Reviewerly/exHarmony huggingface dataset:
A Signal processing method of OFDM communication receiver based on CNN

Orthogonal frequency division multiplexing (OFDM) the signal processing is a key issue in wireless communication research. The multipath effect and Doppler shift of wireless communication channels can lead to distortion of the transmitted signal, which poses a considerable challenge to the information recovery of communication receivers. This paper presents the signal processing method of OFDM communication based on convolutional neural network (CNN). The method replaces all signal processing modules of the OFDM communication receiver with CNN, and the information is recovered by the CNN. In order to adapt to the processing of communication signals, we designed a one-dimensional convolutional neural network (1D-CONV-CNN) model as the neural network structures by this method. Simulation results indicate that the signal processing method effectively reduces the bit error rate (BER) and improves its performance compared with the conventional reception method under different channel conditions.
Smartwatch In-Air Signature Time Sequence Three-Dimensional Static Restoration Classification Based on Multiple Convolutional Neural Networks

In-air signatures are promising applications that have been investigated extensively in the past decades; an in-air signature involves gathering datasets through portable devices, such as smartwatches. During the signing process, individuals wear smartwatches on their wrists and sign their names in the air. The dataset we used in this study collected in-air signatures from 22 participants, resulting in a total of 440 smartwatch in-air signature signals. The dynamic time warping (DTW) algorithm was applied to verify the usability of the dataset. This paper analyzes and compares the performances of multiple convolutional neural networks (CNN) and the transformer using median-sized smartwatch in-air signatures. For the four CNN models, the in-air digital signature data were first transformed into visible three-dimensional static signatures. For the transformer, the nine-dimensional in-air signature signals were concatenated and downsampled to the desired length and then fed into the transformer for time sequence signal multi-classification. The performance of each model on the smartwatch in-air signature dataset was thoroughly tested with respect to 10 optimizers and different learning rates. The best testing performance score in our experiment was 99.8514% with ResNet by using the Adagrad optimizer under a 1×10−4 learning rate.
Differential Privacy Preserving Using TensorFlow DP-SGD and 2D-CNN for Large-Scale Image Data

Abstract: Although smart wearables have many potential advantages, their widespread and ongoing use raises a number of privacy issues and difficult information security challenges. This article, present a thorough analysis of current wearable sensorbased big data analytics applications that protect user privacy. We draw attention to the fundamental aspects of privacy and security for applications on wearable technology. Then, we look at how deep learning techniques like 2D CNN are used for better evaluation and privacy preservation as well as for differential privacy of Tensor flow. DP-SGD (Differentially private stochastic gradient descent)The metrics are epsilon as well as accuracy, with 0.56 epsilon and 85.17% accuracy for three epochs and 100.09 epsilon and 95.28 accuracies for twenty epochs, respectively. Model training Accuracy is 94.71%. Also, present a case study on privacy-preserving machine learning techniques. Herein, we theoretically and empirically evaluate the privacypreserving deep learning framework's performance. We explain the implementation details of a case study of a secure prediction service using the 2D convolutional neural network (2D CNN) model
Scaffolding as a specific pedagogical support for students studying a foreign language in university e-learning environment

The aim of the study is to reason the usage of scaffolding as a specific kind of pedagogical support in university e-learning environment. The research paper describes the essence, functions, strategies and forms of pedagogical support of students learning a foreign language. The special features of foreign language training e-course based on scaffolding principles of e-learning environment are considered. The efficiency of teaching students according to scaffolding principles to develop foreign language communicative competence is examined by the example of using the e-learning course ‘Empower A2-B2’ in university e-learning environment ‘do.sevsu.ru’. The novelty of this study consists in the definition of the three main forms of scaffolding – individual scaffolding, affiliate scaffolding and computer-based scaffolding. As a result, the e-learning foreign language course ‘Empower A2-B2’ was worked out on scaffolding principles and introduced into the educational process of Sevastopol State University.
A robust and high embedding capacity watermarking technique for telemedicine

The medical field has been greatly influenced by the rapid expansion of technology and globalization. Medical images are routinely transferred by wired or remote media amongst professionals, medical experts, analysts, and patients themselves in order to improve the diagnostic outcomes. To achieve investigative conclusions, physicians concentrate on the ROI (Region of Interest) in a medical image. To retrieve the ROI when it has been damaged by noise or altered by intruders during transmission, the ROI data are hidden within the RONI (Region of Non-Interest) in medical images. This paper intends a high embedding capacity watermarking system grounded on Finite Ridgelet Transform (FRT). Proposed method supports the high embedding capacity for concealing entire ROI data inside RONI and simultaneously provides a high level of robustness to the concealed data.
Performance comparison of ORB, SURF and SIFT using Intracranial Haemorrhage CTScan Brain images

Medical images are crucial for both the doctor's accurate diagnosis and the patient's subsequent therapy. It is feasible to swiftly identify lesions in medical photos by using clever algorithms, and it is crucial to extract information from images. Feature extraction is an important step in image classification. It allows the representation of the content of images as perfectly as possible. The intention of this study is to certain overall performance assessment among the feature detector and the descriptor method, especially while there are numerous combos for assessment. Three techniques were decided on for the feature descriptors: ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transformation), and SURF (Accelerated Robust Feature) and to calculate matching evaluation parameters, for example, the number of key points in the image, Execution time required for each algorithm and to find the best match. The dataset was taken from Kaggle, which contained 170 CTScan images of the brain with intracranial hemorrhage masks. The brute force method is used to achieve feature matching. Performance analysis shows the discriminative power of various combinations of detector and descriptor methods. SURF algorithm is the best and most robust in CTScan imaging to help medical diagnosis.
TouchWheel: Enabling Flick-and-Stop Interaction on the Mouse Wheel

The mouse wheel may induce physical fatigue by requiring users to clutch the wheel repeatedly when scrolling long distances. To address this problem, some mouse products provide a free-spinning mode in which users can spin the wheel to initiate inertial scrolling. However, the time and effort required to toggle the free-spinning mode may lower the usability of the mouse wheel. To enable inertial scrolling without the overhead of mode switching, we present TouchWheel, a physical mouse wheel with touch sensitivity and a virtual wheel model that enables users to perform flick-and-stop operations as they do on touchscreens. A user experiment revealed that participants could actively use TouchWheel’s flick-and-stop operation according to their different scrolling needs. For tasks that frequently required long-distance scrolling, TouchWheel outperformed a normal mouse wheel in task completion time and required fewer clutching and mode-switching actions than the normal mouse wheel and a spin-enabled mouse wheel. For tasks that required short-distance scrolling, TouchWheel performed similarly to other baseline options.
A Multimodal Teaching Model of College English Based on Human–Computer Interaction

The experimental results of this paper showed that the final grades of most students would be improved in the class with low English proficiency in the case of textbooks of the same difficulty. However, when multimodal instruction was used, the failure rate in the second semester decreased by 13 percentage points compared with the first semester, while the high-achieving students increased by 6 percentage points. Therefore, by using the multimodal teaching method, the learning effect of students with low English proficiency from the mid-term to the end of the term has been significantly improved, which showed that the multimodal teaching method was more superior than the conventional teaching method. Before and after the use of multi-modal teaching, students in class B had a 24% increase in grades from mid-term to final, mainly because the English level of class B was generally higher.
The size of a Markovian SIR epidemic given only removal data

Abstract During an epidemic outbreak, typically only partial information about the outbreak is known. A common scenario is that the infection times of individuals are unknown, but individuals, on displaying symptoms, are identified as infectious and removed from the population. We study the distribution of the number of infectives given only the times of removals in a Markovian susceptible–infectious–removed (SIR) epidemic. Primary interest is in the initial stages of the epidemic process, where a branching (birth–death) process approximation is applicable. We show that the number of individuals alive in a time-inhomogeneous birth–death process at time $t \geq 0$ , given only death times up to and including time t , is a mixture of negative binomial distributions, with the number of mixing components depending on the total number of deaths, and the mixing weights depending upon the inter-arrival times of the deaths. We further consider the extension to the case where some deaths are unobserved. We also discuss the application of the results to control measures and statistical inference.
Effects of Computer-Assisted Language Learning (CALL) on ESL Reading Comprehension at a Private Sector University in Pakistan

English is used globally as an international language. It is considered as a prestigious language which provides greater opportunities for its users. Second language acquisition is not an easy task, for language acquisition it is necessary to learn all the aspects of language such as oral and written. Learner efficiently learn language rules, pronunciation, grammar and structure which can be learned more effectively by using Computer Assisted Language Learning (CALL) method. The recent study aims to explore the effectiveness of CALL in Pakistan at tertiary level which focuses on the improvement of apprentices’ reading skills. This is a quasi-experimental study, the data was obtained by 15 participants through pretest and posttest. Posttest was conducted after three weeks of pretest, during the intervention period CALL was integrated in the language classroom for reading skill lessons. A paired one-tailed t-test was used to analyze test scores and a descriptive data analysis method was used to detect learners’ perceptions. The result of this research study showed that CALL is a highly effective technique used to increase the proficiency level of learners in reading comprehension.
Rethinking symbolic and visual context in Referring Expression Generation

Situational context is crucial for linguistic reference to visible objects, since the same description can refer unambiguously to an object in one context but be ambiguous or misleading in others. This also applies to Referring Expression Generation (REG), where the production of identifying descriptions is always dependent on a given context. Research in REG has long represented visual domains through symbolic information about objects and their properties, to determine identifying sets of target features during content determination. In recent years, research in visual REG has turned to neural modeling and recasted the REG task as an inherently multimodal problem, looking at more natural settings such as generating descriptions for objects in photographs. Characterizing the precise ways in which context influences generation is challenging in both paradigms, as context is notoriously lacking precise definitions and categorization. In multimodal settings, however, these problems are further exacerbated by the increased complexity and low-level representation of perceptual inputs. The main goal of this article is to provide a systematic review of the types and functions of visual context across various approaches to REG so far and to argue for integrating and extending different perspectives on visual context that currently co-exist in research on REG. By analyzing the ways in which symbolic REG integrates context in rule-based approaches, we derive a set of categories of contextual integration, including the distinction between positive and negative semantic forces exerted by context during reference generation. Using this as a framework, we show that so far existing work in visual REG has considered only some of the ways in which visual context can facilitate end-to-end reference generation. Connecting with preceding research in related areas, as possible directions for future research, we highlight some additional ways in which contextual integration can be incorporated into REG and other multimodal generation tasks.
Digital Fringe Projection-Based Clamping Force Estimation Algorithm for Railway Fasteners

The inspection of railway fasteners to assess their clamping force can be used to evaluate the looseness of the fasteners and improve railway safety. Although there are various methods for inspecting railway fasteners, there is still a need for non-contact, fast inspection without installing additional devices on fasteners. In this study, a system that uses digital fringe projection technology to measure the 3D topography of the fastener was developed. This system inspects the looseness through a series of algorithms, including point cloud denoising, coarse registration based on fast point feature histograms (FPFH) features, fine registration based on the iterative closest point (ICP) algorithm, specific region selection, kernel density estimation, and ridge regression. Unlike the previous inspection technology, which can only measure the geometric parameters of fasteners to characterize the tightness, this system can directly estimate the tightening torque and the bolt clamping force. Experiments on WJ-8 fasteners showed a root mean square error of 9.272 N·m and 1.94 kN for the tightening torque and clamping force, demonstrating that the system is sufficiently precise to replace manual measurement and can substantially improve inspection efficiency while evaluating railway fastener looseness.
Age invariant face recognition using discriminative tensor subspace learning with fuzzy based classification

Abstract Aging is a complex process that affects both the shape and texture of a face. In the aging domain, various methods have been proposed for age estimation or simulation of the face based on an image at a given age. Face recognition methods that are designed for automatic face recognition are still a challenging issue. We present a novel age invariant face recognition method using tensor subspace learning with fuzzy synthetic classification. Local Binary Pattern (LBP) processed face images are used to learn the tensor sub‐space, which converts high dimensional feature space to age‐invariants while retaining key elements of the local geometrical structure of the face. Tensor sub‐space is better in terms of sub‐space learning and feature extraction than Principal Component Analysis (PCA)/Linear Discriminant Analysis (LDA) that been used in many techniques in the literature. Tensor Normalized Face images are generated which exhibit maximum inter‐class distances and minimum intra‐class distances. Further local patches are used for synthesizing global Fuzzy membership scores to classify the test face images. Experiments performed on standard face‐aging datasets, namely FG‐NET, AGEDB, and MORPH‐Album‐II, and received accuracy of 99.15%, 99.20%, and 99.8%, respectively. Experimental results outperform the current state‐of‐the‐art techniques, and results show the promise of the proposed system for personal identification despite the aging process. It also proved that the local descriptor gives better performance over the global descriptor like PCA for the aging process. The method also demonstrated improved performance as compared with compute‐intensive methods that required training on deep networks.
BPF: a novel cluster boundary points detection method for static and streaming data

Abstract Data points situated near a cluster boundary are called boundary points and they can represent useful information about the process generating this data. The existing methods of boundary points detection cannot differentiate boundary points from outliers as they are affected by the presence of outliers as well as by the size and density of clusters in the dataset. Also, they require tuning of one or more parameters and prior knowledge of the number of outliers in the dataset for tuning. In this research, a boundary points detection method called BPF is proposed which can effectively differentiate boundary points from outliers and core points. BPF combines the well-known outlier detection method Local Outlier Factor (LOF) with Gravity value to calculate the BPF score. Our proposed algorithm StaticBPF can detect the top- m boundary points in the given dataset. Importantly, StaticBPF requires tuning of only one parameter i.e. the number of nearest neighbors $$(k)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and can employ the same $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> used by LOF for outlier detection. This paper also extends BPF for streaming data and proposes StreamBPF . StreamBPF employs a grid structure for improving k -nearest neighbor computation and an incremental method of calculating BPF scores of a subset of data points in a sliding window over data streams. In evaluation, the accuracy of StaticBPF and the runtime efficiency of StreamBPF are evaluated on synthetic and real data where they generally performed better than their competitors.
How digital innovation affects women's entrepreneurship in Africa? An analysis of transmission channels

Digital innovations in Africa have increased dramatically over the past two decades, and are fueling a rich literature. In this paper, we examine their impact and transmission channels on women's entrepreneurship in a sample of 54 African countries. We specify and estimate a cross-sectional data model by generalized least squares (GLS) over the period of 1996–2020. Our results show that digital innovations (measured by researchers in R&amp;D, firms in R&amp;D, patents owed by residents, patents owed by non-residents) significantly reduce women's entrepreneurship in Africa. Their effects are mediated by energy, female education, the internet, mobile phones, migration and marriage. We suggest strengthening the digital infrastructure for sustainable women's entrepreneurship in Africa.
Fusion and Enhancement of Consensus Matrix for Multi-View Subspace Clustering

Multi-view subspace clustering is an effective method that has been successfully applied to many applications and has attracted the attention of scholars. Existing multi-view subspace clustering seeks to learn multiple representations from different views, then gets a consistent matrix. Until now, most of the existing efforts only consider the multi-view information and ignore the feature concatenation. It may fail to explore their high correlation. Consequently, this paper proposes a multi-view subspace clustering algorithm with a novel consensus matrix construction strategy. It learns a consensus matrix by fusing the different information from multiple views and is enhanced by the information contained in the original feature direct linkage of the data. The error matrix of the feature concatenation data is reconstructed by regularization constraints and the sparse structure of the multi-view subspace. The feature concatenation data are simultaneously used to fuse the individual views and learn the consensus matrix. Finally, the data is clustered by using spectral clustering according to the consensus matrix. We compare the proposed algorithm with its counterparts on six datasets. Experimental results verify the effectiveness of the proposed algorithm.
The electric mountain bike as pharmakon: examining the problems and possibilities of an emerging technology

In the last decade there has been an upsurge in the popularity of electric mountain bikes. However, opinion is divided regarding the implications of this emerging technology. Critics warn of the dangers they pose to landscapes, habitats, and ecological diversity, whilst advocates highlight their potential in increasing the accessibility of the outdoors for riders who would otherwise be socially and/or physically excluded. Drawing on interview data with 30 electric mountain bike users in England, this paper represents one of the first attempts to explore empirically the experiential, ecological and socio-cultural implications of this activity. Utilising Stiegler’s account of the pharmakon, in which technology is positioned as both remedy and poison, we suggest that the e-mountain bike’s role in the promotion of social and environmental responsibility is both complex and contradictory. Specifically, findings indicate that while this assistive technology can play a key role in facilitating deeper connections between riders as well as an ethic of care towards others, it can, at the same time, generate more individualised and automated experiences of recreational mobility in outdoor environments.
Research on the Path of Cultivating Innovation Ability and Legal Consciousness of Talents in Innovative Enterprises

Under the new economic perspective, talent has become an important factor promoting the development of enterprises, for innovative enterprises, innovation ability of talents is the important performance of enterprise core competitiveness, and in a new round of technological revolution and industrial revolution, how to realize the effective cultivation of talent innovation ability and legal consciousness in innovative enterprises is a problems that enterprises need to solve. Legal consciousness, as the most basic literacy requirements, plays a guiding role in the cultivation of talents. In order to achieve the cultivation of innovation ability of talents, it is necessary to base on the cultivation of talent legal consciousness, to make the enterprise talent on the premise of basic legal literacy, constantly improve its innovation ability, and then promote the efficient development of innovative enterprises.