Reviewerly/exHarmony huggingface dataset:
Word-level beam search decoding and correction algorithm (WLBS) for end-to-end ASR

A key challenge in resource-constrained speech recognition applications is the unavailability of a large, domain-specific audio corpus to train the models. In such scenarios, models may not be exposed to a wide range of domain-specific words and phrases. In this work, we propose an approach to improve the in-domain automatic speech recognition results using our word-level beam search decoding and correction algorithm (WLBS). We use a token-based language model to mitigate the data sparsity and the out of vocabulary issues in the corpus. We evaluate the proposed approach for airplane-cabin specific announcements use case. The experimental results show that the WLBS algorithm with its handling of misspellings and missing words achieves better performance than state-of-the-art beam search decoding and n-gram LMs. We report a WER of 11.48% on our airplane-cabin announcement test corpus.
Medical Image Registration Based on Mutual Information and Hybrid Algorithm

Aiming at the problem that the existing traditional medical image registration algorithm is easy to fall into the local extremum and low registration accuracy, this paper proposes a hybrid optimization algorithm which combines the downhill simplex algorithm and firefly algorithm as an optimization strategy and takes mutual information as the similarity measure to complete the chest image registration tasks. The hybrid optimization algorithm takes the parameter optimization result of decreasing simplex as the initial solution of the firefly algorithm, and further optimizes to obtain the global optimal value. In the case of local optimum, it can avoid the mutual information function when the downhill simplex algorithm is used alone., and it can effectively improve the computational efficiency of the firefly algorithm. The experimental results show that the proposed algorithm can improve the registration time and registration effect significantly, and can complete the medical image registration tasks better.
Classification of Classroom Teachers’ Speech Intention Based on Deep Learning

Teachers use language to guide classroom teaching activities. The automatic classification of teacher speech according to intention is helpful for the quantitative analysis and evaluation of classroom teaching process. Teachers’ speech in real classroom teaching of middle school Chinese and mathematics is used to construct a corpus, and deep convolutional neural network (CNN) is trained to classify teachers’ speech and identify three kinds of teacher-led teaching activities, including teaching, questioning and classroom management. The experimental data show that:(1) compared with the classical shallow network classification algorithm SVM, the classification accuracy of CNN is increased by 10% to 95.5%, which can meet the requirements for accuracy of automatic analysis of classroom teaching process; (2) Classifying and statistical analysis of classroom teaching behaviors by using CNN classification algorithm can provide useful ideas for classroom analysis and research.
A novel reduced-order method for analysis of hyperspectral images

Hyperspectral imaging technology has been broadly applied in remote sensing because it collects echoed signals from across the electromagnetic (EM) spectrum and provides fruitfully helpful information. However, the processing or transformation of high-data-volume hyperspectral images, also viewed as snapshots varying with the EM spectrum, burdens the hardware resources, especially for the high spectral resolution and spatial resolution cases. To tackle this challenge, a novel reduced-order method based on the dynamic mode decomposition (DMD) algorithm is presented here to analyze hyperspectral images. The method decomposes the spatial-spectral hyperspectral images in terms of spatial dynamic modes and corresponding spectral patterns. Then, these spatial-spectral patterns are utilized to recover the raw hyperspectral images. Our proposed approach is benchmarked by the actual hyperspectral images measured at the Salinas scene. It is demonstrated that the proposed approach can represent the hyperspectral images with a low-rank model in spectral dimension. Our proposed approach could provide a useful tool for the model order reduction of hyperspectral images.
Kernel-Based Clustering Driven by Density Index

We propose a clustering method that can deal with non-linearly separable groups by means of a search process in which we look for the best values for parameters of a set of kernel functions that induce, on the objects to be clustered, the best partition relative to quality in terms of a density-based criterion. In summary, our proposal is an iterative adaptation of a set of kernel parameters, guided by a density-based criterion, which is able to yield clustering solutions that improve those results obtained from the separate application of kernel and density-based methods from the state-of-the-art.
Discovering Causal Models with Optimization: Confounders, Cycles, and Feature Selection

We propose a new method for learning causal structures from observational data, a process known as causal discovery. Our method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. We consider a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do. We formulate the discovery problem as an integer program, and propose a solution technique that leverages the conditional independence structure in the data to identify promising edges for inclusion in the output graph. In the large-sample limit, our method recovers a graph that is equivalent to the true data-generating graph. Computationally, our method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. We demonstrate our approach by showing how it can be used to examine the validity of instrumental variables, which are widely used for causal inference. In particular, we analyze US Census data from the seminal paper on the returns to education by Angrist and Krueger (1991), and find that the causal structures uncovered by our method are consistent with the literature.
Using DNI forecasts provided by all sky imager to improve control of parabolic trough solar fields

The solar field of a parabolic trough power plant is a complex thermal and hydraulic system. It is used to bring the heat transfer fluid (HTF) up to the desired temperature. The mass flow for the entire solar field is usually controlled from the plant control room. In the ideal case, the controller will adjust the mass flow so that the desired temperature is reached and all collector in the solar field are in full focus. Under transient conditions like a cloud passing through, different temperatures of the HTF can occur in the solar field. For example, the area not covered by a cloud can become hotter whereas temperatures in the area with cloud cover drop. In case the mass flow is adapted to the average irradiance level in the field, this scenario can lead to significant defocusing of those collector rows seeing high DNI but suffering from reduced mass flow. State of the art solar field controllers can use information on current DNI derived from one or a few pyrheliometers distributed over the solar field. Information on DNI distribution or an appropriate average DNI are thus not available. Recent developments in all sky imager (ASI) systems provide such spatially resolved DNI information and open the way for new control approaches. In this study, an irradiance forecasting system is used based on all sky imagers (ASI) to provide spatially resolved DNI maps of the whole solar field. With the ASI system, the controller can access information about the spatial DNI distribution and thus has knowledge about individual clouds in the solar field. In this paper, a model based approach to improve the feed forward control of the mass flow is investigated The detailed model used by the controller represents the current state of the solar field using most recent sensor data from the real solar field. With the forecasts of the ASI system as input, several simulations are performed to determine a suitable mass flow for the upcoming minutes. Under cloudy conditions, the new system shows advantages compared to a reference controller using only the DNI information from the elapsed time step. The investigation are carried out with ideal forecast and forecasts including uncertainties. For a test set of 27 days, the new control concept is able to reduce the number of emergency defocusing events by 9% under ideal forecasts and 5.7% when considering uncertainties. The average focus state of the field is increased by about 0,25%.
Denoising Heterogeneous Malayalam Document Images Through Binarization

Malayalam is an Indian language with a vast collection of traditional documents from heterogeneous sources like palm-leaf manuscripts containing valuable information on traditional medicine and culture, the majority of which are not available in digital formats. Development of Optical Character Recognition (OCR) systems is essential to digitize such documents for effective utilization and preservation. On account of degradation due to ageing and mishandling, noises such as stains, creases, shadows, uneven illumination, complex backgrounds, bleed-through ink, and faint text, historical documents are particularly difficult to process. The presence of such noises degrades the overall performance of the OCR system, if it is not preprocessed appropriately. This paper proposes a novel approach to denoise the document image in the preprocessing stage using division normalization and Otsu's thresholding to enhance its overall quality. The efficacy of the proposed approach is evaluated on document images with natural and synthetically introduced noise elements and it is observed that this work outperforms the pure Otsu thresholding-based method.
Linguistic Mathematical Morphology w-operators in Fuzzy Color Space

Color is a very important visual feature used in computer vision and image processing. Compared with grayscale images, color images can provide richer information. However, the direct extension of grayscale image algorithms to color is not always straightforward. Usually, Mathematical Morphology (MM) is based on lattice theory; therefore, the most elementary requirement to define morphological color operators was thought to establish an ordering of the space of the pixel intensities. Several attempts have been made, and different approaches have been presented in the last years, aiming at building a fuzzy mathematical morphology model. The situation has become more complex when trying to apply fuzzy set theory in color images because of the existence of many different ordering schemes and different definitions for the basic morphological operators. The use of fuzzy set theory is appropriate to manage the imprecision in color description. Moreover, in practical applications, it is usual to work with different color terms, whose number and design depend on the application itself. In this sense, the concept of linguistic color space is useful, among other things, for representing the set of fuzzy colors that are relevant to a certain application. In this article, we propose a novel definition of linguistic w-operators of the mathematical color morphology using diffuse definitions of color spaces, based on the original idea of ​​binary morphology without the need to establish a grid or an order. This innovative proposal allows to reduce the ambiguity in the color description and avoid false colors.
Monitoring Foreign Language Teacher’s Training on the Basis of Surveying Pedagogical Education Bachelors

The research objective is as follows: to identify the prospects of foreign language teacher’s training relying on analytical survey conducted among bachelor students in the field of training 44.03.01"Pedagogical Education" (profile "Foreign Language"). The article examines the main directions of foreign language teacher’s training as they are formulated in present-day methodological works, describes monitoring of foreign language teacher’s training on the basis of surveying pedagogical education bachelors, summarizes respondents' answers. Analyzing students’ survey results the researchers identify the main directions of professional training improvement, and herein lies scientific originality of the study. The research findings are as follows: the authors justify the necessity to take into account the acquired data when reconsidering the process of foreign language teacher’s training.
A Study on Smart contract for efficient learner problem recommendation in distance education environment

AbstractFor learner's self-directed distance education, the need for Problem Recommendation learning guidesreflecting accurate learning patterns based on learner data is increasing. In this paper, based onblockchain based smart contract technology, various learner data generated in the distance educationenvironment were collected and accurately managed to analyze Problem Recommendation patternsfor individual learners and gave weights for each learning situation. It is possible to present the optimalProblem Recommendation path when individual learners solve problems based on the assigned weightsfor each learning situation. To evaluate the performance of this study, the learning satisfaction with theexisting similar learning environment, the usefulness of the Problem Recommendation guide, and theprocessing speed of learner data were analyzed. As a result, compared to the existing learningenvironment, the proposed learning environment improved learning satisfaction by 19% or more, and itwas confirmed that the learning data processing speed was improved by more than 20%.
A Non-autoregressive Decoding Model Based on Joint Classification for 3D Human Pose Regression

Recently, the graph convolution networks (GCN) has been widely applied in 3D human pose regression and has showed encouraging performance. One limitation of this method is that it only models the semantic correlation between 2D joints feature, but ignores the variability of the semantic correlation between 2D joints feature. To address this limitation, we propose a non-autoregressive decoding model based on joint classification (JC-NARD), which realizes the 3D joints regression with the method of sequence analysis. The model splits joints into several joint sub-sequences according to the connection and semantic correlation, and then models the correlation between 3D-3D joints feature and 2D-3D joints feature by attention mechanism in each sub-sequence to establish 3D spatial constraint between joints. In order to verify the accuracy and generalization of the model, we combine our model with several 3D human pose regression networks, and the performance of the models are all improved by 1.2–4.5 mm.
R-Calculi for Logic Programming

This chapter will give R-calculi \(\mathbf{S}^\mathrm{LP}, \mathbf{T}^\mathrm{LP}\) and \(\mathbf{U}^\mathrm{LP}\) for logic programming [1,3,4,6] such that they are sound and complete with respect to \(\subseteq /\preceq /\vdash \)-minimal changes, respectively [7]. Specially, for \(\mathbf{U}^\mathrm{LP},\) we have the following conclusions.
Delivering Digital Transformation with Self-reporting Data Assets

The pharmaceutical industry needs to reverse current cost and revenue trends in the research and development process. Digital transformation is the process of turning a classical industry into a data-driven industry that leverages new technologies and opportunities arising from data science, such as artificial intelligence and machine learning. For pharma to successfully maneuver this digital transformation, changes to strategy, platforms, and cultures are necessary. Within this paper, we review the current challenges for the digital transformation of the pharma enterprise and offer a solution in form of a data centric architecture powered by a digital hub and Self-reporting data assets (SRDAs). SRDAs represents an efficient subset of the more generic FAIR – Findable, Accessible, Interoperable, Reusable – principles in pharma, as they were developed explicitly to solve important data sharing and reuse cases in this domain. Along with multiple use cases, we show how SRDAs harmonize information and make it accessible in a reusable fashion to data scientists. We present a variety of dashboards that can be created on a solid foundation of SRDAs and outline future business benefits that can follow from this transformative technology.
Privacy preserving defect prediction using generalization and entropy-based data reduction

The software engineering community produces data that can be analyzed to enhance the quality of future software products, and data regarding software defects can be used by data scientists to create defect predictors. However, sharing such data raises privacy concerns, since sensitive software features are usually considered as business assets that should be protected in accordance with the law. Early research efforts on protecting the privacy of software data found that applying conventional data anonymization to mask sensitive attributes of software features degrades the quality of the shared data. In addition, data produced by such approaches is not immune to attacks such as inference and background knowledge attacks. This research proposes a new approach to share protected release of software defects data that can still be used in data science algorithms. We created a generalization (clustering)-based approach to anonymize sensitive software attributes. Tomek link and AllNN data reduction approaches were used to discard noisy records that may affect the usefulness of the shared data. The proposed approach considers diversity of sensitive attributes as an important factor to avoid inference and background knowledge attacks on the anonymized data, therefore data discarded is removed from both defective and non-defective records. We conducted experiments conducted on several benchmark software defect datasets, using both data quality and privacy measures to evaluate the proposed approach. Our findings showed that the proposed approach outperforms existing well-known techniques using accuracy and privacy measures.
A Semantic Retrieval Method Based on the Knowledge Graph of Ethnic Clothing Culture

There is no mature ontology construction method in the field of ethnic costume culture. Based on the analysis of the existing ontology construction methods and the characteristics of ethnic clothing cultural information, this paper combines the seven-step method and IDEF-5 (ICAM DEFinition method, IDEF) to propose an ontology construction method suitable for the ethnic clothing cultural field, and completes the ethnic clothing cultural knowledge graph construct. On the basis of the knowledge graph, in order to solve the problem of inaccurate retrieval results caused by the lack of semantic information in traditional information retrieval, a semantic retrieval model based on the knowledge graph is proposed, which realizes a full understanding of user retrieval intentions by adding semantics in the retrieval process. Finally, it is evaluated by experiments that this model can return accurate retrieval results to users.
Online Certificate Generation and Verification Using Blockchain Framework

Jadhav, Prasad Godambe, Aseem Gaikwad, Rutwik Deshpande, KiranThere are many cases reported of certificate forgery every day, and many of them go undetected. The purpose of this study is to develop a system that would authenticate certificates to their real owners and could verify the same on a specific portal. To achieve this, in this paper we focus on Ethereum Blockchain as it is immutable, transparent, scalable, and also cost-effective. Universities would be able to generate their own certificates on the web portal itself by uploading their certificate template in Scalable Vector Graphics (SVG) format and the student data in comma-separated values (CSV) format. Each generated certificate would be allotted to the respective student within the system. Each certificate would be given a unique hash code so that it could be verified easily and there would be no scope of duplication. The authentication and verification would be done on the same web portal. To sum up, this method would save time and efforts that are required to verify a certificate manually and would result in an effective, secure way to generate certificates.
Various strategies &amp; methods for successful improvement in English oral communication skills

It has been claimed that ‘no individual’s linguistic repertoire is perfect’ (Maleki 2007, as cited in (Hmaid, 2014)). Both indigenous and non-native participants of any community frequently encounter obstacles, relying on restricted verbal tools to convey their intention. Communication strategies are the methods through which speakers seek to bridge the barrier amongst all individuals who wish to convey and the instantly accessible language assets. The significance of English cannot be overstated. Paragraph fragmentation, run-on phrases, and general topic alignment are the major opponents of proper grammar. Communicating is the most fundamental mode of interaction. That is the only fundamental human privilege with the fewest limits and is regarded as extremely essential by the legislation. Currently, fluency in English is practically a must for accomplishment in the globe. This research paper reveals a mixed-method methodology in scrutinizing possible effective modules and types for oral communication skills in the English language. Both quantitative and qualitative approaches were taken into consideration throughout the whole investigation, where the studies of Dhani Ram Sharma, Apisara Sritulanon, Panna Chaturongakul, Thapanee Thammetar, Anne Syomwene, Zhanli Yang, Yaseen Hmaid, JD Singh had a huge impact on the results and discussions of the research. The main obstacles illustrated throughout the research were lack of preparation, lack of spoken English, poor vocabulary, inability to clearly understand native speakers or others speaking this language, and inability to express ideas that were discussed and analyzed with the offered solutions. The following research objectives are being analyzed throughout the investigation: making a significant commitment for persuading instructors &amp; others; developing a conceptual method for instructors; promoting the benefits of communication skills; offering a skill different from conventional learning speaking skills to learning English; examining the efficacy of conversation skills. Examples, strategies, and recommendations illustrated by the authors in the following sections of the research paper underline the importance of oral communication skills both in the teaching environment and in the professional sphere. Furthermore, the analysis of statistics made by Dhani Ram Sharma, stressed the positive impacts of strategies used by them in improving and developing students’ communication skills. Moreover, the results and discussions concerning the revealing of research findings affirm that activities and strategies implemented were successful enough for future professional accomplishments of oral communication skills.