Reviewerly/exHarmony huggingface dataset:
ANALISIS K-MEANS CLUSTERING PADA PEMETAAN PROVINSI INDONESIA BERDASARKAN INDIKATOR RUMAH LAYAK HUNI

Liveable houses, based on national and global have various indicators criteria, they are proper access to sanitation, adequate living space, building resilience, drinking water access, and living security. In addition, the government also pays attention to the housing ownership status variable. This research aims to map Indonesia provinces based on liveable houses indicator that consists of 9 variables, they are access to adequate housing, decent drinking water, electric lighting, decent sanitation, houses area &lt;7.2 m2, the widest land floor, the widest bamboo wall, top of the widest fibers palm, house for rent/contract. This study uses K-Means Clustering analysis and coefficient silhouette width validation method to determine the level of cluster validation. The analysis results are 4 clusters, which cluster 1 consists of 10 members, cluster 2 consists of 19 members, cluster 3 consists of 3 members, and cluster 4 consists of 2 members. Cluster 1 is the lowest percentage for houses size variable with &lt;7,2 m2, the widest fibers roof and ownership of rent/contract houses compared to other clusters. Cluster 2 has the highest percentage of liveable access variable and descent drinking water. Cluster 3 has the lowest percentage of liveable access, descent drinking water, electric lighting, and decent sanitation, while the highest percentage of houses size variable is &lt;7,2 m2, The Widest Land Floor and Wall. Cluster 4 has the highest percentage of other variables of electric lighting variable, descent sanitation, the widest fibers roof, and ownership of temporary rent/contract houses. Meanwhile, has the lowest percentage of the widest land floor and bamboo wall, compared to other clusters
Tracking using Human Pose Matching with Deep Association Metric

This paper proposes a novel approach to track multiple people utilizing skeletal information combined with visual appearance features to improve the accuracy of tracking people across different frames of a video. We extracted the appearance feature vectors and skeletal feature vectors for each detected person in every frame. Each individual was tracked by considering the cosine distance between the skeletal feature vectors along with the euclidean distance between the appearance feature vectors across different frames of a video. This reduces the dependency of the tracker over appearances of people thus making it more consistent, especially in videos with people having similar appearances such as sports videos with players wearing similar jerseys. The stance of an individual in continuing frames is expected to be similar considering the high frame rate of modern camera devices. Therefore it is befitting to consider skeletal features along with appearance features for tracking. Our paper is an incremental paper demonstrating improvement over SORT with a deep association metric approach. Our approach utilizing skeletal information combined with visual appearance information returns better MOT results on the MOT17 dataset using the yolov3 detector.
SHARED KNOWLEDGE AND PRESENTATION OF THE RESEARCH RESULTS IN SPECIALISED SCIENTIFIC PUBLICATIONS

Many and of different character documents define the relationships and roles in education and sciencefrom laws and legal acts, to regulations, standards, simple rules and many others.Every such document plays a certain role in terms of their meaning and content.Within a certain framework, the development of every participant -a teacher, a mentor, a scientist, etc -is related to these systems.All these are a necessary, yet insufficient condition to be a good teacher, lecturer, and scientist.A teacher, a lecturer, a scientist teach, transfer and share knowledge.And knowledge itself is a special category.How knowledge and research results can reach their users and is passing through specialized scientific publications the only way to it.This research paper considers the possibilities to stimulate scientific publications in Bulgaria and their role.
Reward Systems for Trustworthy Medical Federated Learning

Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models. In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems. We first evaluate how to measure the contributions of institutions toward predictive performance and bias in cross-silo medical FL with a Shapley value approximation method. In a second step, we design different reward systems incentivizing contributions toward high predictive performance or low bias. We then propose a combined reward system that incentivizes contributions toward both. We evaluate our work using multiple medical chest X-ray datasets focusing on patient subgroups defined by patient sex and age. Our results show that we can successfully measure contributions toward bias, and an integrated reward system successfully incentivizes contributions toward a well-performing model with low bias. While the partitioning of scans only slightly influences the overall bias, institutions with data predominantly from one subgroup introduce a favorable bias for this subgroup. Our results indicate that reward systems, which focus on predictive performance only, can transfer model bias against patients to an institutional level. Our work helps researchers and practitioners design reward systems for FL with well-aligned incentives for trustworthy ML.
A Study to Find the Impact of Emotional Intelligence on Employee Motivation in the Organization

Purpose: The purpose of the study is to examine the impact of emotional intelligence on employee motivation in the organization and to find the relationship between six factors namely, Organizational commitment, Reward, Recognition, Performance Appraisal, Job Satisfaction, and Emotional Exhaustion.Methodology/Approach: An online survey was conducted to collect the data from 57 employees, who are currently working in various IT Companies. The conceptual model was tested, and the findings were validated and statistically analyzed using SmartPLS.Findings: The factor Emotional Exhaustion was found to be significant on Organizational Commitment, whereas Reward, Recognition, Performance Appraisal, and Job Satisfaction were found to be not significant on Organizational Commitment.Implications: Employers can use this study to find out which factor is affecting the motivation level of employees in the organization.
Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies

The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian False Discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.
RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems

Interactive autonomous applications require robustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the task of action detection. We develop a novel ranking method to rank videos based on the degree of global camera motion. For the high ranking camera videos we show that the accuracy of action detection is decreased. We propose an action detection pipeline that is robust to the camera motion effect and verify it empirically. Specifically, we do actor feature alignment across frames and couple global scene features with local actor-specific features. We do feature alignment using a novel formulation of the Spatio-temporal Sampling Network (STSN) but with multi-scale offset prediction and refinement using a pyramid structure. We also propose a novel input dependent weighted averaging strategy for fusing local and global features. We show the applicability of our network on our dataset of moving camera videos with high camera motion (MOVE dataset) with a 4.1% increase in frame mAP and 17% increase in video mAP.
Combined Learning of Neural Network Weights for Privacy in Collaborative Tasks

We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network architecture and base parameters to train a model using only locally available data. Locally trained models are then submitted to a combining agent, which produces a combined model. The new model's parameters can be sent back to hosts, and can then be used as initial parameters for a new training iteration. CoLN is capable of combining several distributed neural networks of the same kind but is not restricted to any single neural architecture. In this paper we detail the combination algorithm and present experiments with feed-forward, convolutional, and recurrent Neural Network architectures, showing that the CoLN combined model approximates the performance of a hypothetical ideal centralized model, trained using the combination of the local datasets. CoLN can contribute to secure collaborative research, as required in the medical area, where privacy issues preclude data sharing, but where the limitations of local data demand information derived from larger datasets.
Pointed semibiproducts of monoids

Semibiproducts of monoids are introduced here as a common generalization to biproducts (of abelian groups) and to semidirect products (of groups) for exploring a wide class of monoid extensions. More generally, abstract semibiproducts exist in any concrete category over sets in which map addition is meaningful thus reinterpreting Mac Lane's relative biproducts. In the pointed case they give rise to a special class of extensions called semibiproduct extensions. Not all monoid extensions are semibiproduct extensions but all group extensions are. A categorical equivalence is established between the category of pointed semibiproducts of monoids and the category of pointed monoid action systems, a new category of actions that emerges from the equivalence. The main difference to classical extension theory is that semibiproduct extensions are treated in the same way as split extensions, even though the section map may fail to be a homomorphism. A list with all the 14 semibiproduct extensions of 2-element monoids is provided.
KU Leuven RDR: Institutional Research Data Repository

In January 2022 KU Leuven released its institutional research data repository, RDR. This poster describes what RDR is and how KU Leuven researchers can use it to publish and archive finished research data in a FAIR and secure way.
Smart IoT Techniques to Improve Pandemic Outreach

The COVID-19 pandemic has crossed the provincial, radical, philosophical, spiritual, educational, and pedagogical limits of the latest global problem. The healthcare infrastructure allowed by the internet of things (IoT) is beneficial using an integrated network for careful surveillance of COVID-19 patients. This device aims to improve the comfort of patients and decrease the hospital readmission rate. Implementation of the IoT influences reducing healthcare costs and improving the affected patient’s medical outcome. Therefore, by presenting a theoretical framework to solve the COVID-19 pandemic, this new study-based analysis aims to investigate, analyze, and illustrate the overall implications of the well-proven IoT theory. Finally, it describes and addresses twelve essential IoT implementations. Ultimately, scholars, educators, and scientists have been compelled to suggest some productive ways to solve or cope with this pandemic.
A Scalable 5,6-Qubit Grover's Quantum Search Algorithm

Recent studies have been spurred on by the promise of advanced quantum computing technology, which has led to the development of quantum computer simulations on classical hardware. Grover's quantum search algorithm is one of the well-known applications of quantum computing, enabling quantum computers to perform a database search (unsorted array) and quadratically outperform their classical counterparts in terms of time. Given the restricted access to database search for an oracle model (black-box), researchers have demonstrated various implementations of Grover's circuit for two to four qubits on various platforms. However, larger search spaces have not yet been explored. In this paper, a scalable Quantum Grover Search algorithm is introduced and implemented using 5-qubit and 6-qubit quantum circuits, along with a design pattern for ease of building an Oracle for a higher order of qubits. For our implementation, the probability of finding the correct entity is in the high nineties. The accuracy of the proposed 5-qubit and 6-qubit circuits is benchmarked against the state-of-the-art implementations for 3-qubit and 4-qubit. Furthermore, the reusability of the proposed quantum circuits using subroutines is also illustrated by the opportunity for large-scale implementation of quantum algorithms in the future.
A walk through of time series analysis on quantum computers

Because of the rotational components on quantum circuits, some quantum neural networks based on variational circuits can be considered equivalent to the classical Fourier networks. In addition, they can be used to predict the Fourier coefficients of continuous functions. Time series data indicates a state of a variable in time. Since some time series data can be also considered as continuous functions, we can expect quantum machine learning models to do many data analysis tasks successfully on time series data. Therefore, it is important to investigate new quantum logics for temporal data processing and analyze intrinsic relationships of data on quantum computers. In this paper, we go through the quantum analogues of classical data preprocessing and forecasting with ARIMA models by using simple quantum operators requiring a few number of quantum gates. Then we discuss future directions and some of the tools/algorithms that can be used for temporal data analysis on quantum computers.
Quality Assurance in the Context of Contemporary Software Practice

We review the literature on the nature of quality assurance in the context of comnplex systems developed using iterative and incremental approaches.
Distributional Uncertainty Propagation via Optimal Transport

This paper addresses the limitations of standard uncertainty models, e.g., robust (norm-bounded) and stochastic (one fixed distribution, e.g., Gaussian), and proposes to model uncertainty via Optimal Transport (OT) ambiguity sets. These constitute a very rich uncertainty model, which enjoys many desirable geometrical, statistical, and computational properties, and which: (1) naturally generalizes both robust and stochastic models, and (2) captures many additional real-world uncertainty phenomena (e.g., black swan events). Our contributions show that OT ambiguity sets are also analytically tractable: they propagate easily and intuitively through linear and nonlinear (possibly corrupted by noise) transformations, and the result of the propagation is again an OT ambiguity set or can be tightly upper bounded by an OT ambiguity set. In the context of dynamical systems, our results allow us to consider multiple sources of uncertainty (e.g., initial condition, additive noise, multiplicative noise) and to capture in closed-form, via an OT ambiguity set, the resulting uncertainty in the state at any future time. Our results are actionable, interpretable, and readily employable in a great variety of computationally tractable control and estimation formulations. To highlight this, we study three applications in trajectory planning, consensus algorithms, and least squares estimation. We conclude the paper with a list of exciting open problems enabled by our results.
Identifying Relation-gaps in Ontologies using TOPSIS

Though many ontologies have a huge number of classes, one cannot find a good number of object properties connecting the classes in most of the cases. Adding object properties makes an ontology richer and more applicable for tasks such as Question Answering. In this context, determining the pair of classes that are most likely to have object properties between them becomes very important. We address the above question in this paper. We propose a simple yet powerful framework based on the popular multi-criteria decision making algorithm TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for identifying relation-gaps. In order to identify such relation-gaps (i.e class-pairs) the proposed system utilizes a mix of criteria derived from the ontology itself and from external sources such as Wikipedia text corpora. Our experimental results show that by means of these carefully chosen criteria and their corresponding weights, the proposed system yields promising results with respect to the precision of the relation-gaps identified.
Safety-Critical Manipulation for Collision-Free Food Preparation

Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of quick, robust, and ultimately collision-free behaviors. In this work, we showcase a novel framework for modifying previously generated trajectories of robotic manipulators in highly detailed and dynamic collision environments using Control Barrier Functions (CBFs). This method dynamically re-plans previously validated behaviors in the presence of changing environments -- and does so in a computationally efficient manner. Moreover, the approach provides rigorous safety guarantees of the resulting trajectories, factoring in the true underlying dynamics of the manipulator. This methodology is extensively validated on a full-scale robotic manipulator in a real-world cooking environment, and has resulted in substantial improvements in computation time and robustness over re-planning.
A Two Parameters Equation for Word Rank-Frequency Relation

Let $f (\cdot)$ be the absolute frequency of words and $r$ be the rank of words in decreasing order of frequency, then the following function can fit the rank-frequency relation \[ f (r;s,t) = \left(\frac{r_{\tt max}}{r}\right)^{1-s} \left(\frac{r_{\tt max}+t \cdot r_{\tt exp}}{r+t \cdot r_{\tt exp}}\right)^{1+(1+t)s} \] where $r_{\tt max}$ and $r_{\tt exp}$ are the maximum and the expectation of the rank, respectively; $s&gt;0$ and $t&gt;0$ are parameters estimated from data. On well-behaved data, there should be $s&lt;1$ and $s \cdot t &lt; 1$.
An efficient topology optimization method based on adaptive reanalysis with projection reduction

Efficient topology optimization based on the adaptive auxiliary reduced model reanalysis (AARMR) is proposed to improve computational efficiency and scale. In this method, a projection auxiliary reduced model (PARM) is integrated into the combined approximation reduced model (CARM) to reduce the dimension of the model in different aspects. First, the CARM restricts the solution space to avoid large matrix factorization. Second, the PARM is proposed to construct the CARM dynamically to save computational cost. Furthermore, the multi-grid conjugate gradient method is suggested to update PARM adaptively. Finally, several classic numerical examples are tested to show that the proposed method not only significantly improves computational efficiency, but also can solve large-scale problems that are difficult to solve by direct solvers due to the memory limitations.
Problems of training competitive higher education graduates in today’s environment

One of the drivers of economic development is human capital, which can transform the economic space and reveal the innovative potential of the socio-economic landscape’s systems and subsystems. The key role in shaping the human resources of the future belongs to the higher school. Current institutional conditions impose new requirements on graduates of higher educational institutions and to the higher education system, in particular, to university teachers. Purpose of the study: to reveal the necessary competencies of a modern teacher of the Higher School, necessary for the preparation of competitive specialists and managers. The article uses historical, logical and comparative research methods, systems approach and modelling method. The author substantiates the factors, influencing the transformation of the modern ecosystem and the modern student, the characteristic features of students at universities, identifies the, and indicates on need to change the mission of the higher school and the teacher. A list of key competencies of a lecturer and seminarian of the higher school, which helps him to prepare the competitive graduate, was highlighted.
Korean-Specific Dataset for Table Question Answering

Existing question answering systems mainly focus on dealing with text data. However, much of the data produced daily is stored in the form of tables that can be found in documents and relational databases, or on the web. To solve the task of question answering over tables, there exist many datasets for table question answering written in English, but few Korean datasets. In this paper, we demonstrate how we construct Korean-specific datasets for table question answering: Korean tabular dataset is a collection of 1.4M tables with corresponding descriptions for unsupervised pre-training language models. Korean table question answering corpus consists of 70k pairs of questions and answers created by crowd-sourced workers. Subsequently, we then build a pre-trained language model based on Transformer and fine-tune the model for table question answering with these datasets. We then report the evaluation results of our model. We make our datasets publicly available via our GitHub repository and hope that those datasets will help further studies for question answering over tables, and for the transformation of table formats.