner. As a result, DISUSE can learn and establish more precise corresponding question-clue pairs, and thus gives more precise answers of new questions in present of their contexts via reasoning the key and correct corresponding clues to the questions. Extensive experiment results on the RC dataset (ReClor) and VQA datasets (GQA and VQA 2.0) demonstrate the superiority of our DISUSE over the state-of-the-arts.
Speaker Verification Based on Log-Likelihood Score Normalization

Due to differences in the distribution of scores for different trials, the performance of a speaker verification system will be seriously diminished if raw scores are directly used for detection with a unified threshold value.As such, the scores must be normalized.To tackle the shortcomings of score normalization methods, we propose a speaker verification system based on log-likelihood normalization (LLN).Without a priori knowledge, LLN increases the separation between scores of target and non-target speaker models, so as to improve score aliasing of "same-speaker" and "different-speaker" trials corresponding to the same test speech, enabling better discrimination and decision capability.The experiment shows that LLN is an effective method of scoring normalization.
Let’s Not Make It Complicated—Using Only LightGBM and Naive Bayes for Macro- and Micro-Activity Recognition from a Small Dataset

We propose a model that combines only simple techniques to meet the challenge of cooking activity recognition. The challenge dataset is basically small, consisting only of four subjects where three are used for training and one for validation. In order not to overfit the small training data, we employed two simple classifiers, LightGBM and Naive Bayes, which suited the task. To prevent leakage from other subject data during training, we used Leave One Subject Out cross validation. Further, we incorporated a post-processing step wherein the Naive Bayes corrects the macro-activity classification outcomes that have been derived by LightGBM, based on the combinations of macro and micro activities that are likely to occur. We hypothesized that this added post-processing will improve the macro-activity recognition, and with it, our model may be able to adapt well and generalize to other small datasets. As a result, our proposed model achieved an average accuracy of 0.557 when classifying macro and micro activities from a small dataset.