Reviewerly/exHarmony huggingface dataset:
AdvisOnt: Semantization of Agricultural Advisory Services for Validation of Outcomes of Non-Formal and Informal Learning. I

We propose an advisory system AdvisOnt that analyses the outcomes of non-formal and informal learning and ensures their validation for more efficient matching of information about potential employees, employers and agricultural educational resources. AdvisOnt is based on ontological representation of this knowledge formalized by competencies, vacancies, training courses, user profiles, etc. The system is aimed to generate recommendations for employment or further learning of necessary competencies by matching these objects. External knowledge bases are used for semantic formalization of vacancies and resumes for their more pertinent matching with the help of agricultural domain knowledge and competence classifications. AdvisOnt users receive recommendations on employment and about training courses that provide advisable competencies.
<i>Esplorare (con) i Dizionari Digitali</i> e trasferire competenze di consultazione

As Internet became more widespread, the use of printed dictionaries decreased. However, users’ abilities to consult online dictionaries are still not adequately developed. The project Esplorare (con) i Dizionari Digitali – through Moodle platform – allows users to carry out online exercises in order to develop both their linguistic knowledge and their ability to consult online dictionaries of different languages. We present the results of an study that involved around 300 students of Italian secondary schools, showing how the structure of the platform and the exercises, common to all languages, leads to transfer of skills. The exercises were created using Möbius Assessment, an automatic assessment system, already tested for scientific subjects.
An Enhanced Differential Evolution Algorithm with Sorted Dual Range Mutation Operator to Solve Key Frame Extraction Problem

This paper proposes a modified Differential Evolution (DE) algorithm in which the conventional mutation operation of DE is replaced by a ‘sorted population’ based mutation operation. This ‘sorted population’ based mutation operation, proposed by authors, differs from the conventional mutation operation in the way in which it selects the candidates for the mutation process and the values it sets for the mutation scale factor (F). The modified DE was implemented, to verify its superiority, on solving 14 different standard benchmarking problems. A comparative study, based on the results obtained, revealed that the proposed algorithm solved the problems providing optimal solutions with lesser time, for higher dimensional problems. Next, the experiments were extended to solve the key frames problem from videos. This part of the experiment combined the conventional SSIM (Structural Similarity Index) approach of key frame extraction with the proposed DE. The results showed that the proposed DE was giving comparatively better results than classical DE.
A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.
Rotation of bits: a classical and quantum perspective

Abstract Bit rotation is an operation similar to shift except that the bits that fall off at one end are put back to the other end. In left rotation, the bits that fall off at left end are put back at right end. In right rotation, the bits that fall off at the right end are put back at the left end. Applications of bit rotation include; registers, cryptography, computing with a single bit string circularly shifted to the right or left based on some position but no work has been done with respect to shifting the bits one position at a time generating emergent bit strings equal to the number of bits-1 from the incident bit string, and then recombining or extracting bit(s) from each of the bit strings or words to form back the incident bit string. In this article, the authors present a new approach of rotating classical bit strings known as CRotate. A quantum approach to bit rotation known as QRotate is presented as well. The quantum perspective uses the concept of bit swapping by avenue of the quantum swap gate in jsqubits. Models and algorithms are duly presented.
Banking news-events representation and classification with a novel hybrid model using DistilBERT and rule-based features

This paper discusses a novel hybrid approach to text classification that integrates a machine learning algorithm along with DistilBERT, a pre-trained deep learning framework for natural language processing, offers a base model fine-tuned on Indian Banking News-Events with a rule-based method that is used by filtering false positives and dealing with false negatives to enhance the results given by the previous classifier. The major benefit is that by incorporating unique rules for certain chaotic or overlapping categories that have not been effectively trained, the system can be quickly fine-tuned. This research also compares the effectiveness of state-of-art deep contextual language representation DistilBERT used in our proposed hybrid model with the most preferred context independent language representation TFIDF, on supervised learning of the classification of multiclass Banking News-Events. Both representations are fed into the machine learning classifiers, Logistic Regression, Linear SVC, Decision Tree, and Random Forest. The findings indicate that DistilBERT can better transfer generic domain knowledge to other domains as compared to the baseline TFIDF and results into higher accuracy with Random Forest. The result is further fed into the Rules based method as per our proposed model. And, our proposed hybrid model resulted into improved accuracy.
Concept of Implementing Computer Voice Control for CNC machines Using Natural Language Processing

Currently, rapidly developing software and hardware technologies in the machine learning field boost natural language processing in many applications: from interactive voice response systems to fully automated customer support with chat-bots and even conversational bots for the entertainment industry. However, there are still fields that do not use machine learning to solve existing tasks and challenges. This paper introduces a high-level architecture of voice user interface, describes its requirements and use-cases in the context of using computer numeric control machines. Moreover, while other researchers suggest trying to automate the manufacturing process as much as possible to exclude humans from the manufacturing process, this article suggests choosing a slightly different approach and taking a fresh look at how the user interacts with the machine. The study proposes a new interaction scheme using computer voice control as an analog to computer numeric control term. Such an approach allows simplifying the human-machine interfaces on a machine and improving the safety of use and efficiency of interaction.
Non-negative Matrix Factorization for Binary Space Learning

Non-Negative matrix factorization (NMF) is a popular research problem in data dimensional reduction. Conventional NMF approaches cannot achieve a subspace made up of binary codes from the high-dimensional data space. To address the above-mentioned problem, we propose a method based on nonnegative matrix factorization to generate a low-dimensional subspace made up of binary codes from the high-dimensional data. The problem can be mathematically expressed as a 0-1 integer mixed optimization problem. For this purpose, We put forward a method based on discrete cyclic coordination descent to obtain a local optimal solution. Experiments show that our means can obtain the better clustering ability than conventional non-negative matrix factorization and its variant approaches.
Predicting Personality of Ancient People based on Transfer Learning

It is important to identify the personality of ancient people, which may help us understand more about culture change. The traditional method, self-report, is difficult to conduct research on ancient Chinese historical individuals. With the development of artificial intelligence, Transfer Learning provides a new way to identify the personality of ancient people. In this paper, we proposed Unsupervised Entropy Regression Domain Adaptation(UERDA) and Semi-supervised Entropy Regression Domain Adaptation (SERDA) based on Transfer Learning. We used the micro-blog data on Sina Weibo and autobiography data of ancient Chinese individuals for distribution adaptation, to train the personality prediction model and predicted the big-five personality traits of ancient individuals. For joint distribution adaptation, UERDA uses information entropy to realize unsupervised domain adaptation based on the regression task. SERDA is a semi-supervised method based on UERDA to improve the accuracy of the Transfer Learning model in the regression task. Compared with traditional algorithm PCA, RMSE of UERDA has decreased by 2.23 on average, and RMSE of SERDA has decreased by 3.16 on average. The experimental results showed that Transfer Learning achieved good performance in terms of the prediction of the big five personality traits of ancient people, which provides a new way for research on ancient people and culture change.
Iterative Deep Learning-based Acoustic Models using Transcription Agreement from Multi-Models Automatic Speech Recognitions

Compared to English or languages with a lot of resources, Indonesian is one of the languages which is categorized as under resource language. Indonesian has a limited transcribed speech corpus that can be used to build an acoustic model of a speech recognition system with a supervised learning approach. To overcome this problem, this research proposes the development of iterative acoustic models using an additional unlabeled speech corpus. We used unlabeled data to rebuild the acoustic models by using segment's transcriptions generated by the previously supervised developed ASR. To get more reliable transcription, we used four ASRs with four types of deep learning-based acoustic models (DNN, LSTM, CNN, and TDNN) and selected segments with consistent transcripts provided by all models or segments with fully agreement labels. This technique relatively improves the accuracy of the three ASRs that were developed using DNN-based acoustic models by 1.95%, CNN by 1.56%, and TDNN by 2.59%.
Augmentation on CNNs for Handwritten Digit Classification in a Small Training Sample Size Situation

Abstract In general, a deep learning needs a lot of samples. However, in a practical pattern recognition problem, the number of training samples is usually limited. We investigate the effect of an image data augmentation by a perspective transformation on a convolution neural network(CNN) for handwritten digit classification in a small training sample size situation. The experimental results show the effectiveness of the image data augmentation by the perspective transformation on the CNN for handwritten digit classification particularly in the small training sample size situation.
A Literature Survey on Identification of Asthma Using Different Classifier and Clustering Techniques

Asthma disease are the scatters, gives that influence the lungs, the organs that let us to inhale and it’s the principal visit disease overall particularly in India. During this work, the matter of lung maladies simply like the trouble experienced while arranging the sickness in radiography are frequently illuminated. There are various procedures found in writing for recognition of asthma infection identification. A few agents have contributed their realities for Asthma illness expectation. The need for distinguishing asthma illness at a beginning period is very fundamental and is an exuberant research territory inside the field of clinical picture preparing. For this, we’ve survey numerous relapse models, k-implies bunching, various leveled calculation, characterizations and profound learning methods to search out best classifier for lung illness identification. These papers generally settlement about winning carcinoma discovery methods that are reachable inside the writing. The probability of endurance of patients with maladies is frequently made conceivable if the sickness is recognized and analyzed in perfect time. (SVM), (KNN) and vector machine, Random help in the recognition of lung mass. A numeral of procedures has been started in malignancy recognition strategies to advance the productivity of their identification. Different applications like as help vector machines, neural systems, picture preparing methods are widely used in for asthma illness recognition which is explained during this work.
Internet communications in the information age: socio-cultural and environmental context

The article deals with specifics of Internet communications functioning in the information technology era. The emergence and development of Internet communications in human life promote the creation of virtual reality, which significantly changes the forms of relationships between people. Instead of personal meetings and direct communication, people get used to communicating in virtual space. It affects their professional activity, studying, personal life, leisure, etc. The latest forms of Internet communication not only open up to mankind the unique opportunities for a person’s creative self-realization but also make a new socio-cultural reality. Therefore, it is necessary to study the peculiarities of the new forms of Internet communications’ functioning and their impact on social development.
Technical Note: Analyzing the effect of repeated fusing on toner to examine printing alterations made by the same laser printer

With continuous innovations in laser printing technology, the number of cases of falsification using laser printers is increasing annually. Difficulties in identifying printing alternations, especially laser-printed alternations by the same printer are increasing. Effectively, repeated printing on the same page by the same printer means the original toner on the document was fused repeatedly. Thus, this study mainly investigates the effects of second fusing on the microscopic morphology of toner to examine whether documents have been tampered with. To detect the influence of fusing on the microscopic morphology of toner, 170 documents printed by 17 different models of laser printers from 5 brands were studied. It was found that, according to microscopic observations, the micrographs of toner may become brighter, darker or both with repetition of the fusing process. By calculating the average gray value of the micrographs of toner, it was found that, in some laser printers, the gray value of the thermoplastic accumulation area will increase after a second fusing, and the gray value of the toner particle dispersion area will decrease. In conclusion, by comparing the micrographs of toner from suspicious and reliable contents on the same page or in the same document, together with the measurement and analysis of average gray values, it is possible to examine the once- and twice-fused document contents, and further determine whether the document has been altered.
I futuri insegnanti d’inglese alla scuola primaria Un’indagine sulla percezione delle competenze

In this paper we explore primary school trainee teachers’ perception of their linguistic-communicative competence in EFL and of their training as language teachers. 44 questionnaires were administered and 17 semi-structured interviews were conducted with Italian trainee teachers. The findings revealed that the respondents considered themselves only partly ready to handle pluralistic approaches for teaching in a plurilingual, multicultural and socially fluid context. In fact, while they expressed confidence in their general pedagogical knowledge, they perceived their linguistic-cultural education and training insufficient to enable them to teach English for interactional purposes. A few suggestions are put forward at the end of the paper.
Balancing guidance and flexibility in the design of an inclusive handweaving loom

The paper describes a design case of an accessible handweaving loom that allows people with different kinds of disabilities to weave autonomously promoting self-expression and stimulating creativity. The project developed through several iterative cycles resulting in the redesign of a commercial loom enhanced and transformed using digital fabrication technology. People with various types of disabilities, their caregivers and weaving experts were engaged in a series of participatory design workshops focused on eliciting requirements and desires and inspiring the design process. The discussion of the design case offers a contribution on design for accessibility and a concrete case to reflect on the importance of supporting and stimulating self-expression and creativity in people with special needs.
Locally-Adaptive Naïve Bayes Framework Design via Density-Based Clustering for Large Scale Datasets

In this chapter, local conditional probabilities of a query point are used in classification rather than consulting a generalized framework containing a conditional probability. In the proposed locally adaptive naïve Bayes (LANB) learning style, a certain amount of local instances, which are close the test point, construct an adaptive probability estimation. In the empirical studies of over the 53 benchmark UCI datasets, more accurate classification performance has been obtained. A total 8.2% increase in classification accuracy has been gained with LANB when compared to the conventional naïve Bayes model. The presented LANB method has outperformed according to the statistical paired t-test comparisons: 31 wins, 14 ties, and 8 losses of all UCI sets.
WITHDRAWN: Thyroid tissue segmentation and classification in ultrasound image of Artificial intelligence

This paper suggests that it be based on a new device method for benign or malignant ultrasound examination of thyroid tissues. Ultrasound imaging is a medical instrument that is mostly used to identify and classify thyroid disorders. The suggested solution consists of four essential phases: Segmentation, grouping, pre-processing and extraction of features. Rayleigh trimmed anisotropic diffusion filter is used for pre-processing purposes. An ABC algorithm is used to break down the nodule field. Artificial neural network (ANN) and support vector machine (SVM) classifiers are used for classification tasks, using feature vectors extracted from the Gray Level Co-occurrence (GLCM) functions. Classification findings are analyzed on the basis of precision, resilience and specificity. It is derived that the SVM classifier offers better performance than the ANN for distinguishing benign and malignant nodules, achieving precision of 92.5 per cent, and sensitivity of 96.66 per cent and specificity of 80 per cent.
Belief base change as priority change: A study based on dynamic epistemic logic

AGM's belief revision is one of the main paradigms in the study of belief change operations. In this context, belief bases (prioritised bases) have been primarily used to specify the agent's belief state. While the connection of iterated AGM-like operations and their encoding in dynamic epistemic logics have been studied before, few works considered how well-known postulates from iterated belief revision theory can be characterised by means of belief bases. Particularly, it has been shown that some postulates can be characterised through transformations in priority graphs, while others may not be represented that way. This work investigates the expressive power of prioritized bases, employing priority graphs as a representation for an agent's belief state and of transformations on such bases as representations for iterated relational belief change operators. As such, we propose a new representation for an agent's belief state, which we show can be used to characterize different iterated belief change postulates not previously representable using transformations on priority graphs.
A new approach in model selection for ordinal target variables

Abstract Multi-class predictive models are generally evaluated averaging binary classification indicators without a distinction between nominal and ordinal dependent variables. This paper introduces a novel approach to assess performances of predictive models characterized by an ordinal target variable and a new index for model evaluation is proposed. The new index satisfies mathematical properties and it can be applied to the evaluation of parametric and non parametric models. In order to show how our performance indicator works, empirical evidences obtained on toy examples and simulated data are provided. On the basis of the results achieved, we underline that our approach can be a more suitable criterion for model selection than the performance indexes currently suggested in the literature.
Information and analytical collective decision-making support using intelligent technologies

Abstract The issues of organizing collective decision-making support for innovative project teams are considered. Research relevance is justified by the need to reduce the degree of uncertainty in decision-making during the projects set implementation, using accumulated corporate experience and knowledge. A synthesis of the subject area and information systems functionality ontological models is proposed. This will improve the efficiency of information system reengineering and the quality of made decisions, among them during the process of collective decision-making support system development.
Research on Multidimensional Teaching Mode of College English Based on Data Mining

The rapid development of computer technology and Internet technology has promoted the development of the era of big data. The acquisition and application of data information is an important foundation for improving the level of data application in the era of big data. In the current college English teaching process, the multi-dimensional English teaching model based on data mining has a positive effect on improving college English teaching efficiency and ensuring teaching quality. In the context of big data, teachers not only need to understand the problems in college English teaching, but also teachers must analyze the way of multi-dimensional college English teaching mode based on data mining, so as to effectively improve the quality of English teaching.