Reviewerly/exHarmony huggingface dataset:
Discretization Based Framework to Improve the Recommendation Quality

Recommendation systems are information filtering software that delivers suggestions about relevant stuff from a massive collection of data. Collaborative filtering approaches are the most popular in recommendations. The primary concern of any recommender system is to provide favorable recommendations based on the rating prediction of user preferences. In this article, we propose a novel discretization based framework for collaborative filtering to improve rating prediction. Our framework includes discretization-based preprocessing, chi-square based attribution selection, and K-Nearest Neighbors (KNN) based similarity computation. Rating prediction affords some basis for the judgment to decide whether recommendations are generated or not, subject to the ratio of performance of any recommendation system. Experiments on two datasets MovieLens and BookCrossing, demonstrate the effectiveness of our method.
Consortium Blockchain for Military Supply Chain

Few countries are deploying a blockchain technology for their military supply chain management. Blockchain implementation shows a positive impact on a Military Supply Chain Management (MSCM). The existing military blockchain is adopting a private blockchain setting due to sensitivity level of military data. However, the private blockchain allows non-military authorities’ involvement. This research is focusing on the secondary data analysis. This paper proposes a new consortium military blockchain for defense shipment. Blockchain layers are introduced to accommodate military authorities and suppliers. Further research in this area may strengthen the traceability and tracking features in the military logistics including military asset shipment and life cycles
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains. We show that a new family of algorithms based on model selection ideas benefits from very favorable guarantees in this scenario and discuss some theoretical obstacles affecting some alternative techniques. We also report the results of several experiments with our algorithms that demonstrate their practical effectiveness.
Findings of the VarDial Evaluation Campaign 2023

Noëmi Aepli, Çağrı Çöltekin, Rob Van Der Goot, Tommi Jauhiainen, Mourhaf Kazzaz, Nikola Ljubešić, Kai North, Barbara Plank, Yves Scherrer, Marcos Zampieri. Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023). 2023.
On the benefits of robust models in modulation recognition

Given the rapid changes in telecommunication systems and their higher dependence on artificial intelligence, it is increasingly important to have models that can perform well under different, possibly adverse, conditions. Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. However, in other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations, which consist of imperceptible crafted noise that when added to the data fools the model into misclassification. This puts into question the security of DNNs in communication tasks, and in particular in modulation recognition. We propose a novel framework to test the robustness of current state-of-the-art models where the adversarial perturbation strength is dependent on the signal strength and measured with the “signal to perturbation ratio” (SPR). We show that current state-of-the-art models are susceptible to these perturbations. In contrast to current research on the topic of image classification, modulation recognition allows us to have easily accessible insights on the usefulness of the features learned by DNNs by looking at the constellation space. When analyzing these vulnerable models we found that adversarial perturbations do not shift the symbols towards the nearest classes in constellation space. This shows that DNNs do not base their decisions on signal statistics that are important for the Bayes-optimal modulation recognition model, but spurious correlations in the training data. Our feature analysis and proposed framework can help in the task of finding better models for communication systems.
On the Characterization, Detection and Impact of Batch Refactoring in Practice

Up to 60% of the refactorings in software projects are constituted of a set of interrelated transformations, the so-called batches (or composite refactoring), rather than single transformations applied in isolation. However, a systematic characterization of batch characterization is missing, which hampers the elaboration of proper tooling support and empirical studies of how (batch) refactoring is applied in practice. This paper summarizes the research performed under the context of a Master's dissertation, which aimed at taming the aforementioned problems. To the best of our knowledge, our research is the first work published that provides a conceptual foundation, detection support and an large impact analysis of batch refactoring on code maintainability. To this end, we performed two complementary empirical studies as well as designed a first heuristic aimed at explicitly detecting batch refactorings. Our first study consisted of a literature review that synthesizes the otherwise scattered, partial conceptualization of batch refactoring mentioned in 29 studies with different purposes. We identified and defined seven batch characteristics such as the scope and typology of batches, plus seven types of batch effect on software maintainability, including code smell removal. All batches' characteristics and possible impacts were systematized in a conceptual framework, which assists, for instance, the proper design of batch refactoring studies and batch detection heuristics. We defined a new heuristic for batch detection, which made it possible to conduct a large study involving 4,607 batches discovered in 57 open and closed software projects. Amongst various findings, we reveal that most batches in practice occur entirely within one commit (93%), affect multiple methods (90%). Surprisingly, batches mostly end up introducing (51%) or not removing (38%) code smells. These findings contradict previous investigations limited to the impact analysis of each transformation in isolation. Our findings also enabled us to reveal beneficial or harmful patterns of batches that respectively induces the introduction or removal of certain code smells. These patterns: (i) were not previously documented even in Fowler's refactoring catalog, and (ii) provide concrete guidance for both researchers, tool designers, and practitioners.
BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian

In this paper we describe a transformer model pre-trained on 8 billion tokens of crawled text from the Croatian, Bosnian, Serbian and Montenegrin web domains. We evaluate the transformer model on the tasks of part-of-speech tagging, named-entity-recognition, geo-location prediction and commonsense causal reasoning, showing improvements on all tasks over state-of-the-art models. For commonsense reasoning evaluation we introduce COPA-HR - a translation of the Choice of Plausible Alternatives (COPA) dataset into Croatian. The BERTic model is made available for free usage and further task-specific fine-tuning through HuggingFace.
ONLINE PRACTICE FOR SPEAKING ENGLISH

The study deals with the main resources of online speaking practice in learning a foreign language. Speaking practice is long acknowledged to contribute greatly to the development of fluency, confidence and experience of language learners. Although much research has long been devoted to various educational aspects along with different issues of language study, there is a necessity of more detailed survey of online speaking practice in a foreign language learning. Consequently, the study aimed at examining the key online means of English speaking practice. The key online practice sources for English speaking have been explored by the methods of monitoring and comparative analysis of internet resources. Further research perspectives of English speaking practice online have been specified due to the study findings.
Improving Neural Question Generation using Deep Linguistic Representation

Question Generation (QG) is a challenging Natural Language Processing (NLP) task which aims at generating questions with given answers and context. There are many works incorporating linguistic features to improve the performance of QG. However, similar to traditional word embedding, these works normally embed such features with a set of trainable parameters, which results in the linguistic features not fully exploited. In this work, inspired by the recent achievements of text representation, we propose to utilize linguistic information via large pre-trained neural models. First, these models are trained in several specific NLP tasks in order to better represent linguistic features. Then, such feature representation is fused into a seq2seq based QG model to guide question generation. Extensive experiments were conducted on two benchmark Question Generation datasets to evaluate the effectiveness of our approach. The experimental results demonstrate that our approach outperforms the state-of-the-art QG systems, as a result, it significantly improves the baseline by 17.2% and 6.2% under the BLEU-4 metric on these two datasets, respectively.
Explicit Vocabulary Instruction with English Language Learners: a Charade Game

The present study aimed to determine the efficacy of students' vocabulary mastery by Charade Game. This research has used 8th students of SMPN 18 KOTA TANGERANG SELATAN as a sample population. There had been 30 students throughout this group. The researchers used a simple cluster sampling technique to select the sample. The method of this study was quantitative and a quasi-experimental design of the pre-test and post-test. The data used for this study were gathered through pre-test and post-test vocabulary. Pre-test and post-test data then were analyzed numerically via SPSS19. The findings of this research clarified that the student pre-test score was 57 but then after treatment, the student post-test score was 81.83. The findings also indicate that the t obtained value was 15.79 and the t critical value was 0.05. These findings mean that Charade Game was likely to succeed in mastering vocabulary.
Monitoring studies of university graduates’ satisfaction with the quality of education: the main approaches

Introduction. The article actualizes the problem of monitoring the qualitative indicators of higher education in modern Russian universities. The opportunities for monitoring to identify the levels of university graduates’ satisfaction with the obtained education considering the rapidly changing socio-cultural and economic conditions are disclosed. Goal: substantiation of heuristic possibilities for monitoring the quality of higher education in accordance with the social demand. Methods. At the basis of the study methodology lies the systemic approach allowing the authors to conduct a comprehensive analysis of the specific features of monitoring. The analysis, generalization, and classification of the main indicators constituting the foundation for the study of university graduates’ satisfaction with the quality of education are conducted. Results. The article presents the results of monitoring studies and proposes a classification of methodological and methodical approaches to the study of the topic. Scientific novelty. The topic of monitoring university graduates’ satisfaction with the quality of education allows the authors to explore the main relevant problems of modern universities and disclose the potential of higher education. Practical significance. The acquired study results can be used in further research, scientific and methodical developments, and project activity aimed at improving students’ satisfaction with the educational process and promote the quality of pedagogical work.
Assessing User Experience of a Mobile Application Using Usability Questionnaire Method

Eden Farm application is an application system used by customers to order vegetables and fruits. Eden Farm collaborates with hundreds of local farmers and dozens of raw material suppliers to provide all the culinary business needs of thousands of Eden Farm customers. However, there are still many who do not understand the usability of the Eden Farm application. In building a good system, one important part is good usability. Usability is closely related to user experience. User experience plays an important role in the development of a system, especially in the Eden Farm application, because user experience can show the ease that users feel and efficiency through user experience in using the system. The author tries to use the USE Questionnaire to 100 respondents. The analysis resulted in an average value of the Usefulness parameter of 85%. Then the parameter Ease of Learning with 82%, and Ease of Use with 80%, and the smallest is Satisfaction with 79%. These results indicate that the Eden Farm application is good. The results of measuring the usability of Eden Farm application with the classification "Very Appropriate" to use, with a usability value of 84.18%. The results of usability measurement are expected to help the development and improvement of the Eden Farm application in the future.
Digital Marketing And Digital Transformation Of The University

The article examines the issues of the transformation of the university and the development of the levels of the digital university.Special attention is paid to digital marketing as a core component of a digital university.The topicality of this problem consists in the fact that this is about not only the digitalization of educational processes, but also about their transformation.Modern digital technologies provide new tools for the development of universities and other educational institutions world over.The digital marketing is a new space for Russian universities.The most important digital marketing tools are covered.To move to the modern level, the university must adequately close all levels of the digital university model described above and constantly maintain feedback with key stakeholders -students, research workers, industry and academic partners, school-leaver and applicants.The article analyzes the system of digital marketing activities in education.For transformation it is necessary to develop skills in working with modern digital tools, to digitize certain directions or blocks of educational programs, to provide the university with the reputation of a modern innovative university that operates in accordance with global trends and state development programs.The analysis of the vector of digital transformation of the university using digital marketing is carried out.The main directions of research and the formation of digital transformation of the university are determined.
Color-Based Fusion of MRI Modalities for Brain Tumor Segmentation

Most attempts to provide automatic techniques to detect and locate suspected tumors in Magnetic Resonance images (MRI) concentrate on a single MRI modality. Radiologists typically use multiple MRI modalities for such tasks. In this paper, we report on experiments for automatic detection and segmentation of tumors in which multiple MRI modalities are encoded using classical color encodings. We investigate the use of 2D convolutional networks using a classic U-Net architecture. Slice-by-slice MRI analysis for tumor detection is challenging because this task requires contextual information from 3D tissue structures. However, 3D convolutional networks are prohibitively expensive to train. To overcome this challenge, we extract a set of 2D images by projecting the 3D volume of MRI with maximum contrast. Multiple MRI modalities are then combined as independent colors to provide a color-encoded 2D image. We show experimentally that this led to better performance than slice-by-slice training while limiting the number of trainable parameters and the requirement for training data to a reasonable limit.
Challenges in using Machine Learning to Support Software Engineering

In the past few years, software engineering has increasingly automating several tasks, and machine learning tools and techniques are among the main used strategies to assist in this process.However, there are still challenges to be overcome so that software engineering projects can increasingly benefit from machine learning.In this paper, we seek to understand the main challenges faced by people who use machine learning to assist in their software engineering tasks.To identify these challenges, we conducted a Systematic Review in eight online search engines to identify papers that present the challenges they faced when using machine learning techniques and tools to execute software engineering tasks.Therefore, this research focuses on the classification and discussion of eight groups of challenges: data labeling, data inconsistency, data costs, data complexity, lack of data, non-transferable results, parameterization of the models, and quality of the models.Our results can be used by people who intend to start using machine learning in their software engineering projects to be aware of the main issues they can face.
FaceBlit

We present FaceBlit---a system for real-time example-based face video stylization that retains textural details of the style in a semantically meaningful manner, i.e., strokes used to depict specific features in the style are present at the appropriate locations in the target image. As compared to previous techniques, our system preserves the identity of the target subject and runs in real-time without the need for large datasets nor lengthy training phase. To achieve this, we modify the existing face stylization pipeline of Fišer et al. [2017] so that it can quickly generate a set of guiding channels that handle identity preservation of the target subject while are still compatible with a faster variant of patch-based synthesis algorithm of Sýkora et al. [2019]. Thanks to these improvements we demonstrate a first face stylization pipeline that can instantly transfer artistic style from a single portrait to the target video at interactive rates even on mobile devices.
Flexible multi-view semi-supervised learning with unified graph

At present, the diversity of data acquisition boosts the growth of multi-view data and the lack of label information. Since manually labeling is expensive and impractical, it is practical to enhance learning performance with a small amount of labeled data and a large amount of unlabeled data. In this study, we propose a novel multi-view semi-supervised learning (MSEL) framework termed flexible MSEL (FMSEL) with unified graph. In this framework, two flexible regression residual terms are introduced. One is a linear penalty term, which adaptively weighs the diverse contributions of different views and properly learns a well structured unified graph. The other is a relaxation regularization term, which finds the optimal prediction and the linear regression function. Both the prediction of samples in the database and new-coming data are supported. Moreover, during the process, the unified graph learns depending on the data structure and dynamically updated label information. Further, we provide an alternating optimization algorithm to iteratively solve the resultant objective problem and theoretically analyze the corresponding complexities. Extensive experiments conducted on synthetic and public datasets demonstrate the superiority of FMSEL.
An Alternating Training Method of Attention-Based Adapters for Visual Explanation of Multi-Domain Satellite Images

Recently, satellite image analytics based on convolutional neural networks have been vigorously investigated; however, in order for the artificial intelligence systems to be applied in practice, there still exists several challenges: (a) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model explanability</i> to improve the reliability of the artificial intelligence system by providing the evidence for the prediction results; (b) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dealing with domain shift</i> among images captured by multiple satellites of which the specification of the image sensors is various. To resolve the two issues in the development of a deep model for satellite image analytics, in this paper we propose a multi-domain learning method based on attention-based adapters. As plug-ins to the backbone network, the adapter modules are designed to extract domain-specific features as well as improve visual attention for input images. In addition, we also discuss an alternating training strategy of the backbone network and the adapters in order to effectively separate domain-invariant features and -specific features, respectively. Finally, we utilize Grad-CAM/LIME to provide visual explanation on the proposed network architecture. The experimental results demonstrate that the proposed method can be used to improve test accuracy, and its enhancement in visual explanability is also validated.
Offline signature recognition system using oriented FAST and rotated BRIEF

Despite recent developments in offline signature recognition systems, there is however limited focus on the recognition problem facet of using an inadequate sample size for training that could deliver reliable and easy to use authentication systems. Signature recognition systems are one of the most popular biometric authentication systems. They are regarded as non-invasive, socially accepted, and adequately precise. Research on offline signature recognition systems still has not shown competent results when a limited number of signatures are used. This paper describes our proposed practical offline signature recognition system using the oriented FAST and rotated BRIEF (ORB) feature extraction algorithm. We focus on the practicality of the proposed system, which requires only the minimum number of signatures per user to achieve a high level of fidelity. We manifest the practicality of our approach with a signature database of 300 signatures from 100 different individuals, implying that only two signatures are needed per person to train the proposed system. Our proposed solution achieves a 91% recognition rate with a median matching time of only 7 ms.
Water Management System Using Machine Learning

ARIMA model, unlike conventional models, is capable of self-learning data features layer by layer in supervised or unsupervised manner and creating a data-driven model with the given dataset. A detailed analysis along with automation in system would help reduce wastage of resources. This report presents an integrated data-driven modeling framework that couples machine learning with a well-developed IoT device. The integrated framework enables users to monitor water levels as well as forecast usage patterns using an Android application. We aim at unraveling usage patterns, and correlation of various factors would help in better analyzing and predicting the consumption pattern of the most valuable resource-potable water. Water consumption in India is rising rapidly due to the ever-increasing population and hence necessary precautions need to be taken in order to regulate the water flow in different parts of the country and minimize wastage.