Reviewerly/exHarmony huggingface dataset:
Comment: On Focusing, Soft and Strong Revision of Choquet Capacities and Their Role in Statistics

We congratulate Ruobin Gong and Xiao-Li Meng on their thought-provoking paper demonstrating the power of imprecise probabilities in statistics. In particular, Gong and Meng clarify important statistical paradoxes by discussing them in the framework of generalized uncertainty quantification and different conditioning rules used for updating. In this note, we characterize all three conditioning rules as envelopes of certain sets of conditional probabilities. This view also suggests some generalizations that can be seen as compromise rules. Similar to Gong and Meng, our derivations mainly focus on Choquet capacities of order 2, and so we also briefly discuss in general their role as statistical models. We conclude with some general remarks on the potential of imprecise probabilities to cope with the multidimensional nature of uncertainty.
Data Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network

Abstract For the problems of privacy data protection caused by massive data sharing in the construction of power Internet of things, a data sensitivity measurement and classification model based on information entropy and BP neural network is proposed. Firstly, a recognition matching algorithm is proposed to identify the sensitive level of attributes in the dataset, and the information entropy is used to determine the weight of attributes sensitivity level, so as to calculate the sensitivity measurement value of records in the dataset; finally, the BP neural network is used to output the data classification results. The experimental results show that the model can achieve accurate measurement and classification of data, with low incorrect judgment rate and error rate.
ELECTRAMed: a new pre-trained language representation model for biomedical NLP

The overwhelming amount of biomedical scientific texts calls for the development of effective language models able to tackle a wide range of biomedical natural language processing (NLP) tasks. The most recent dominant approaches are domain-specific models, initialized with general-domain textual data and then trained on a variety of scientific corpora. However, it has been observed that for specialized domains in which large corpora exist, training a model from scratch with just in-domain knowledge may yield better results. Moreover, the increasing focus on the compute costs for pre-training recently led to the design of more efficient architectures, such as ELECTRA. In this paper, we propose a pre-trained domain-specific language model, called ELECTRAMed, suited for the biomedical field. The novel approach inherits the learning framework of the general-domain ELECTRA architecture, as well as its computational advantages. Experiments performed on benchmark datasets for several biomedical NLP tasks support the usefulness of ELECTRAMed, which sets the novel state-of-the-art result on the BC5CDR corpus for named entity recognition, and provides the best outcome in 2 over the 5 runs of the 7th BioASQ-factoid Challange for the question answering task.
Fast pupil tracking based on the study of a boundary-stepped image model and multidimensional optimization Hook-Jives method

A new fast method for pupil detection and eyetracking real time is being developed based on the study of a boundary-step model of a grayscale image by the Laplacian-Gaussian operator and finding a new proposed descriptor of accumulated differences (point identifier), which displays a measure of the equidistance of each point from the boundaries of some relative monotonous area (for example, the pupil of the eye). The operation of this descriptor is based on the assumption that the pupil in the frame is the most rounded monotonic region with a high brightness difference at the border, the pixels of the region should have an intensity less than a predetermined threshold (but the pupil may not be the darkest region in the image). Taking into account all of the above characteristics of the pupil, the descriptor allows achieving high detection accuracy of its center and size, in contrast to methods based on threshold image segmentation, based on the assumption of the pupil as the darkest area, morphological methods (recursive morphological erosion), correlation or methods that investigate only the boundary image model (Hough transform and its variations with two-dimensional and three-dimensional parameter spaces, the Starburst algorithm, Swirski, RANSAC, ElSe). The possibility of representing the pupil tracking problem as a multidimensional unconstrained optimization problem and its solution by the Hook-Jeeves non-gradient method, where the function expressing the descriptor is used as the objective function, is investigated. In this case, there is no need to calculate the descriptor for each point of the image (compiling a special accumulator function), which significantly speeds up the work of the method. The proposed descriptor and method were analyzed, and a software package was developed in Python 3 (visualization) and C ++ (tracking kernel) in the laboratory of the Physics and Mathematics Faculty of Kamchatka State University of Vitus Bering, which allows illustrating the work of the method and tracking the pupil in real time.
Improving neural network using a sine tree-seed algorithm for tuning motor DC

A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.
Adapting MARBERT for Improved Arabic Dialect Identification: Submission to the NADI 2021 Shared Task.

In this paper, we tackle the Nuanced Arabic Dialect Identification (NADI) shared task (Abdul-Mageed et al., 2021) and demonstrate state-of-the-art results on all of its four subtasks. Tasks are to identify the geographic origin of short Dialectal (DA) and Modern Standard Arabic (MSA) utterances at the levels of both country and province. Our final model is an ensemble of variants built on top of MARBERT that achieves an F1-score of 34.03% for DA at the country-level development set—an improvement of 7.63% from previous work.
Exploring Data Augmentation for Multi-Modality 3D Object Detection

It is counter-intuitive that multi-modality methods based on point cloud and images perform only marginally better or sometimes worse than approaches that solely use point cloud. This paper investigates the reason behind this phenomenon. Due to the fact that multi-modality data augmentation must maintain consistency between point cloud and images, recent methods in this field typically use relatively insufficient data augmentation. This shortage makes their performance under expectation. Therefore, we contribute a pipeline, named transformation flow, to bridge the gap between single and multi-modality data augmentation with transformation reversing and replaying. In addition, considering occlusions, a point in different modalities may be occupied by different objects, making augmentations such as cut and paste non-trivial for multi-modality detection. We further present Multi-mOdality Cut and pAste (MoCa), which simultaneously considers occlusion and physical plausibility to maintain the multi-modality consistency. Without using ensemble of detectors, our multi-modality detector achieves new state-of-the-art performance on nuScenes dataset and competitive performance on KITTI 3D benchmark. Our method also wins the best PKL award in the 3rd nuScenes detection challenge. Code and models will be released at https://github.com/open-mmlab/mmdetection3d.
Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices

Cloud applications are increasingly shifting from large monolithic services, to complex graphs of loosely-coupled microservices. Despite their advantages, microservices also introduce cascading QoS violations in cloud applications, which are difficult to diagnose and correct. We present Sage, a ML-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised learning models to circumvent the overhead of trace labeling, determines the root cause of unpredictable performance online, and applies corrective actions to restore performance. On experiments on both dedicated local clusters and large GCE clusters we show that Sage achieves high root cause detection accuracy and predictable performance.
Automatic Adjustment Method for Rotor Dynamic Balance System

This paper aims to develop an automatic adjustment method (AAM) that integrates 3D sensor and dynamic balancing platform using 3D computer vision technology and dynamic balance algorithm to improve the efficiency of rotor dynamic balancing. Active rotor dynamic balancing process relies on technicians to mount washers on particular balance columns based on their experience, therefore uncertainty causes productivity decline. The proposed AAM introduce 3D sensors on active rotor dynamic balance machine to get 3D point cloud of rotor and balance columns. First, by 3D depth data, the background noise can be removed to detect the positions of axis center, key phasor and balance columns of rotor automatically. Second, combine with unbalance vector from dynamic balancing machine, the AAM system calculated the optimal balance configuration by the vector analysis algorithm. Experiments in industrial examples showed that, compared to conventional method, the proposed AAM requires fewer rounds to achieve acceptance. In contrast, the industrial rotor balancing method previously used by operators requires more than three rounds in average. That is, the proposed AAM can accurately analyze the dynamic balance and greatly reduce the redundant dynamic balance operations. In conclusion, compared to the conventional approach, the proposed AAM is superior in terms of efficiency, effectiveness and robustness in online optimization of rotor dynamic balance. Notably, the effectiveness of the AAM has also been confirmed by Taiwan manufacturers that use rotor dynamic balance machines.
RASA Conversational Agent in Romanian for Predefined Microworlds

Technology is becoming omnipresent in our lives due to its accessibility and ease of use.Conversational agents facilitate interactions in natural language and are frequently employed to perform repetitive tasks in a specific context.We introduce a conversational agent for Romanian built on top of the open-source RASA framework, capable to communicate in predefined microworlds.Two scenarios were considered, namely: a smart home assistant which interprets commands to IoT devices, and an interactive infopoint for our university focusing on providing guidance to students.Several enhancements were considered, including an NLP pre-processing pipeline from spaCy and a knowledge graph implemented using Grakn for conceptualizing the information accessible to the agent.Our agent can quickly classify intents and extract entities with high accuracy for a given microworld (F1-score of 97% for the first microworld and 93% for the second).A survey on 10 users showed high satisfaction in terms of the usefulness and the succinctness of the provided information.
Development of Personalized Learning Resources Recommendation System Based on Knowledge Graph

This study focuses on how to efficiently and accurately recommend personalized learning resources for users when they are in the face of massive learning resources. A recommendation system is developed with software engineering methods. Knowledge graph technology is integrated in the system; curriculum knowledge graphs are constructed to solve the problems of semi-structured data storage and knowledge fragmentation. Two kinds of recommendation methods are adopted to achieve the goal of learners' personalized learning, one is Euclidean distance recommendation algorithm based on user behavior graph library, the other is learning mode recommendation algorithm and sequential mode recommendation algorithm based on user session library. The recommendation system maintains the interpret-ability and the accuracy of recommendation based on user historical behavior data; and realizes recommendation based on user session library in the context of lacking users’ historical data.
Methods for Rock-hewn Worksite Analysis: The Church of Göreme n° 4b (Cappadocia): A Case Study

Some rock-hewn structures present oddities in their layout, far from the models shared by the majority of similar monuments. These particularities challenge and question the very process of their design. While a technical survey may explain part of these singular cases, a careful comparative selection of the available methods remains paramount to draw a complete picture of the progress and organisation of these peculiar worksites. Goreme n° 4b is a rock-hewn church located on a ridge on the w...
Comics as Learning Medium During the Covid-19 Pandemic

Abstract: This development research aims to develop products in the form of learning media which are packaged in the form of digital comics. The development method used is R & D-4D and comic design using Adobe Pothoshop and Comic Life software. Then to test the feasibility of the media, validation was carried out regarding the material and design. The development of learning media in the form of digital comics aims to facilitate teachers and students in the learning process and parents of students in accompanying their children to study at home during the Covid-19 pandemic. This research succeeded in developing comic learning media based on the scientific approach for class students. IV on the material around the rectangle in jpg and pdf format, so that it can be accessed via Android-based mobile phones. The percentage of validation test design experts and material experts was 81.667% and 83.571%. This shows that the media developed is suitable for use as learning media.
Data Mining Technology in Business Data Analysis

Abstract With the vigorous development of e-commerce, people pay more and more attention to data mining technology. This research mainly discusses the application of data mining technology in business data analysis. This study uses the clustering algorithm of buyers’ purchasing behavior as an example to illustrate the definition of VMML in the clustering analysis of purchasing behavior. In this piece of XML, through the integration of data, an e-commerce research data warehouse is constructed, which is also used as the source data of EBizDAS. A certain amount of research has been done on buyers, sellers, commodities and transaction data, and the results obtained by EBizDAS analysis are displayed on the C2C e-commerce data analysis web platform for C2C users to browse and reference online. In this study, the 10 queries in CoHadoop took a total of 849 seconds, while the distributed strategy took a total of 748 seconds, with a performance improvement of about 12%, and the overall performance improvement was still considerable. This research has positive significance for the development of e-commerce.
Readiness to introduce digital currency: is the central bank independence important?

The paper put forward the hypothesis suggesting that central bank’s advances on the way of CBDC projects depend on the level of their independence. At the same time, the theory demonstrates some ambiguity of how to interpret independence in respect of CBDC involvement especially in the case of empirical tests because indexes of central banks independence are tailored to the assessment of relations between monetary authorities’ status and inflation. A high level of the central bank independence index rank may mean a narrow focus on monetary policy, while a low level of independence may mean a lack of resources to be allocated to execute the innovative projects. To avoid such theoretic ambiguity in the empirical test the linearly and quadratic functions are applied in the case of the central bank independence proxy. Such proxy is added by proxies for economic development, innovations and financial development. The role of political regimes is also examined. The empirical results show that the intermediate central banks independence level might play the role in how deeply central banks are plunged in CBDC projects. The Intermediate level is interpreted as a situation when central banks operate with enough financial independence to realize innovative projects but scope of responsibilities goes beyond a narrow focus on monetary policy. But central banks status proxy is valid in the multivariate regression. The most statistically significant driver of advances toward CBDC is financial development. Proxy for innovations is valid only in the univariate regression. In the case of multivariate regression the innovation capacity of the country is less important than financial development and central bank’s status.
Consideration of the Basic Competencies of a Preschool Teacher in Curriculum Modernization

The work deals with an important aspect of teacher training — the formation of competence in the field of technologization of the pedagogical process. An updated theoretical model of a holistic pedagogical process is presented, which is the basis for coverage of the criteria for the formation of abilities for the constructive and systematic introduction of technologies into pre-school education. The author’s questionnaire The Scale of Self-Assessment of Pedagogical Abilities for the Technologization of the Pedagogical Process was presented. The article provides the results of its testing on a sample of a total of 412 respondents, including 170 preschool teachers with work experience from 1 to 25 years, 200 — students-future teachers, 42 - PEF coordinators. Based on the comparison of samples of teachers with different work experience, it describes how the nature of professional activity determines the specific structure of competences in the field of TPP. The experiment was a part of research; authors showed the conditions for the implementation of a special curriculum, which helps effectively prepare teachers for the use of modern educational technologies.
Using Term Frequency - Inverse Document Frequency to find the Relevance of Words in Gujarati Language

TF-IDF, term frequency-inverse document frequency, is used to evaluate the relevance of a word to a document in a collection. It is used in information retrieval and text mining field. In this technique, the weight of the word is calculated which signifies the importance of the word in the document. This paper discusses about the approach to determine TF-IDF for documents inGujarati language, which is a morphologically rich Indo-Aryan language.The approach calculates TF-IDF, based on the method to find the frequency of the words in the document.The system shows significant results, which is discussed in detail.
A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint

In teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multiagent reinforcement learning (MARL), where agents need to cooperate with one another, a student may fail to cooperate well with others even by following the teachers' suggested actions, as the polices of all agents are ever changing before convergence. When the number of times that agents communicate with one another is limited (i.e., there is budget constraint), the advising strategy that uses actions as advices may not be good enough. We propose a partaker-sharer advising framework (PSAF) for cooperative MARL agents learning with budget constraint. In PSAF, each Q-learner can decide when to ask for Q-values and share its Q-values. We perform experiments in three typical multiagent learning problems. Evaluation results show that our approach PSAF outperforms existing advising methods under both unlimited and limited budget, and we give an analysis of the impact of advising actions and sharing Q-values on agents' learning.
Attribute dependency data analysis for massive datasets by fuzzy transforms

Abstract We present a numerical attribute dependency method for massive datasets based on the concepts of direct and inverse fuzzy transform. In a previous work, we used these concepts for numerical attribute dependency in data analysis: Therein, the multi-dimensional inverse fuzzy transform was useful for approximating a regression function. Here we give an extension of this method in massive datasets because the previous method could not be applied due to the high memory size. Our method is proved on a large dataset formed from 402,678 census sections of the Italian regions provided by the Italian National Statistical Institute (ISTAT) in 2011. The results of comparative tests with the well-known methods of regression, called support vector regression and multilayer perceptron, show that the proposed algorithm has comparable performance with those obtained using these two methods. Moreover, the number of parameters requested in our method is minor with respect to those of the cited in the above two algorithms.
BERT meets Cranfield: Uncovering the Properties of Full Ranking on Fully Labeled Data

Recently, various information retrieval models have been proposed based on pre-trained BERT models, achieving outstanding performance.The majority of such models have been tested on data collections with partial relevance labels, where various potentially relevant documents have not been exposed to the annotators.Therefore, evaluating BERTbased rankers may lead to biased and unfair evaluation results, simply because a relevant document has not been exposed to the annotators while creating the collection.In our work, we aim to better understand a BERTbased ranker's strengths compared to a BERTbased re-ranker and the initial ranker.To this aim, we investigate BERT-based rankers performance on the Cranfield collection, which comes with full relevance judgment on all documents in the collection.Our results demonstrate the BERT-based full ranker's effectiveness, as opposed to the BERT-based re-ranker and BM25.Also, analysis shows that there are documents that the BERT-based full-ranker finds that were not found by the initial ranker.