Reviewerly/exHarmony huggingface dataset:
Optimization of a DIY parallel-optical-axes profilometer for compensation of fringe divergence

In profilometry systems, the conventional optical configuration of fringe projection with the oblique projection and camera axis perpendicular to the field of view requires complicated calculus and exact ray trace designs to get the 3D shape under question accurately. Moreover, when the optical components of the camera and projector and their internal placement are unknown, as is the case with commercial digital video projectors, the correct alignment of these components is uncertain, needing complex methods of calibration to correct the measured shape. In this work, we re-introduce a parallel-optical-axes configuration for a DIY profilometer that simplifies the calibration by a simple trigonometric relationship obtaining the topography with a resolution and accuracy similar to conventional arrangements that require non-simple relationships among phase and height. Furthermore, an experimental method is presented to locate the divergence point within the projector, which allows optimizing the projection and observation distances. The proposed calibration procedure allows proper compensation for the apparent scale change caused by perspective. The performance of the proposed method is tested in an optimized configuration and a non-optimized one, comparing the measurements with a Coordinate-measuring machine. The optimized optical system using simple spatial phase unwrapping shows an improved RMS error of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>53</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mspace width="thinmathspace" /> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> versus <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>203</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mspace width="thinmathspace" /> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> of the non-optimized.
Combined DCT-DWT Color Image Digital Watermarking Technique

In present time, information transmission is becoming increasingly different through digitization. But security is a measure concern between information transmissions. Therefore, the digital watermarking technique gives a better method to prevent illegal access, unlike exploitation and allocation and information about the copyright protection of digitized data. It must be strong; therefore, if one part of this is changed then it will be recoverable. Several different methods are provided for embedding and extracting watermarks from the original image. In proposed paper, watermarking is completed by applying the combined approach of Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT). DWT technique is applied on the Red color (or R-channel) component of the color image for embedding the watermark. Once completing the embedding process, DCT technique is applied to 8X8 block of the color image. The results of proposed algorithm strongly maintain image quality and also it is sufficient for general image processing tasks.
Clustering Methods for Particle Filters With Gaussian Mixture Models

New clustering methods are proposed to develop novel particle filters with Gaussian mixture models (PFGMM). In the PFGMM, the propagated samples are clustered to recover a Gaussian mixture model (GMM) using a clustering algorithm, which plays a fundamental role in the filter’s performance. Two clustering methods are introduced that simultaneously minimize the covariance of each of the GMM components and maximize the likelihood function. Under the scenarios considered in this article, it is shown through numerical simulation that the PFGMMs with the proposed clustering algorithms lead to better performance than the PFGMM employing the K-means or the expectation-maximization algorithms as well as the regularized particle filter (PF) and the Gaussian sum PF.
3D-BitNet: Flow-Agnostic and Precise Network for video Bit-Depth Expansion

Bit-depth expansion(BDE) algorithms have made great progress on single image. However, it is hard to directly apply on videos with no temporal constraint. Aiming at the problem of video BDE, we adopt an encoder-decoder structure with 3D convolution to fuse spatial and temporal domain information. The encoder utilizes 3-stage down sampling with 3D ResBlocks to align the features of different time series, the decoder adopt the inverse structure with Coordinate Attention to fuse the aligned features and reconstruct the high bit-depth frame. The quantitative and qualitative experimental results show that the proposed video BDE network is superior to other methods. Compared with the best BDE algorithm, we obtain a 1.51db improvement on PSNR. With no additional temporal information such as optical flow, our method is also superior in running speed.
An Enhanced Cos-Neuro Bio-Inspired Approach for Document Clustering

Data mining is a dynamic and attractive research domain that has become known to discover information from the vast amount of constantly created data. Clustering is an unsupervised approach to data mining in which a group of similar items is assembled in one cluster. The quality of documents retrieved within a lesser amount of time has always been a fundamental problem in web document clustering. The authors introduce similarity technique-based K-means clustering using bee swarm optimization and artificial neural networks in this work. The artificial neural network helps classify the best centroid location based on the similarity index of the document and according to the trained structure of ANN to organize the best cluster number to test queries. The quality of papers returned is improved significantly with lesser execution time and improved efficiency through the projected method.
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition

As end-to-end automatic speech recognition (ASR) models reach promising performance, various downstream tasks rely on good confidence estimators for these systems. Recent research has shown that model-based confidence estimators have a significant advantage over using the output softmax probabilities. If the input data to the speech recogniser is from mismatched acoustic and linguistic conditions, the ASR performance and the corresponding confidence estimators may exhibit severe degradation. Since confidence models are often trained on the same in-domain data as the ASR, generalising to out-of-domain (OOD) scenarios is challenging. By keeping the ASR model untouched, this paper proposes two approaches to improve the model-based confidence estimators on OOD data: using pseudo transcriptions and an additional OOD language model. With an ASR model trained on LibriSpeech, experiments show that the proposed methods can greatly improve the confidence metrics on TED-LIUM and Switchboard datasets while preserving in-domain performance. Furthermore, the improved confidence estimators are better calibrated on OOD data and can provide a much more reliable criterion for data selection.
Monte Carlo Modeling of Employment Distribution of College Graduates based on Data Mining Technology

This study considers the university as the sample to follow general process of knowledge discovery. Also applying a data mining technology constructs the prediction model of graduates' employment. This research work has made a scientific analysis of the academic data and employment related data of college graduates over the years, and finds out the available knowledge and potential laws, which can provide reference for colleges and universities to deal with the situation of increasing employment pressure and prominent employment structural contradictions. This provides a scientific data-based reference for students' career guidance work to make correct decisions.
Self-Supervised Image Prior Learning with GMM from a Single Noisy Image

The lack of clean images undermines the practicability of supervised image prior learning methods, of which the training schemes require a large number of clean images. To free image prior learning from the image collection burden, a novel Self-Supervised learning method for Gaussian Mixture Model (SS-GMM) is proposed in this paper. It can simultaneously achieve the noise level estimation and the image prior learning directly from only a single noisy image. This work is derived from our study on eigenvalues of the GMM’s covariance matrix. Through statistical experiments and theoretical analysis, we conclude that (1) covariance eigenvalues for clean images hold the sparsity; and that (2) those for noisy images contain sufficient information for noise estimation. The first conclusion inspires us to impose a sparsity constraint on covariance eigenvalues during the learning process to suppress the influence of noise. The second conclusion leads to a self-contained noise estimation module of high accuracy in our proposed method. This module serves to estimate the noise level and automatically determine the specific level of the sparsity constraint. Our final derived method requires only minor modifications to the standard expectation-maximization algorithm. This makes it easy to implement. Very interestingly, the GMM learned via our proposed self-supervised learning method can even achieve better image denoising performance than its supervised counterpart, i.e., the EPLL. Also, it is on par with the state-of-the-art self-supervised deep learning method, i.e., the Self2Self. Code is available at https://github.com/HUST-Tan/SS-GMM.
Mixer-TTS: Non-Autoregressive, Fast and Compact Text-to-Speech Model Conditioned on Language Model Embeddings

This paper describes Mixer-TTS, a non-autoregressive model for mel-spectrogram generation. The model is based on the MLP-Mixer architecture adapted for speech synthesis. The basic Mixer-TTS contains pitch and duration predictors, with the latter being trained with an unsupervised TTS alignment framework. Alongside the basic model, we propose the extended version which additionally uses token embeddings from a pre-trained language model. Basic Mixer-TTS and its extended version achieve a mean opinion score (MOS) of 4.05 and 4.11, respectively, compared to a MOS of 4.27 of original LJSpeech samples. Both versions have a small number of parameters and enable much faster speech synthesis compared to the models with similar quality.
Sustaining Successful ICT Integration in Remote Rural Schools

This paper aims to provide insights into how teachers can sustain ICT integration practices in geographically isolated and disadvantaged areas. A mixed-method case study design was used for the study. Data were collected through interviews, questionnaires, classroom observation, and document examination. Qualitative analysis and descriptive statistics were used to analyse the data. For data validity, triangulation of responses and member checking was carried out. The study identified successful technology integration factors in geographically isolated and disadvantaged schools: a strong community of practice, an adequate support system, and contextual ICT integration practices. It suggests that teachers’ pedagogical practices related to ICT toned to undergo a continuous process of review and reflection to ensure that ICT-related practices remain pedagogically effective and relevant to changing needs and contexts. The account of the teachers in this study, focusing on their teaching experiences in a school in a remote, low-income area, creates a paradigm for comparative research on education in challenging contexts. Comparative studies on ICT integration practices in poverty-stricken, post-disaster, and conflict-affected areas, could provide inputs for formulating context-specific policy recommendations that could support successful ICT integration in small rural schools in the Philippines and other developing countries.
Gengar: An RDMA-based Distributed Hybrid Memory Pool

Byte-addressable Non-volatile Memory (NVM) technologies promise higher density and lower cost than DRAM. They have been increasingly employed for data center applications. Despite many previous studies on using NVM in a single machine, there remain challenges to best utilize it in a distributed data center environment. This paper presents Gengar, an RDMA-enabled Distributed Shared Hybrid Memory (DSHM) pool with simple programming APIs on viewing remote NVM and DRAM in a global memory space. We propose to exploit semantics of RDMA primitives to identify frequently-accessed data in the hybrid memory pool, and cache it in distributed DRAM buffers. We redesign RDMA communication protocols to reduce the bottleneck of RDMA write latency by leveraging a proxy mechanism. Gengar also supports memory sharing among multiple users with data consistency guarantee. We evaluate Gengar in a real testbed equipped with Intel Optane DC Persistent DIMMs. Experimental results show that Gengar significantly improves the performance of public benchmarks such as MapReduce and YCSB by up to 70 % compared with state-of-the-art DSHM systems.
Using Interaction as Nudge to Increase Installation Rate of COVID-19 Contact-Confirming Application

For the COVID-19 Contact Confirming Application (COCOA) to be fully effective, it is important that many people install COCOA on their smartphones. However, because the current COCOA implements only a minimum number of functions, it is difficult to motivate people to install it. Therefore, we focus on an extrinsic approach that motivates the installation of COCOA by adding fun functions through interaction with a ubiquitous system on the environment side. In this paper, we introduce an interactive system that nudges users to install COCOA by reframing the act of installing COCOA as getting a ticket to participate in fun experiences.
Online Credibilistic Fuzzy Clustering Method Based on Cauchy Density Distribution Function

The problem of clustering arrays of multidimensional data, given in the form of feature vectors, sometimes quite large dimensions is an integral part of the overall problem of Data Mining, and to solve it currently offers a large number of approaches, methods, algorithms from the simplest type K-means to a rather complex ones based on an analysis of the density distribution data of high dimensionality in large arrays. A special place here is occupied by fuzzy clustering methods, which are synthesized under the assumptions of mutual intersection of classes, which are formed in the process of data analysis.
Mobile application to improve the learning of children with Attention Deficit Disorder and Hyperactivity

The research work focuses on students with Attention Deficit Hyperactivity Disorder. That is why this mobile application prototype was designed to improve their learning, concentration and social skills in the development of educational classes. A hybrid methodology and the Balsamiq tool were used, since they were the most appropriate to develop this work. As a result, a mobile application design was obtained so that students with Attention Deficit and Hyperactivity disorder can improve their learning through the use of technology and educational games. In this way, we can take advantage of the benefits that technology gives us and implement it as an educational strategy to improve student learning, using the most important areas such as communication, mathematics and logical ability.
Constructing a Blueprint of a Kolek

Abstract Kolek, a Malay Traditional Boat, is slowly forgotten by the younger generation due to several reasons. In this paper, an in-depth study was conducted to construct a Kolek blueprint for preservation using the Reverse Engineering method on the existing artifact in Malay Traditional Boat Yard located at Pulau Duyung. The study was conducted through several phases namely boat measurement, digitization, and analyses. As a result, a Kolek Lines Plan was produced using Rhinoceros 5 for reference by future generations. Then analyses were carried out using PolyCAD version 10.4 to obtain hydrostatic properties of the Kolek for its stability, and resistance to determine the power requirements needed for the Kolek. The results show the blueprint of the Kolek that can be develop as well as mechanical properties of the Kolek can be obtained using modern naval architecture techniques. This blueprint can be used to build new boats by the next generation using the hybrid method which is a combination of modern and traditional methods.
Terminologia e lessicografia: riflessioni sulla denominazione delle pratiche terminologiche nel XXI secolo

This contribution aims to propose a reflection that is inserted in a perspective of the history of lexicography, on the one hand, and of semantics, on the other. The first objective of this work is to retrace how the denominations generally attributed to lexicographic and terminographic practice have been structured and lexicalized over the centuries. In the second part of this work, the paper mainly reflects on the use of the names of lexicographic resources (“dictionary”, “glossary”, “vocabulary”) in terms of terminology. This investigation objective arises from the hypothesis of a improper use of these names in different fields of application, especially when non-linguists use these names for products that claim to be of a terminological nature.
Virtual microscopy: The future of pathological diagnostics, dental education, and telepathology

Objectives: The objectives of this study were as follows: (1) to provide an overview of the feasibility, utility, merits, and demerits of virtual microscopy (VM) in pathological diagnostics and (2) to evaluate its advantages in dental and medical teaching over conventional microscopes. Materials and Methods: A literature survey was carried out in August 2020 in electronic databases such as PubMed using keywords such as virtual microscopy and whole slide imaging. The searches revealed 956 articles out of which 36 were selected after reading the full-text articles. Results: The review of 36 articles revealed that VM or whole-slide imaging (WSI) allows access to entire scanned sections of tissue that can be viewed on a computer in exactly the same way as conventional glass slides with a microscope and can be explored at several magnifications. VM offers many advantages like ease of accessibility through the Internet anytime or in any place, and moreover, the virtual slide does not get damaged or fade away like glass slide. Since it increases learning and comprehension, virtual microscopes have been used extensively in dental and medical education. Conclusions: VM opens a new world in tissue-based diagnosis through digitization of slides. The experiences of telepathology applications have already proved that it is a promising technology which has been adopted by histopathologists and cytologists across the world and contributing immensely to telepathology consultation in underserved areas. It is a feasible, valid, and approved technology for pathological diagnosis.
Coremoni-WE: Individual Core Training Monitoring and Support System Using an IMU at the Waist and the Ear

Due to the influence of the new coronavirus, many people are interrupting fitness clubs and exercise/sports performed by multiple people. Under these circumstances, ”core training,” which can be easily performed indoors by individuals, attracts attention as an exercise to improve health. However, it is not easy to recognize whether the posture during the training is correct or not, which may significantly reduce the effect of the exercise. To tackle these issues, we have been developing ”Coremoni-WE,” a core training monitoring and support system that combines wearables and earables to judge posture.
Design of Handwritten Numeral Recognition System Based on BP Neural Network

Abstract In the case of pattern recognition, handwritten numeral recognition is an important research topic in pattern recognition, and it has a very wide application in today’s information society. However, the research on numeral recognition is still in the development stage, and the recognition effect is not ideal. A handwritten numeral recognition method based on BP neural network is proposed. Firstly, the image is grayed, binarized, smoothed, denoised and normalized to extract the pixel value; Then the designed BP neural network is trained, compared with the expected results and expected structure, and the BP neural network is adjusted and modified; Finally, the trained neural network is obtained. Experiments show that the accuracy of this method for handwritten digit recognition is 85.88%.
Using Machine Learning to Predict the Users Ratings on TripAdvisor Based on Their Reviews

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we intend to facilitate the acquiring information process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning algorithms (Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors and Recurrent Neural Networks) and Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. The experiments carried out showed that the developed models can predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users.
MaxRI: A method for discovering maximal rare itemsets

Rare itemset mining got extensive attention due to its high importance in real-life applications. Rare itemset mining methods aim at discovering the whole set of rare itemsets in a dataset. Although current algorithms perform reasonably well in finding interesting rare itemsets, they also reveal a large number of rare itemsets, including redundant ones. As a result, skimming through these massive amounts of (partly redundant) itemsets is a big overhead in many applications. On the other hand, generating a massive number of rare itemsets also compromises the performance of algorithms in terms of time and memory. To address these limitations, we propose an efficient algorithm called maximal rare itemset (MaxRI) to discover maximal rare patterns (long rare itemset). Then, we propose another method RRI (Recover Rare Itemsets from maximal rare itemsets) to retrieve the interesting subset of rare itemsets of a user-given length, k, from the set of maximal rare itemsets. To the best of our knowledge, this is the first paper proposed for rare itemset mining by considering the representative rare patterns without redundant ones. Our experimental results indicate that our proposed methods’ performance is better than the up-to-date algorithms in terms of time and memory consumption.