Reviewerly/exHarmony huggingface dataset:
Graph-Inceptor: Towards Extreme Data Ingestion, Massive Graph Creation and Storage

Graph processing is increasingly popular given the wide range of phenomena represented as graphs (e.g., social media networks, pharmaceutical drug compounds, or fraud networks, among others). The increasing amount of data available requires new approaches to efficiently ingest and process such data. In this research, we describe a solution at a conceptual level in the context of the Graph-Massivizer architecture. Graph-Inceptor aims to bridge the void among ETL tools enabling data transformations required for graph creation and enrichment and supporting connectors to multiple graph storages at a massive scale. Furthermore, it aims to enhance ETL operations by learning from data content and load and making decisions based on machine-learning-based predictive analytics.
Incremental Change Detection Method For Data Center Power Efficiency Metrics (Work In Progress Paper)

We propose an incremental change detection method for data center (DC) energy efficiency metrics and consider its application to the power usage efficiency (PUE) metric. In recent years, there is an increasing focus on the sustainability of DCs and PUE is playing an important role to evaluate the DC's energy efficiency. Publicly reported PUE values are mostly calculated over a whole year as there are many fluctuations caused by outside influences as outdoor air temperature (OAT). In this paper, we propose a method to detect short-term changes in the DC energy efficiency (e.g., PUE) , while considering outside influences (e.g., OAT) observing related daily aggregated DC data. We also conduct a few preliminary experiments for PUE change detection based on real-world DC data, where we have manually labeled changes in the PUE using visualization tools. The experimental results show that the method can detect important major and minor changes in the PUE with a very low false positive rate. However, due to the small number of positive labels, the recall rate is currently between 57% and 70%. Further investigation is necessary to see how representative the current recall rates are and what kind of improvements are necessary to make the change detection method more stable.
GraalVM Compiler Benchmark Results Dataset (Data Artifact)

Systematic testing of software performance during development is a persistent challenge, made increasingly important by the magnifying effect of mass software deployment on any savings. In practice, such systematic performance evaluation requires a combination of an efficient and reliable measurement procedure integrated into the development environment, coupled with an automated evaluation of the measurement results and compact reporting of detected performance anomalies.
Software Mining -- Investigating Correlation between Source Code Features and Michrobenchmark's Steady State

Microbenchmarking is a widely used method for evaluating the performance of a piece of code. However, the results of microbenchmarks for applications that utilize the Java Virtual Machine (JVM) are often unstable during the initial phase of execution, known as the warmup phase. This is due to the JVM's use of just-in-time compiler optimization, which is to identify and compile a "hot set" of important code regions. In this study we examine the static features of 586 microbenchmarks from 30 Java applications. To do so, we first extract static source code features of the benchmarks and then employ manual and descriptive data mining methods to identify meaningful correlations between these static features and the benchmarks' ability to reach a steady state. Our findings indicate that the number of function calls and lines of code have a considerable influence on whether or not the microbenchmarks reach a steady state.
Synchronous Glitch: The Serious Play of Digital Matter

Journal of Analytical PsychologyVolume 68, Issue 2 p. 443-447 Film and Culture Synchronous Glitch: The Serious Play of Digital Matter Briony O Clarke, Corresponding Author Briony O Clarke [email protected] orcid.org/0000-0001-9172-4351 London, UKSearch for more papers by this author Briony O Clarke, Corresponding Author Briony O Clarke [email protected] orcid.org/0000-0001-9172-4351 London, UKSearch for more papers by this author First published: 02 April 2023 https://doi.org/10.1111/1468-5922.12892Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Davis, A. (2015). The laws that balance the wave: Ruskin and the art of Briony Clarke. The Ruskin Review, 11(2), 19–35. Hillman, J. (1975). Re-visioning psychology. HarperCollins. Montfort, N. (2005). Twisty little passages: An approach to interactive fiction. MIT Press. Rowland, S., & Weishaus, J. (2021). Jungian arts-based research and the nuclear enchantment of New Mexico. Routledge. Saunders, C., Rutkowski, A., van Genuchten, M., Vogel, D., & Molina Orrego, J. (2011). Virtual space and place: Theory and test. MIS Quarterly, 35(4), 1079–1098. Retrieved December 20, 2022 from (https://www.academia.edu/72767536/Virtual_space_and_place_theory_and_test) Online etymology dictionary. (n.d.). Scry. Retrieved December 20, 2022, from https://www.etymonline.com/word/scry Tyson, D. (1997). Scrying for beginners: Tapping into the supersensory powers of your subconscious. Llewellyn Worldwide, Ltd. Voss, A. (2014). Scrying. In C. Partridge (Ed.), The occult world (pp. 580–591). Taylor & Francis Group. Volume68, Issue2April 2023Pages 443-447 ReferencesRelatedInformation
Fuzzy Controller-empowered Autoencoder Framework for anomaly detection in Cyber Physical Systems

In recent times, Cyber Physical Systems (CPSs) have been extensively deployed in vital infrastructures to provide crucial services to society. In the smart factory scenario, the Industrial Control Systems (ICSs) are CPSs that autonomously regulate the manufacturing process through sensors and actuators. Protection of these industrial control systems against cyber threats is essential to prevent any malfunction in the production procedure. In this article, a Fuzzy Controller-enabled Autoencoder Framework (FCAF) is proposed. The proposed framework can detect anomalies that may arise due to cyberattacks in the smart factory environment. The results show evidence that the model can sense any rapid deviation from normal behaviour once the attack commences and also detects unforeseen anomalies. The model further reduces the bias arising from the machine learning algorithm by introducing a fuzzy controller. The highest specificity and sensitivity reported by the proposed technique are 93% and 49.9%, respectively.
Joint project-based training of IT students based on the use of version control tools and task separation in the project

Problem statement. The relevance of the work lies in the proposed combination of methods and tools for application development during the educational process, which repeats frequent situations in industrial software development. The goal is to create conditions that allow to gain collective development skills close to real participation in software production. Methodology. During the training, students will write separate parts of the overall project, embedding their solution into the general logic of the system based on the implementation of the class interface set by the teacher. At the same time, frequently used design patterns are described and applied, such as model-view-controller, strategy, etc. The augmented project is synchronized between the participants using a version control system. As a result, students have a full-fledged application, parts of which are written by individual participants. Results. The implementation of the approach is considered on the example of the game “Chess”, in which each of the students can write the code of a separate figure; the possibilities of applying the approach within one or more classes and the potential risks of its use are indicated. Conclusion. The proposed approach allows students to gain work experience close to industrial programming and master such competencies as joint development, work in version control systems and building modular systems.
Cross‐version defect prediction using threshold‐based active learning

Abstract Because defects in software modules (e.g., classes) might lead to product failure and financial loss, software defect prediction enables us to better understand and control software quality. Software development is a dynamic evolutionary process that may result in data distributions (e.g., defect characteristics) varying from version to version. In this case, effective cross‐version defect prediction (CVDP) is not easy to achieve. In this paper, we aim to investigate whether the defect prediction method of the threshold‐based active learning (TAL) can tackle the problem of the different data distribution between successive versions. Our TAL method includes two stages. At the active learning stage, a committee of investigated metrics is constructed to vote on the unlabeled modules of the current version. We pick up the unlabeled module with the median of voting scores to domain experts. The domain experts test and label the selected unlabeled module. Then, we merge the selected labeled module and the remaining modules with pseudo‐labels from the current version into the labeled modules of the prior version to form enhanced training data. Based on the training data, we derive the metric thresholds used for the next iteration. At the defect prediction stage, the iterations stop when a predefined threshold is reached. Finally, we use the cutoff threshold of voting scores, that is, 50%, to predict the defect‐prone of the remaining unlabeled modules. We evaluate the TAL method on 31 versions of 10 projects with three prevalent performance indicators. The results show that TAL outperforms the baseline methods, including three variations methods, two common supervised methods, and the state‐of‐the‐art method Hybrid Active Learning and Kernel PCA (HALKP). The results indicate that TAL can effectively address the different data distribution between successive versions. Furthermore, to keep the cost of extensive testing low in practice, selecting 5% of candidate modules from the current version is sufficient for TAL to achieve a good performance of defect prediction.
Detecting translation borrowings in huge text collections using various methods

The purpose of this work is to investigate the problem of detecting transportable borrowings and text reuse. The article proposes a monolingual solution to this problem: translating the suspicious material into language collections for additional monolingual analysis. One of the major requirements for the suggested technique is robustness against machine learning ambiguities. The next step in the document analysis is split into two parts. The authors begin by retrieving documents-candidates that are similarity to other types of text recurrence. The paper proposes retrieving texts utilizing word clusters formed using distributional semantic for robustness. In the second stage, the authors use deep learning neural networks to compare the suspected document to candidates utilizing phrase embedding. The experimentation is carried out for the language pair “English-Arabic” on both articles and synthetic data.
Disentangled convolution for optimizing receptive field

Convolutional neural network (CNN) is one of the primary techniques for high-performance image recognition. The convolution operation with small-sized filters is a key ingredient of CNN and the receptive field of the whole CNN is enlarged by stacking lots of convolution layers. The convolution layer, however, is problematic in terms of the receptive field. It provides fixed small receptive field due to the fixed filter size in convolution, which requires us to manually control it in advance. Besides, the larger-sized convolution filters significantly increase computation cost. Thus, in this study, we propose a method to adaptively tune the receptive field of the convolution operation in an end-to-end manner as well as to enlarge the receptive field in a low computation cost. Based on the biological studies and scale-space theory, we can disentangle convolution operation into Gaussian envelope filtering for smoothing and derivative-related filtering, both of which are heterogeneously parameterized. Those two types of filters are jointly optimized in the end-to-end CNN training and the receptive field of the convolution is adequately optimized via learning the Gaussian envelope with a low extra computation cost. The experimental results on image classification tasks demonstrate that the proposed method effectively enlarges receptive field to improve performance.
Pedagogical design in education using augmented reality: a systematic review

There is a growing interest in using Augmented Reality technologies in education due to common perceived advantages like its accessibility and the increase in academical performance and motivation it promotes. Yet, most research done does not offer a deeper analysis of how AR technologies are used to reach different educational goals or the scenarios for which the technology is better or worse suited for. We conducted a systematic literature review to find educational projects applying AR technologies during the 2017–2021 period and analysed them through a pedagogical lens using Bloom’s Revised Taxonomy (BRT). 67 research articles were surveyed to identify the pedagogical design used and the reported results. We found that AR technologies are predominantly used in and better suited for training or practice scenarios, as well as helping in understanding complex, abstract or hard-to-find information. Most problems reported are associated to usability issues or a poor pedagogical design.
Performance Enhancement of Eulerian Video Magnification

In this study, a linear and phase-based Eulerian video magnification (EVM) methods are developed to minimize magnified noises and processing time.The developed approaches utilize the Lanczos resampling algorithm to reduce the frames' size of the source video so that the size of the processed data is significantly reduced.Then spatial decomposition is applied to the resized frames.Subsequently, temporal filters with specific cut-off frequencies are also used to filter only the desired frequencies to be amplified and then add them to the decomposed frames.The magnified frames are processed by a wavelet denoising algorithm to locate distributed noise over the different frequency bands and then remove it.The resulted denoised-magnified frames are resized up and then reconstructed by the spatial synthesis process.The experiments show the superiority and effectiveness of the developed EVM approaches compared to the conventional ones and other related approaches in terms of the execution time and the quality of the magnified video.The developed EVM approach can be used in several applications such as the detection of human vital signs without contact so that it is very useful to avoid infection in several diseases such as Covid-19.Furthermore, it can be used in detection of human mood and lying detection, detection and localization of material and liquid variations.
Informatization of vocational education through the implementation of the model of the center of immersive technologies

Problem statement . Every year, immersive equipment becomes more accessible, and the material and technical base of educational organizations is filled with new innovative devices. An urgent scientific task is the development of methods and tools for the implementation and usage of immersive devices in the educational process. Methodology. The authors describe approaches to the informatization of education based on the model of the center of immersive technologies (CIT), which contributes to the solution of managerial, methodical, pedagogical and technical problems associated with the application of immersive technologies in the educational and career guidance processes of educational organizations. The main functions of the CIT model are divided into three components: training students of secondary vocational education (SVE) in working professions, the realization of short programs for advanced training and assessment of qualifications, career guidance of schoolchildren. The key users of the CIT model are SVE students, schoolchildren and their parents, the management team of the educational organizations, teachers. The CIT model can be implemented in any organization that has immersive equipment (VR/AR) and human resources. Results. The implementation of immersive simulators in teaching students in professional programs, educational practice, as well as in organizing and conducting professional excellence championships demonstrates approximately equal effectiveness in comparison with traditional teaching methods, except for the cases in which it is important to develop tactile skills in working with certain equipment. Using the example of the execution of VR simulators in the teaching of the subject “Technology” in the school course, the advantage of learning on real machines is demonstrated. It was discovered that the maximum pedagogical effect is achieved by adding VR simulators to the training course and by using a combined approach. VR simulators are necessary for work in the absence of workshops and serve as an important didactic propaedeutic tool when they are available. Conclusion. The domestic and foreign experience of using immersive technologies in educational and outreach activities is analyzed, the development projects of the Russian Federation in the field of immersive technologies are considered. The relevance, theoretical and practical significance of the development and implementation of the CIT model are substantiated. The results of approbation in educational institutions of secondary vocational and basic general education are presented.
A Novel Computationally Efficient Approach to Identify Visually Interpretable Medical Conditions from 2D Skeletal Data

Timely identification and treatment of medical conditions could facilitate faster recovery and better health. Existing systems address this issue using custom-built sensors, which are invasive and difficult to generalize. A low-complexity scalable process is proposed to detect and identify medical conditions from 2D skeletal movements on video feed data. Minimal set of features relevant to distinguish medical conditions: AMF, PVF and GDF are derived from skeletal data on sampled frames across the entire action. The AMF (angular motion features) are derived to capture the angular motion of limbs during a specific action. The relative position of joints is represented by PVF (positional variation features). GDF (global displacement features) identifies the direction of overall skeletal movement. The discriminative capability of these features is illustrated by their variance across time for different actions. The classification of medical conditions is approached in two stages. In the first stage, a low-complexity binary LSTM classifier is trained to distinguish visual medical conditions from general human actions. As part of stage 2, a multi-class LSTM classifier is trained to identify the exact medical condition from a given set of visually interpretable medical conditions. The proposed features are extracted from the 2D skeletal data of NTU RGB + D and then used to train the binary and multi-class LSTM classifiers. The binary and multi-class classifiers observed average F1 scores of 77% and 73%, respectively, while the overall system produced an average F1 score of 69% and a weighted average F1 score of 80%. The multi-class classifier is found to utilize 10 to 100 times fewer parameters than existing 2D CNN-based models while producing similar levels of accuracy.
THE FORMATION OF FUTURE FOREIGN LANGUAGE TEACHERS’ PANORAMIC-PEDAGOGICAL THINKING IN THE CONTEXT OF NEW UKRAINIAN SCHOOL

The article touches upon the problem of the future foreign language teachers' panoramic-pedagogical thinking formation during their professional training in view of the Concept "New Ukrainian School".It is said in the article that the challenges of the 21 st century require highly qualified many-sided specialists, who are able to make independent decisions, find solutions in any professional situations, cooperate with Ukrainian and foreign partners.It is stated that only a new generation teacher is capable to cope with the new tasks of the school reform, to realize successfully the aims of developing school learners' intercultural communicative competence.The authors of the article claim that traditional forms of future foreign language teachers' professional training at the university can't fully satisfy the needs of the New Ukrainian School.The necessity of future foreign language teachers' panoramic-pedagogical thinking formation during their professional training is considered.The article highlights the skills of panoramic-pedagogical thinking which the students should have. The authors worked out the stages of panoramic-pedagogical thinking skills formation on the basis of the course "Methods of Teaching English".A particular attention is paid to the students' independent getting theoretical knowledge on the course with the help of suggested innovative techniques.The experience and effectiveness of such a form of future foreign language teachers' professional training was experimentally proved.The article substantiates the possibilities of the students' panoramic-pedagogical thinking formation during their practice at school, which takes place throughout the whole methodology course.The importance of specifically-organised tasks, which enable future foreign language teachers to demonstrate their creativity, ability to design new teaching aids and teaching materials, to work out innovative techniques has been noted.It is proved that panoramic-pedagogical thinking formation during future teachers' professional training gives them an opportunity to perceive innovations and changes of New Ukrainian School entirely thanks to the learners' self-education and self-development.
Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant features. The invariance characteristics of the proposed Gabor Block Local Binary Patterns (GBLBP) are demonstrated using a publicly available texture dataset. We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy (CH) images for the classification of cancer lesions. The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images. The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training. The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.