Reviewerly/exHarmony huggingface dataset:
Atenea: Digitalizing Educational Games with Light Tables

Along the last years, the use of technology has started at younger ages. It is also known that it is used by parents to calm their children down and by teachers to make learning more attractive for children. It seems natural to combine and put technology and teaching together to improve the learning process. This is the core principle of Atenea. It is based on technology that is already used in classrooms, such as projectors, laptops or touch screens. But these, now classical, technologies are not the only ones that appear there; light tables have also been already included. But their classical use is by placing different figures on them, even sand. Atenea consists of the digitization of light tables, allowing children to play games. The main goal is to provide teachers with an innovative tool for teaching. It is also aimed at providing a more attractive and enjoyable way of learning. The system makes children feel like they are using the light table in a very similar way as if it was not digitalized. It follows the principles of calm technology. To evaluate the quality in usability terms, the SUS questionary has been used, which shows very good results.
On some theoretical developments of interval metric space

The goal of this work is to introduce the concept of interval metric in a compact way by modifying the existing definitions of interval metric. Then, a result regarding the necessary and sufficient criterion for interval metric is established. Thereafter, to illustrate the idea of interval metric, a set of examples is provided. Then, several results regarding the formation of interval metric are derived. Also, the concept of interval diameter, boundedness of a set under interval metric and interval distance are introduced. All the theoretical results are illustrated with the help of some numerical examples. Finally, as an application of interval metric, all the theoretical developments of transformation of multi-objective interval optimization problem into interval single objective optimization problem by Global criterion method, Tchebycheff method and Weighted Tchebycheff method are established.
Robust Collision Avoidance of Quadric and Polygonal Surfaces Moving in Planar Environments

The determination of robot trajectories that can achieve reactive collision avoidance with moving objects is of great interest. In cluttered environments with tight spaces, it becomes important to consider the shapes of the objects in computing these trajectories. The literature largely models the shapes of the objects as circles, and this can make the avoidance maneuvers very conservative, especially when the objects are elongated and/or non-convex. In this paper, we model the shapes of the objects using combinations of quadric surfaces or polygons, and employ a collision cone approach to achieve reactive collision avoidance, in the presence of measurement noise. The collision avoidance design employs a dual-loop control architecture where the inner loop uses a dynamic inversion-based method and the outer loop uses Linear Matrix Inequalities (LMIs). Simulation results demonstrating the collision avoidance laws for dynamic, heterogeneous quadric and polygonal surfaces are presented.
An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System*

The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patient’s health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats.
Model-Based Fairness Metric for Speaker Verification

Ensuring that technological advancements benefit all groups of people equally is crucial. The first step towards fairness is identifying existing inequalities. The naive comparison of group error rates may lead to wrong conclusions. We introduce a new method to determine whether a speaker verification system is fair toward several population subgroups. We propose to model miss and false alarm probabilities as a function of multiple factors, including the population group effects, e.g., male and female, and a series of confounding variables, e.g., speaker effects, language, nationality, etc. This model can estimate error rates related to a group effect without the influence of confounding effects. We experiment with a synthetic dataset where we control group and confounding effects. Our metric achieves significantly lower false positive and false negative rates w.r.t. baseline. We also experiment with VoxCeleb and NIST SRE21 datasets on different ASV systems and present our conclusions.
A Comparative Study of Voice Conversion Models With Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023

This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023. For both in-domain and cross-domain English singing voice conversion (SVC) tasks (Task 1 and Task 2), we adopt a recognition-synthesis approach with self-supervised learning-based representation. To achieve data-efficient SVC with a limited amount of target singer/speaker’s data (150 to 160 utterances for SVCC 2023), we first train a diffusion-based any-to-any voice conversion model using publicly available large-scale 750 hours of speech and singing data. Then, we fine-tune the model for each target singer/speaker of Task 1 and Task 2. Large-scale listening tests conducted by SVCC 2023 show that our T13 system achieves competitive naturalness and speaker similarity for the harder cross-domain SVC (Task 2), which implies the generalization ability of our proposed method. Our objective evaluation results show that using large datasets is particularly beneficial for cross-domain SVC.
SLM: Bridge the Thin Gap Between Speech and Text Foundation Models

We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM freezes the pretrained foundation models to maximally preserves their capabilities, and only trains a simple adapter with just 1% (156M) of the foundation models’ parameters. This adaptation not only leads SLM to achieve strong performance on conventional tasks such as automatic speech recognition (ASR) and automatic speech translation (AST), but also unlocks the novel capability of zero-shot instruction-following for more diverse tasks. Given a speech input and a text instruction, SLM is able to perform unseen generation tasks including contextual biasing ASR using real-time context, dialog generation, speech continuation, and question answering. Our approach demonstrates that the representational gap between pretrained speech and language models is narrower than one would expect, and can be bridged by a simple adaptation mechanism. As a result, SLM is not only efficient to train, but also inherits strong capabilities already present in foundation models of different modalities.
Findings of the 2023 ML-Superb Challenge: Pre-Training And Evaluation Over More Languages And Beyond

The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification. The challenge comprises a research track focused on applying ML-SUPERB to specific multilingual subjects, a Challenge Track for model submissions, and a New Language Track where language resource researchers can contribute and evaluate their low-resource language data in the context of the latest progress in multilingual speech recognition. The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages. The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks, and a variety of speech/voice types present significant challenges in multilingual speech processing.
Impact of Meteorological Parameters on Solar Radiation over Langtang National Park, Nepal

The main objective of this research is to study the impact of meteorological parameters on atmospheric turbidity index on Langtang National Park (28.2112° N, 85.5663° E, 3862 m asl) for a period of one year (2018). Daily data of Aerosol optical depth (AOD) and meteorological parameters are obtained from AERONET and NASA website respectively. The maximum and minimum values of direct solar radiation (IB) during the study period are found 1286 ± 24 W/m2 in January and 1123 ± 3 W/m2 in August respectively. The maximum and minimum value of diffuse solar radiation (ID) is found 119 ± 21 W/m2 in April and 59 ± 2 W/m2 in August respectively. The annual average of direct solar radiation and diffuse solar radiation are found 1202 ± 63 W/m2 and 90 ± 20 W/m2 respectively. There is a negative effect of maximum temperature, minimum temperature, dew point, relative humidity, water contend and rainfall on IB, but no significant effect of those parameters on ID. The result of this research work is beneficial for the further identification, impact and analysis of solar radiation at different places with same geographical and meteorological condition. The study of solar radiation is important for energy harvesting and agriculture.
Dyna-Validator: A Model-based Reinforcement Learning Method with Validated Simulated Experiences

Dyna is a planning paradigm that naturally weaves learning and planning together through environmental models. Dyna-style reinforcement learning improves the sample efficiency using the simulation experience generated by the environment model to update the value function. However, the existing Dyna-style planning methods are usually based on tabular methods, only suitable for tasks with low-dimensional and small-scale space. In addition, the quality of the simulation experience generated by the existing methods cannot be guaranteed, which significantly limits its application in tasks such as continuous control of high-dimensional robots and autonomous driving. To this end, we propose a model-based approach that controls planning through a validator. The validator filters high-quality experiences for policy learning and decides whether to stop planning. To deal with the exploration and exploitation dilemma in reinforcement learning, a combination of ϵ-greedy strategy and simulated annealing (SA) cooling schedule control is designed as an action selection strategy. The excellent performance of the proposed method is demonstrated in a set of classical Atari games. Experimental results show that learning dynamic models in some games can improve sample efficiency. This benefit is maximized by choosing the proper planning steps. In the optimization planning phase, our method maintains a smaller gap with the current state-of-the-art model-based reinforcement learning (MuZero). In order to achieve a good compromise between model accuracy and optimal programming step size, it is necessary to control the programming reasonably. The practical application of this method in a physical robot system helps reduce the influence of an imprecise depth prediction model on the task. Without human supervision, it is easier to collect training data and learn complex skills (such as grabbing and carrying items) while being more effective at scaling tasks that have never been seen before.
PROBLEMATIKA PEMBELAJARAN BAHASA INDONESIA KELAS III DI SEKOLAH DASAR

&lt;p&gt;&lt;em&gt;This research examines further related problems or problems in learning Indonesian in class III at Gombang Blora State Elementary School. The research method used a qualitative research method with a descriptive approach. The sampling technique used total sampling, where the respondents were a teacher and all third-grade students. The results showed that there were several problems in learning the Indonesian language for third-grade elementary schools, which were divided into four, including (1) learning media which were limited to textbooks from the Ministry of Education, there was no innovation in teaching materials, (2) listening skills were still low, the concentration of students is not good, and the motivation of students is still lacking in participating in the learning process, (3) learning strategies are monotonous and not yet innovative so that students are easily bored and less interested, (4) the ability of teachers in delivering material. Based on the problems, it is necessary to innovate the delivery of learning material by the teacher.&lt;/em&gt;&lt;/p&gt;
Examining the Interconnected Dynamics of Work Ethics, Job Satisfaction, and Organizational Commitment: An Investigation within Private Banking Institutions in Pakistan

In private banking institutions in Pakistan, there exists a notable gap in comprehending the intricate dynamics of work ethics, job satisfaction, and organizational commitment. This gap hinders the development of effective strategies to enhance employee engagement and organizational performance. The study's insights offer valuable implications for enhancing work ethics, job satisfaction, and organizational commitment within private banking institutions, thereby contributing to improved employee performance and overall organizational effectiveness. This study aimed to investigate work ethics and its impact on employee performance. This study was conducted on the employees of four selected private banks in Pakistan. The quantitative research methodology was used, and a structural questionnaire (close-ended) was employed for data collection. Smart PLS was used to analyse and check the contributory relationship among variables. The results revealed that work ethics are positively and significantly related to job satisfaction, organizational commitment and employee performance, as well as a significant relation between job satisfaction employee performance and organizational commitment to employee performance in private sector banks in Pakistan.
Study of Alternative Inundation Management Based on Water Conservation in Sub Drainage System of Parung River, Kediri City

Parung River drainage sub system is located in Kediri City. At the peak of the rainy season inundation often occurs on hos cokroaminoto road, kilisuci road and terusan kaliombo road, the cause is reduced water catchment areas and drainage capacity can’t accommodate discharge, so it is necessary to handle water conservation-based inundation. The analysis conducted was modelling the sub-system using the Storm Water Management Model (SWMM) version 5.1 with a 5-year return period. Rainfall calculation used 10 years of daily rain data (2012-2021) from 3 rain stations (Mrican, UPT Kediri, and Pesantren). The 5-year return period results is 129.4 mm/day, then the intensity of rain is calculated using the Mononobe formula. To calibrate the model, comparing observation discharge with simulated discharge of SWMM resulted the Nash-Sutcliffe efficiency number is 0.62 and the correlation R is 0.91. The results of the existing simulation show that Parung River drainage sub-system was not able to accommodate the 5-year return period discharge, impacting 27 overflow points. Water conservation based handling with the application of infiltration wells and rainwater harvesting can reduce discharge on land ranging from 51.58% to 90.91%, while in the channel successfully reduces discharge from 1.73% to 90.40%.
Sistem Pendukung Keputusan Pemilihan Rumah Kost menggunakan Metode Simple Additive Weighting (SAW)

Housing development is increasing, one of which is boarding houses, due to the many workers or immigrants wanting to work. However, many workers or local immigrants have difficulty in choosing a boarding house due to the many considerations regarding the aspects of cost, facilities, and location, followed by a large number of boarding houses offering different prices, facilities, and locations, which will make workers or immigrants confused in deciding which place to go. they will stay. Decision Support Systems (DSS) are a type with the capability to solve a problem in semi-systematic and unsystematic terms. This decision support system will later be created using the Simple Additive Weighting (SAW) method to solve problems experienced by workers or immigrants. The author's research technique is determining criteria and alternatives, collecting data, carrying out calculations, and ranking alternatives from the calculation results. The research sample was a boarding house in the Kalibata area, Pancoran, South Jakarta. The results of this research are aimed at making it easier for students or migrant workers who need housing according to their criteria.
Utilization of Artificial Intelligence in Predicting Crime

The problem of crime in Indonesia is an urgent issue, with crime rates continuing to increase. High crime rates have serious impacts on societal security, social stability, and economic development. Amidst the complexity of types of crime, motives and methods of handling them, Artificial Intelligence (AI) and Machine Learning (ML) technology has emerged as a promising solution. Through analysis of a literature review with the keywords "AI and crime," this research aims to understand the differences between the use of AI in crime prediction and traditional methods. The literature review method will identify and analyze the latest knowledge regarding the use of AI technology in overcoming crime problems. The use of AI in analyzing crime data, identifying complex patterns, and providing accurate predictions will be emphasized. The research will also explain how AI is able to overcome problems that are difficult to solve with conventional methods. It is hoped that the results of this literature review will provide deeper insight into the potential of AI in reducing crime rates and c reating a safer environment for people in Indonesia.
Study on the Tripartite Collaborative Training Mechanism for Secondary School Mathematics Teachers

Based on the principle of "local colleges and universities serving the local community", the university has established a tripartite collaborative training mechanism with the local secondary schools and government with clear rights and responsibilities, stable coordination and win-win cooperation. Based on the theoretical teaching and practical guidance of teacher education, and oriented to the improvement of educational and teaching ability, the university is striving to build a cooperative community integrating the training of secondary school mathematics teachers, the training of in-service secondary school mathematics teachers, and the research and service of backbone teacher education.
Analysis of Discrete Wavelet Spectra of Broand Signals

The article is devoted to the development of a mathematical model for autoregressive and autocoherent analysis of discrete wavelet spectra with approbation on known experimental data.The model is used to detect anomalies or emergency states of the object after wavelet filtering of noise and zero crossing.The proposed method for processing noisy signals, combining noise filtering and Mahalanobis proximity analysis, shows better machine learning results than neural networks and other methods for detecting classification and recognition anomalies.The wavelet spectra were processed using autoregressive and autocoherent analyses.The possibility of using these functions as classification features for identifying possible anomalous states of devices is shown.Before forming the wavelet spectra, the procedure of direct with filtering and inverse transformation of the already filtered signal is carried out.As a result, it seems possible to identify the characteristic features of the signal -anomalies or emergency conditions.The main factor in the classification of device status signals is the moment when changes in the signal spectrum begin to develop, therefore, all analysis, both in the time and frequency domain, is tied either to the number of samples by the time the signal was received, and in some cases even to the extended date format -month number.
High-speed and energy-efficient non-volatile memristive III-V-on-silicon photonic phase shifter

We demonstrated a non-volatile III-V-on-silicon photonic phase shifter based on HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> memristor with ~400 fJ switching energy, 100 ns switching speed, and an excellent endurance of over 800 cycles.
Optoelectronic Neuromorphic Accelerator at 523.27 GOPS Based on Coherent Optical Devices

An integratable and scalable optoelectronic neuromorphic accelerator based on coherent optical devices is proposed and demonstrated. It can achieve the computing speed for convolutions of 523.27 GOPS and accuracy up to 96.67% for handwritten digit recognition as a neural network.
ChimeraPy: A Scientific Distributed Streaming Framework for Real-time Multimodal Data Retrieval and Processing

Multimodal data analysis provides profound insights into behaviors and interactions within various settings. However, the collection and analysis of this data in real-world scenarios are intricate and resource-intensive. To streamline these processes, we introduce ChimeraPy: an open-source, distributed streaming platform optimized for high-throughput data transfer across processing nodes within a computer cluster. The utility and performance of ChimeraPy are showcased through two benchmark applications, highlighting its capability to handle complex data environments.
Automated Multi Face Recognition and Identification using Facenet and VGG-16 on Real-World Dataset for Attendance Monitoring System

The goal of this study is to create a facial recognition-based automatic attendance tracking system. We suggested a Convolutional Neural Network (CNN) method for this system's real-time recognition and identification of many faces. For face embedding and identification, we use the FaceNet model, which has demonstrated outstanding performance in recent research. The suggested method uses a camera to take pictures of people, then extracts face traits and compares them to the database of registered users. The recognition of registered individuals is used to record attendance. The proposed system's accuracy is 75%. For single faces, the accuracy of Facenet and VGG-16 are 99.20% and 51.30% respectively.The system can be used in a number of places, including companies, colleges, and schools, where keeping track of attendance is crucial for administrative reasons. The automated attendance monitoring process of the suggested system can greatly reduce human error and save time. Furthermore, recognizing unwanted visitors helps improve the facility's security and safety.