Reviewerly/exHarmony huggingface dataset:
A mobile pedagogical framework for enhancing online teaching and learning in higher education

While information and communication technology has permeated education, institutions and faculty are seeking innovative pedagogical approaches to meet the demands of online teaching and learning. The aim of this study is to design a mobile pedagogical framework to enhance online teaching in higher education system. Based on the literature review and extant framework analysis, we developed a conceptual framework for mobile pedagogy. Then we conducted interviews with 25 lecturers and 4 policy makers to investigate reflections and assessments of mobile pedagogy. Thematic analysis was used to analyze the interview data. Based on the results, the mobile pedagogical framework was expanded. The revised framework provides a roadmap for faculty and institutions to enhance mobile pedagogy and online learning in the higher education.
Emotional analysis of safeness and risk perception of London and Rome railway stations during the COVID-19 pandemic

The evaluation of perceived safeness and risk by persons is particularly valuable for safety and security handling. Each individual is influenced by others' opinions on safety and security and the Internet embodies the place where these opinions are mostly pursued, acquired, and estimated. From this point of view, social networks play a considerable influential role. For this reason, Opinion Mining and Sentiment Analysis have found noteworthy uses in a variety of situations and one of the most interesting is embodied by public safety and security. The goal of this work is to examine the perception of safety and risk within the railway stations of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the railway stations of London Victoria and Waterloo and the railway stations of Rome Termini and Tiburtina were considered, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents present in Twitter.
An Accurate Matching Query Method of Natural Language Knowledge Graph Based on Hierarchical Graph Topological Sequence

In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph.Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem.However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for largescale natural language knowledge graph query.Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph.Through experiment analysis, compared with the traditional graph model matching algorithm applied in knowledge graph query, this method can quickly filter the unqualified knowledge graph candidate sets, effectively reduce the number of knowledge graph candidate sets, and make it have more advantages in matching efficiency and time performance.In addition, compared with two algorithms of GIndex and FG-Index, this method has better performance in index construction time, average size of candidate set and average running time of online update.
Recognition of human activities based on decision optimization model

Human activity recognition is a technology that uses a variety of sensors to identify and classify its activity data. Human activity recognition is widely used in the fields of medical treatment, daily health care and elderly care. Human activities are characterized by complexity and diversity, and it is difficult to clearly divide actions. The current human identification system is still extremely challenging. Therefore, we propose a decision optimization recognition model, which improves the recognition accuracy through model decision selection. The decision optimization recognition model adopts four typical statistical learning algorithms to construct a human activity recognition model. Algorithms include K-nearest neighbor algorithm (KNN), Bayesian algorithm (NB), support vector machine algorithm (SVM), and random forest algorithm (RF). The classification accuracy is used as the performance index of the algorithm to judge the recognition accuracy. The experimental results show that the recognition accuracy rate of human activities through the decision-making optimization recognition model is as high as 96.8%. Among the four typical statistical learning algorithms, the random forest algorithm has the best performance.
Deduplication of IoT Data in Cloud Storage

Distributed computing assumes a genuine job inside the business space, these days as figuring IoT, i.e., sensor data are conveyed as a utility on interest to clients over the net. Sensor storage is one among the administrations given in distributed computing that has been expanding enormously. The most favorable position of exploitation distributed storage from the client’s motivation is that clients will reduce their use in purchasing and keeping up capacity foundation though exclusively paying for the amount of IoT data asked for which might be scaled upon interest. With the developing IoT sensor data size of distributed computing, a rebate in learning volumes may encourage providers diminishing the costs of running more capacity cache and decreasing power utilization. In this manner, learning deduplications methods are dropped at enhance accumulate power in cloud cache. With the dynamic idea of IoT data in distributed storage, usage of cloud changes time to time, some sensor information pieces could likewise be measureable in time. Some sensor datasets could be used or refreshed by different clients at a comparable time, while others may have the abnormal state of high request. Due to this, it is essential to help this dynamic element in distributed storage. However, current methodologies are principally centered on static topic that restricts their full congruity in on sensor data in distributed storage. In this chapter, we tend to propose a dynamic deduplication method for distributed storage for IoT data that wanting to enhance cache power and keeping up repetition for adaptation to non-critical failure.
A novel watermarking scheme based on discrete wavelet transform‐singular value decomposition

Abstract Due to the rapid advancement in information technology, individuals as well as organizations frequently transfer or share their digital content via cyberspace. The security and integrity of this digital content become a big concern, as any middleman can misuse or temper this content. Digital watermarking is a blessing to protect it from any unwanted tampering. Even though many researchers have implemented various watermarking approaches, but still ownership protection and reliability concerns are not fully mitigated. This paper proposes an improved novel hybrid watermarking scheme by combining discrete wavelet transform (DWT) with singular value decomposition (SVD) to minimize these concerns. At first, the host image is decomposed by multi‐level DWT and a two‐level SVD is applied to the fourth lower‐level coefficient (LL5) of the host image. Then the watermark is inserted into the specified sub‐band of the host image. The watermark is extracted in a reverse manner. The hybridization of DWT and SVD ensures that the watermarking technique performs better in terms of visual distortion and robustness. The outcomes of the experiment show that our proposed method is more robust, imperceptible, and secure than the existing DWT‐SVD based methods, even with a broad watermark scale.
A Deconvolutional Deblurring Algorithm Based on Short- and Long-Exposure Images

An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing algorithms, thus realizing the restoration of degraded images. The effective priori side information provided by the short-exposure image was utilized to improve the accuracy of kernel estimation, and then increased the effect of image restoration. For the kernel estimation, a priori filtering non-dimensional Gaussianity measure (BID-PFNGM) regularization term was raised, and the fidelity term was corrected using short-exposure image information, thus improving the kernel estimation accuracy. For the image restoration, a P norm-constrained relative gradient regularization term constraint model was put forward, and the restoration result realizing both image edge preservation and texture restoration effects was acquired through the further processing of the model results. The experimental results prove that, in comparison with other algorithms, the proposed algorithm has a better restoration effect.
Color Correction Technique using an Artificial Color Board and Root-polynomial Color Correction for Smartphone-Based Urinalysis

Urinalysis is a method that uses urine test strips containing indicators in the form of reagents that will change color when reacting with certain substances in urine that have been determined. Many researchers have developed readings of color change test strips using smartphones due to being portable and easier to use. However, the color information of the obtained image is unstable due to several factors, such as environment light, sensor characteristics, and other stability factors. Color correction was performed to consistently produce color information using the Root-polynomial color correction (RPCC) algorithm based on the color reference standard in Artificial Color Board. The color correction technique was evaluated using images taken with a color temperature variation of 2500°K – 8500 °K that were recorded using Huawei Nova 5T and Samsung Galaxy A51. The results show that the RPCC method has a good and stable performance on every cellphone used with a color correction evaluation value ($\Delta$E) of 1.8 – 2.6. The results show that the artificial color board and RPCC method can also minimize color temperature so that the color measurement results remain accurate.
Data Assimilation Network for Generalizable Person Re-Identification

In this paper, a data assimilation network is proposed to tackle the challenges of domain generalization for person re-identification (ReID). Most of the existing research efforts only focus on single-dataset issues, and the trained models are difficult to generalize to unseen scenarios. This paper presents a distinctive idea to improve the generality of the model by assimilating three types of images: style-variant images, misaligned images and unlabeled images. The latter two are often ignored in the previous domain generalization ReID studies. In this paper, a non-local convolutional block attention module is designed for assimilating the misaligned images, and an attention adversary network is introduced to correct it. A progressive augmented memory is designed for assimilating the unlabeled images by progressive learning. Moreover, we propose an attention adversary difference loss for attention correction, and a labeling-guide discriminative embedding loss for progressive learning. Rather than designing a specific feature extractor that is robust to style shift as in most previous domain generalization work, we propose a data assimilation meta-learning procedure to train the proposed network, so that it learns to assimilate style-variant images. It is worth mentioning that we add an unlabeled augmented dataset to the source domain to tackle the domain generalization ReID tasks. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art domain generalization methods.
Token replacement-based data augmentation methods for hate speech detection

Abstract Hate speech detection mostly involves the use of text data. This data, usually sourced from various social media platforms, have been known to be plagued with numerous issues that result in a reduction of its quality and hence, the quality of the trained models. Some of these issues are the lack of diversity and the diminutive class of interest in the dataset which results in overfitted models that do not generalize well on other or newly collected data. The different ways of handling these issues include augmenting the data with diverse samples, engineering non-redundant features or designing robust classification models. In this study, the focus is on the data augmentation aspect. Data augmentation is a popular method for improving the quality of existing datasets by generating synthetic samples that mimic the distribution of the original samples. There is a lack of extensive studies on how hate speech texts respond to varying textual data augmentation techniques and methods. Specifically, we provide further insight into the token replacement method of textual data augmentation by performing empirical studies that investigate which embedding method(s) is a robust source of synonym for replacement process, what effective method(s) can be used to select words to be replaced, and how to confirm if the label within each class is preserved. Our proposed methods, validated on two commonly used hate speech datasets affected by a known lack of diversity and diminutive class of interest issues, significantly improve classification performance and provides insights into token replacement methods.
Rigid-aware self-supervised GAN for camera ego-motion estimation

Learning-based camera ego-motion estimation has attracted increasing attention and has made impressive improvements. However, the accuracy of unsupervised paradigm is still limited, especially in complex dynamic environment. In this paper, we propose a rigid-aware self-supervised generative adversarial network (GAN) for camera ego-motion estimation, which can effectively learn the rigidity of the scene and improve the accuracy of ego-motion estimation by combining the pixel- and the structure-level perception. Specifically, a rigid-aware generator is first designed for joint unsupervised learning of optical flow, stereo depth and camera pose from two consecutive frames. Then, an iterative pose refinement strategy with rigidity learning is presented to reduce the impact of moving objects in scenes. To overcome the limitation of the purely pixel-wised photometric methods, a rigidity mask embedded discriminator is attached to perceive structural distortion artifacts in synthesized fake images, which encourages the generator to learn additional structure-level information to improve the accuracy of pose estimation. Experiments on the benchmark datasets show that our model achieves the state-of-the-art performance in terms of both RPE and ATE compared to recent GAN-based methods.
Hybridization of the Higher Order Neural Networks with the Evolutionary Optimization Algorithms—An Application to Financial Time Series Forecasting

Multilayer artificial neural networks have characteristics of structural complexity, computational overhead and black box visualization. In contrast, higher order neural networks (HONNs) have single layer trainable weights, fast learning properties, stronger approximation, and higher fault tolerance capability. This chapter discusses about popular HONNs such as Pi-Sigma neural network (PSNN), Sigma-Pi neural network (SPNN), and functional link artificial neural network (FLANN), their architecture, learning process, and applications to financial time series forecasting. To eradicate the limitations of gradient descent-based training, we discuss few evolutionary optimization algorithms for HONN training. The hybrid models obtained are applied to forecast financial time series. Performance analysis is carried out to establish the suitability of evolutionary optimization-based HONNs.
Attention-Based Multi-level Network for Text Matching with Feature Fusion

Text matching is a basic and common task in natural language processing. Recently, deep learning has achieved excellent performance in text matching tasks. The major process of the existing model is to pass two sentences through shallow encoder and interact with each other, then only the last layer of feature representation is utilized to conduct the final matching, which lacks sufficient semantic feature extraction and sentence interaction. To address the above limitation, we design an Attention-Based Multi-level Network(ABMN) for text matching, which utilizes the multi-level interaction layer with feature fusion to obtain more refined text information across all levels. We evaluate our proposed architecture on the three public real-world datasets:SNLI, Quora, and LCQMC. Experimental results show that the proposed model achieves the state-of-the-art performance.
A Multilayered Adaptive Recurrent Incremental Network Model for Heterogeneity-Aware Prediction of Derived Remote Sensing Image Time Series

<i>Catastrophic forgetting</i> of previously acquired knowledge is a major setback suffered by the neural networks (NNs) when these are trained on tasks in sequential fashion. The NN variants, including the deep network models, as commonly used in remote sensing data prediction are also not free from this limitation. The issue becomes more prominent when the prediction is performed over data collected from spatial zones with a large degree of subregional variations or heterogeneity. In order to tackle this problem, in this article, we propose a multilayered adaptive recurrent incremental network (MARINE) model, offering a spatial heterogeneity-aware self-adaptation scheme that resolves the catastrophic forgetting issue in an autonomous manner. Typically, the proposed MARINE is equipped with an intrinsic mechanism of clustering the spatial subregions as per their heterogeneity levels and auto-constructing the recurrent network layers in an ensemble fashion so that the knowledge acquired about one group of heterogeneity level does not overwrite that acquired about other groups. With respect to spatio-temporal prediction of normalized difference vegetation index (NDVI) time series, as derived from MODIS Terra satellite remote sensing imagery, we demonstrate that our proposed MARINE achieves competitive results when compared with the state-of-the-arts. More significantly, in the presence of higher degree of spatial heterogeneity, MARINE outperforms others by better <i>avoiding the catastrophic forgetting issue</i>.
Applied sentiment analysis on a real estate advertisement recommendation model

Recently, the data generated are exploding in the information age. In the post-COVID-19 era, some real estate contracts have been signed online, and online advertisement recommendation has become a new way to reduce the searching cost. Therefore, the model in which real estate online recommendations can be made suitable without user preferences has become a tricky problem. This study uses sentiment and economic data to predict real estate sales and then made an advertisement recommendation from the forecast results. The 2SA-RERec (Two Sentiment Analysis of Real Estate Recommendation) model is proposed, which shows the highest accuracy among the others.
An empirical framework for developing and evaluating a Virtual Assembly Training System in learning factories

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Intelligent Video Surveillance Systems

In the past decades, there has been tremendous growth in security-related issues throughout the world. With such a multifold increase in demand for security, video surveillance has become a critical research area. In the most recent studies, there were many attempts to integrate image processing algorithms. It was also possible to achieve artificial intelligence capabilities using computer vision. Intelligent video surveillance systems can be described as a technique to assist security personnel by providing reliable real-time alerts, thus supporting the investigations with efficient video analysis. This paper discusses a comprehensive and systematic overview of the existing video surveillance techniques and possible enhancements.
Embedding Novel Views in a Single JPEG Image

We propose a novel approach for embedding novel views in a single JPEG image while preserving the perceptual fidelity of the modified JPEG image and the restored novel views. We adopt the popular novel view synthesis representation of multiplane images (MPIs). Our model first encodes 32 MPI layers (totally 128 channels) into a 3-channel JPEG image that can be decoded for MPIs to render novel views, with an embedding capacity of 1024 bits per pixel. We conducted experiments on public datasets with different novel view synthesis methods, and the results show that the proposed method can restore high-fidelity novel views from a slightly modified JPEG image. Furthermore, our method is robust to JPEG compression, color adjusting, and cropping. Our source code will be publicly available.
Advanced forensic procedure for the authentication of audio recordings generated by Voice Memos application of <scp>iOS14</scp>

In this study, we propose an advanced forensic examination procedure for audio recordings generated by the Voice Memos application with iPhone Operation System (iOS)14, to verify that these are the original recordings and have not been manipulated. The proposed examination procedure consists of an analysis of the characteristics of audio recordings and of the file system of the device storing the audio recordings. To analyze the characteristics of audio recordings, we compare the encoding parameters (bitrate, sampling rate, timestamps, etc.) and the file structure to determine whether audio recordings were manipulated. Next, in the device examination step, we analyze the media-log history and temporary files of the file system obtained by mobile forensic tools. For comparative analysis, a total of 100 audio recording samples were obtained through the Voice Memos application from five iPhone mobile handsets of different models with iOS14 installed using Advanced audio coding (AAC) or Apple lossless audio codec (ALAC). As a result of analyzing the encoding parameters between the original and manipulated audio recordings, as well as the temporary files contained in the device file system, the difference in the encoding parameters and the very unique trace of the original audio recordings in the temporary files were confirmed when manipulating the audio recordings. In particular, the primary advantage of our proposed method is its potential ability to recover original audio recordings that were subsequently manipulated via the temporary files examined in the device file system analysis.
End-to-End Unsupervised Document Image Blind Denoising

Removing noise from scanned pages is a vital step before their submission to optical character recognition (OCR) system. Most available image denoising methods are supervised where the pairs of noisy/clean pages are required. However, this assumption is rarely met in real settings. Besides, there is no single model that can remove various noise types from documents. Here, we propose a unified end-to-end unsupervised deep learning model, for the first time, that can effectively remove multiple types of noise, including salt & pepper noise, blurred and/or faded text, as well as watermarks from documents at various levels of intensity. We demonstrate that the proposed model significantly improves the quality of scanned images and the OCR of the pages on several test datasets.