Reviewerly/exHarmony huggingface dataset:
Designing Porthole Aluminium Extrusion Dies on the Basis of Explainable Artificial Intelligence

This paper shows the development of a tool with which to solve the most critical aspect of the porthole die design problem using a predictive model based on machine learning (ML). The model relies on a large amount of geometrical data regarding successful porthole die designs, information on which was obtained thanks to a collaboration with a leading extrusion company. In all cases, the dies were made of H-13 hot work steel and the billet material was 6063 aluminium alloy. The predictive model was chosen from a series of probes with different algorithms belonging to various ML families, which were applied to the analysis of geometrical data corresponding to 596 ports from 88 first trial dies. Algorithms based on the generation of multiple decision trees together with the boosting technique obtained the most promising results, the best by far being the CatBoost algorithm. The explainability of this model is based on a post-hoc approach using the SHAP (SHapley Additive exPlanations) tool. The results obtained with this ML-based model are notably better than those of a previous model based on linear regression as regards both the R2 metric and the results obtained with the application examples. An additional practical advantage is its explainability, which is a great help when deciding the best way in which to adjust an initial design to the predictive model. This ML-based model is, therefore, an optimal means to integrate the experience and know-how accumulated through many designs over time in order to apply it to new designs. It will also provide an aid in generating the starting point for the design of high-difficulty dies, in order to minimise the number of FEM (finite element method) simulation/correction iterations required until an optimal solution is achieved. It is not aimed to eliminate FEM simulation from the design tasks, but rather to help improve and accelerate the whole process of designing porthole dies. The work presented herein addresses a validation model for a very common porthole die typology: four cavity and four port per cavity dies for 6xxx series aluminium alloys. However, a wide range of research regarding the generalisation of this model or its extension to other porthole die typologies must still be carried out.
The Experience of Implementing a Digital Library in the Educational and Research Activities of the National Defense University of Ukraine Named after Ivan Cherniakhovskyi

Attention is focused on the relevance of the use of distance learning technology, in particular on the development, use and integration of a digital library with the modular object-oriented dynamic learning environment Moodle. The problem of development, integration and use of digital libraries, in particular the National Defence University of Ukraine named after Ivan Cherniakhovskyi (NDUU) digital library, is analyzed.
Scripting Languages

At some point in your life as a professional working on the Web, you’re going to have to work with scripting languages. I know it’s a pain in the butt, but I resisted it as long as possible. But I eventually gave in, and so will you (cue dramatic music). This chapter leads you through configuring Apache for PHP and Node.js.
A Novel Framework for Malpractice Detection in Online Proctoring

Online exams have become increasingly popular in recent years for assessing students' content knowledge, especially during the COVID-19 outbreak. Proctoring for online tests is, however, challenging due to the lack of a face-to-face connection. Furthermore, according to a prior study, online assessments are more vulnerable to various types of cheating, putting their validity at risk. Suspicious student head and mouse movements are identified and depicted in three degrees of detail, allowing course instructors and professors to proctor online tests in a rapid, fast, and trustworthy manner. Our thorough evaluations, which include usage scenarios, well-designed user research, and expert interviews, indicate that our method is effective and practical.
It’s Too Noisy in Here: Using Projection to Improve Differential Privacy on RDF Graphs

Differential privacy is one of the most popular and prevalent definitions of privacy, providing a robust and mathematically rigid definition of privacy. In the last decade, adaptation of DP to graph data has received growing attention. Most efforts have been dedicated to unlabeled homogeneous graphs, while labeled graphs with an underlying semantic (e.g. RDF) have been mildly addressed. In this paper, we present a new approach based on graph projection to adapt differential privacy to RDF graphs, while reducing query sensitivity. We propose an edge-addition based graph projection method that transforms the original RDF graph into a graph with bounded typed-out-degree. We demonstrate that this projection preserves neighborhood, allowing to construct a differentially private mechanism on graphs given a similar mechanism on graphs with bounded typed-out degree. Experimental and analytical evaluation through a realistic twitter use-case show that this provide up to two orders of magnitude of utility improvement.
PROFESIONALISME GURU BAHASA BALI PADA MASA PANDEMI

This article analyzes the professionalism of Balinese language teachers during the pandemic. The analysis process is carried out by conducting relevant studies by adapting indicators of professional teachers in general and then adapting them to Balinese language teachers. This article takes pictures of professional Balinese teachers during the pandemic which is indeed a challenge for every subject teacher. The results of the analysis found that there were ten indicators of professional teachers covering aspects of language use, learning approaches, classroom management, mastery of materials, mastery of technology. Information and the ability of teachers to motivate students.keywords: professionalism, Balinese language teacher.
Semantic Vector Learning and Visualization with Semantic Cluster Using Transformers in Natural Language Understanding

Natural language understanding (NLU) is a fundamental technology for implementing natural interfaces. The embedding of sentences and correspondence between text and its extracted semantic knowledge, called semantic frame, has recently shown that a semantic vector representation is key in the implementation or support of robust NLU systems. Herein, we propose an extension of cluster-aware modeling with various types of pre-trained transformers for consideration of the many-to-1 relationships of text-to-semantic frames and semantic clusters. To attain this, we define the semantic cluster, and design the relationships between cluster members to learn semantically meaningful vector representations. In addition, we introduce novel ensemble methods to improve the semantic vector applications around NLU, i.e., similarity-based intent classification and a semantic search. Furthermore, novel semantic vector and corpus visualization techniques are presented. Using the proposed framework, we demonstrate that the proposed model can learn meaningful semantic vector representations in ATIS, SNIPS, SimM, and Weather datasets.
High Throughput VLSI Architecture for Bilateral Filtering in Computer Vision

Bilateral (BL) filtering is becoming de-facto noise filtering for computer vision and analytics processing systems due to its edge-preserving property. The prior literature address - Bilateral filtering by its direct implementation, which has higher computational complexity and is restricted to non-embedded applications. This paper proposes a novel solution to Bilateral filtering with significant complexity reduction enabling 720 MPixel/second throughput hardware IP in 16nm FinFET. The paper proposes multiple novelties namely Space and amplitude quantized Fixed point 2D-LUT, mixed-mode 1D Division LUT, and an efficient content-adaptive algorithm for Bilateral Filtering. Additionally, the given solution is flexible to support Octave scaling for image pyramid generation and generic 2D convolution engine in computer vision applications. The proposed solution requires 1/80 amount of 2D LUT compared to direct implantation. The image quality results show negligible pixel deviation in noise filtering output with a range of 1-3-pixel rms compared to pc based floating reference.
Multi-class SVM based C3D Framework for Real-Time Anomaly Detection

The conventional multi-class anomaly detection models are independent of noise elimination and feature segmentation due to large number of feature space and training images. As the number of human anomaly classes is increasing, it is difficult to find the multi-class anomaly due to high computational memory and time. In order to improve the multi-class human anomaly detection process, an advanced multi-class segmentation-based classification model is designed and implemented on the different human anomaly action databases. In the proposed model, a hybrid filtered based C3D framework is used to find the essential key features from the multiple human action data and an ensemble multi-class classification model is implemented in order to predict the new type of actions with high accuracy. Experimental outcomes proved that the proposed multi- class classification C3D model has better human anomaly detection rate than the traditional multi-class segmentation models.
Socalart: A Sensor-Based Wearable Device for Enhanced Social Distancing to Minimize the Spread of COVID-19

No Abstract.
Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.
Global trends and the future of education

What impact will climate change have on our educational institutions in the next decade? Are our research and innovation systems prepared for an era of global, open and internet intensive science? What does it mean for schools that our societies are becoming more individualistic and diverse?
HISTORICAL ANALYSIS OF INTERACTION BETWEEN SCHOOL AND THEATER IN NATIONAL EDUCATION

The article presents a retrospective analysis of the interaction between school and theater at different stages of the national education development. The author reveals a unique advanced experience of interaction between school and theater in the framework of the activities organized by the theater pedagogical departments for children and youth; describes various forms of interaction between school and theater at the present stage by classifying them according to the source of interaction initiative: theater, school, the third party. The main direction of the state policy in the field of supporting theatrical art in education is given.
The Association between The Patient’s Age Groups with Stage, Grading, and Molecular Subtype of Breast Cancer

In 2020, Global Cancer Observatory reported breast cancer incidence was 11.7% and mortality was 6.9%, most presenting a late-stage and delaying diagnosis. The study aimed to assess the breast cancer clinicopathologic characteristics according to the age groups. The research was conducted on 98 Medical Records from Bethesda Hospital Yogyakarta. The variable data were analyzed by univariate and the association between variables were analyzed with binary logistic regression with odds ratio and 95% confidence interval. The presentation of age groups ≤ 30 years (1,02%), 31-40 years (12,24%), 41-50 years (25,51%), 51-60 years (46,94%), and ≥ 61 years (15,31%); right breast (48,98%) and left breast (51,02%); stage I (3,06%), II (28,57%), III (55,10%), dan IV (13,27%); ductal type of breast cancer (89,80%) and others (10,20%); molecular subtype luminal A (43,88%), luminal B (14,29%), HER-2 neu enriched (29,59%), and basal-like/triple-negative (12,24%). There wasn’t any association between age groups and stages (p=0.368). There was an association between age groups and molecular subtype (p=0.013, OR 2.993 CI95% 1.239-7.230). Conclusions: The commonest clinicopathologic characteristic of breast cancer patients were in the age group 51-60 years, left breast, high stage, ductal type, and luminal A. Hormone receptors in breast cancer expressed more common in patients ≤50 years.
Exploring the meaning of the play rules

Objectives This study attempted to explore the meaning of the play rules that appear in situations where young children play. Methods The study was conducted on 26 5-year-old young children from April to July 2021, and data were collected by means of participatory observation, young children’s interview, researcher's journal, young children’s play outputs, video video and audio recording. Data were collected and analyzed through transcription, abbreviation and application for each subject. Results As a result of the study, it was found that the play rules enable play, connect play and learning, and add fun to play. Conclusions First, it was found that the play rules are the way of learning for children and play important roles and functions in play. Second, it was found that the children made and transformed the rules necessary for play by themselves and expanded their play world. Third, it was found that the rules of play framed and refined the play, and maintained the order of the play. Fourth, It was found that the sometimes rules were not followed and the process of adjusting the rules among the children led them to learn. Fifth, It was found that the rules of play add to the fun of play.&#x0D;
Negative Perceptions About the Applicability of Source-to-Source Compilers in HPC: A Literature Review.

cases compiler directives have been used as an easy way to offload parallel regions of a CPU-based program to a GPU accelerator, they may also be an attractive programming model for providing portability across different GPU vendors, in which case the porting process may proceed in the reverse direction: from low-level, architecture-specific code to higher-level directive-based abstractions. MiniMDock is a new mini-application (miniapp) designed to capture the essential computational kernels found in molecular docking calculations, such as are used in phar-maceutical drug discovery efforts, in order to test different solutions for porting across GPU architectures. Here we extend MiniMDock to GPU offloading with OpenMP directives, and compare to performance of kernels using CUDA and HIP on NVIDIA and AMD GPUs, respectively, as well as across different compilers, exploring performance bottlenecks. We document this reverse-porting process, from highly optimized device code to a higher-level version using directives, compare code structure, and describe barriers that were overcome in this effort.
Fractals and resistors run in parallel in the classroom

Using fractal design to learn about both resistor networks and fractional dimensions
Face Recognition Attendance System Method Based on Fusion of LBP and HOG

As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by centre symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.
Implementation of a Reliable VUI System on Edge Device

Voice user interface (VUI) is becoming highlighted as an effective tool for customized user interactions. In this paper, we implement a reliable VUI system using speech-to-text technology. The user"s utterance is converted into text using Google STT API and followed by keyword determination. Then, the system provides appropriate response corresponding to this keyword query to the user. We use four response types according to voice characteristics (male vs. female) and amount of information (reference vs. short). Our proposed VUI shows a real-time performance on the edge device and has been evaluated on two separate experiments.
Phase Completion for Fringe Projection Profiler Based on Neural Networks

Fringe projection profiler (FPP) measures the geometry of the target surface by projecting the pre-modulated stripe map onto the surface, and then capture the phase map with a camera.However, the inaccurate exposure or the characteristics of the surface reflectance may influence the imaging quality of the phase map, leaving some over-exposure and under-exposure regions.Addressing to this problem, this paper propose to apply a neural network to complete the phase map.Firstly, we propose a synthetic dataset to simulate the phase map of the inaccurate exposure regions, based on a physical rendering model.After that, we implement a transformer neural network to complete the missing phase information.Experiments show that the proposed neural network can complete the missing information from its neighbouring information, and provide precise completion results.
Occam's Razor Description of Sudden Energy Transitions in a Rabi-Driven Quantum Bit Under Frequent Observation

We wish to illustrate Occam's razor principle by revisiting the issue related to the long-lasting continuous experimental measurement of a Rabi- driven qubit ( Z. Minev et al. Nature 2019, 570, 200 ). We propose a simple and basic explanation of the results that proceeds from the variance properties of the Hamiltonian. We show that the standard deviation is comparable to the energy expectation value for a weak Rabi drive. Therefore it becomes instrumental in providing a large statistical time-dependent range of available values for the qubit energy that spread out across its well-known harmonic state-flipping mean value. In the presence of a long-lasting continuous measurement process, the resulting “quantum freezing” of the observed system keeps its energy within the standard deviation as long as this remains possible. Otherwise the system suddenly experiences a quantum jump between the two energy levels in order to resume its state freezing trajectory.
Fine-grained Encryption for Secure Research Data Sharing

Research data sharing requires provision of adequate security. The requirements for data privacy are extremely demanding for medical data that is reused for research purposes. To address these requirements, the research institutions must implement adequate security measurements, and this demands large effort and costs to do it properly. The usage of adequate access controls and data encryption are key approaches to effectively protect research data confidentiality; however, the management of the encryption keys is challenging. There are novel mechanisms that can be explored for managing access to the encryption keys and encrypted files. These mechanisms guarantee that data are accessed by authorised users and that auditing is possible. In this paper we explore these mechanisms to implement a secure research medical data sharing system. In the proposed system, the research data are stored on a secure cloud system. The data are partitioned into subsets, each one encrypted with a unique key. After the authorisation process, researchers are given rights to use one or more of the keys and to selectively access and decrypt parts of the dataset. Our proposed solution offers automated fine-grain access control to research data, saving time and work usually made manually. Moreover, it maximises and fortifies users' trust in data sharing through secure clouds solutions. We present an initial evaluation and conclude with a discussion about the limitations, open research questions and future work around this challenging topic.
THE EFFECT OF DIGITAL LITERATURE ON STUDENTS' LEARNING INDEPENDENCE IN THE 2019 COVID SITUATION

This study is a quantitative study through a causal approach with the purpose of determining the effect of digital literacy on the results of students' learning autonomy in the 2019 COVID situation. This study was conducted to test the validity of the instrument using the formula Person Product Moment correlation and reliability test using Cronbach's Alpha formula. The sample in this study was 42 students of the Elementary School, which was obtained using the Taro Yamane formula. This quantitative research was continued with the requirements test of data analysis using the Normality, Homogeneity, and Linearity tests. The average value for the digital literacy variable is 84.73 and the average value for the learning independence variable is 78.29. The results showed that there was an effect of digital literacy on learning independence, which was indicated by the path coefficient (pxy) of 0.70. This shows the influence of digital literacy on learning independence, while the coefficient of determination (r2) is 0.49 or 49%. This means that the value of the influence of digital literacy on learning independence is 49%, through the regression equation Y =(10.49 + 0.8X). Based on the results of the research above, it can be concluded that there is a positive influence of digital literacy on the learning independence of students in the 2019 COVID situation.