Reviewerly/exHarmony huggingface dataset:
Tuple Interpretations and Applications to Higher-Order Runtime Complexity

Tuple interpretations are a class of algebraic interpretation that subsumes both polynomial and matrix interpretations as it does not impose simple termination and allows non-linear interpretations. It was developed in the context of higher-order rewriting to study derivational complexity of algebraic functional systems. In this short paper, we continue our journey to study the complexity of higher-order TRSs by tailoring tuple interpretations to deal with innermost runtime complexity.
ANALYSIS OF PUBLIC REACTION TO E-LEARNING ON TWITTER

Over the past few decades, due to the explosive growth of social media, online resources, and microblogging sites such as Twitter. There was an influx of user-generated content. The data obtained from these resources is a rich source of information for data mining. Sentiment analysis is a current and important area of research that attempts to determine the polarity of a text. The definition of feelings about current events in the world has become crucial. This article focuses on data mining on Twitter and defining opinions regarding e-learning. The focus is on identifying sentiment from e-learning-related texts that are shared on Twitter. About 3,000 tweets were extracted and the polarity of those tweets was detected, and then visualize the resulting data.
Geological and Geochemical Study of Gold and Base Metals Mineralization in the Surupa Area, Borena Zone, Southern Ethiopia

The paper reported the information on geology, alteration pattern, ore minerals and geochemical investigation for gold and base metals and to delineate potential area of gold and base metals mineralization. The results showed that the study area consists of heterogeneous sequences of medium to high-grade poly-deformed gneisses with igneous and sedimentary precursors and structurally affected by three phases of deformation (D1 to D3) like foliations, mesoscale folds, veins and fractures/joints respectively. The nickel and cobalt are highly anomalous elements indicating the source for the mineralization controlled by different structures such as fractures and joints. Pyrite, magnetite, goethite and hematite are ore mineral assemblages characterize the mineralization of the area.
Nextformer: A ConvNeXt Augmented Conformer For End-To-End Speech Recognition

Conformer models have achieved state-of-the-art(SOTA) results in end-to-end speech recognition. However Conformer mainly focuses on temporal modeling while pays less attention on time-frequency property of speech feature. In this paper we augment Conformer with ConvNeXt and propose Nextformer structure. We use stacks of ConvNeXt block to replace the commonly used subsampling module in Conformer for utilizing the information contained in time-frequency speech feature. Besides, we insert an additional downsampling module in middle of Conformer layers to make our model more efficient and accurate. We conduct experiments on two opening datasets, AISHELL-1 and WenetSpeech. On AISHELL-1, compared to Conformer baselines, Nextformer obtains 7.3% and 6.3% relative CER reduction in non-streaming and streaming mode respectively, and on a much larger WenetSpeech dataset, Nextformer gives 5.0%~6.5% and 7.5%~14.6% relative CER reduction in non-streaming and streaming mode, while keep the computational cost FLOPs comparable to Conformer. To the best of our knowledge, the proposed Nextformer model achieves SOTA results on AISHELL-1(CER 4.06%) and WenetSpeech(CER 7.56%/11.29%).
Features of Vocal and Choral Skills and Abilities Formation in Younger Schoolchildren Using a Kinaesthetic Approach

The study aims to provide a methodological substantiation of the effectiveness of the author’s “Vocal Kinaesthetics” technology for vocal and choral skills and abilities formation in younger schoolchildren. The use of the proposed technology significantly optimises the process of vocal and choral training in general. The paper deals with the issue of vocal and choral skills and abilities formation in younger schoolchildren in modern conditions. The researcher formulates and defines the notion of a kinaesthetic approach and proposes a route of using the kinaesthetic approach in the process of vocal and choral skills and abilities formation in younger schoolchildren through its manifestation in the author’s “Vocal Kinaesthetics” technology. Scientific novelty of the study lies in defining the kinaesthetic approach as a methodological basis for vocal and choral training. As a result, an increase in the effectiveness of vocal and choral skills and abilities formation in the process of applying the kinaesthetic approach in classes with younger schoolchildren has been identified.
Optimizing Character Animations using Online Crowdsourcing

This paper presents a novel approach for exploring diverse and expressive motions that are physically correct and interactive. The approach combining user participation in with the animation development process using crowdsourcing to remove the need for data-driven libraries while address aesthetic limitations. A core challenge for character animation solutions that do not use pre-recorded data is they are constrained to specific actions or appear unnatural and out of place (compared to real-life movements). Character movements are very subjective to human perception (easily identify underlying unnatural or strange patterns with simple actions, such as walking or climbing). We present an approach that leverage's crowdsourcing to reduce these uncanny artifacts within generated character animations. Crowdsourcing animations is an uncommon practice due to the complexities of having multiple people working in parallel on a single animation. A web-based solution for analysis and animation is presented in this paper. It allows users to optimize and evaluate complicated character animation mechanism conveniently on-line. The context of this paper introduces a simple animation system, which is integrated into a web-based solution (JavaScript/HTML5). Since Web browser are commonly available on computers, the presented application is easy to use on any platform from any location (easy to maintain and share). Our system combines the expressive power of web pages for visualising content on-the-fly with a fully fledged interactive (physics-based) animation solution that includes a rich set of libraries.
MULTIVARIATE PIECEWISE REGRESSIVE AFRICAN BUFFALO OPTIMIZATION-BASED RESOURCE-AWARE TASK SCHEDULING IN CLOUD

Distributed cloud computing handles a large number of tasks and provides many dynamic virtualized resources that aim to share as a service through the internet. While handling a large volume of tasks, task execution times, throughput, and makespan are the most significant metrics in practical scenarios. So, the scheduling task is essential to achieve accuracy and correctness on task completion. A novel technique called Multivariate Piecewise Regressive African Buffalo Optimization-based Resource Aware Task Scheduling (MPRABO-RATS) is introduced for improving the task scheduling efficiency and minimizing time consumption. First, the cloud user dynamically generates numerous heterogeneous tasks in the cloud environments. After receiving the tasks, the task scheduler in the cloud server finds the resource-optimized virtual machine using the Multivariate Piecewise Regressive African Buffalo Optimization technique. The proposed optimization technique uses the Multivariate Piecewise Regression function for analyzing the different resources availablity such as CPU Time, Memory, Bandwidth, and Energy before the task scheduling. Initially, the population of the virtual machine is defined. After that, the fitness is measured using Multivariate Piecewise Regression. Based on the fitness estimation, the resource-efficient virtual machine is determined. Finally, the task scheduler assigns the tasks to the resource-optimized virtual machine with higher efficiency. Experimental evaluation is carried out in the CloudSim simulator on the factors such as task scheduling Efficiency, Throughput, Makespan, and Memory Consumption with respect to a number of tasks. The observed results indicate that the MPRABO-RATS technique offers an efficient solution in terms of achieving higher task scheduling Efficiency, Throughput, and Minimizing the Makespan as well as Memory Consumption than the conventional scheduling techniques
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics

Traditional approaches based on finite element analyses have been successfully used to predict the macro-scale behavior of heterogeneous materials (composites, multicomponent alloys, and polycrystals) widely used in industrial applications. However, this necessitates the mesh size to be smaller than the characteristic length scale of the microstructural heterogeneities in the material leading to computationally expensive and time-consuming calculations. The recent advances in deep learning based image super-resolution (SR) algorithms open up a promising avenue to tackle this computational challenge by enabling researchers to enhance the spatio-temporal resolution of data obtained from coarse mesh simulations. However, technical challenges still remain in developing a high-fidelity SR model for application to computational solid mechanics, especially for materials undergoing large deformation. This work aims at developing a physics-informed deep learning based super-resolution framework (PhySRNet) which enables reconstruction of high-resolution deformation fields (displacement and stress) from their low-resolution counterparts without requiring high-resolution labeled data. We design a synthetic case study to illustrate the effectiveness of the proposed framework and demonstrate that the super-resolved fields match the accuracy of an advanced numerical solver running at 400 times the coarse mesh resolution while simultaneously satisfying the (highly nonlinear) governing laws. The approach opens the door to applying machine learning and traditional numerical approaches in tandem to reduce computational complexity accelerate scientific discovery and engineering design.
KEBIASAAN MENONTON VLOG DIARI DAN KORELASI TERHADAP KETERAMPILAN RETORIKA MAHASISWA (Habits of Watching Vlog Diary and the Correlation to the College Student’s Rhetoric Skills)

Rhetoric skills are important for students. Through rhetoric, students can convey a purpose and objective persuasively. Students' rhetorical skills can be developed through the habit of watching vlog diaries. By doing the habit of watching diary vlogs, it is hoped that it will affect students' rhetorical skills. The purpose of this study was to find out whether or not there was a relationship between the habit of watching diary vlogs and rhetorical skills in Postgraduate students of the UHAMKA Indonesian Language Education Masters Program. This research uses quantitative research with cross sectional method. Test (Chi-Square) used found the PValue value of 0.043, meaning that this 0.043 value &lt;alpha (0.05) with the results of the odds ratio (OP) obtained 0.043 with a range (0.079-2.066), which means that the habit of watching this vlog diary has potential 2 (double) strength will have to do with rhetorical skills. The conclusions that can be summarized in this study are that there is a moderate relationship between the habit of watching diary vlogs and rhetorical skills.
亚院三年级学生使用汉语形容词 AABB 与 ABAB 重叠式的偏误分析 MISTAKES OF USING THE ADJECTIVE REDUPLICATION AABB AND ABAB BY SEMESTER VI STBA-PIA STUDENTS

This study aims to examine the types of errors found in the sixth semester students of STBA-PIA, and explain the factors that cause these errors. This study used a qualitative descriptive method design. Data collection techniques were carried out by elicitation and interview techniques. The results showed that the errors in the use of reduplication of AABB and ABAB adjectives by semester VI STBA-PIA students consisted of errors of omission of 28 sentences, errors of addition of as many as 28 sentences. 18 sentences, error of selection is 58 sentences and error of ordering is 7 sentences. The total errors in the use of adjective reduplication are 111 sentences. The results of the analysis also prove that the factors that cause the error are interlingual factors and intralingual factors. Keywords: Error, Adjective Reduplication, AABB, ABAB
Causality for Inherently Explainable Transformers: CAT-XPLAIN

There have been several post-hoc explanation approaches developed to explain pre-trained black-box neural networks. However, there is still a gap in research efforts toward designing neural networks that are inherently explainable. In this paper, we utilize a recently proposed instance-wise post-hoc causal explanation method to make an existing transformer architecture inherently explainable. Once trained, our model provides an explanation in the form of top-$k$ regions in the input space of the given instance contributing to its decision. We evaluate our method on binary classification tasks using three image datasets: MNIST, FMNIST, and CIFAR. Our results demonstrate that compared to the causality-based post-hoc explainer model, our inherently explainable model achieves better explainability results while eliminating the need of training a separate explainer model. Our code is available at https://github.com/mvrl/CAT-XPLAIN.
Internet of medical things

Internet of Medical Things (IOMT) is playing critical role in healthcare business to boost the accuracy, reliability and efficiency of electronic equipment. Researchers are working towards a computerized healthcare system by integrating the existing medical resources and healthcare services. As IOT converging different sectors but our emphasis is connected to research contribution of IOT in healthcare domain. This study covers the peoples contribution of IOT in healthcare sector, application and future problems of IOT in term of medical services in healthcare. We do expect that our study will be valuable for academics and practitioners in the area, allowing them to comprehend the tremendous potential of IoT in medical domain and identification of important problems in IOMT. This investigation would also allow the academics to comprehend applications of IOT in healthcare area. This input would allow the academics to comprehend the prior contribution of IOT in healthcare business.
Lookback for Learning to Branch

The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure. Specifically, we find that with the strong branching heuristic, a child node's best choice was often the parent's second-best choice. We call this the "lookback" phenomenon. Surprisingly, the typical branching GNN of Gasse et al. (2019) often misses this simple "answer". To imitate the target behavior more closely by incorporating the lookback phenomenon in GNNs, we propose two methods: (a) target smoothing for the standard cross-entropy loss function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term. Finally, we propose a model selection framework to incorporate harder-to-formulate objectives such as solving time in the final models. Through extensive experimentation on standard benchmark instances, we show that our proposal results in up to 22% decrease in the size of the B&B tree and up to 15% improvement in the solving times.
No Reference Image Quality Algorithm based on Human-Eye Sensitive Conception for Contrast-Distortion Evaluation

No reference image quality assessment IQA algorithms are widely used for finding distortions in images without comparing them to references.Image' areas with no details may indicate a brightness saturation change, as well as the presence of noise in the background, which are more visible types of distortions.Thus, the design of such IQA should take into account the human visual conception.This paper proposes a no-reference image quality evaluation algorithm that takes into account the finest edge detection process and entropy deployment in regard to human visual sensitive HVS to quantify brightness saturation variations and noise in pixels.Statistic objective metrics for correlation coefficient CC which person PCC, spearman rank order SROCC, root mean squared error RMSE are used in the objective evaluation in corresponding with the subjective evaluation.The proposed algorithm is tested and significantly has well correlation PCC > 0.87, SROCC > 0.93, RMSE < 0.34.The findings of this research could be used to improve the performance of no-reference HVS-based IQA algorithms currently in use.
NeRF, meet differential geometry!

Neural radiance fields, or NeRF, represent a breakthrough in the field of novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Numerous recent works have been focusing on making the models more robust, by means of regularization, so as to be able to train with possibly inconsistent and/or very sparse data. In this work, we scratch the surface of how differential geometry can provide regularization tools for robustly training NeRF-like models, which are modified so as to represent continuous and infinitely differentiable functions. In particular, we show how these tools yield a direct mathematical formalism of previously proposed NeRF variants aimed at improving the performance in challenging conditions (i.e. RegNeRF). Based on this, we show how the same formalism can be used to natively encourage the regularity of surfaces (by means of Gaussian and Mean Curvatures) making it possible, for example, to learn surfaces from a very limited number of views.
Work climate as a determining factor in organizational commitment

The perspective of an organization is its continuous improvement, conditioned by the factors of the perception and characteristics of the employees, as well as the actions of the institution. The objective of this article is to know the incidence of the work environment in the organizational commitment of the workers of the Regional Agrarian Directorate of Amazonas, 2019. The study is quantitative, cross-sectional, and correlational. The survey was applied to a group of 95 collaborators, the results showed that there is a dependency relationship identified according to the Pearson correlation of 0.564; with a significance level of 0.001. In this sense, we can indicate that the work environment is directly related to organizational commitment, in such a way that the greater the organizational commitment, the better the work environment, highlighting two aspects at work, such as interpersonal relationships and working conditions, which lead to growth and institutional development.&#x0D; Keywords: Work environment, organizational commitment, continuous improvement.
Leadership Style as Correlate of Management Decision Making in School Administration

Leadership influences the decision making process and set the goals for an organization. In educational management, the teachers and school heads provide the leadership and direction essential to the smooth day-today running of the school. Both leadership and management are essential for effective and successful school administration. The decision’s school leaders make affect students, parents, teachers in the school and the larger society. Thus, since wrong decisions can bring consequences in an organization or institution, it becomes necessary to explore leadership styles and management of decision making that can guarantee the best results for all stakeholders in the workplace. The paper recommended among others, that democratic leadership style should be adopted by management in decision making because it is most likely to guarantee industrial peace and institutional harmony as well as goal attainment
FORMATION OF LEADERSHIP POTENTIAL OF INTELLECTUAL

The importance of the leadership qualities development of representatives of various strata of the intelligentsia as an important prerequisite for the successful implementation of their social mission is substantiated. It is shown that proper leadership potential in combination with high general and professional culture of an intellectual and his rich spiritual world significantly increases the opportunities and creative activity of an intellectual and his influence on the spirituality and culture of the general population. The meaning of the concepts of intelligence of a leader and leadership of an intellectual is analyzed, and their role in the processes of social development is revealed. The goals, content and nature of the formation of traits and qualities of leadership and intelligence of future professionals are revealed. The possibility of using the synergetic effect that arises as a result of targeted development of the qualities of intelligence and leadership of students as future professionals in the process of forming their professional and social competence is considered.
Robust Chaos-Based Medical Image Cryptosystem

In In this paper, we propose an efficient cryptosystem for medical images. While the confusion stage is ensured by an Arnold's cat map allowing the permutation of pixels; the diffusion stage is alleviated by an improved logistic map used by the chaotic key-based algorithm (CKBA). The simulation results attest that the proposed algorithm has superior security and enables efficient encryption/decryption of medical images. Performances were evaluated by several security analyses: the NPCR and UACI are improved over 99.60% and 33.46% respectively, and entropy is reported close to 7.8. What makes this new cipher much stronger security.
Airplane Flight Route Optimization Problem with Multi-constraints

The main goal of this paper is to formulate, design, as well as test algorithms that will increase the efficiency of large civil airplanes flying long-distance routes. The algorithms will reduce the total time spent flying long-distance routes by optimizing airplane trajectory in a three-dimensional wind environment constrained by the different time of arrival requirements. This research work focuses on utilizing MATLAB graphical functionality as well as modularity as an analytical tool. MATLAB simulation, as well as visualization capabilities, are hugely beneficial in both engineering education as well as research. MATLAB has been used to create a test-bed to visually depict mission planning work to provide a visible representation of flight routes and their variability.
Best Practices for Relational Database Optimization using Microsoft SQL

The main problem when querying a database is the response time. The research was of an applied type, using two databases: control and experimental. Three computers have been used to execute ten queries to the two databases, running 4 consecutive times and obtaining an average. The results found were that, of the ten consultations carried out, seven consultations obtained better results in the experimental DB and three in the control DB. It is concluded that the best practices to optimize a database are: to create clustered indexes on columns frequently used in searches or to perform sorts, create non-clustered indexes on primary or foreign keys that do not have clustered indexes, use calculated columns, operators, and listing of proper columns in queries, however, the use of indexes should be restricted because they affect insert, update, and delete operations.&#x0D; Keywords: relational database, database optimization, SQL, indexing.