Reviewerly/exHarmony huggingface dataset:
Machine Learning based Stream Selection of Secondary School Students in Bangladesh

In the Bangladeshi education system, there are three stages up to the secondary school certificate (SSC)- the primary (Primary Education Completion Certificate, or PEC), middle school (Junior School Certificate, or JSC), and SSC. A separate stream has to be chosen after the eighth grade, which could be any of the following streams: Science, Business Studies, and Humanities. The selection of a stream is very important for their future higher studies and career planning. Usually, students take the decision of selecting a stream based on PSC and JSC results only. To address this challenge, we have collected a dataset from different Bangladeshi schools, which consists of PSC and JSC students' records. There are 26 data for each student record including subject-wise student results, parent’s academic qualification, parent’s profession, parent’s monthly income, sibling information, district, etc. In the experimental analysis, a series of machine learning regression algorithms have been utilized. Moreover, we have employed various performance metrics in order to validate our model’s performance. The experimental results demonstrate that among the regressors, extreme gradient boosting algorithm’s performance were superior in both science and humanities streams. In the business stream however, Support Vector Machine’s performance is considerably better. It is expected that the analysis will help prospective students and stakeholders in their future decisions. Moreover, we have utilized Local Interpretable Model Agnostic Explanations that helps to increase the interpretability of the model.
Beyond the Game: Charting the Future of Game Data Science

A.S. Fraenkel introduces a new (s, t)-Wythoff's game which is the generalization of both a-Wythoff's game and Wythoff's game. Fraenkel determines all P-positions of (s, t)-Wythoff's game under normal play convention. In this paper, the (s, t)-Wythoff's ...
Detecting Security Bad Smells of Software by using Data Mining

Abstract Bad smells of code can lead to significant software vulnerabilities that negatively affect the security attribute of software and make the software easily attacked. Method implementation is the lowest code level that produces bad smells in software. Several programming guidelines have been introduced to reduce the number of bad code smells. Some are related to security, while others are related to other software quality attributes. We can detect some of these guidelines by using quality metrics (e.g., the number of lines of code and the number of parameters). This project proposes applying data mining classifiers to automatically detect security code smells and non-security code smells based on software quality metrics. We developed eight models using eight classifiers: Logistic Regression, Random Forest, XG Boost, J48, SVM, Naive Bayes, LightGBM, and Neural Network. We evaluated the eight models using several performance measurements (e.g., accuracy and confusion metrics). The results showed that it could detect the security code smell from the non-security code smell with the highest accuracy of 95\%.
APPLICATION OF THE AGILE – SCRUM METHODOLOGY TO REVAMP KPI (KEY PERFORMANCE INDEX) SYSTEM AT PT SOHO GLOBAL HEALTH

PT. Soho Global Health (SOHO) is a provider of health products and supplements that has 2500 employees. The overall performance of these employees is assessed on an annual KPI (Key Performance Index). Problems arise due to technology systems that were previously perceived to be very slow for processing KPI data online. New system changes that are designed with faster performance need to be done or in other words revamp the system. Substitution of a whole new system or revamp system at Soho needs to use the Agile methodology approach. Agile methodology especially the Scrum model can overcome problems that arise in the system development process, especially in the speed of processing time, many modules and complex, and also involves many teams both the development team and many elements of related users. Given that, the KPI system at SOHO processes data from thousands of employees from many divisions as a whole.
Hermeneutic Study in The Novel “Tuyet” Bur Rasuanto's Works

The purpose of this research is to describe hermeneutics in the novel Tuyet by Bur Rasuanto. This research is a type of library research. Source of data in this research is primary and secondary data. Primary data is the main data taken from the novel Tuyet by Bur Rasuanto and secondary data is taken from several literatures that support primary data. The method used to obtain data in this study is a qualitative descriptive analysis method, namely reading repeatedly and then analyzing and recording words, sentences, expressions that contain hermeneutics. After the data is collected, the data is analyzed by identifying the data based on the problem items and research objectives. From the results of the analysis above, it can be concluded that the novel Tuyet by Bur Rasuanto is full of meaning. The novel teaches us that the amount of love for parents can cripple the human mind. In addition, living with idealism will lead humans to exile to the ruling regime. The more dangerous is that power that is too prominent also increasingly acts arbitrarily against its own people which is also capable of diminishing the spirit of human life.
Application of Random Forest for The Classification Diabetes Mellitus Disease in RSUP Dr. M. Jamil Padang

Diabetes Mellitus is a disease in which blood sugar levels go beyond normal (GDS&gt;200 mg/dl). Diabetes Mellitus may be defined as an insulin function disorder in the pancreatic organ. Diabetes Mellitus is a world health problem as incidents of this disease are increasing in every part of the world, including Indonesia. Prevention and control of the disease need to be made so as not to cause complications in other organs even to death. Because of this, one needs to study a method to predict the occurance of this disease and to knows the variable that most affect a person suffered from it. This could be accomplished by using a classification methods. One of classification methods is Random Forest. In this case study using randomForest packages in RStudio software. In general, the result of this study are the smallest OOB’s error rates (%) and Variable Importance Measure (VIM) using Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) values.The classification by a Random Forest methods on the incidence of Diabetes Mellitus in RSUP Dr. M. Jamil Padang results in OOB’s error rate was 1,2% or accuracy rates was 98,8%. The most optimal model produced using mtry = 4 and ntree = 1000. If used MDA, the variables that most affect are Age, Polyphagia, Polyuria, HB, and BMI. While if used MDG, the variables that most affect are Age, Polyphagia, BMI, HB, and Delayed Healing.
Nonparametric Regression Modeling with Fourier Series Approach on Poverty Cases in West Sumatra Province

Poverty is a complex problem that has an impact on various social problems such as education, unemployment, health and economic growth. Therefore, the problem of poverty is important to overcome in order to create population welfare. One of the analyses that can be used to model the percentage of poverty is regression analysis. Regression analysis is divided into two approaches, namely parametric and nonparametric. Parametric regression has several assumptions while, the only assumption nonparametric regression shape of the curve does not form a certain pattern. There are several approaches to nonparametric regression, one of which is the Fourier Series. The purpose of this study is to model the percentage of poverty in West Sumatra Province. The unclear shape of the curve in the data used is a consideration for using nonparametric regression. Then it is known that the data used in this study is data per region which tends to have a fluctuating nature. So it is suitable to use the Fourier series approach. In this research, nonparametric regression modeling with one, two, and three oscillation parameters was attempted. The best model was obtained which consisted of two oscillation parameters with a Generalized Cross Validation (GCV) value of 2.110 and R² of 92.44%.
Application of Random Forest to Identify for Poor Households in West Sumatera Province

Poverty is a socioeconomic problem in Indonesia. The number of people who were living in poverty in West Sumatera increases for 26.44 thousands from 2020 to 2021. The government has created programs to cope with poverty by taking into account the criteria for the poor households. These criteria have been developed by using the data obtained through The National Socioeconomic Survey (Susenas). However, instead of.showing the actual location of poor household, the existing data only interprets the number of poor household. Thus make the program less effective. This could be overcome by classification analysis of random forest (RF). RF is collection of many decision trees. Before fitting RF, one has to determine the values if three tuning parameters, mtry, ntree and node size. The result are the smallest OOB’s error rate (%) and Variable Importance Measure(VIM). The classification by RF in this research results in OOB’s error rate was 5.65% or accuracy rate was 94.35% with tuning parameter using mtry=5 and ntree=500. Based on the VIM, the poor household’s criteria include sources of drinking water such as protected or unprotected spring water and surface water, lighting tools such as non-PLN electricity or no usage of electricity, fuel for cooking such as charcoal and firewood, and the head of the household being self-employed, a family worker, or unpaid with at least a junior high degree.
PKM DEVELOPMENT OF HOUSEHOLD BUSINESS AROMATHERAPY BRICKETS KHOLY ZAINI

People of Martapura City and its surroundings often use incense/bukhur as an air freshener for religious activities. One of the home businesses that produce it is Kholy Zaini. The business was founded 12 years ago with the premise that when pioneering it was that people in the city liked incense/bukhur, while most of it was obtained from imports from the island of Java. Even though it has been running for a long time until now there is still no IPRT and in its manufacture, I t still uses synthetic fragrances, which hurts health. This program is implemented to provide understanding and solutions to partners so that the products produced don’t negatively impact the future. The methods implemented include socialization activities, counseling, discussions, training, and mentoring. The solution that the PKM Team provides to partners is to replace synthetic fragrances with natural fragrances obtained from the distillation of ylang flowers. The process used to make incense/bukhur from natural fragrances is like making it in general, but the difference is da distillation of ylang flowers to get a natural fragrance. The PKM team succeeded in making a natural fragrance from the distillation of ylang flowers. In addition, the PKM team also teaches the company's bookkeeping to partners and develops a website to market their products. The advantages of this PKM activity are making incense/bukhur more environmentally friendly, partners can make the right decisions and increasing sal, es and making products known more widely.
MySQL Database Server: Deploying Software Application to Enhance Visibility and Accountability

This study explored the design and development of customized enterprise web-based application management systems, which introduced a better approach on how to promote Insecticide Treated Nets (ITNs) supply chain management visibility, accountability, integration of inventory, real time location tracking and monitoring. The literature reveals that the pool of Web-based supply chain management apps and use represents the degree of responsiveness to institutional pressures, the effect of top management perception and support, and the role of organizational characteristics in the choice to adopt Web-based software application. A software development life cycle method was adopted combining a logistics management system with Web Services technology using waterfall approach. The result of this study provides an important information, for ITNs company to consider the implementation of Web-based application over the existing excel platform which will improve several ITNs processes across the supply chain such as better-quality supply chain transparency, reduction of costs related to labor, greater inventory visibility and management of transport channels as well as improvements in client’s service experience. The study recommends that the company (ITNs) should invest in IT hardware infrastructures (internet, computers, and software), create a better awareness and develop a comprehensive training for staff across all the state on how to use the web base application in addition to increasing the number of employees who are specifically assigned to become company administrators in order to provide faster and user-friendly services.
Concurrent Multi-path quantum entanglement routing based on segment routing in quantum hybrid networks

Abstract The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Symbolic Algorithm for Inverting General k-Tridiagonal Interval Matrices

The k-tridiagonal matrices have received much attention in recent years. Many different algorithms have been proposed to improve the efficiency of k-tridiagonal matrix estimation. A novel method based on interval analysis has been identified to improve the efficiency of the calculation. This paper presents efficient and reliable computational algorithms for determining the determinant and inverse of general k-tridiagonal interval matrices built on generalized interval arithmetic. This study is based on the Doolittle LU factorization of the interval matrix. Finally, examples are presented to illustrate the algorithms.
John Tuthill

An interview with Drosophila neuroscientist John Tuthill.
Classical Dance Hand Gesture Recognition using Image Processing and Deep Learning

Recognition of Dance posture &amp; hand gesture in dance is most important. To get the exact names of these mudras some expertise should be there. So without expertise our proposed system recognizes the dance posture and hand geture. Indian classical dance traditionally performed as an expressive show dance type of strict execution workmanship, connected with Vedic writing. It's likewise a method of articulation of inward sentiments, assisting with creating self-conviction and fearlessness. Significance of old style dance is the activity of the whole self. Due to picture names, picture quality, uneven dataset and pixels values the accuracy is not good. So our proposed system implies the CNN and Deep Learning algorithm to recognizes the Dance posture and Hand gesture. Our system is used for all users who are interested in dance. Without a teacher can get the dance posture details. Introduced here Kathakali and Bharatnatyam Hand Mudras. Referred Kaggale site for dataset and also we added our own dataset. Each image recognizes the proper name and gives in the form sound also of Hand Mudra. Gaussian algorithm is used for more accuracy purpose.
EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVE

Sentiment analysis has been rapidly employed for business decision support. New data mining researchers are yet to have an adequate understanding of the various applications of sentiment analysis while utilising social media data. As a result, it is critical to define the data mining and text analytics research trend holistically using existing literature. The study explores sentiment analysis research for its application in transforming social media data and identifies relevant research aspects through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis are mainly related to innovation, transparency, and efficiency. Our review also highlights the distinctiveness of sentiment analysis for synthesising social media information to investigate various features, including the knowledge-domain map that detects author collaboration networks in the past.
Use of blockchain in the internet of things: a survey

This paper provides a comprehensive and high level technical overview of BC technology by creating a common language through business and technology about BC. Then an overview of a BC technology, its architectures, classification, challenges, etc. is presented. Finally, some recommendations are provided to re-searchers that will have to be tackled before deploying the next generation of IoT applications based on BC technology.
Two coupled qubits under the influence of a minimal, phase-sensitive environment

In this work, we investigate the influence of a minimal, phase-sensitive environment on a system of two coupled qubits. The environment is constituted by a single-mode field initially prepared in a type of Schrödinger cat state, a quantum superposition of two squeezed coherent states. We present an analytical solution to the model and investigate the degradation of the quantum features of the system due to the action of the environment. In particular, we find that the time-averaged linear entropy for long times, $\bar{S}_T$, has approximately a linear dependence on Mandel's $Q$ parameter as well as on the variance of the $\hat{X}$ quadrature of the initial state of the environment.
Decision-Making Under Uncertainty: Beyond Probabilities

This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion.
An Alternative Formulation of the Quantum Phase Estimation Using Projection-Based Tensor Decompositions

In this paper an alternative version of the quantum phase estimation is proposed, in which the Hadamard gates at the beginning are substituted by a quantum Fourier transform. This new circuit coincides with the original one, when the ancilla is initialized with $|0\rangle$. With the help of a projection-based tensor decomposition and closed-form expressions of its exponential, this new method can be interpreted as a multiplier coupled to the Hamiltonian of the corresponding target unitary operator. Based on this observation a recursive decomposition is derived.
Robust Knowledge Distillation from RNN-T Models With Noisy Training Labels Using Full-Sum Loss

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. Soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNN-T architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data.
Neural Gromov-Wasserstein Optimal Transport

We present a scalable neural method to solve the Gromov-Wasserstein (GW) Optimal Transport (OT) problem with the inner product cost. In this problem, given two distributions supported on (possibly different) spaces, one has to find the most isometric map between them. Our proposed approach uses neural networks and stochastic mini-batch optimization which allows to overcome the limitations of existing GW methods such as their poor scalability with the number of samples and the lack of out-of-sample estimation. To demonstrate the effectiveness of our proposed method, we conduct experiments on the synthetic data and explore the practical applicability of our method to the popular task of the unsupervised alignment of word embeddings.
Information Theoretic I-MMSE generalize Time-Frequency Signal Processing Tools

In this paper, we capitalize on information theoretic-estimation theoretic result, called the I-MMSE [1]-[2] to show that such tool generalizes time-frequency signal processing tools urgent for the analysis of non-stationary non-Gaussian signals.
Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples

Conversational recommender systems (CRSs) are improving rapidly, according to the standard recommendation accuracy metrics. However, it is essential to make sure that these systems are robust in interacting with users including regular and malicious users who want to attack the system by feeding the system modified input data. In this paper, we propose an adversarial evaluation scheme including four scenarios in two categories and automatically generate adversarial examples to evaluate the robustness of these systems in the face of different input data. By executing these adversarial examples we can compare the ability of different conversational recommender systems to satisfy the user's preferences. We evaluate three CRSs by the proposed adversarial examples on two datasets. Our results show that none of these systems are robust and reliable to the adversarial examples.
Degree bounds for fields of rational invariants of $\mathbb{Z}/p\mathbb{Z}$ and other finite groups

Degree bounds for algebra generators of invariant rings are a topic of longstanding interest in invariant theory. We study the analogous question for field generators for the field of rational invariants of a representation of a finite group, focusing on abelian groups and especially the case of $\mathbb{Z}/p\mathbb{Z}$. The inquiry is motivated by an application to signal processing. We give new lower and upper bounds depending on the number of distinct nontrivial characters in the representation. We obtain additional detailed information in the case of two distinct nontrivial characters. We conjecture a sharper upper bound in the $\mathbb{Z}/p\mathbb{Z}$ case, and pose questions for further investigation.