Reviewerly/exHarmony huggingface dataset:
Migratismi di moda

The lexicon of immigrant writers in Italy is full of words derived from their mother tongues. Among the semantic fields most involved is undoubtedly that of fashion. We find many clothes, dresses, fabrics typical of the areas of emigration to Italy: Arab world, states of sub-Saharan Africa, Middle East and Asia. Some of these are attested in Italian for a long time now (burnus, fez, sari); others are very recent, in correlation with migration and the presence of immigrant communities in Italy. The term ‘migratismo’ has been proposed for this class of words. Some ‘migratismi’ have already spread in Italian and recorded in vocabularies (for example the veils of Islamic women: burqa, hijab, niqab). Their circulation has produced derived and compound words (burkini, antiburqa). Others are well detectable in the italian literature of migration, where the authors explain the meaning with a gloss or in a note. Searches in archives and databases allow to understand the real circulation of these words.
Processing of Data Concerning National Health Insurance in the Context of the COVID-19 Outbreak: Applications and Limitations

With automated medical information and multiple intelligent functions, the health insurance IC card can open a real-time medical data management system to control and track the epidemic In addition to legal compliance, travel and contact history linked to the health insurance database must still pay attention to the principle of proportionality and comply with the basic principles of personal data protection law As for the processing and application of personal data obtained from epidemic prevention, when it conforms to the personal data law and there are express provisions of the law, important interests must be balanced in a strict EU data protection law regime As far as the legal system is concerned, however, Formosan personal data protection law does not contain provisions for balancing interests, nor does it clearly define the concept of public interest Therefore, it may be handled exclusively by a specific law and pay attention to the completeness of procedures and organizations
Designing Learning Games and Tools for Karelian Language - Considerations Based on Beginners' Experiences of Gameplay

According to our knowledge, there are only few existing mobile games for learning Viena Karelian dialect and culture. Karelian is classified as a minority and endangered language. Different actions for revitalizing the Karelian language and culture have been made during the several past years in Finland and Russia. However, more effort is needed, especially to developed digital and easily accessible ways to learn and teach Karelian. This paper presents simple games developed for beginners to learn Viena Karelian. We also present findings from a small-scale user study. In addition, we propose a concept idea of the tool for teachers to create simple learning exercises.
Efficient Sampling for Generative Adversarial Networks with Reparameterized Markov Chains

Recently, sampling methods have been successfully applied to enhance the sample quality of Generative Adversarial Networks (GANs). However, in practice, they typically have poor sample efficiency because of the independent proposal sampling from the generator. In this work, we propose REP-GAN, a novel sampling method that allows general dependent proposals by REParameterizing the Markov chains into the latent space of the generator. Theoretically, we show that our reparameterized proposal admits a closed-form Metropolis-Hastings acceptance ratio. Empirically, extensive experiments on synthetic and real datasets demonstrate that our REP-GAN largely improves the sample efficiency and obtains better sample quality simultaneously.
A Comparative Analysis of EU Homegrown Crypto-Asset Regulatory Frameworks

The article critically analyses recent policy initiatives at international and national level regarding the regulation of the crypto-asset sector, including the European Commission’s consultation document. It also aims to provide a comparative analysis of two notable crypto-asset frameworks which have been established in the EU, namely the frameworks established by Malta and France, which cover the conduct, prudential and market integrity aspects of the sector. The article identifies the relative strengths and weaknesses of the respective frameworks and extract the positive elements which could be incorporated into a holistic, harmonised crypto-asset framework for EU adoption.
Towards A Unified Understanding and Improving of Adversarial Transferability

In this paper, we use the interaction inside adversarial perturbations to explain and boost the adversarial transferability. We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations. The negative correlation is further verified through different DNNs with various inputs. Moreover, this negative correlation can be regarded as a unified perspective to understand current transferability-boosting methods. To this end, we prove that some classic methods of enhancing the transferability essentially decease interactions inside adversarial perturbations. Based on this, we propose to directly penalize interactions during the attacking process, which significantly improves the adversarial transferability. We will release the code when the paper is accepted.
Curating the past : Asine and PRAGMATA

Digitalization and web-based repositories of information have become increasingly important in archaeologyas resources both for students and researchers. This paper discusses the work to make publi ...
A Secure Opus Pulse Steganographic Scheme Based on Message Transform

This paper proposes a secure steganography scheme in Opus pulse domain. Opus is a versatile audio codec designed for interactive Internet applications and is widely used in the Internet communication applications. In Opus pulse domain, there exists a large embedding space, which makes it an ideal carrier for steganography. The challenge is that the generation of pulse samples depends on the value of previous pulse samples, so the embedding operation must be carried out at a single sample, otherwise it will lead to distortion drift. This paper proposes a scheme with message transform and rate-distortion evaluation to improve statistical security. The experiment results show that the proposed scheme achieves good auditory quality and statistical security with a large embedding capacity.
Prediction of Deleterious Protein Mutations

Human Single Amino Acid Polymorphisms (SAPs) or Single Amino Acid Variants (SAVs) represent the most frequent type of genetic variation among the population. They originate from non-synonymous single nucleotide variations (missense variants) where a single base pair substitution alters the genetic code in such a way that it produces a different amino acid at a given position. Since mutations are commonly associated with the development of various genetic diseases, it is of utmost importance to understand and predict which variations are deleterious and which are neutral. Computational tools based on machine learning are becoming promising alternatives to tedious and highly costly mutagenic experiments. Generally, varying quality, incompleteness and inconsistencies of SAVs datasets degrade the usefulness of machine learning approaches. Consequently, robust and more accurate approaches are essential to address these issues. In this paper, we present the application of a consensus classifier based on the holdout sampling, which shows robust and accurate results, outperforming currently available tools. We generated 100 holdouts to sample different classifiers’ architectures and different classification variables during the training stage. The best performing holdouts were selected to construct a consensus classifier and tested by blindly utilizing a K5-fold cross-validation approach. We also performed an analysis of the best protein attributes for predicting the effects of SAVs by calculating their discriminatory power.
Small Data-Driven Electrical Insulator Defect Detection

The inspection and maintenance of insulator equipment has always adopted the traditional manual detection. It is very significant to study the automatic Insulator defect detection by drone inspection. However, in practical industrial applications, the number of available defect insulator samples is limited. It is difficult to construct a sufficient and high-quality dataset to support the training of the object detection model. In this paper, we propose a detection framework which combines the super-resolution reconstruction and the object detection model. In our model, we use the super-resolution reconstruction and traditional data augmentation to amplify the amount of data and avoid the overfitting caused by the small sample data. The model has excellent performance on the training set which only contains 80 images, and achieves 61% mAP. We also show that the super-resolution reconstruction can rich image texture features and is more effective than some traditional data augmentation methods.
The Root Causes of Data Breaches

What are the root causes that have allowed attackers to break into so many organizations? This chapter mainly focuses on six technical root causes. Before delving into those, we first discuss three of the “meta-level” root causes: failure to prioritize security, failure to invest in security, and failure to execute on security initiatives. For anything important in life or business, one may argue that these three types of failures (to prioritize, invest, and execute) can apply to almost anything, but we will cover some of the specifics to security in this chapter.
Pengenalan Isyarat Tangan dan Pengaruh Lingkungan pada Ekstraksi Fitur

Communication can be done in verbal or nonverbal way. Nonverbal communication means communicate without using voice from the mouth but using letters, pictures, or sign language. In communication with the gestures there will be difficulties if the other person does not understand the meaning of the sign given. This final project propose to develop an application that able to translate alphabet c, i, l, o, u, and v from BISINDO sign language using SVM. For computers able to receive data from the given sign language,  computer vision methods is used.  To find out the ideal environment used to make detection, exploring combination of background colors and direction of light is done. From the results of obtained evaluation, the black background and front light results in the best evaluation value.
The Effectiveness of Identifying Learning of the Information in Explanatory Texts By Using the SQ3R Method Based on the Reading Intensity of the XI Grade Students at Madrasah Aliyah

Identifying learning of the information in explanatory text requires creativity in its implementation. One of the things that affect identifying learning of the information in explanatory text is the students’ reading intensity. Therefore, it takes the most effective learning method to be applied and the learning method chosen is the SQ3R method. The purpose of this study was to explain the effectiveness of identifying learning of information in explanatory text by using the SQ3R method based on the reading intensity of the XI grade students at Madrasah Aliyah. The method used in this study was an experimental research design with a quasi-experimental factorial. The sample of this research is students of class IX MAN 2 Kudus. The collection of data uses identifying learning of the explanatory text information and reading intensity test to determine high or low reading intensity. Non-test data collection uses observation and photo documentation. The results of this study were (1) The SQ3R learning method was effective in identifying information in explanatory text for class XI students of Madrasah Aliyah with an increase of 12.23, from an average of 66.51 to 78.74. This is evidenced by the sign value=0.000&lt;0.05, which means that there is a significant difference in the learning outcomes of the experimental group pre-test post-test which is used the SQ3R learning method. Meanwhile, the ability to identify information in explanatory text to students with low reading intensity treated with the SQ3R method, and an average value is 67. Identifying Learning of the information in explanatory text based on reading intensity of the XI grade students at Madrasah Aliyah by using the SQ3R is an effective learning method. The result of this learning has increased by 12.23, from an average of 66.51 to 78.74. This is evidenced by the sign value=0.000&lt;0.05, which means that there is a significant difference in the learning outcomes of the experimental group pretest posttest using the SQ3R learning method
Whole slide semantic segmentation: large scale active learning for digital pathology

Deep learning for digital pathology is a challenging problem. Small patient datasets limit generalizability of trained deep learning models, while the large size of whole slide images (WSIs) represents a bottleneck for training. Additionally, annotations are difficult to obtain at scale due to image size and the volume of samples needed for accurate and generalizable training. We have investigated the use of Active Leaning (AL) to alleviate this burden; AL is a training approach where a small subset of samples is used to create a bootstrap classifier, which in turn selects new samples for annotation to maximize the performance gain for each additional training sample. In our previous work, we have found AL to be more efficient than the more common Random Learning (RL) approach in terms of segmentation performance per training sample. In the current work, we extend our investigation of AL by using our region-of-interest (ROI) trained classifier and perform WSI-level segmentation of multiple classes. We compare the results of the AL- to RL-based training, and generate inference results for a dataset of 75 WSIs spanning 61 patients. After four rounds of training, AL yielded a validation loss 0.566 lower as well as dice coefficients an average of 0.022 higher for classes present in images for the holdout testing set. This work demonstrates the generalizability of AL from patch-based segmentation to WSI-based, and provides a path forward for rapid development of complex digital pathology datasets in deep learning.
Retaining pianistic virtuosity in #MIs: Exploring pre-existing gestural nuances for live sound modulation through a comparative study

This chapter focuses on Reach, a keyboard-based gesture recognition system for live piano sound modulation, and the comparative user testing conducted to evaluate it. Reach is a system built using the Leap Motion Orion SDK, a custom C++ OSC mapper and a Pure Data environment. It provides control over the sound modulation of a live piano feed, taking advantage of pre-existing gestural nuances offering a touch-free experience to the pianist.The user testing compared the Reach system with two commercially available keyboard-based systems for augmented live sound modulation: Seaboard and TouchKeys. The approach taken during the user tests is illustrated and test results are discussed. The results that emerged suggest an underlying importance of recognising and utilising the musician’s existing technique when designing Digital and Augmented Musical Instruments (#MIs), and the potential of reducing the requirement to learn additional instrumental technique. The comparative user testing discussed in this chapter is part of a larger research project that seeks to study and understand how a low degree of invasiveness in digital systems for live sound modulation can reduce the learning curve of new systems, allowing greater access to music making with technology.
Neural Disjunctive Normal Form: Vertically Integrating Logic With Deep Learning For Classification

We present Neural Disjunctive Normal Form (Neural DNF), a hybrid neuro- symbolic classifier that vertically integrates propositional logic with a deep neural network. Here, we aim at a vertical integration of logic and deep learning: we utilize the ability of deep neural networks as feature extractors to extract intermediate representation from data, and then a Disjunctive Normal Form (DNF) module to perform logical rule-based classification; we also seek this integration to be tight that these two normally-incompatible modules can be learned in an end-to-end manner, for which we propose the BOAT algorithm. Compared with standard deep classifiers which use a linear model or variants of additive model as the classification head, Neural DNF provides a new choice of model based on logic rules. It offers interpretability via an explicit symbolic representation, strong model expressity, and a different type of model inductive bias. Neural DNF is particularly suited for certain tasks that require some logical composition and provides extra interpretability.
20세기 초 서양 선교사들의 어학교재 -한국어 Fifty Helps와 일본어 Fifty Helps의 비교 연구-

This paper aims to analysis the characteristics of language textbooks used by Western missionaries in early 20th century through the comparison between Korean Fifty Helps and Japanese Fifty Helps.The Korean Fifty Helps written by Annie Baird was very popular textbook to Western missionaries, while revising six times from 1896 to 1926. Pastor Winn wrote Japanese Fifty Helps by adapting Annie Baird''s learning method during the Japanese colonial period as missionaries in Korea were under the burden of learning Japanese to communicate with Japanese officers.The reason Korean Fifty Helps had been used as a language textbook for a long time was that it prioritized the acquisition of expressive whole sentences to learning of grammatical forms. Meanwhile Japanese Fifty Helps lost the learning effect of acquiring whole sentential expressions by matching them with Korean words by small units. In addition, it missed a meaningful practical processes in which learners could produce various expression according to their situation.The way of learning sentential expressions prior to grammatical forms is connected with modern communicative teaching methods in language learning. As the characteristics of Korean Fifty Helps are reflected quite a lot in modern Korean textbooks, it is worth saying that Korean Fifty Helps has become a predecessor of Korean textbooks for foreigners. (Jeonju University)
Combining Creativity and Feedback for Pupils Activation

In modern society and modern school systems, we are all lifelong learners. And two most important paths and goals for a lifelong learner are creativity and receiving and giving feedback. Feedback - developing the practice of feedback to enhance learning through arts is a project that set the foundations to define a clearer model of feedback. The main goal is to create innovative learning resources about practice of feedback, supported by art. Feedback toolkits were developed for achieving these goals. In this Feedback project we had an opportunity to learn new toolkits for effective feedback, to perform them in our classrooms and to creatively design our own toolkits. Toolkits were well accepted by students. We were training our competences, gaining knowledge and assessing it. Here, we would like to present new creative toolkit. It was developed in Erasmus+ KA2 Feedback project. We named our toolkit Potica. Potica is Slovenian traditional layered desert. This feedback toolkit is also made up of several layers. It is a quick and effective insight into our background, current learning and added value for the future. The method is suitable for introduction when gaining new knowledge or for feedback. The positive sides of these toolkits are: high motivation of pupils, fun way of learning, team work - communication, helping each other, immediate feedback and new creative experience.
Study Of Tremulous Signatures In Devanagari Nepali Script

Signatures, as one of the behavioral human characteristics are generally recognized as legal means for verifying an individual's identity by administrative and financial institutions. A signature is developed by a person and it can change over time. Signatures are prone to influences by age, physical and mental conditions. In this study tremulous signatures including questioned and standards related to 50 real cases resolved by National Forensic Science Laboratory of Nepal were collected. The time interval between questioned and standard signatures was up to ten years. Sixteen major characteristics along with their forty-one sub characteristics of each case were studied. Among sixteen major characteristics in the tremulous signatures, study shows that the characteristics like the style of writing in hand printed form, pen pauses, medium pen pressure, poor line quality, pen lifts are most prominently observed. Tremors were absent mostly in the terminal strokes of letters. The letters written were varied in size and those were 6% small, 70% medium and 24% large. Interestingly no any retouch attempts were found to complete the shape of letters.
A Mammographic Images Classification Technique via the Gaussian Radial Basis Kernel ELM and KPCA

Computer Assisted Diagnosis (CAD) and Artificial Intelligence (AI) are hot topics in the field of medical imaging. Recently, many methods have been proposed. In this research work, a novel mammography image classifier based on Kernel Extreme Learning Machine (KELM) and Kernel Principal Component Analysis (KPCA) is presented. The proposed algorithm, called KELM-KPCA, aims to detect breast tumors by classifying mammographic images. The system first used discrete Tchebichef transform (DTT) to extract features from the images. After normalization of the feature vectors, the kernel principal component analysis (KPCA) is applied to reduce the dimensionality of the features. The reduced characteristics were then subjected to classification by KELM. The k-factor cross-validation strategy was used to improve the generalization of the proposed algorithm. The Mammographic Image Analysis Society (MIAS) dataset is used in this work. The simulation results were compared to the existing algorithms and it was observed that the proposed work outperforms other algorithms. Work on the same dataset in terms of accuracy, F-score, sensitivity and specificity.
FINDING SIMILARITIES IN LASER MICROMETERS‘ SCANNED DATA USING CROSS-CORRELATION

This paper demonstrates a signal processing approach designed to overlay data sets measured using two optical micrometers.Two sensors are used in order to measure object's shape, but measured signals are relatively shifted, because it is impossible to mount sensors into the same position.Correlation method are applied to measurement data and the shift between these sets are evaluated.
PRACTICAL APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PYTHON PROGRAM LANGUAGE

In this paper, practical application of Python programming language for artificial intelligence (AI) and machine learning (ML) usage in terms of application in different class of problems has been demonstrated. This work investigates possibilities and finding advantages and issues in comparison with other related languages and tools. The data sets used for practical examples represent an excellent basis for demonstrating the capabilities of the considered technologies. Although they are not too complex, they fit perfectly into what is actually the main goal of the paper in order to show principles of using AI and ML in solving statistically based problems of clustering and classification.