Reviewerly/exHarmony huggingface dataset:
Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada

In this paper, we present specially designed automatic speech recognition (ASR) systems for the highly agglutinative and inflective languages of Tamil and Kannada that can recognize unlimited vocabulary of words. We use subwords as the basic lexical units for recognition and construct subword grammar weighted finite state transducer (SG-WFST) graphs for word segmentation that captures most of the complex word formation rules of the languages. We have identified the following category of words (i) verbs, (ii) nouns, (ii) pronouns, and (iv) numbers. The prefix, infix and suffix lists of subwords are created for each of these categories and are used to design the SG-WFST graphs. We also present a heuristic segmentation algorithm that can even segment exceptional words that do not follow the rules encapsulated in the SG-WFST graph. Most of the data-driven subword dictionary creation algorithms are computation driven, and hence do not guarantee morpheme-like units and so we have used the linguistic knowledge of the languages and manually created the subword dictionaries and the graphs. Finally, we train a deep neural network acoustic model and combine it with the pronunciation lexicon of the subword dictionary and the SG-WFST graph to build the subword-ASR systems. Since the subword-ASR produces subword sequences as output for a given test speech, we post-process its output to get the final word sequence, so that the actual number of words that can be recognized is much higher. Upon experimenting the subword-ASR system with the IISc-MILE Tamil and Kannada ASR corpora, we observe an absolute word error rate reduction of 12.39% and 13.56% over the baseline word-based ASR systems for Tamil and Kannada, respectively.
Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada

We present automatic speech recognition (ASR) systems for Tamil and Kannada based on subword modeling to effectively handle unlimited vocabulary due to the highly agglutinative nature of the languages. We explore byte pair encoding (BPE), and proposed a variant of this algorithm named extended-BPE, and Morfessor tool to segment each word as subwords. We have effectively incorporated maximum likelihood (ML) and Viterbi estimation techniques with weighted finite state transducers (WFST) framework in these algorithms to learn the subword dictionary from a large text corpus. Using the learnt subword dictionary, the words in training data transcriptions are segmented to subwords and we train deep neural network ASR systems which recognize subword sequence for any given test speech utterance. The output subword sequence is then post-processed using deterministic rules to get the final word sequence such that the actual number of words that can be recognized is much larger. For Tamil ASR, We use 152 hours of data for training and 65 hours for testing, whereas for Kannada ASR, we use 275 hours for training and 72 hours for testing. Upon experimenting with different combination of segmentation and estimation techniques, we find that the word error rate (WER) reduces drastically when compared to the baseline word-level ASR, achieving a maximum absolute WER reduction of 6.24% and 6.63% for Tamil and Kannada respectively.
Improved Lightweight DeepLabv3+ Algorithm Based on Attention Mechanism

DeepLabv3+ has a wide range of applications in autonomous driving, geographic information systems, etc. However, its deployment on the mobile terminal faces a trade-off between model size and accuracy. Consecutive downsampling operations also result in a great loss of detail information. To solve these problems, this paper proposes an improved algorithm based on DeepLabv3+. Firstly, backbone is replaced by MobileNetv2 to reduce the size of the model; Secondly, the improved Atrous Spatial Pyramid Pooling module is proposed to augment the segmentation result while reducing the parameters. The performance is further ameliorated by applying attention mechanism; Finally, through refining decoder module, the proposed network makes up for lost detail information. Experiment shows that the algorithm achieves an mIoU of 73.31% on the validation set of the PASCAL VOC2012 dataset. Compared with typical algorithms, proposed algorithm has a better effect on trade-off between model size and accuracy.
Computer Numerical Experiment Results of Zhang Neural Net Connected to Jacobi Iteration Algorithm for Static Linear Equation System Solving

It is very common and vital to solve linear equation system (LES) in numerical fields. Generally, LES problems mainly include two types, i.e., the time-dependent LES problem and the static (i.e., time-independent) LES problem. With the rapid development of artificial intelligence, neural network has rich application scenes in many fields. For example, Zhang neural net (ZNN) is an effective neural network when solving time-dependent problems. In this paper, we present a special ZNN model termed elegant-formula ZNN (EFZNN) model. In addition, the specific EFZNN model has close relation with the traditional algorithm, i.e., Jacobi iteration (JI) algorithm, after ingenious construction and discretization by Euler forward discretization formula. Especially, when we fix the step-size in the discretization EFZNN algorithm as 1, it is the same as the JI algorithm. Besides, the ZNN and EFZNN models including the corresponding discretization algorithms for solving the LES are introduced, and the feasibility and efficiency of them in solving the LES are verified by, more importantly, computer numerical experiments, being the main merit of the paper.
Towards multimodal interaction for needlebased procedures in a virtual radiology suite

Abstract Touchless interaction is popular in the medical domain because it maintains sterility and ensures physicians’ autonomy. Evaluating these technologies, however, proves difficult due to technical and human hurdles. Virtual reality leaves these limitations behind and allows for the exploration of promising concepts by simulating an environment and the interactions that takes place within it.We present a virtual radiology suite in the context of needle-based MR-interventions to evaluate touchless interactions. Hand and foot inputs were implemented on a custom interface and evaluated in a user study (n= 16). Results show that activating the system and manipulating values was faster with foot input. However, multimodal interaction is preferable because it is less demanding.
Face and Emotion Recognition in Real Time using Machine Learning

In present era there are various methods and different features which can be used to perform face recognition and emotion recognition like face, text, speech, and so on. The facial feature among these is the best. Emotion detection is also widely used in many fields like understanding human behaviour, detecting mental disorders, finding the emotions of people in a crowd, etc. In the proposed framework, face-emotion detection can be defined in three stages. The first stage is detecting the human face from the camera feed then in the second stage, the input is analysed based on the features with help of the convolutional neural networks (CNN) model. The last stage is to classify human emotion into 7 basic categories: fear, anger, disgust, sad, surprise, neutral and happy. Thus, proposed framework has mainly three objectives like face detection, face recognition and emotion classification. Face recognition has many significant applications in various fields like identity verification, security, biometrics, smart card, surveillance system, etc. Facial expression identification eminently comes from a few parts of the face, like eyes and mouth. The framework proposes to capture the facial features in real-time and process them to identify if there are any similarities between the real-time input and any of the trained faces. The outcome of the proposed framework is to display the name of the recognized individual and classify the face emotion with probabilities of the emotion. It also displays the emotion of an unknown person with the name given as ‘unidentified’, along with the probabilities of the emotions.
Developing Students’ Skill in Writing Procedure Text Using YouTube Videos

The purpose of this study is to determine whether YouTube videos are effective to use in teaching writing procedure text during the online learning process. It is experimental research with the population of all students of Class XI TMI in the first semester of SMK YPT Tegal in the academic year 2020/2021 with a total of 327 students. The sample was 40 students taken by using the cluster random sampling technique. The samples were grouped into two; (1) control group consisting of 20 students who were not taught using YouTube videos, and (2) experimental group consisting of 20 students who were taught using YouTube videos. The data analyzed using SPSS version 22 obtained the average post-test results of the two groups 78.50&gt; 64.50. Based on the results of hypothesis testing using an independent sample t-test, a significance value of 0.000 was obtained, which was smaller than the significant value of a, which was 0.05, meaning that Ha was accepted. This means that there is a significant difference between the experimental group and the control group. In conclusion, the use of YouTube videos in teaching writing has a positive effect on the students’ writing skill.
The Effect of Role Playing Strategy on Students’ Speaking Ability At Eighth Grade of SMP Negeri 9 Pematangsiantar

The objective of this research was to find the effect of role play strategy on students’ speaking ability at eighth grade of SMP Negeri 9 Pematangsiantar.This research was conducted at SMP Negeri 9 Pematangsiantar with method of this research used quantitative methhod with true experimental method. Population of this research was eighth grade students and sample of this research was two class of eighth grade divide into control and experimental class that consist of 64 students. The technique of collecting data was pre-test and post-test. The technique of analyzing data was use SPSS version 21.0. The result of this test. If the tcount value is higher than the ttable at the significance level 0.05 and the degree fredoom (df) N-1, thus the alternative hypothesis (Ha) was accepted and null hypothesis (Ho) is rejected. The researcher found that tcount 4.50 &gt; ttable 1.66 at significance level 0.05 with the degrees of fredoom (df) (32-1=31) for each class so the degrees of freedom is 62. It means that Ho is rejected and Ha is accepted.Using role play strategy is able to develop the students’ speaking ability at the eighth grade students of Smp Negeri 9 Pematangsiantar. It can be seen in the result of the analysis data which shows that there is a significant progress between the students’ speaking ability before and after the researcher given the trearment. The score in experimental class is more significant increase than in control class because of the researcher give the treatment
A Nondestructive Alternative for Kerogen Type Determination in Potential Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning

In Petroleum Geology, to assess the hydrocarbon generation potential in source rocks involves the determination of the kerogen type by some destructive method. The usage of such methods is a bottleneck in the process because it is time-consuming, requires specialized tools and personnel, and ends up destroying the rock sample, so it is not possible to do any posterior analysis. This study presents an alternative method for determination of the kerogen type that is fast and non-destructive using hyperspectral data and machine learning techniques. The method is validated using five distinct supervised learning algorithms that were applied to spectral data collected in rock samples from Taubat&#x00E9; Basin, Brazil, of an outcrop whose rocks have a wide range of hydrocarbon generation potential. Cores and samples were collected from the outcrop and had their kerogen type determined by geochemical analyses performed in the laboratory. The robustness of the method is evaluated in two distinct experiments. In the first one, the hyperspectral dataset was collected using a non-imaging spectroradiometer; in the second one, the method uses non-imaging hyperspectral data as training and is tested in hyperspectral images collected. In both experiments, the method was able to establish a relationship between selected spectral features and the kerogen type of the source rocks sampled. The results obtained in this paper are prospective for non-destructive classification of kerogen type (and, consequently, the hydrocarbon generation potential), since most of the models generated achieved accuracy above 0.8 in the validation step and 0.75 in the test step.
Architecture for Automatic Recognition of Group Activities Using Local Motions and Context

Currently, the ability to automatically detect human behavior in image sequences is one of the most important challenges in the area of computer vision. Within this broad field of knowledge, the recognition of activities of people groups in public areas is receiving special attention due to its importance in many aspects including safety and security. This paper proposes a generic computer vision architecture with the ability to learn and recognize different group activities using mainly the local group&#x2019;s movements. Specifically, a multi-stream deep learning architecture is proposed whose two main streams correspond to a representation based on a descriptor capable of representing the trajectory information of a sequence of images as a collection of local movements that occur in specific regions of the scene. Additional information (e.g. location, time, etc.) to strengthen the classification of activities by including it as additional streams. The proposed architecture is capable of classifying in a robust way different activities of a group as well to deal with the one-class problems. Moreover, the use of a simple descriptor that transforms a sequence of color images into a sequence of two-image streams can reduce the curse of dimensionality using a deep learning approach. The generic deep learning architecture has been evaluated with different datasets outperforming the state-of-the-art approaches providing an efficient architecture for single and multi-class classification problems.
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

For over a decade now, robotics and the use of artificial agents have become a common thing.Testing the performance of new path finding or search space optimization algorithms has also become a challenge as they require simulation or an environment to test them.The creation of artificial environments with artificial agents is one of the methods employed to test such algorithms.Games have also become an environment to test them.The performance of the algorithms can be compared by using artificial agents that will behave according to the algorithm in the environment they are put in.The performance parameters can be, how quickly the agent is able to differentiate between rewarding actions and hostile actions.This can be tested by placing the agent in an environment with different types of hurdles and the goal of the agent is to reach the farthest by taking decisions on actions that will lead to avoiding all the obstacles.The environment chosen is a game called "Flappy Bird".The goal of the game is to make the bird fly through a set of pipes of random heights.The bird must go in between these pipes and must not hit the top, the bottom, or the pipes themselves.The actions that the bird can take are either to flap its wings or drop down with gravity.The algorithms that are enforced on the artificial agents are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement Learning.The NEAT algorithm takes an "N" initial population of artificial agents.They follow genetic algorithms by considering an objective function, crossover, mutation, and augmenting topologies.Reinforcement learning, on the other hand, remembers the state, the action taken at that state, and the reward received for the action taken using a single agent and a Deep Q-learning Network.The performance of the NEAT algorithm improves as the initial population of the artificial agents is increased.
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions

Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. But what exactly in the training data causes a model to make a certain prediction? We seek to answer this question by providing a language for describing how training data influences predictions, through a causal framework. Importantly, our framework bypasses the need to retrain expensive models and allows us to estimate causal effects based on observational data alone. Addressing the problem of extracting factual knowledge from pretrained language models (PLMs), we focus on simple data statistics such as co-occurrence counts and show that these statistics do influence the predictions of PLMs, suggesting that such models rely on shallow heuristics. Our causal framework and our results demonstrate the importance of studying datasets and the benefits of causality for understanding NLP models.
Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

The objective was to identify previously unknown groups in a dataset using various techniques. Significant progress has been made in this field in recent years, resulting in the development of novel and promising clustering algorithms. With the constant advancement of big data technology, research on study tours has also become crucial. Clustering can unearth the potential hidden information in large datasets, thereby facilitating more efficient work. Diverse measures have been proposed to quantify similarity, including the Euclidean distance and data space density. As a result, clustering becomes a multi-objective optimization problem. Clustering algorithms are extensively utilized in data preprocessing, data classification, and big data prediction. In this study, we examine clustering methods for big data from a theoretical perspective to comprehend their correlations across a large number of datasets. In addition, we predicted customer demand for research products using fabricated metrics.
MATHEMATICAL MODEL OF CHEMICAL PURIFICATION OF WATER IN A MECHANICAL FILTER

Based on the thermodynamic approach, a mathematical model of chemical water purification in a mechanical filter is constructed. For each of the components of the system, the mass transfer equation is obtained, on the basis of which the initial-boundary value problem of convective diffusion accompanied by a chemical reaction is formulated. Considering the terms corresponding to the components generated as a result of chemical reactions as internal sources, the system of integro-differential equations is assigned with the original initial-boundary value problem. The solution of this system is found by the method of successive iterations.
Exact Solutions of Three Types of Conformable Fractional-Order Partial Differential Equations

Fractional calculus is widely used in biology, control systems, and engineering, so it has been highly valued by scientists. Fractional differential equations are considered an important mathematical model that is widely used in science and technology to describe physical phenomena more accurately in terms of time memory and spatial interactions. The study of exact solutions of fractional differential equations helps to understand these complex physical phenomena and dynamic processes. The results show that the method is simple and clear. In addition, with the help of MAPL, we provide some 3D maps with accurate solutions.
Digital Literacy and TPACK’s Impact on Preservice Elementary Teachers’ Ability to Develop Science Learning Tools

The transition of education from the 4.0 era of the Industrial Revolution to the 5.0 era of the Social Revolution necessitates educators’ skill development. Particularly, technology-based learning devices must be designed with digital literacy and Technological Pedagogical Kontent Knowledge (TPACK) skills in mind so that learning tools for teachers are more creative and innovative. The purpose of this study was to determine the impact of digital literacy and Technological Pedagogical Content Knowledge (TPACK) on the ability of prospective Madrasah Ibtidaiyah science teachers to create learning tools. This study employed a survey approach. Madrasah Ibtidaiyah (Islamic Primary School) IAIN Lhokseumawe sixth-semester preservice teacher students were the subjects of this study. The data collection method employed a questionnaire and a straightforward regression analysis. The results demonstrate a strong correlation between digital literacy and the ability to compose science learning tools. A t-table value of 9.880 indicated a positive and significant relationship between digital literacy and the capacity to organize science learning tools. With a t-table value of -0.562, TPACK has no significant effect on the ability to compose learning tools. Therefore, it can be concluded that digital literacy has a positive effect on the ability of preservice Islamic primary teachers to create science learning tools for their students.
$$\mathtt {IP.LSH.DBSCAN}$$: Integrated Parallel Density-Based Clustering Through Locality-Sensitive Hashing

Locality-sensitive hashing (LSH) is an established method for fast data indexing and approximate similarity search, with useful parallelism properties. Although indexes and similarity measures are key for data clustering, little has been investigated on the benefits of LSH in the problem. Our proposition is that LSH can be extremely beneficial for parallelizing high-dimensional density-based clustering e.g., DBSCAN, a versatile method able to detect clusters of different shapes and sizes. We contribute to fill the gap between the advancements in LSH and density-based clustering. We show how approximate DBSCAN clustering can be fused into the process of creating an LSH index, and, through parallelization and fine-grained synchronization, also utilize efficiently available computing capacity. The resulting method, $$\mathtt {IP.LSH.DBSCAN}$$ , can support a wide range of applications with diverse distance functions, as well as data distributions and dimensionality. We analyse its properties and evaluate our prototype implementation on a 36-core machine with 2-way hyper threading on massive data-sets with various numbers of dimensions. Our results show that $$\mathtt {IP.LSH.DBSCAN}$$ effectively complements established state-of-the-art methods by up to several orders of magnitude of speed-up on higher dimensional datasets, with tunable high clustering accuracy through LSH parameters.