Reviewerly/exHarmony huggingface dataset:
Smart Voice Assistant for Library System

Library plays an important role in student’s life. It is also helpful for the research scholars. Today libraries are established in all places. Finding a book in the library is a herculean task. Some people even loose interest while searching the required book. Searching for books in computer also takes much time. In our project we use voice recognition to find books which will make the herculean task easier. Even the people without system knowledge can access this and find books in an easier way. It reduces the time in searching books and makes the library user friendly.
Semantic search with domain-specific word-embedding and production monitoring in Fintech

We present an end-to-end information retrieval system with domain-specific custom language models for accurate search terms expansion. The text mining pipeline tackles several challenges faced in an industry-setting, including multi-lingual jargon-rich unstructured text and privacy compliance. Combined with a novel statistical approach for word embedding evaluations, the models can be monitored in a production setting. Our approach is used in the real world in risk management in the financial sector and has wide applicability to other domains.
Implementasi Inferensi Fuzzy Tsukamoto pada Prediksi Penjualan Telur Ayam Eropa pada Bisnis Raffa Telur

Eggs are an animal product that comes from poultry. Eggs are known as a food that contains nutrients that are very good for the body because they contain a high protein source. Apart from being nutritious, people consume a lot of eggs because the price is relatively cheaper than other protein foods. This research was conducted on the Raffa Egg business located in Pematangsiantar City. The data collection process was carried out by means of interviews and observations with Raffa Eggs. This study aims to predict the number of purchases of European chicken eggs from suppliers. The research was conducted using Tsukamoto fuzzy logic with 3 variables, namely sales (x), inventory (y) and purchases (z). where the sales variable (x) consists of 2 fuzzy sets, including increasing and decreasing, inventory (y) consisting of many and few fuzzy sets and purchasing (z) consisting of many and few fuzzy sets. The results of the calculation of the prediction of the number of purchases of European chicken eggs with sales of 6500 and inventory of 25 000 are 29583 items. Keywords : Eggs, European chicken eggs, Fuzzy, Tsukamoto, Pematangsiantar
Artificial Intelligence in Causality Healthcare Sector

The ideology of explainability in Artificial Intelligence (AI) is a prevailing issue which requires attention in the healthcare sector. The issue of explain ability is as ancient as AI and the sophisticated AI signified an understandable retraceable technique. Nonetheless, their demerits were in handling the uncertainties of the actual world. As a result of the advent of probabilistic education, applications have now been considered successful and considerably invisible. Comprehensive AI handles the implementation of traceability and transparency of statistical black box techniques of Machine Learning (ML), certainly Deep Learning (DL). Based on the approach of this paper, it can be argued that there is need for researchers to go beyond the comprehensive AI. To accomplish the dimension of explainability in the healthcare sector, causability aspects have to be incorporated. In the same manner that usability incorporates measurements for the quality of usage, causability incorporates the evaluation of explainable quality. In this research, we provide a number of fundamental definitions to effectively discriminate between causability and explainability, including the application case of DL and human comprehensibility in the field of histopathology. The fundamental contribution of this paper is the ideology of causability that has been differentiated from the notion of explainability whereby causability is based on personal property whereas explainability is the system property.
The Surge of e-learning during The 2020 Covid-19 Crisis: Threat or Promise? An Exploration and Conversation with a Professional in Education

The obligation for students to stay at home and take distance education due to the quarantine has turned virtual learning or e-learning into the most promising tool for safe teaching. Distance education appears to be the answer for the democratization of higher education in Latin America to eradicate poverty and inequality in work and living opportunities, still has to be accomplished. The Covid-19 crisis has forced the world education system to embrace e-learning without be prepared but does this mean a threat or as promise? In this article we explore the question at a point when the pandemic is still raging and definite answers are not yet available, firstly be presenting a framework based on previous research on e-learning in Latin-America, followed by a conversation with an e-learning professional engaged in Africa.
Can Model Checking Assure, Distributed Autonomous Systems Agree? An Urban Air Mobility Case Study

Advancement in artificial intelligence, internet of things and information technology have enabled the delegation of execution of autonomous services to autonomous systems for civil applications.It is envisioned, that with an increase in the demand for autonomous systems, the decision making associated in the execution of the autonomous services will be distributed, with some of the responsibility in decision making, shifted to the autonomous systems.Thus, it is of utmost importance that we assure the correctness of distributed protocols, that multiple autonomous systems will follow, as they interact with each other in providing the service.Towards this end, we discuss our proposed framework to model, analyze and assure the correctness of distributed protocols executed by autonomous systems to provide a service.We demonstrate our approach by formally modeling the behavior of autonomous systems that will be involved in providing services in the Urban Air Mobility framework that enables air taxis to transport passengers.
LEARNING FROM PISA EXPERIENCES: READING TASKS TO EXPLORE BIASED INFORMATION

The paper presents the results from a study involving 70 students in grade 9, from two technological highschools situated in a large town in Romania. In September 2020, the 15 year olds read and answered the questions from one of the released units after PISA 2018, and then reflected on their reading experience in a self assessment sheet. The results show good results in working with information from one article and poor ability in corroborating conflicting information from two sources that focus on a same topic. The students display little familiarity with biased information even if the majority enjoyed the reading challenge of two divergent points of view. The results are consistent with the PISA reading trend for Romania. In order to improve reading comprehension, teachers should renovate the reading practices by challenging students with work on multiple texts and tasks that focus on evaluation and reflection and on the sources credibility.
Deep Learning-based Image Analysis Model for Diagnosing Thyroid Carcinoma in Fine Needle Aspiration Cytology (FNAC) Images

In this work, the diagnostic accuracy of an automated diagnosis system is evaluated using two pre-trained convolutional neural network models, namely AlexNet and VGG16.The diagnosis system is used to identify the cancerous and normal thyroid cells in Fine Needle Aspiration Cytology (FNAC) photographs.The proposed Alexnet and VGG16 models are implemented using deep learning based Transfer Learning (TL) to process multi-stained FNAC images.Initially, the image patches are derived from the cytology images based on the thyroid cell population.These patches are fed to the 8-deep layered AlexNet and 16-deep layered VGG16 as inputs and they are passed through multiple convolution layers, max pooling and dense layers.Through optimal implementation and testing of the models, the AlexNet model achieves a diagnostic accuracy of 92.5% whereas the VGG16 model results a diagnostic accuracy of 96.66% and sensitivity and specificity of 98.75% and 92.5% respectively.
AN ANALYSIS OF THREE ENGLISH TEXTBOOKS FOR ELEVENTH GRADE OF SENIOR HIGH SCHOOL

The objectives of this study were to: (1) see whether the textbooks used for learning English meet the criteria of a good textbook and (2) reveal the strengths and weaknesses of the textbook. This research was conducted using content analysis because the data were collected from textbooks. Observation data through and document analysis. The data were analyzed through qualitative research. The object of this research is the English textbook used for class XI SMA students. The results of the study found that English to stop bullying currently fulfills the twelve criteria of a good textbook by Mukundan containing 1) Books related to syllabus and curriculum, 2) Methodology, 3) Suitability with students, 4) Physical and utilitarian attributes, and 5 ) Efficient spending of complementary materials, 6) General, 7) Speaking, 8) Reading, 9) Writing, 10) Vocabulary, 11) Grammar, and 12). Sports. Furthermore, the study also found that the advantages of English in relation to current bullying are providing appropriate communicative activities with students, providing teachers' books, and samples of material that are familiar to students. Meanwhile, the weakness of this textbook is that it does not provide assignments to develop students' listening and pronunciation skills. Also, the text and images are not up-to-date.
Neural Pooling for Graph Neural Networks

Tasks such as graph classification, require graph pooling to learn graph-level representations from constituent node representations. In this work, we propose two novel methods using fully connected neural network layers for graph pooling, namely Neural Pooling Method 1 and 2. Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs. In addition, compared to existing graph pooling methods, our proposed methods are able to capture information from all nodes, collect second-order statistics, and leverage the ability of neural networks to learn relationships among node representations, making them more powerful. We perform experiments on graph classification tasks in the bio-informatics and social network domains to determine the effectiveness of our proposed methods. Experimental results show that our methods lead to an absolute increase of upto 1.2% in classification accuracy over previous works and a general decrease in standard deviation across multiple runs indicating greater reliability. Experimental results also indicate that this improvement in performance is consistent across several datasets.
Mining Indonesia Tourism's Reviews to Evaluate the Services Through Multilabel Classification and LDA

The tourism sector is one of the mainstay factors, one of the most significant economic contributors in Lamongan. There are two leading tourism destinations in Lamongan, namely WBL and Mazoola. Evaluation of tourist experience in a tourist destination can use the reviews provided at the end of the trip. Tourists review various aspects of tourism, such as price, services, and location. Classify more than one aspect from reviews is a challenging task. Five labels used, namely: Price, Location, Safety, Services and Facilities, and Environment and Ambiance. This study was conducted to determine the aspects that should be evaluated from the reviews that visitors provide. This research uses five multi-label classifier algorithms commonly used for multi-label classification: NBSVM, Binary Relevance-Naive Bayes, Binary Relevance-Logistic Regression, Classifier Chains-Naive Bayes, and Multilabel kNN. NBSVM was a robust performer. For WBL data in scenario 1, the highest accuracy is BR-LR, which is 92%. Whereas in scenario 2, NBSVM has the highest value of 91,32%. However, in other assessments, NBSVM is still superior. Likewise to Mazola's data, NBSVM has the highest accuracy in both scenarios: 87,38% and 87,28%. This study also extracts three trend topics for each data set, WBL and Mazoola. Trend topics aim to find out what topics are discussed more frequently in each tourist destination review-topic extraction using LDA with the Gensim library.
Data Imputation for Symbolic Regression with Missing Values: A Comparative Study

Symbolic regression via genetic programming is considered as a crucial machine learning tool for empirical modelling. However, in reality, it is common for real-world data sets to have some data quality problems such as noise, outliers, and missing values. Although several approaches can be adopted to deal with data incompleteness in machine learning, most studies consider the classification tasks, and only a few have considered symbolic regression with missing values. In this work, the performance of symbolic regression using genetic programming on real-world data sets that have missing values is investigated. This is done by studying how different imputation methods affect symbolic regression performance. The experiments are conducted using thirteen real-world incomplete data sets with different ratios of missing values. The experimental results show that although the performance of the imputation methods differs with the data set, CART has a better effect than others. This might be due to its ability to deal with categorical and numerical variables. Moreover, the superiority of the use of imputation methods over the commonly used deletion strategy is observed.
Proactive Anomaly Detection Using Time Series Data of A Large Scale Platform

Ensuring high availability of large scale e-commerce platforms and promptly detecting anomalies in system components is a challenging issue. In this paper, a novel end-to-end system that constantly tracks system components and clusters similar ones, in addition, detects and reports anomalies in the tracked system component has been proposed. This study has been tested on a real platform and its performance has been thoroughly analysed in terms of success and duration.
Continual Learning with Sparse Progressive Neural Networks

Human brain effectively integrates prior knowledge to new skills by transferring experience across tasks without suffering from catastrophic forgetting. In this study, to continuously learn a visual classification task sequence (PermutedMNIST), we employed a neural network model with lateral connections, sparse group Least Absolute Shrinkage And Selection Operator (LASSO) regularization and projection regularization to decrease feature redundancy. We show that encouraging feature novelty on progressive neural networks (PNN) prevents major performance decrease on sparsification, sparsification of a progressive neural network produces fair results and decreases the number of learned task-specific parameters on novel tasks.
Collective Visioning: Igniting the Radical Imagination

As the spaces which once existed within the university for the co-imagining of liberatory political praxes diminish, social movements are increasingly reverting to methods of collective visioning which have been developed on the streets to theorise radical social change. I argue that such methodologies provide important opportunities for a reappraisal of our hierarchical student/teacher relations, and make the case that we can collectively learn much more from within our movements than anyone might hope to teach us from outside. The article traces our current movement wave, and locates contemporary forms of collective visioning within a wider lineage of knowledge co-production in social movements. The neoliberal subversion of university education is examined, and proposals made for ways in which we might challenge these hegemonic conditions. I conclude that the liberatory epistemologies and forms of knowledge co-production explored in this article offer significant potential for developing new modes of praxis for our current wave of ecological and anti-capitalist activists both inside and outside the university.
Multilingual Practices in Internet Discourse on the Social Network “Facebook” (An Analysis of Written Language of Russophone Women Living in Finland)

Russian speakers make up the biggest group of foreign-language speakers in Finland. Their use of Finnish words in Russian discourse can be interpreted as an example of multilingual practices, such as code-switching. It can also be viewed as an example of how loanwords are assimilated. While speakers of Russian in Finland are part of a worldwide internet community, they also represent a local community that can have a language form of its own. This article presents an analysis of about 500 cases of Finnish lexical items and word combinations usage in written Russian (260 different lemmas). The material for the research was selected from written internet discourse of a Russophone community on Facebook, a social media platform. Members of this community are Russian women who have been living in Finland for some time. The Finnish words were studied in the context of posts and replies to them. The 475 Finnish words found amounted to 4 % of the total 12,022 words used in the source. The analysis of the material took into account semantic and grammatical features of the items. Semantic features included the categories of proper nouns, terms and other words related to life in Finland. The grammatical analysis began by studying the choice of writing system, i.e. whether the units retained their original spelling which is either in Latin or in Cyrillic. After that, the Finnish words that had been transliterated were studied for the presence or lack of declension as compared to the Russian norm in similar uses. It was suggested that the tendency not to decline Finnish words written both in Cyrillic and Latin in the discourse also affected the syntactic positions in which they were used, making positions that did not require declension overrepresented. The number of examples subjected to the assimilation rules for loanwords in Russian (transliteration and using declension) was small. Therefore, most of the examples represent code-switching, a natural consequence of those living in Finland, and provide evidence for the existence of a local version of Russian.
The Existence of Fourier Coefficients and Periodic Multiplicity Based on Initial Values and One-Dimensional Wave Limits Requirements

Physical systems in partial differential equations can be interpreted in a visual form using a wave simulation. In particular, the interpretation of the differential equations used is in the nonlinear hyperbolic model, but in its completion, there are some limitations to the stability requirements found. The aim of this study is to investigate the analytical and numerical analysis of a wave equation with a similar unit and fractal intervals using the Fourier coefficient. The method in this research is to use the analytical solution approach, the spectral method, and the finite difference method. The hyperbolic wave equation's analytical solution approach, illustrated in the Fourier analysis, uses a pulse triangle. The spectral method minimizes errors when there is the addition of the same sample grid points or the periodic domain's expansion with a trigonometric basis. Meanwhile, different ways offer a more efficient solution. Based on the research results, the information obtained is that the Fourier analysis illustrates the pulse triangle use to solve the solution. These results are also suitable for adding sample points to the same spectra. Fourier analysis requires a relatively long time to solve one pulse triangle graph to need another solution, namely the finite difference method. However, its use is still limited in terms of stability when faced with more complex problems.
The Localization of Deleted Frames in the Forged Videos

The manipulation processes made by deleting frames on videos are the fastest and laborless method of video forgery. The process of misleading people is done by cutting the frames of the scene or object to be hidden from the related video. With this study, designing a fast-running algorithm to detect in which location the frame deletion forgery is aimed for localization the forgery location. The test of the algorithm's resistance against different compression formats and different compression qualities was carried out. Frame deletion forgeries were performed in random sizes and locations on the videos obtained from SULFA and TRACE LIBRARY databases which used frequently in the literature. Forgery location was detected with 100% accuracy at uncompressed videos. In MPEG4 and H264 compressed videos, accuracies are %85 and %90 respectively. Average processing time is 0.0007 second for frame. Experimental results show that the proposed method can detect the forgery location with high accuracy.
CONCEPTUAL FRAMEWORK FOR UNDERSTANDING EMERGING TECHNOLOGIES THAT SHAPE THE ACCOUNTING AND ASSURANCE PROFESSION OF THE FUTURE

We are currently on the verge of the fourth industrial revolution (industry 4.0) that will fundamentally change the way in which businesses and individuals operate. It is characterized by the integration of both physical and virtual world with a new pulse for greater automation and autonomy of business processes without or limited need of human intervention. Cloud computing, the internet of things, big data and data analytics, artificial intelligence and machine learning, blockchain, augmented reality are among the most exploited contributing technologies. This paper provides a conceptual framework for emerging technologies with greatest disruptive potential for the accounting and assurance profession. We have surveyed the scarce literature with the aim of investigating the relationship and interplay between most potent emerging technologies and accounting and assurance services. Our findings suggest that cloud accounting, big data, blockchain, artificial intelligence and machine learning will most likely shape the accounting and assurance world for many organisations and future skills set pursued by professionals in order to add value. We conclude that current accounting research efforts are insufficient to understand and grasp the possible future interactions between the vibrant digital economy, its emerging technologies and the accounting profession, including the changing information needs of investors.
Automatic Segmentation Method of Breast Tumor Ultrasonic Images Based on Attention-Enhancing Unet

In order to improve the accuracy of the segmentation of the breast ultrasound image lesion, Attention-Unet was improved, and an Attention-enhancing Unet (AE-Unet) model is proposed. First, the network loss function was improved. Based on the output value of the traditional network end, output weights of all attention gate were integrated. Compared with the standard lesion template, it was used to obtain accurate network loss values; Secondly, the network training method was improved, and the strategy of combining thickness and fineness was adopted. The overall loss function was used to train the overall network to make the network basically stable; then the partial loss function was used to alternately train the backbone network and the attention gate module in turn. Fine-tuning was used to further improve the accuracy of network parameters. The combination of the two greatly improves the accuracy of segmentation of the breast ultrasound lesion area. The experimental results on the breast ultrasound data actually collected in the hospital show that the proposed AE-Unet model has an M-IOU of 81.24%, precision of 85.88%, F1 of 80.58%, Acc of 93.85% and specificity of 97.48%, PPV is up to 85.88%, which has achieved better segmentation results than existing advanced algorithms.