Reviewerly/exHarmony huggingface dataset:
Splitting-based randomized iterative methods for solving indefinite least squares problem

The indefinite least squares (ILS) problem is a generalization of the famous linear least squares problem. It minimizes an indefinite quadratic form with respect to a signature matrix. For this problem, we first propose a simple and effective splitting (SP) method according to its own structure, and prove that it converges for any initial value. Further, to avoid implementing some matrix multiplications and calculating the inverse of large matrix, and considering the acceleration and efficiency of the randomized strategy, we develop two randomized iterative methods on the basis of the SP method as well as the randomized Kaczmarz, Gauss-Seidel and coordinate descent methods, and describe their convergence properties. Numerical results show that our three methods all have quite decent performance in both computing time and iteration numbers compared with the latest iterative method of the ILS problem, and also demonstrate that the two randomized methods indeed yield significant acceleration in terms of computing time.
Validating distortion measurements of wide-field-of-view near-eye displays

Compact near-eye displays covering a large field of view require complex optical imaging systems. While the optical system is designed to minimize distortion, material, design or production constrains may lead to perceptible residual distortion. Imaging light measurement devices (ILMD) are used to detect distortion for product quality assurance. However, imaging measurement systems, especially with wide-angle lenses, may also add distortion to the measurement. Therefore, the accurate calibration of ILMDs is crucial for the accuracy of AR/VR device distortion measurements. Here, we quantify and validate the accuracy of methods of distortion correction for an imaging light measurement device with a field of view (FOV) of 120° and use calibrated ILMDs to quantify geometric distortion and chromatic aberration of virtual reality (VR) headsets.
Prevalence of oculomotor changes following the near work in stereoscopic augmented reality

Extended reality human factors studies commonly utilize an approach in which group behavior is reported. This possibly masks the true prevalence of oculomotor changes that appear in response to stereoscopic augmented reality. Thus, the study aimed to elucidate the prevalence, direction, and magnitude of oculomotor changes after the near work in stereoscopic augmented reality. The task of fifty-three subjects (18-28 years old, normal visual acuity, no vision complaints) was to type the text displayed at 60 cm as accurately and quickly as possible. Each subject participated in two sessions – the text was displayed in stereoscopic augmented reality and on the computer screen. Clinical assessments of visual parameters were performed before and immediately after 30 minutes of the task. As a result, individual variations were found in the magnitude and direction of oculomotor changes after the near work. After the use of stereoscopic augmented reality, adverse changes in vergence and accommodation were observed in about 40% of the group. Despite the prevalence of adverse oculomotor changes being similar in the case of text displayed on the computer screen, only less than 20% of the group showed a decline of visual parameters in both viewing conditions. The exploratory study highlights the necessity to consider individual variations in visual responses and identify groups that might benefit or be disadvantaged in using stereoscopic augmented reality technologies.
Differential addition on binary elliptic curves

This paper presents new and fast differential addition (i.e., the addition of two points with the known difference) and doubling formulas, as the core step in Montgomery scalar multiplication, for various forms of elliptic curves over binary fields. The formulas are provided for binary Edwards, binary Hessian and binary Huff elliptic curves with cost of 5M+4S+1D when the given difference point is in affine form. Here, M,S,D denote the costs of a field multiplication, a field squaring and a field multiplication by a constant, respectively. In this paper, we show that every ordinary elliptic curve E over binary field F2n has Lopez and Dahab differential addition and doubling formulas with suitable function. This paper also presents, new complete differential addition formulas for binary Edwards curves with cost of 5M+4S+2D. Also, new complete Montgomery ladder and scalar multiplication algorithms are presented using the complete differential addition formulas.
Introducing the Volume of Extremity (VoX) method to integrate prosodic data into discourse analysis

This study aims to integrate acoustic analysis into discourse analysis. We developed a method that targets the fifth and the ninety-fifth percentiles of acoustic parameters of the four prosodic dimensions: pitch (F0), power (amplitude), duration, and voice quality per each speaker. We then defined a new measure to express the Volume of Extremity (VoX) of a speaker's voice. To demonstrate the strength of our model, we analyzed two political interviews from the Israeli election campaign in 2019, one with Benjamin Netanyahu and the other with Benny Gantz. Findings suggest that the acoustically targeted utterances are meaningful to the speaker but in a complex manner.
Study on Learning of Communication Channels at the Grassroots Level With Reference to Social Issues

Communication helps an individual to survive, grow, progress, and develop within a structure. Communication also creates a common pool of ideas and beliefs through a systematic process. When it comes to rural areas, the role of communication and communicators becomes more important because of the educational background of people. The Indian rural population faces a lot of social issues like girl's education, dowery, domestic violence, etc. The government always initiates number of programs in rural areas to make people aware regarding the social issues. Also, the change agents like educators and NGO workers work along with the government agencies, but most of the time, the campaign fails to impress the people at grassroot level. The communication at grass root level allows rural people to be at the centre of any initiatives. There are so many formal channels of communication in rural areas and there are some informal channels of communication at grass root level which helps people to understand information as they are.
Design and Implementation of Practical Workshops for Teaching STEM Content

The main objective of this work has been to assess the potential cognitive and emotional benefits of several practical experiences integrating STEM areas. A sample of 13 students in the 6th grade of primary school was accessed. Several questionnaires were designed, depending on the contents to be dealt with, and were applied at different times in order to ascertain the evolution of the variables under study. The data from the post-tests applied after the workshops confirm that the practical and integrated sessions carried out with the students improve the level of knowledge, even finding statistically significant differences (Sig.&amp;lt; 0.05) with respect to the initial values. The emotional results show that following an active and integrated methodology has benefited the promotion of positive emotions in the participants.
A Technique for Determining True Deformation Diagrams Under Dynamic Tension Using DIC

This article describes a pneumodynamic experimental setup created in the laboratory of dynamic tests of the Research Institute of Mechanics of the Nizhny Novgorod State University and designed to study the dynamic behavior of structural materials at average strain rates of the order of 10-100 s−1. Modern means of registration of parameters of dynamic processes are applied. The results of approbation are given on the example of dynamic tests of samples of sheet steel 09G2S. The technical and true strain diagrams in the static and dynamic range of strain rates are determined.
A classification model for continuous responses: Identifying risk perception groups on health‐related activities

In the current literature on latent variable models, much effort has been put on the development of dichotomous and polytomous cognitive diagnostic models (CDMs) for assessments. Recently, the possibility of using continuous responses in CDMs has been brought to discussion. But no Bayesian approach has been developed yet for the analysis of CDMs when responses are continuous. Our work is the first Bayesian framework for the continuous deterministic inputs, noisy, and gate (DINA) model. We also propose new interpretations for item parameters in this DINA model, which makes the analysis more interpretable than before. In addition, we have conducted several simulations to evaluate the performance of the continuous DINA model through our Bayesian approach. Then, we have applied the proposed DINA model to a real data example of risk perceptions for individuals over a range of health-related activities. The application results exemplify the high potential of the use of the proposed continuous DINA model to classify individuals in the study.
An Electronic Bandoneon with a Dynamic Sound Synthesis System Based on Measured Acoustic Parameters

Abstract The bandoneon is a distinctive free-reed instrument with profound ties to tango culture and Latin American music. The scarcity of manufacturers and the related high retail prices, however, are restricting access to the instrument for new generations of musicians. By combining modern technologies and scientific research, the Bandoneon 2.0 project aims to create an expressive and accessible new version of the instrument. In this article, we present an electronic bandoneon with a custom sound synthesis system. We also present an acoustic measurement system with which we analyzed the sound and air pressure signals of an acoustic bandoneon. Through this, we characterized several sound attributes that are utilized in a synthesis model made in Faust DSP. Combining the controller interface and the custom synthesizer, the electronic bandoneon we created can achieve a good level of expressiveness and engagement for the performer. We aim to produce an instrument that can be used in recreational, academic, and professional contexts to address the current sociocultural demand.
CHR vs. Human-Computer Interaction Design for Emerging Technologies: Two Case Studies

Recent years have seen a surge in interest in the multifaceted topic of human-computer interaction (HCI). Since the advent of the Fourth Industrial Revolution, the significance of human-computer interaction in the field of safety risk management has only grown. There has not been a lot of focus on developing human-computer interaction for identifying potential hazards in buildings. After conducting a comprehensive literature review, we developed a study framework for the use of human-computer interaction in the identification of construction-related hazards (CHR-HCI). Future studies will focus on the intersection of computer vision, VR, and ergonomics. In this research, we have built a theoretical foundation for past studies’ findings and connections and offered concrete recommendations for the improvement of HCI in danger identification in the future. Moreover, we analyzed two cases studies related to the domain of CHR-HCI in terms of wearable vibration-based systems and context aware navigation.
An adaptive infrared image denoising method based on two-dimensional empirical mode decomposition for distribution network inspection UAV

An adaptive denoising method based on 2D empirical mode decomposition (EMD) is proposed to improve the infrared image quality of inspection UNMANNED aerial vehicles (UAVs) and provide guarantee for improving the inspection level of distribution network. Through rapid adaptive two-dimensional empirical mode decomposition algorithm decomposition of a UAV collected for distribution network inspection original noise of infrared image, get more than the IMF component and the residual amount, a forecast noise dominated the IMF component parameters such as threshold value and the variance of noise, using the estimated parameters in combination with the optimal linear interpolation algorithm of noise threshold function of leading the IMF component implementation of threshold denoising. After the denoised IMF component is obtained, the denoised infrared image is obtained after reconstruction with the signal-dominated IMF component, and the adaptive denoising of the infrared image of the distribution network inspection UAV is realized. The experimental results show that the method in this paper can maintain the details of the image, improve the definition, significantly improve the visual effect, the overall denoising performance is stable and feasible, and ensure the quality inspection UAV to collect infrared images.
A 250-mW 5.4G-Rendered-Pixel/s Realistic Refocusing Processor for High-Performance Five-Camera Mobile Devices

Digital refocusing in multi-camera mobile devices is becoming crucial. Realistic refocusing, which is a subset of digital refocusing, provides physically-correct quality; however, its intense computational complexity results in low processing speed and restricts its applicability. Moreover, its complex computation flow requires substantial DRAM bandwidth and a large SRAM area, making it more challenging to implement in hardware. In this paper, we present a high-performance refocusing processor based on a hardware-oriented realistic refocusing algorithm. The proposed compact computation flow saves 92% of the DRAM bandwidth and 32% of the SRAM area without noticeable quality degradation. To support high-performance refocusing, we develop highly paralleled engines for view rendering. They deliver 5.4G rendered-pixel/sec throughput. The hardware accelerator improves the processing speed by 100× to 350× that of the original refocusing algorithm running on a general purpose processor. The chip is fabricated with 40nm CMOS technology and comprises 271KB of SRAM and 2.3M logic gates. The chip processes Full-HD light fields up to 40 frames per second under 250mW power consumption.
Optimal Constructions of Low-Hit-Zone Frequency-Hopping Sequence Sets via Cyclotomy

In complex environments of wireless communication, to improve the communication antiinterference performance, quasi-synchronous frequency-hopping (FH) communication system requires low-hit-zone (LHZ) FH sequence sets with optimal Hamming correlation (HC) properties and flexible parameters.In this paper, based upon cyclotomy theory, two classes of LHZ FH sequence sets with flexible parameters not included in the literatures are designed, and the periodic HC properties of which are analyzed.It turns out that the designed LHZ FH sequence sets are optimal on the maximum periodic HC.And thus the new constructions can be used to provide optimal FH sequence sets for quasi-synchronous FH communication system.
Knowledge Graph Empowered Machine Learning Pipelines for Improved Efficiency, Reusability, and Explainability

Artificial intelligence (AI) pipelines are complex, heavily parameterized, and expensive to execute in terms of time and computational resources. Consequently, it is onerous to run experiments with all possible parameter combinations to achieve an optimal solution. However, these AI experiments can be optimized by recommending relevant parameters to commence the experiments, reducing search space significantly, which can be fine tuned further. The relevant parameters can be identified by observing the metadata of pipelines executed in the past, and the relevant pipeline with relevant parameters can be recommended to the user. Currently, there are various metadata frameworks that automatically record the metadata of AI pipelines. Developing a recommendation system requires understanding pipeline metadata components and their interactions. There is a need to represent the metadata generated by these AI pipelines that capture the relationship among these pipeline entities. This article presents a knowledge-infused recommender that utilizes prior knowledge and metadata of already executed pipelines represented using the proposed metadata schema to recommend a relevant pipeline per user queries. Unlike black-box models, the use of knowledge graphs makes recommendations explainable, improving transparency and trustworthiness for the users.
Making COVID dis-connections: designing intra-active and transdisciplinary sound-based narratives for phenomenal new material worlds

In this article, we reflect on the design and implementation of an interactive transhistorical and transmedial web-based digital narrative audio experience, PATTER(n)INGS: Apt 3B, 2020 that we developed in 2020. This work is an immersive audio-only application, and it focuses on the complex, material living conditions during the COVID-19 pandemic. Drawing inspiration from PATTER(n)INGS and its complex, material audio and narrative design, we propose a model for creating the content and delivery for similar sound-based interactive digital narratives. Our proposed model focuses primarily on the creative process for designing such sound-based work. To construct our analytical model, the New Material/Spectral Morphology Design Model (or NM/SM Design Model), we draw on theoretical influences from critical posthumanism, feminist new materialism and non-human narrative that critique notions of stable subjectivity as sites for power and authority over semiotic meaning-making. We combine these views with foundational theoretical research in electroacoustic musical composition notation, and audio experimentation that complicate notions of sound, sound making, spatial perception, psychoacoustic phenomena, and listening practices. Together, this theoretical/compositional framework provides a unique method to consider how one can sustain and maximize sonic agents as core phenomena to create anti-cognitive worlds and stories.
Local bias adjustment, duration-weighted probabilities, and automatic construction of tariff cells

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Classification of Cervical Cancer Using an Autoencoder and Cascaded Multilayer Perceptron

Cervical cancer is the most frequent and potent form of cancer in women. The complications caused by it can be avoided if it is detected and treated promptly. In oncology, artificial intelligence has improved the prediction accuracy in the preliminary stages of cervical cancer. In this work, a novel machine learning-based approach is introduced to predict and categorize the healthy and anomalous cervical cells. Initially, an adaptive median filter is utilized to eliminate the noise artefacts in the pap smears from the Herlev dataset. The auto-encoder (AE) is employed to extract the features and reduce the dimension of features to progress the training process. Consequently, a cascaded multilayer perceptron (c-MLP) is employed for the classification of the normal and abnormal cervical cells. The c-MLP was trained using the Bayesian Regulation algorithm to generate the best classification accuracy of 97.63%. As a result, the classification using the c-MLP is more accurate and effective for classifying healthy and malignant cervical cells than the classic ML classifiers. The proposed ML-based framework progresses the overall accuracy range by 3.61%, 4.40%, 5.07%, and 4.66% better than SVM, XGB, RF, and DT, respectively.
Classification of Renal Function Trends in Time Series Data Using Machine Learning

As a preventive measure for chronic kidney disease requiring dialysis, some municipal governments and medical associations make comments to family doctors and examinees based on the results of health checkups. However, the judgment and preparation of comments are mostly done manually by physicians and public health nurses and require much labor. In this study, we examine the trend determination of kidney function level using machine learning as part of the study to realize the automation of this task. We propose a preprocessing method for time-series data dealing with different numbers of checkups and different duration of checkups. As a machine learning model, we propose ensemble learning methods using gradient boosting decision trees. The effectiveness of the proposed methods is demonstrated in evaluation using about 3,000 cases of specific health checkup data.
Formulation of pdi-Bagging and its Evaluation

For pattern classification problems, there is ensemble learning method that identifies multiple weak classifiers by the learning data and combines them together to improve the discriminant rate of testing data. We have already proposed pdi-Bagging (possibilistic data interpolation-Bagging) which improves the discriminant rate of testing data by adding virtually generated data to learning data. However, the accuracy of the correct virtual data type is not stable because the virtual data generate in the wide area of the data space. In addition, the discriminant accuracy is not high because the evaluation index for changing the generation class of virtual data is defined in each dimension. In this paper, we propose a new method to specify the generation area of virtual data and change the generation class of virtual data. As a result, the discriminant accuracy is improved since five new bagging methods which generate virtual data around correct discriminant data and error discriminant data are formulated, and the class of virtual data is determined with the proposed new evaluation index in multidimensional space. We formulate a new pdi-Bagging algorithm, and discuss the usefulness of the proposed method using numerical examples.
Comparison Between Daytime, Twilight and Nighttime on Detection of Approaching Vehicles by Pedestrians

This study examined the causes of traffic accidents between pedestrians and vehicles at twilight, based on the visual characteristics of pedestrians at twilight. To obtain data on the visual characteristics of pedestrians, we conducted an experiment in which a participant wearing an eye tracker walked along a sidewalk on the right side of the road passing vehicles during the daytime, twilight, and nighttime. The experimental data were analyzed by dividing the visual objects into static and dynamic ones. The results showed that the visual characteristics at twilight were not intermediate between those at daytime and nighttime, but were particular ones. It was also revealed that at twilight, people tend to neglect looking carefully at dynamic objects, and the timing of looking at the dynamic objects is apt to be limited to a specific distance.
A deep penetration network for sentence classification

Sentence classification is an important task in natural language processing. The task makes use of deep networks to enclose a mass of features with different granularities in a sentence. However, the classification usually suffer from severe performance degradation when stacking a large number of networks. The main reason is that, in a deep architecture, the silent feature representations are easily weakened and mixed with noisy information, which is not effective in learning contextual features and constructing semantic dependencies in a sentence. In this paper, a deep penetration network (DPN) is designed to improve deep architectures’ ability to preserve the favourable semantic features. The DPN enables salient features to penetrate through a deeper architecture and to construct long semantic dependencies between them. This approach is evaluated on seven public datasets. Our experiments show that the DPN exhibits a stable performance with deeper architectures. It improves the performance on three types of sentence classification tasks, outperforming the existing state-of-the-art models.