Reviewerly/exHarmony huggingface dataset:
Dementia Detection Using Language Models and Transfer Learning

Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.
A Blockchain Based Research on Comprehensive Quality Evaluation of Emerging Engineering College Students

In the context of engineering education transformation and education evaluation reform, this research empirically investigates the status quo of comprehensive quality evaluation of T University students in quantitative research methods, and finds that there are some problems in the current evaluation of students' comprehensive quality, such as incomplete evaluation content, incomplete participation of evaluation subjects, imperfect supervision mechanism in the evaluation process, and weak development function of evaluation. Furthermore, this research uses blockchain to build a comprehensive quality evaluation system for students based on a reputation incentive mechanism, forming a more realistic and objective evaluation atmosphere, and helping to further improve the quality of engineering education.
Trinary tools for continuously valued binary classifiers

Classification methods for binary (yes/no) tasks often produce a continuously valued score. Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment. Such tasks involve examining classifier results, typically using summary statistics and manual examination of details. In this paper, we provide an interactive visualization approach to support such continuously-valued classifier examination tasks. Our approach addresses the three phases of these tasks: calibration, operating point selection, and examination. We enhance standard views and introduce task-specific views so that they can be integrated into a multi-view coordination (MVC) system. We build on an existing comparison-based approach, extending it to continuous classifiers by treating the continuous values as trinary (positive, unsure, negative) even if the classifier will not ultimately use the 3-way classification. We provide use cases that demonstrate how our approach enables machine learning practitioners to accomplish key tasks.
Echo State Network for Soft Actuator Control

Conventional model theories are not suitable to control soft-bodied robots as deformable materials present rapidly changing behaviors. Neuromorphic electronics are now entering the field of robotics, demonstrating that a highly integrated device can mimic the fundamental properties of a sensory synaptic system, including learning and proprioception. This research work focuses on the physical implementation of a reservoir computing-based network to actuate a soft-bodied robot. More specifically, modeling the hysteresis of a shape memory alloy (SMA) using echo state networks (ESN) in real-world situations represents a novel approach to enable soft machines with task-learning. In this work, we show that not only does our ESN model enable our SMA-based robot with locomotion, but it also discovers a successful strategy to do so. Compared to standard control modeling, established either by theoretical frameworks or from experimental data, here, we gained knowledge a posteriori, guided by the physical interactions between the trained model and the controlled actuator, interactions from which striking patterns emerged, and informed us about what type of locomotion would work best for our robot.
Automatic Modulation Recognition Using Minimum-Phase Reconstruction Coefficients and Feed-Forward Neural Network

Identification of signal waveforms is highly critical in 5G communications and other state-of-the-art radio technologies such as cognitive radios. For instance, to achieve efficient demodulation and spectrum sensing, cognitive radios need to implement automatic modulation recognition (AMR) of detected signals. Although many works have been reported in the literature on the subject, most of them have mainly focused on the additive white Gaussian noise (AWGN) channel. However, addressing the AWGN channel, only, does not sufficiently emulate real-time wireless communications. In this paper, we created datasets of six modulation schemes in GNU Radio. Wireless signal impairment issues such as center frequency offset, sample rate offset, AWGN, and multipath fading effects were applied for the dataset creation. Afterward, we developed AMR models by training different artificial neural network (ANN) architectures using real cepstrum coefficients (RCC), and minimum-phase reconstruction coefficients (MPRC) extracted from the created signals. Between these two features, MPRC features have the best performance, and the ANN architecture with Levenberg-Marquardt learning algorithm, as well as logsig and purelin activation functions in the hidden and output layers, respectively, gave the best performance of 98.7% accuracy, 100% sensitivity, and 99.33% specificity when compared with other algorithms. This model can be leveraged in cognitive radio for spectrum sensing and automatic selection of signal demodulators.
Web Mining and Web Usage Mining for Various Human-Driven Applications

The current century has seen a huge amount of data being produced every day by various computational platforms. These data are very large in amount when compared to the past. These data have emerged in form of big data (BD), and it's vital to manage these data for processing as well as extracting. The big data are emerging from Facebook, Instagram, and various other social as well as private platforms. Web mining has the capabilities to mine or extract data from a very huge set of data. The mining of useful information is essential for optimal functioning of various systems. In this chapter, the usage of web mining and one of its sub categories, web usage mining, for various human-driven applications has been discussed. This chapter focuses on the background that leads to the emergence of web mining. The chapter also deeply explores various subdomains that are essential for tackling data extraction using web mining.
Study and implementation of encryption algorithm based on SM2

SM2 is a public key cryptosystem based on elliptic curve. Because of its high security and difficulty to crack, it is adopted by the national commercial cryptosystem to replace RSA. This paper studies and implements the encryption and decryption algorithm process based on SM2. Users can generate key pairs using the system, and quickly encrypt various types of files and generate cipher files. And then decrypt the encrypted files with public keys. The whole encryption and decryption process is displayed with a convenient interface, and various operation processes are recorded in logs for easy inspection. Finally, the test results of encrypting and decrypting different data files are displayed.
Assessing Together the Trends in Newspaper Topics and User Opinions

This chapter proposes and explores all features of a model capable of capturing trends within large corpora of texts. Not only are trends assessed through a numerical index, but they are displayed alongside rhetorical and attitudinal information on all topics concerned for all relevant epochs. This way, trend evolutions can be analyzed in the light of wordings and thinking evolutions, thus allowing for a co-evolutionary approach to trend assessing. Each and every step is methodologically explained, as well as the interactions between them. Variations and adaptations are also discussed for a greater adaptability of the model to all use cases.
Structure-guided attention network for fine-grained vehicle model recognition

In view of the difficulty in accurately recognizing vehicle models due to the wide variety of vehicle models and little differentiation between some models, a structure-guided attention network for weakly supervised learning fine-grained vehicle model recognition is proposed. First, we utilize the structure-guided attention location module to locate key features of images, extracting the strong and weak attention areas of the image by generating attention maps. Second, we investigate multichannel convolutional neural network for the learning of the strong-weak feature pairs and solve the problem of different network feature maps with different sizes by using feature map alignment. Third, an adaptive weighting method is proposed to balance the loss function. The experimental results show that our algorithm effectively improves the accuracy of the fine-grained vehicle recognition dataset and has universal applicability in other fine-grained recognition datasets.
“This” and “That” in Teleshopping with Possessive Telepresence Systems using 5G Mobile Networks

With the growth of communication technology, it has become possible to use telepresence systems to allow people to feel present when they are in a remote location. The way that people in remote locations share knowledge and act together toward people in local locations can be thought of as the possession of people in remote locations by people in local locations. Therefore, by using possession, users can consume the service as if they were two people. In this paper, we propose a possession-based teleshopping system that allows a person with the intention to purchase something in a remote location to possess a person wearing an MR-HMD in a local location, thereby enabling a local purchasing experience. To carry out this study, we visited a clothes shop, a convenience store, and a movie theater using two patterns of communication lines over 4G and 5G, where the remote possessor instructed the local possessee to do the shopping. The fieldwork revealed that the possessee could respond flexibly to the possessor’s directive words such as “this” and “that” due to the ease of sharing visibility. In addition, because the possessee was able to accurately convey visual information such as the price and color of the product, the possessor was able to make smoother purchasing decisions, and the possessee was able to make payments and perform other tasks without delay. Furthermore, the relationship between the possessor and the possessee was such that they were able to compensate for each other’s lack of knowledge. As a result, the possession system makes it possible to perform a task with the knowledge of two people, even though they appear to be one person face-to-face with other people in economic activities.
CWCT: An Effective Vision Transformer using improved Cross-Window Self-Attention and CNN

In the process of metaverse construction, in order to achieve better interaction, it is necessary to provide clear semantic information for each object. Image classification technology plays a very important role in this process. Based on CMT transformer and improved Cross-Shaped Window Self-Attention, this paper presents an improved Image classification framework combining CNN and transformers, which is called CWCT transformer. Due to the high resolution of the image, vision transformers will lead to too high model complexity and too much calculation. To solve this problem, CWCT captures local features by using optimized Cross-Window Self-Attention mechanism and global features by using convolutional neural networks (CNN) stack. This structure has the flexibility to model at various scales and has linear computational complexity concerning image size. Compared with the original CMT network, the classification accuracy has been improved on ImageNet-1k and randomly screened Tiny-ImageNet dataset. Thanks to the optimized Cross-Window Self-Attention, the CWCT proposed in this paper has a significant improvement in operation speed and model complexity compared with CMT.
Multiqubit state learning with entangling quantum generative adversarial networks

The increasing success of classical generative adversarial networks (GANs) has inspired several quantum versions of GANs. Fully quantum mechanical applications of such quantum GANs have been limited to one- and two-qubit systems. In this paper, we investigate the entangling quantum GAN (EQ-GAN) for multiqubit learning. We show that the EQ-GAN can learn a circuit more efficiently compared with a SWAP test. We also consider the EQ-GAN for learning eigenstates that are variational quantum eigensolver (VQE)-approximated, and find that it generates excellent overlap matrix elements when learning VQE states of small molecules. However, this does not directly translate into a good estimate of the energy due to a lack of phase estimation. Finally, we consider random state learning with the EQ-GAN for up to six qubits, using different two-qubit gates, and show that it is capable of learning completely random quantum states, something which could be useful in quantum state loading.
Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.
A Study on the Stress Factors Analysis of On-board Training for Cadet in Shipping Company

The purpose of this study is to analyze the factors influencing the stress that the cadet feel during the on-board training by measuring the stress. The participants of this study were 129 cadets from Mokpo Maritime University who completed the on-board training in shipping company and the collected data were analyzed by SPSS Statistics 27. In this study, stress measurement, general characteristic analysis for participants, stress difference analysis, correlation analysis, multiple regression analysis, and multinomial logistic regression analysis were performed. As a result of this study, the overall average stress score was 2.88±0.65, in the order of technical factors, psychological factors, crew
(Retracted) Automatic reading recognition of pointer barometer based on machine vision

To develop a calibration system for a pointer barometer based on machine vision, an automatic reading recognition scheme of the barometer based on color images is proposed. To locate the dial while extracting the pointer, an optimized three-point-based circle determination method is proposed to calibrate the dial center and segment the dial. At the same time, an improved angle method is proposed to realize the automatic reading recognition of pointer instruments. First, the pointer area is detected and extracted by the three frame difference method, and the pointer centroids are extracted by least circumscribed rectangle fitting, and the optimal three points are selected to determine the center of the circle and locate the dial. Then, the two-color image matrix elements corresponding to different pointers are compared point by point to obtain the maximum value, and the dial without the pointers is also obtained. Through cropping, color channel component extraction, and binarization processing on the dial without pointers, a noninterference dial scale image is refitted, and each scale is fitted with a minimum circumscribed rectangle to calibrate the centroid of the scale line. Finally, using the centroids of the calibrated scale lines, the centroid of the fitted pointer, and the circle center obtained by three-point-based circle determination, the improved angle method proposed is used to realize the automatic reading recognition of the pointer instrument. Experimental results show that this scheme can effectively improve the accuracy of machine vision-based automatic reading recognition for pointer instruments.
Creating an open source, customizable accessibility checker for content authors

Enforcing website accessibility and usability in a large organization poses its challenges. Implementing web accessibility at scale requires distributing responsibilities and assigning accountability. Enterprise accessibility suites are effective tools, however are often expensive and can be overwhelming to content authors who may not have technical expertise or have a development background. Sa11y is an open source, highly customizable, in-page accessibility checker designed for content authors, with a unique focus on quality accessibility. Sa11y is platform agnostic, and was created with JavaScript, CSS, and HTML.
Textural and geochemical distinction between supergene and hypogene Cu sulfide phases at the Mammoth copper deposit, Queensland, Australia

Mammoth is a sediment-hosted, structurally controlled, Proterozoic Cu deposit located in the Mt Isa Inlier. Hypogene and supergene chalcocite are identified within the deposit and they are texturally and paragenetically distinct. Pyrite, chalcopyrite, bornite and chalcocite (both hypogene and supergene) were analyzed using Laser Ablation (LA) ICP-MS for trace element geochemistry. Supergene chalcocite is enriched in Bi (83 ppm) and depleted in both Ag (23 ppm) and As (7 ppm) compared to hypogene chalcocite (26 ppm Bi, 302 ppm Ag and 45 ppm As). Conclusions of this study are: 1) chalcocite is a component of the hypogene ore mineralogy; 2) meteoric oxidation processes produced supergene chalcocite; and 3) clear distinction as to the genesis of the chalcocite mineralisation is possible using mineralogical, textural and geochemical criteria.
New Deep Spatio-Structural Features of Handwritten Text Lines for Document Age Classification

Document age estimation using handwritten text line images is useful for several pattern recognition and artificial intelligence applications such as forged signature verification, writer identification, gender identification, personality traits identification, and fraudulent document identification. This paper presents a novel method for document age classification at the text line level. For segmenting text lines from handwritten document images, the wavelet decomposition is used in a novel way. We explore multiple levels of wavelet decomposition, which introduce blur as the number of levels increases for detecting word components. The detected components are then used for a direction guided-driven growing approach with linearity, and nonlinearity criteria for segmenting text lines. For classification of text line images of different ages, inspired by the observation that, as the age of a document increases, the quality of its image degrades, the proposed method extracts the structural, contrast, and spatial features to study degradations at different wavelet decomposition levels. The specific advantages of DenseNet, namely, strong feature propagation, mitigation of the vanishing gradient problem, reuse of features, and the reduction of the number of parameters motivated us to use DenseNet121 along with a Multi-layer Perceptron (MLP) for the classification of text lines of different ages by feeding features and the original image as input. To demonstrate the efficacy of the proposed model, experiments were conducted on our own as well as standard datasets for both text line segmentation and document age classification. The results show that the proposed method outperforms the existing methods for text line segmentation in terms of precision, recall, F-measure, and document age classification in terms of average classification rate.
Metallogeny of gold in the Tien Shan and Urals Paleozoic orogenic belts: a GIS-based approach

Based on a GIS-orientated approach, the metallogeny of gold in the Tien Shan and Urals Paleozoic orogenic belts has been studied. The pilot model of the GIS-orientated digital database created has a wide range of forecasting and metallogenic applications in regional geological investigations at various scales.
Multi-label learning with missing features and labels and its application to text categorization

In multi-label learning, researchers usually assume that the training data set is complete. However, this assumption is not always hold in real applications, e.g., the features or labels are missing for some data examples. Existing algorithms mainly focus on the problem of missing labels, and ignore the problem of missing features. This paper proposes a novel multi-label learning algorithm named MMFL, i.e., Multi-label learning with Missing Features and Labels, which can deal with the problem of missing features and labels simultaneously. First, we try to recover the missing values of features and labels by matrix factorization, and then learn a classification model from the latent feature space to the latent label space. Second, to overcome the problem of tail labels in matrix factorization, we build an extra classifier for the sparse tail labels. Besides, the manifold regularization technology is used to keep the manifold structures of instance similarity and label correlation. The effectiveness of our proposed method is verified by comparing it with the state-of-the-art approaches over eight multi-label benchmark data sets.