Reviewerly/exHarmony huggingface dataset:
Instrumental, Hermeneutic, and Ontological Indeterminacy in Hugh Davies’s Live Electronic Music

While many previous studies have explored indeterminacy as a compositional technique, in this article we explore the concept of indeterminacy, not only from the perspective of the composer, but also from the perspectives of performer and archival researcher, drawing upon our experiences of researching and performing several live electronic music compositions by British experimental musician Hugh Davies (1943–2005). Our core argument is that beneath the surface level of composed indeterminacy—that is, beyond the notations and instructions that a composer employs to prescribe indeterminate musical results—there exist further 'nested' planes of indeterminacy that reveal themselves through the acts of archival research, rehearsal and performance. 'Instrumental' indeterminacy has to do with the instruments that are used to perform the music, and specifically to situations where the boundaries of the instruments are experienced (by performers or audience members) as ambiguous, fluid, reconfigurable, or undefinable, or where the behaviour of the instrument(s) is unpredictable or uncontrollable in the moment of performance. 'Hermeneutic' indeterminacy concerns the composer's intentions and the ways in which these are revealed, through the processes of archival and performance research, to be incompletely, ambiguously, contradictorily, and/or diffusely represented in documents (including but not limited to scores) and material configurations (including the instruments and apparatus used to perform the music). 'Ontological' indeterminacy is signalled by uncertainty (on the part of the researchers) about the ontological status of the piece to be performed. By sharing these perspectives, we aim to contribute to scholarly understandings of the 'afterlives' of indeterminacy, beyond the circumscriptions of a composer.
RES: An Interpretable Replicability Estimation System for Research Publications

Reliable and faithful research is the cornerstone of breakthrough advancements and disruptive innovations. Assessing the credibility of scientific findings and claims in research publications has long been a time-consuming and challenging task for researchers and decision-makers. In this paper, we introduce RES - an intelligent system that assists humans in analyzing the credibility of scientific findings and claims in research publications in the field of social and behavioral sciences by estimating their replicability. The pipeline of RES consists of four major modules that perform feature extraction, replicability estimation, result explanation, and sentiment analysis respectively. Our evaluation based on human experts' assessments suggests that the RES has achieved adequate performance. The RES is also built with a Graphical User Interface (GUI) that is publicly accessible at https://tamu-infolab.github.io/RES/.
Text Mining to Alleviate the Cold-Start Problem of Adaptive Comparative Judgments

Comparative judgments permit the assessment of open-ended student works by constructing a latent quality scale through repeated pairwise comparisons (i.e., which works “win” or “lose”). Adaptive comparative judgments speed up the judgment process by maximizing the Fisher information of the next comparison. However, at the start of a judgment process, such an adaptive algorithm will not perform well. In order to reliably approximate the Fisher Information of possible pairs well, multiple comparisons are needed. In addition, adaptive comparative judgments have been shown to inflate the scale separation coefficient, which is a reliability estimator for the quality estimates. Current methods to solve the inflation issue increase the number of required comparisons. The goal of this study is to alleviate the cold-start problem of adaptive comparative judgments for essays or other textual assignments, but also to minimize the bias of the scale separation coefficient. By using text-mining techniques, which can be performed before the first judgment, essays can be adaptively compared from the start. More specifically, we propose a selection rule that is based both on a high (1) cosine similarity of the vector representations and (2) Fisher Information of essay pairs. At the start of the judgment process, the cosine similarity has the highest weight in the selection rule. With more judgments, this weight decreases progressively, whereas the weight of the Fisher Information increases. Using simulated data, the proposed strategy is compared with existing approaches. The results indicate that the proposed selection rule can mitigate both the cold-start. That is, fewer judgments are needed to obtain accurate and reliable quality estimates. In addition, the selection rule was found to reduce the inflation of the scale separation reliability.
Application of Machine Learning for Fraud Detection – A Decision Support System in the Insurance Sector

Introduction: The insurance sector is playing a crucial role in the sustainable growth of the Indian economy. But in India, this sector loses crores of rupees every year due to the increasing fraud cases. With the increase in insurance customers, insurance companies need to efficiently equip themselves with a robust system to handle claims fraud. Detection of insurance fraud is a pretty challenging problem. Nowadays, machine learning (ML) and artificial intelligence (AI) are the strategic choices of many leading organisations that want to proceed in a new digital arena.Purpose: This chapter's main objective is to highlight the fundamental market forces driving the adoption of AI and ML and showcase the traditional and modern methods to predict insurance claims fraud intelligently.Methodology: Various research papers have been reviewed, and ML methods have been discussed, which are all being used to predict insurance fraud claims. This chapter also highlights various driving forces influencing the adoption of ML.Findings: This study highlights the introduction of blockchain technology in fraud detection and in combatting insurance fraud. Literature indicates that the quantity and quality of data significantly impact predictive accuracy. ML models are beneficial to identify the majority of fraudulent cases with reasonable precision. Insurance companies should explore the benefits of experienced resource persons from the same domain and develop unique business ideas/rules.
Extracting Skeletons of Two-Manifold Triangular Mesh Surface for Planning Skeleton-Guided Five-Axis Surface Inspection Path

Abstract Skeleton curve-guided five-axis sweep scanning is an emerging surface inspection technique with marvelous inspection efficiency. The precondition of applying such a technique to a complex surface is to extract the skeleton curves of a two-manifold surface associated with partitioning the surface into compact surfaces. However, the work on extracting skeletons of two-manifold triangle mesh surfaces is scarce, as existing skeletonization methods mostly focus on either 2D planar shapes or 3D solid shapes. In this paper, we present a new approach to extract the skeletons on a two-manifold triangle mesh with boundaries. The skeletons are formed in the most intuitive manner of wave front propagation which is based on computing the initial value problem of geodesics on a triangle mesh. The step-length of wave front propagation is adaptively controlled to guarantee the appropriate density of skeleton points for having good connectivity. Experiments show that, as a direct application of the proposed skeleton generation and its associated surface partitioning result, the five-axis sweep scanning path of complex free-form surface can be generated conveniently. Experimental results also validate that the computation of our proposed approach is simpler and faster than the state-of-the-art geodesic Voronoi diagram (GVD) method when most of the two-manifold triangle mesh is nearly planar. Additionally, the augmented parameter used for tracing the wave front’s geodesic propagation information is helpful for the skeleton-based surface partition, which is necessary for skeleton curve-guided five-axis sweep scanning.
A slope deformation measurement method based on laser datum and complex target motion model

Highway slope stability analysis and monitoring is of great significance to ensure the safety of highway infrastructure. Based on the consideration of complex target motion model, a high-precision laser datum highway slope multi-dimensional deformation measurement method is proposed in this paper, which overcomes the problem of low measurement accuracy in traditional methods, and realizes high-precision real-time monitoring of highway slope stability. Firstly, the measurement principle and deformation data solution method combining the laser benchmark and the intelligent photoelectric imaging target are given. Then, considering the multi-dimensional complex motion of the photoelectric imaging target, a solution model of the multi-dimensional motion parameters of the measuring point considering the multi-dimensional complex motion vector of the target is deduced. Then, the inclination sensor is used as the measurement method of the multi-dimensional inclination of the target, combined with the high-precision processing algorithm of target spot center based on image processing, the high-precision measurement of multi-dimensional deformation of target plane is realized; Finally, the prototype of the measurement system is built and compared in the laboratory, and the actual highway slope is measured and verified in the outfield. The experimental results show that the measurement error considering the complex target movement is less than 0.25 mm, which is greatly improved compared with the traditional method, and meets the needs of high-precision slope relative deformation measurement.
Software Package Testability Prediction Using Object-Oriented Cohesion Metrics

Software development costs can be greatly reduced by creating well-designed software. Object-oriented software metrics can assist in designing high-quality systems. Package cohesion metric is found to be highly related to software testability. We have developed a package cohesion metric that has proved its ability in software maintainability and testability predictions as early as the design phase of software development. In this paper, we expand our previous works by proposing a logistic regression model that can predict software testability regarding its costs and efforts. The proposed model can predict the package testability during the design phase, which can save many resources of software development life. This early prediction helps developers to discover the poorly designed packages and take decisions to reduce any further costs and efforts that might happen in the future. The experiments showed that the proposed package cohesion metric could assess the design cohesiveness of software packages.
A general Bayesian nonlinear estimation method using resampled Smooth Particle Hydrodynamics solutions of the underlying Fokker–Planck Equation

The effectiveness of nonlinear filters depends on many factors, but one of the most important is how accurately the filter is able to predict the state dynamics of the underlying system between measurements. For a wide class of Gaussian white noise driven nonlinear systems the Bayesian optimal prior can be obtained by solving the system’s corresponding Fokker–Planck Equation (FPE). Unfortunately the Fokker–Planck Equation is a partial differential equation with dimension equal to the number of states in the underlying dynamical system, making it extremely difficult to solve for realistic systems due to Curse of Dimensionality scaling issues. As a result it has been and still largely remains computationally impractical to simulate higher dimensional Fokker–Planck equations, at least while obtaining very high accuracy across the entire transient probability density function. This paper presents a general nonlinear filter based on solving the transient Fokker–Planck equation via Smooth Particle Hydrodynamics (SPH) at lower resolution, which turns out to still allow for accurate state estimation. The filter is enabled by an efficient heuristic resampling scheme of the SPH solution also presented here. The FPE-SPH Filter is able to replicate the accuracy of the Particle Filter and Extended Kalman filter (EKF) for lower-dimensional systems, while also being more robust than the EKF on certain classes of system. • Techniques to speed up algorithm performance while preserving accuracy. • Resampling algorithm to allow for filter measurement updates. • Smooth Particle Hydrodynamics based filter validated as Bayes optimal. • Performance can match or exceed particle and Extended Kalman filters.
Evaluation Method of English Talent Training Quality Based on Deep Learning Model

This paper studies and analyzes three aspects: DL model, English talent training, and quality evaluation analysis, so as to get more rigorous and accurate quality evaluation results, and make relevant plans for future research directions. This paper focuses on model and method analysis to carry out experiments and analysis on three aspects: DL, personnel training, and quality evaluation. The experiment and inquiry of deep learning are divided into correct rate and loss. In 0-70 epoch, the highest training correct rate is 0.975, and with the increase of training times, the training correct rate is also increasing. According to the statistical investigation, 42.91% of English teachers are satisfied with their academic level, 38.48% with their oral English level, 38.89% with their teaching quality, 41.02% with their teaching methods, 40.29% with their teaching spirit, and 39.88% with their knowledge structure. At the same time, according to the statistics of students, graduates and teachers’ problems in English talent training, the largest proportion is the poor level of teaching resources, so we should improve the efficiency from the level of teaching resources. In order to improve the efficiency of quality evaluation method, this paper combines AF algorithm and BQ algorithm under deep learning. The error rate of algebraic algorithm is compared. Through six groups of sample data, it can be seen that the highest error rate of AF algorithm is 5.86% and the lowest is 0.92%, the highest error rate of BQ algorithm is 10.70% and the lowest is 1.10%, and the highest error rate of algebraic algorithm is 10.70% and the lowest error rate is 5%. In contrast, the error rate of AF algorithm is lower and more stable. Next, this paper compares and analyzes the performance of the AF algorithm, BQ algorithm, and algebraic algorithm. According to the experimental results, it can be seen that the AF algorithm is more accurate than the BQ algorithm and algebraic algorithm in accuracy, recall rate, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> <mn>1</mn> </math> , accuracy rate, etc. Therefore, it is more intuitive and accurate to evaluate the quality of English talents training through AF algorithm under deep learning.
Stepwise Multi-Task Learning Model for Holder Extraction in Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a text analysis technique that categorizes data by aspect and identifies the sentiment attributed to each one and a task for a fine-grained sentiment analysis. In order to accurately perform a fine-grained sentiment analysis, a sentiment word within a text, a target it modifies, and a holder who represents the sentiment word are required; however, they should be extracted in sequence because the sentiment word is an important clue for extracting the target, which is key evidence of the holder. Namely, the three types of information sequentially become an important clue. Therefore, in this paper, we propose a stepwise multi-task learning model for holder extraction with RoBERTa and Bi-LSTM. The tasks are sentiment word extraction, target extraction, and holder extraction. The proposed model was trained and evaluated under Laptop and Restaurant datasets in SemEval 2014 through 2016. We have observed that the performance of the proposed model was improved by using stepwised features that are the output of the previous task. Furthermore, the generalization effect has been observed by making the final output format of the model a BIO tagging scheme. This can avoid overfitting to a specific domain of the review text by outputting BIO tags instead of the words.
Analysis of Lossless Compressors Applied to Integer and Floating-Point Astronomical Data

In this work, lossless compression algorithms are evaluated on a variety of real, current as-tronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial tele-scopes. Compression techniques herein analyzed are chosen to be representative of the most recent algorithms devised for astronomical data, as well as the most commonly employed compressors employed in real observatories. Experimental results suggest that coding techniques such as RICE and HCOMPRESS, typically employed in world-class observatories such as Roque de los Muchachos, do not produce the best possible lossless compression results. Instead, JPEG-LS, LZMA and NDZIP yield the best compression ratio results for 16-bit data (2.72), floating-point data (2.38) and radio data (1.81), respectively. Therefore, the efficiency with which data are stored and transmitted by these observatories could be significantly improved by selectively employing the aforementioned algorithms.
Converting RLBWT to LZ77 in smaller space

Since highly repetitive strings, which can be compressed greatly, are ubiquitous nowadays, algorithms working on compressed space have gained much more attention. In particular, converting one compressed format to another without explicit decompression is of great interest as we can take advantage of various compressed formats with small cost of space. In this paper, we consider algorithms to convert run-length encoded Burrows-Wheeler Transform (RLBWT) to Lempel-Ziv 77 (LZ77). We implement a simplified version of the algorithm proposed by Nishimoto and Tabei and propose a two-pass algorithm to reduce its peak memory usage in practice. Experimental results show that the two-pass algorithm uses 23&#x0025; to 37&#x0025; less space with up to 10&#x0025; increase of computational time when we use 8 threads in the second pass.
Low-Resource Dialect Identification in Ao Using Noise Robust Mean Hilbert Envelope Coefficients

This paper presents an automatic dialect identification system in Ao using a deep Convolutional Neural Network with residual connections. Ao is an under-resourced language belonging to the Tibeto-Burman family in the North-East of India. The three distinct dialects of the language are Chungli, Mongsen and Changki. Ao is a tone language and consists of three tones, viz., high, mid, and low. The recognition of tones is said to be influenced by the production process as well as human perception. In this work, the Mean Hilbert Envelope Coefficients (MHEC) feature is explored to identify the three dialects of Ao as this feature is reported to have information of human auditory nerve responses. Mel Frequency Cepstral Coefficients (MFCC) feature is used as the baseline. In addition, the effect of noise in the dialect identification task at various signal-to-noise ratio scenarios is studied. The experiments show that the MHEC feature provides an improvement of almost 10% average F1-score at high noise cases.
Tweet Classification and Sentiment Analysis of Covid 19 Epidemic by Applying Hybrid Based Techniques

World wide spread of COVID-19 pandemic, is throttling the normal life nearly for two years and claiming millions of life all over the globe. Starting from Wuhan of China it crosses more than 200 countries, thereby imposing a overwhelming challenge to health care system. On the other hand, there has been unprecedented advancement of the social media, namely, Twitter, Facebook, WhatsApp and Instagram etc. in an exponential manner. The essence of this paper is to extract and elucidate the opinion or sentiments of the people all around the globe regarding Coronavirus pandemic based on Twitter data. The analysis are based on both lexicon-based approach followed by machine learning algorithms and aims to express the state-of-the-art of the sentiment analysis on the current Coronavirus epidemic prevailing in the entire world and the awareness of the people regarding the disease, its symptoms and impact followed by the preventive measures that need to be undertaken.
Stochastic Model of Block Segmentation Based on Improper Quadtree and Optimal Code under the Bayes Criterion

Most previous studies on lossless image compression have focused on improving preprocessing functions to reduce the redundancy of pixel values in real images. However, we assumed stochastic generative models directly on pixel values and focused on achieving the theoretical limit of the assumed models. In this study, we proposed a stochastic model based on improper quadtrees. We theoretically derive the optimal code for the proposed model under the Bayes criterion. In general, Bayes-optimal codes require an exponential order of calculation with respect to the data lengths. However, we propose an efficient algorithm that takes a polynomial order of calculation without losing optimality by assuming a novel prior distribution.
Input Multimodal Interactions for IoT Environments: smartphones instead of intermediate equipment

In smart/connected environments, multimodality aims at making services intuitive and user life easier. However, despite the advantages and benefits of multimodal interactions in connected environments, their democratization remains an unaccomplished process. They are generally only found in environments with a large number of equipment and technologies (cameras, Kinect, smart-watches, etc.). Intermediate equipment (between the user and connected devices) that represent an additional cost and a long learning process for end users.In this article, we define our approach which seeks to prove that the smartphone can properly replace the different intermediate equipment used to ensure multimodal interactions with connected objects. The smartphone contains already important embedded sensors, exists in most people’s homes and represents a confidential and comfortable device compared with other equipment. Therefore, we are using smartphones instead of different equipment in the literature studies that use intermediate equipment to ensure multimodality in connected environments. We are starting by extending the solution proposed by Smart Pockets that consists to access/send digital content from/toward the body’s pockets, by replacing the intermediate device used (armband) by the smartphone. Then, we will follow the same process with other studies (other modalities/connected objects) and we will evaluate the users’ feedback. We believe that this will prove the feasibility and allow the multimodality democratization in connected environments.
Voice-Based Human Identification using Machine Learning

Voice and natural language processing, is at the forefront of any human-machine interaction domain. Speech is an effortless and usable method of communication that is based on the sound waves generated by the speaker. It permits the machine to identify and comprehend human spoken language through speech signal processing and pattern recognition. In this work, a methodology for speaker recognition based on machine learning algorithms is proposed. Support Vector Machine (SVM) and Random Forest (RF) models are used with statistical features and Mel-Frequency Cepstral Coefficients (MFCC) as the input features of the models. A new voice dataset was collected for the purpose of training and evaluating speaker recognition models. Samples were obtained from non-native English speakers from the arab region over the course of two months. The performed experiments showed that using the developed methodology and the collected dataset, a 94% identification accuracy can be achieved.