Reviewerly/exHarmony huggingface dataset:
Varieties of Video Lectures

The article is devoted to the typology of the main variants of video lectures (https://vimeo.com/showcase/6184718), which differ in didactic value and laboriousness of creation. For each option of video lectures, the key conditions for effective formation and use are determined. Particular attention is paid to the optimization of hardware for the creation of various options for video lectures in full-time and distance learning. As a basic option, a video recording of a PowerPoint presentation with speech and graphic comments on the slides is considered. The screen (electronic board) videorecording on the left with the lecturer in profile, named ad standard profile version, is recommended for use when there is a shortage of space in the classroom. In the presence of a video camera with optics that ensure the preservation of the detail of images of visual materials, the preferred option for video lecture is a standard frontal one, filmed from the far end of the audience. Didactically optimized editing of video lectures allows you to combine high-quality recording of visual materials of the basic version with mediocre video recordings of the teacher's actions. Integrative versions of video lectures are created by overlaying the video recording of the teacher's actions over a specially designated invariant place in the design of the slides of the basic version of the video lecture. The options for adapting the design of PowerPoint presentations to form a basic and integrative version of video lectures are proposed. The highest ratio of "didactic quality / labor costs" is achieved when creating integrative video lectures directly in the process of face-to-face and distance teaching.
Creating Ensemble Classifiers with Information Entropy Diversity Measure

Ensemble classifiers improve the classification accuracy by incorporating the decisions made by its component classifiers. Basically, there are two steps to create an ensemble classifier: one is to generate base classifiers and the other is to align the base classifiers to achieve maximum accuracy integrally. One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. In this paper, we propose an ensemble classifier generating algorithm to improve the accuracy of an ensemble classification and to maximize the diversity of its component classifiers. In this algorithm, information entropy is introduced to measure the diversity of component classifiers, and a cyclic iterative optimization selection tactic is applied to select component classifiers from base classifiers, in which the number of component classifiers is dynamically adjusted to minimize system cost. It is demonstrated that our method has an obvious lower memory cost with higher classification accuracy compared with existing classifier methods.
Sources of Macroeconomic Instability: Implications on Foreign Direct Investment Inflow in Sub-saharan Africa, A PMG/ARDL Approach

This study investigates the short and long run effect of diverse sources of macroeconomic instability on the inflow of foreign direct investment (FDI). The stability of the business environment of a host country will determine the extent to which these economies will absorb shocks and make the economy attractive to foreign investors. We evaluate the role played by macroeconomic instability conditions in determining the inflow of FDI in Sub-Saharan African (SSA) economies, where FDI inflow have been relatively low and macroeconomic instability variables like inflation, external debt and exchange rate has persistently shown volatile trends as compared to other regions in the world. This study employs the Pooled Mean Group (PMG), Mean Group (MG) and Dynamic fixed effect (DFE) autoregressive distributive lag (ARDL) estimation approaches which account for both non-stationarity and heterogeneity effects, in a panel of 31 SSA countries and covers the period 1990 to 2017. We equally employ first and second generation panel unit root test, cross sectional dependence test and cointegration test. The Hausman test is used to select the most efficient estimation technique between the MG, PMG and DFE techniques. The results reveal a negative but insignificant effect of inflation on FDI both in the short and long run. Equally, a positive but insignificant effect of external debt and a negative but insignificant effect of exchange rate are observed in the short run. In the long run, external debt significantly crowds out FDI while exchange rate significantly crowds in FDI in the sub-Saharan African sub region. Based on the findings, we recommend that debt contracted in the sub region should be channeled to productive use like road infrastructure and renewable energy, while currency depreciation should be encouraged in order to render investors within this region more competitive. The used of monetary and fiscal policies should be employed to tackle macroeconomic uncertainty. Capital account regulation should equally be adopted to ensure a competitive real exchange rate. Equally, the debt carrying capacity of each economy should be identified in order to avoid debt unsustainability or payment and servicing default.
A Hybrid and Improved Isolation Forest Algorithm for Anomaly Detection

Anomalies defined as patterns or data points that do not conform to a well-defined notion of normal behavior. Anomaly detection is a significant research problem that caters to the interest of a large number of research scientists. It is a significant step in every useful data mining framework. Several techniques involving one or more of the following fields, namely statistical analysis, machine learning, soft computing, deep learning, and information theory, which have used for making better anomaly detection systems. Anomaly detection finds its applications in various fields such as detecting malicious behavior in online social media networks, detecting fraud in credit card transactions, fault detection systems. This paper presents a hybrid anomaly detection algorithm that outperforms the existing Isolation forest algorithm. A basic introduction of the existing algorithms given and then a comparative study performed between the existing algorithms and our hybrid algorithm.
Mobile learning development using augmented reality as a biology learning media

Abstract The use of mobile technology changes our daily lives without exception in the learning process. The use of mobile in the learning process aims to change learning and teaching methods to be innovative through visualization techniques, thereby transforming students from passive learners to active students. One of the mobile learning methods that allow us to show a given subject’s visualization is Augmented Reality (AR). The use of AR as a supporting medium in studying biology subjects. AR development uses the Luther-Sutopo Multimedia System methodology. The results of this study are mobile-based learning media to visualize biology subjects using AR.
Memory Effects in Quantum Dynamics Modelled by Quantum Renewal Processes

Simple, controllable models play an important role in learning how to manipulate and control quantum resources. We focus here on quantum non-Markovianity and model the evolution of open quantum systems by quantum renewal processes. This class of quantum dynamics provides us with a phenomenological approach to characterise dynamics with a variety of non-Markovian behaviours, here described in terms of the trace distance between two reduced states. By adopting a trajectory picture for the open quantum system evolution, we analyse how non-Markovianity is influenced by the constituents defining the quantum renewal process, namely the time-continuous part of the dynamics, the type of jumps and the waiting time distributions. We focus not only on the mere value of the non-Markovianity measure, but also on how different features of the trace distance evolution are altered, including times and number of revivals.
Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process.
Methodology of Training Students in Design and Modeling of Clothes Using Information Communication Technologies

The purpose of this article is to develop a methodology for the development of competence in the design and modeling of clothing for university students through the use of information and communication technologies, its introduction into the educational process and increase the effectiveness of education. This article discusses the issues of improving students' knowledge and skills, interest in the learning process and the ability to apply their knowledge in practice in the future, using modern information and communication technology tools in the training of design professionals. Emphasis will be placed on developing and implementing a new teaching method aimed at enhancing the competence of design students in designing and modeling clothing. The role and perspectives of teachers are very important and they are highlighted as key participants in the process. The data show that, the using authoring technology of SCADEduWeb and information communication technologies in the educational process on the basis of interactive teaching methods, that allow students of professional education to effectively teach the design and modeling of clothing through the design of innovative digital tools, modeling creative and reflexive approaches helps to increase the effectiveness of education and facilitates the organization of the learning process. In particular, it shows that the contribution of information communication technologies to the improvement of the educational process in educational institutions, where information communication technologies are an innovative factor. Authoring technology of SCADEduWeb allows each student to achieve good results through independent, creative thinking at each stage of mastering the subject "Design and modeling of sewing objects". Achieving this high level will lead the educational institution not only to modernize the technological tools, but also to change the teaching models. In order to achieve development, we need to master digital knowledge and modern information technologies, use new teaching methods. This allows us to take the shortest path. Indeed, the deep penetration of information technology into all parts of the world today is creating new opportunities.
Can metafeatures help improve explanations of prediction models when using behavioral and textual data?

Machine learning models on behavioral and textual data can result in highly accurate prediction models, but are often very difficult to interpret. Rule-extraction techniques have been proposed to combine the desired predictive accuracy of complex "black-box" models with global explainability. However, rule-extraction in the context of high-dimensional, sparse data, where many features are relevant to the predictions, can be challenging, as replacing the black-box model by many rules leaves the user again with an incomprehensible explanation. To address this problem, we develop and test a rule-extraction methodology based on higher-level, less-sparse metafeatures. A key finding of our analysis is that metafeatures-based explanations are better at mimicking the behavior of the black-box prediction model, as measured by the fidelity of explanations.
AdaMM: Adaptive Object Movement and Motion Tracking in Hierarchical Edge Computing System

This paper presents a novel adaptive object movement and motion tracking (AdaMM) framework in a hierarchical edge computing system for achieving GPU memory footprint reduction of deep learning (DL)-based video surveillance services. DL-based object movement and motion tracking requires a significant amount of resources, such as (1) GPU processing power for the inference phase and (2) GPU memory for model loading. Despite the absence of an object in the video, if the DL model is loaded, the GPU memory must be kept allocated for the loaded model. Moreover, in several cases, video surveillance tries to capture events that rarely occur (e.g., abnormal object behaviors); therefore, such standby GPU memory might be easily wasted. To alleviate this problem, the proposed AdaMM framework categorizes the tasks used for the object movement and motion tracking procedure in an increasing order of the required processing and memory resources as task (1) frame difference calculation, task (2) object detection, and task (3) object motion and movement tracking. The proposed framework aims to adaptively release the unnecessary standby object motion and movement tracking model to save GPU memory by utilizing light tasks, such as frame difference calculation and object detection in a hierarchical manner. Consequently, object movement and motion tracking are adaptively triggered if the object is detected within the specified threshold time; otherwise, the GPU memory for the model of task (3) can be released. Moreover, object detection is also adaptively performed if the frame difference over time is greater than the specified threshold. We implemented the proposed AdaMM framework using commercial edge devices by considering a three-tier system, such as the 1st edge node for both tasks (1) and (2), the 2nd edge node for task (3), and the cloud for sending a push alarm. A measurement-based experiment reveals that the proposed framework achieves a maximum GPU memory reduction of 76.8% compared to the baseline system, while requiring a 2680 ms delay for loading the model for object movement and motion tracking.
Robust preparation of many-body ground states in Jaynes–Cummings lattices

Abstract Strongly correlated polaritons in Jaynes–Cummings (JC) lattices can exhibit quantum phase transitions between the Mott-insulating and superfluid phases at integer fillings. The prerequisite to observe such phase transitions is to pump polariton excitations into a JC lattice and prepare them into appropriate ground states. Despite previous efforts, it is still challenging to generate many-body states with high accuracy. Here, we present an approach for the robust preparation of many-body ground states of polaritons in finite-sized JC lattices by optimized nonlinear ramping. We apply a Landau–Zener type of estimation to this finite-sized system and derive the optimal ramping index for selected ramping trajectories, which can greatly improve the fidelity of the prepared states. With numerical simulation, we show that by choosing an appropriate ramping trajectory, the fidelity in this approach can remain close to unity in almost the entire parameter space. This approach can shed light on high-fidelity state preparation in quantum simulators and advance the implementation of quantum simulation with practical devices.
Smart Healthcare Analytics Solutions Using Deep Learning AI

Machine learning is widely used in various applications such as business organizations, e-commerce, and healthcare industry, scientific and engineering for predicting and discovering relationships among data. In the healthcare industry, the predictive analytics in machine learning is mainly used for disease prediction. ML techniques help in predicting relationships in the electronic health record (EHR) data. The clinical process guidelines in the corpus may be considered as one of the inputs, and various healthcare parameters can be feature scaled, and the resultant architecture provides a positive impact in healthcare systems decision making. The objective of this work is to determine suitable features and optimal classifier design for a Deep Learning Healthcare Diagnosis system (DLHDS) to differentiate the endothelial dysfunction. It is used to predict under-perfused as wells as over-perfused tissues during dynamic contrast material–enhanced magnetic resonance (MR) imaging of the peripheral vascular and muscular system. Early detection of disease and by mapping the drug side effects with patient histories is the needed approach for future prescriptions. The real-time applications of knowledge acquisition of healthcare data research require deep learning healthcare diagnosis systems. This paper presents deep learning service share model architecture for the generalizations of knowledge processing which is available in form of cloud and by using various parameters which enhances the assistive intelligence. By using semi-supervised machine learning techniques, features are reduced based on their nature of correlation in EHR and analyzed by ML techniques classifier and achieved an accuracy of 97%, and it outperforms the other existing prediction models, and this improves the throughput in the prediction of diseases.
Exact Markov chain-based runtime analysis of a discrete particle swarm optimization algorithm on sorting and OneMax

Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain "easy" reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration.
A Capacity Approach to Box and Packing Dimensions of Projections and Other Images

Dimension profiles were introduced by Falconer and Howroyd to provide formulae for the box-counting and packing dimensions of the orthogonal projections of a set E or a measure on Euclidean space onto almost all m-dimensional subspaces. The original definitions of dimension profiles are somewhat awkward and not easy to work with. Here we rework this theory with an alternative definition of dimension profiles in terms of capacities of E with respect to certain kernels, and this leads to the box-counting dimensions of projections and other images of sets relatively easily. We also discuss other uses of the profiles, such as the information they give on exceptional sets of projections and dimensions of images under certain stochastic processes. We end by relating this approach to packing dimension.
Information and psychological impact in the context of strategic communications paradigm

Background. The change in the paradigm of information-psychological confrontation caused changes in the content and architectonics of information-psychological impact, and determined the new trends in the forms of their practical use. Identifying the features of information and psychological impact in the context of strategic communications will make it possible to ensure the information and psychological security of person and society in a more targeted way. Objective. To identify the types, features and trends in the application of information-psychological impact in the context of the strategic communications paradigm. Results. The study reveals the essence of strategic communications as a paradigm of information-psychological confrontation. It analyzes architectonics and content of the main types of information and psychological impact. The study also discusses the main trends in the development of methods and forms of information and psychological impact. Conclusions. The context of strategic communications extends the understanding of information and psychological impacts. It is advisable to consider it as a deliberate dissemination (promotion, blocking and/or selection) of information, interaction and movement between participants in the communication process.Strategic communications use impact methods related to “hard power” meta-strategies based on coercion and “soft power” using attraction technologies. The implementation of these meta-strategies is accompanied by trends reflecting the transition from influence on the military enemy to impact on civilians; from “hard” to “soft” methods of exposure; from monological forms (dissemination of information, influence) to dialogical forms (communication); from an open invasion of consciousness and human behavior to more hidden forms of influence; from tactical forms designed for immediate effect to strategic forms aimed at a distant outcome; from monotype forms of influence to diverse, difficult, complex ones.To ensure the information-psychological security of the person and society, knowledge and consideration of the peculiarities of information and psychological impact are necessary within the framework of the current paradigm of information-psychological confrontation.
The role of self-evaluation and education of radiographers involved in a breast cancer screening program at Clinical Hospital Center Rijeka

Introduction Early breast cancer detection can be achieved by enforcing the quality of mammography screening program. Hence, proper breast positioning is necessary. Systematic education of radiographers on mammographic positioning is a prerequisite for obtaining high-quality mammograms. The aim of this study is to demonstrate the importance of self-evaluation and education of radiographers involved in a breast cancer screening program. Methods The First School of Mammographic Positioning was organized in 2018 at Clinical Hospital Center (CHC) Rijeka and 24 radiographers included in the screening program attended. Four months after the School, an analysis and comparison of randomly selected mammograms was performed. Retrospective analysis was performed, and two sets of collected data were gathered from the period before and after the education. The quality criteria for evaluation were set and the mammograms were independently analyzed by two radiographers with experience in mammography. The chi-square test was used to perform statistical analysis. Results Results showed a statistically significant difference in the quality of mammograms before and after training for all criteria (p < 0.05). Conclusion Self-evaluation and education of radiographers at the CHC Rijeka have led to a significant progress in image quality. Implications for practice Results of this work imply that there is a strong need to formalize breast imaging education and the need to implement systematic education at the national level in Croatia.