Reviewerly/exHarmony huggingface dataset:
THE USE OF INTERACTIVE METHODS IN THE TRAINING OF TACTICAL OFFICERS IN MILITARY EDUCATIONAL INSTITUTIONS

The purpose of this article is to investigate the optimization of the learning process in higher military educational institutions. The paper studies the concept of interactive learning, its basic principles and key features (expanding the boundaries of teachers' cognitive activity, observing the principles of mutual learning, modeling success situations, using different forms of work/formats of learning and activities within one class, problem-based and reflective nature of the class). Methodology. The need to integrate VR-technologies and artificial intelligence technologies into the educational space is emphasized. The disadvantages of interactive learning and situations that exclude the expediency of its use are analyzed. The advantages of the use of interactive learning methods in working with students of higher education institutions are systematized. Results. The classification approaches are defined and the regularity of their formation in the pedagogical literature is characterized. The technologies that are most often used by university teachers when working with students are analyzed. Examples of domestic and foreign educational educational platforms containing high-quality content for independent and collective study of students are given. Practical implications. The need to turn to interdisciplinary assignments based on individual creative work is emphasized. Value/originality. The basic principles of interactive lectures and interactive practical seminars are established. Using the examples of classes on history, foreign language, psychology, mathematical and software support of automated systems functioning, military topography, disciplines related to combat use of units armed with portable anti-aircraft missile systems, use of radio engineering units and military units of the USSR Air Defense Forces, the methodology of interactive technology use is shown.
ACADEMIC MOBILITY IN THE ERA OF TURBULENCE

The relevance of the problems of international academic mobility is due to the growth of export-import of educational services, aggravation of the global level of competition for human and intellectual resources. In the 20s of the 21st century new challenges emerged (COVID-19 pandemic, Russian aggression in Ukraine), which led to a serious transformation of the European and global educational space. In this context, the issue of developing tools to minimize negative challenges while intensifying the processes of academic mobility is relevant. The aim of the article is to analyze the processes of academic mobility in the context of contemporary challenges, in particular, the consequences of Russian aggression on the processes of international migration, redistribution of the global and European higher education market, development of university cooperation in the European and global educational environment. A common trend in higher education is dynamic internationalization: between 2000 and 2019, the number of students worldwide just over doubled (by 235%), and the number of international students tripled from 2.088 million to 6.064 million. And in traditional regions the number of foreign students increased 2-2.5 times (in Europe – 246%, in North America – 221%), and in rapidly developing regions – 4-8 times (in Asia – 440%, South America – 828%, Oceania – 481%). The consequences of the pandemic have been the increased penetration of ICTs in educational processes, the diversification of forms and methods of learning, and the spread of "internationalization at home". The military aggression of 2022 provoked a rapid increase in academic mobility, increased flows from Ukraine, and the exclusion of the Russian Federation from the European and global educational community: all programs and grants were suspended, all opportunities for academic mobility were closed, and it was excluded from the Bologna Process. European and other Western countries have taken unprecedented measures to support students and scholars, both at the level of countries and associations, as well as by individual universities. The consequence of Russian aggression has been a dynamic change in the flow of academic mobility, the main manifestation of which will be a significant influx of students and scholars to European and other countries.
Measure Utility, Gain Trust: Practical Advice for XAI Researcher

Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.
Smart Campus Governance Design for XYZ Polytechnic Based on COBIT 2019

Technological developments drive growth in the industrial revolution and digital transformation era. Technological developments during the industrial revolution 4.0 affect characteristics, especially in work. In responding to the change in technological developments in employment, the Ministry of Public Works has the task of conducting public works affairs in the government environment in an orderly manner to support the president in administering state government. XYZ Polytechnic is a state university as a new pilot under the Ministry of Public Works, Republic of Indonesia. As a basis for future development as well as towards a smart campus and then getting policy directions for the development of smart campus governance at the XYZ Polytechnic, it is necessary to design IT governance, especially in the reconstruction of adaptive and responsive policies and the development of structured governance with structured information systems. This study uses COBIT 2019 as the framework for governance design. With the method from the field preparation stage, interviews then assessed and evaluated existing policies and conditions of field activities to create governance designs according to COBIT 2019. The research results contained a technology governance management design with 17 processes. Based on the capability assessment and gap analysis results, recommendations were made for XYZ Polytechnic, as discussed in the results section. The suggestions are in the form of recommendations related to people, processes, and the use of technology. The recommendations act as evaluation material to improve organizational performance by providing good smart campus governance to students and internal members of XYZ Polytechnic.
Motivating factors of innovative research activities and barriers to R&amp;D in Kazakhstan

While higher education, science, and industry have become key factors in the national and economic growth of a country, they perhaps act as independent performers. This paper aims to describe the development of innovative commercialization activities based on the dissemination of innovations in the fields of research, education, and business. The present research, using empirical studies as its research method, first reviewed the current worldwide status of research studies to see their contribution to knowledge transfer. Second, it portrayed the results of an online survey of 100 respondents studying and teaching or researching at Kazakhstani universities to better understand the innovative research activities and motivations of and barriers to the research and development (RD) in the Republic of Kazakhstan. The results of the proposed approach are presented in the case of Kazakhstani researchers. Based on such findings, some recommendations are made for better Kazakhstani education, industry, and science alignment so that research in this country can have a greater impact on economic growth.
Application Checkpoint and Power Study on Large Scale Systems

Power efficiency is critical in high performance computing (HPC) systems. To achieve high power efficiency on application level, it is vital importance to efficiently distribute power used by application checkpoints. In this study, we analyze the relation of application checkpoints and their power consumption. The observations could guide the design of power management.
Comparing the quantum switch and its simulations with energetically-constrained operations

Quantum mechanics allows processes to be superposed, leading to a genuinely quantum lack of causal structure. For example, the process known as the quantum switch applies two operations ${\cal A}$ and ${\cal B}$ in a superposition of the two possible orders, ${\cal A}$ before ${\cal B}$ and ${\cal B}$ before ${\cal A}$. Experimental implementations of the quantum switch have been challenged by some on the grounds that the operations ${\cal A}$ and ${\cal B}$ were implemented more than once, thereby simulating indefinite causal order rather than actually implementing it. Motivated by this debate, we consider a situation in which the quantum operations are physically described by a light-matter interaction model. While for our model the two processes are indistinguishable in the infinite energy regime, restricting the energy available for the implementation of the operations introduces imperfections, which allow one to distinguish processes using different number of operations. We consider such an energetically-constrained scenario and compare the quantum switch to one of its natural simulations, where each operation is implemented twice. Considering a commuting-vs-anticommuting unitary discrimination task, we find that within our model the quantum switch performs better, for some fixed amount of energy, than its simulation. In addition to the known computational or communication advantages of causal superpositions, our work raises new questions about their potential energetic advantages.
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation

Optical flow estimation in omnidirectional videos faces two significant issues: the lack of benchmark datasets and the challenge of adapting perspective video-based methods to accommodate the omnidirectional nature. This paper proposes the first perceptually natural-synthetic omnidirectional benchmark dataset with a 360-degree field of view, FLOW360, with 40 different videos and 4,000 video frames. We conduct comprehensive characteristic analysis and comparisons between our dataset and existing optical flow datasets, which manifest perceptual realism, uniqueness, and diversity. To accommodate the omnidirectional nature, we present a novel Siamese representation Learning framework for Omnidirectional Flow (SLOF). We train our network in a contrastive manner with a hybrid loss function that combines contrastive loss and optical flow loss. Extensive experiments verify the proposed framework's effectiveness and show up to 40% performance improvement over the state-of-the-art approaches. Our FLOW360 dataset and code are available at https://siamlof.github.io/.
An Introduction to Bayesian Methods for Interaction Design

A summary is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Subject matter: Early software size estimation is one of the project managers' significant problems in evaluating app development efforts because software size is the major determinant of software project effort. Function points (FPs) and lines of code (LOC) are most commonly used as measures of size in existing software effort estimation methods and models. As is known, both these metrics have their advantages and disadvantages when used for software effort estimation. Although the FPs-based measure has the advantage over the LOC in that it does not depend on the technologies used, however, the assessment of efforts requires considering such factors (environmental factors). Considering the above factors can be ensured by appropriate models for estimating the LOC-based effort. Nowadays, many Web apps are created using PHP frameworks making the app development faster. CodeIgniter is one such powerful framework. However, there are no regression models for estimating the software size of Web apps created using the CodeIgniter framework. This requires the construction of the appropriate models. The task of this paper is to develop a nonlinear regression model for estimating the software size (in KLOC, kilo lines of code) of Web apps created using the CodeIgniter framework. Method: We apply the technique for constructing nonlinear regression models based on the multivariate normalizing transformations and prediction intervals. The result is three nonlinear regression models with three predictors: the total number of classes, the average number of methods per class, and the DIT (Depth of Inheritance Tree) average per class. To build these models for estimating the size of Web apps created using the CodeIgniter framework, we used three well-known normalizing transformations: two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation. Conclusions. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results than other regression models based on the univariate transformations.
Optical Character Recognition of Urdu Text using Histogram of Oriented Gradient Features

Abstract Optical character recognition has received significant research focus to digitize the text in images. Urdu OCR is a difficult task as compared to English and similar languages due to its complex nature where a character can have multiple inflections depending upon its position in the word. The proposed research work presents segmentation-free approach (i.e. holistic approach) for offline Urdu printed text detection. To extract text lines in an image, horizontal histogram projection is employed whereas for ligature segmentation in extracted image text line, proposed technique has used connected components labelling. In this model, set of 14 statistical features along with HOG features are extracted for each sub-word/ligature and used for the training of the proposed model. An open-source dataset UPTI [10] has been used to train and test the proposed algorithm. SVM with RBF kernel function is used for the classification of ligatures. The proposed algorithm has achieved 97.3% character recognition rate on given dataset.
Assessing the impact of contextual information in hate speech detection

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research.
Complexity-Based Prompting for Multi-Step Reasoning

We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer, large language models can generate new reasoning chains and predict answers for new inputs. A central question is which reasoning examples make the most effective prompts. In this work, we propose complexity-based prompting, a simple and effective example selection scheme for multi-step reasoning. We show that prompts with higher reasoning complexity, i.e., chains with more reasoning steps, achieve substantially better performance on multi-step reasoning tasks over strong baselines. We further extend our complexity-based criteria from prompting (selecting inputs) to decoding (selecting outputs), where we sample multiple reasoning chains from the model, then choose the majority of generated answers from complex reasoning chains (over simple chains). When used to prompt GPT-3 and Codex, our approach substantially improves multi-step reasoning accuracy and achieves new state-of-the-art (SOTA) performance on three math benchmarks (GSM8K, MultiArith, and MathQA) and two BigBenchHard tasks (Date Understanding and Penguins), with an average +5.3 and up to +18 accuracy improvements. Compared with existing example selection schemes like manual tuning or retrieval-based selection, selection based on reasoning complexity is intuitive, easy to implement, and annotation-efficient. Further results demonstrate the robustness of performance gains from complex prompts under format perturbation and distribution shift.
Nested Sequents for First-Order Modal Logics via Reachability Rules

We introduce the first cut-free nested sequent systems for first-order modal logics that admit increasing, decreasing, constant, and empty domains along with so-called general path conditions and seriality. We obtain such systems by means of two devices: 'reachability rules' and 'structural refinement'. Regarding the former device, we introduce reachability rules as special logical rules parameterized with formal grammars (viz. types of semi-Thue systems) that operate by propagating formulae and/or checking if data exists along certain paths within a nested sequent, where paths are encoded as strings generated by a parameterizing grammar. Regarding the latter device, structural refinement is a relatively new methodology used to extract nested sequent systems from labeled systems (which are ultimately obtained from a semantics) by means of eliminating structural/relational rules, introducing reachability rules, and then carrying out a notational translation. We therefore demonstrate how this method can be extended to the setting of first-order modal logics, and expose how reachability rules naturally arise from applying this method.
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement
Predictive modeling for suspicious content identification on Twitter

The wide popularity of Twitter as a medium of exchanging activities, entertainment, and information is attracted spammers to discover it as a stage to spam clients and spread misinformation. It poses the challenge to the researchers to identify malicious content and user profiles over Twitter such that timely action can be taken. Many previous works have used different strategies to overcome this challenge and combat spammer activities on Twitter. In this work, we develop various models that utilize different features such as profile-based features, content-based features, and hybrid features to identify malicious content and classify it as spam or not-spam. In the first step, we collect and label a large dataset from Twitter to create a spam detection corpus. Then, we create a set of rich features by extracting various features from the collected dataset. Further, we apply different machine learning, ensemble, and deep learning techniques to build the prediction models. We performed a comprehensive evaluation of different techniques over the collected dataset and assessed the performance for accuracy, precision, recall, and f1-score measures. The results showed that the used different sets of learning techniques have achieved a higher performance for the tweet spam classification. In most cases, the values are above 90% for different performance measures. These results show that using profile, content, user, and hybrid features for suspicious tweets detection helps build better prediction models.