Reviewerly/exHarmony huggingface dataset:
Foreign Language Learning Then, Now and After COVID-19: An Exploration of Digital Tools to Augment the Receptive and Productive Skills of Language Learners

ICT in education is playing a vibrant role in both formal and informal settings and has also brought in a paradigm shift from the traditional instructive approach to the modern constructive approach. This digital learning using technology has been seeing steady growth for the past two decades and with the unexpected prevailing pandemic situation, digital teaching and learning has become the order of the day. Teaching using technology was not greatly emphasized then and was not optimally integrated in physical classrooms. But since the advent of COVID-19 pandemic, it has found its active presence in language classrooms and sure can be made the future too. This article discusses the foreign language classroom in twenty-first century in general and specifically during the ongoing pandemic with suggestions for a post pandemic FL classroom. It also lists a few easy to use tools that are freely available to create reading, listening and speaking activities in both physical and online classes. It also elucidates the merits and demerits of using these tools in foreign language classrooms. Technology aided teaching creates maximum engagement in the class, meaningful and purposeful interactions and both active and passive learning for the learners. What is more significant is that the teachers’ activity design constantly evolves to empower students’ learning. It is certainly not about the latest tools, it is about the innovative activities with the known tools too [0000-0002-4949-5222].
Head Movement Detection Using Midpoint Distance For Navigation Menu

Disability is the inability of a person to perform a particular activity. One of the current disabilities is physically disabled. Physical disability is a condition where the limbs cannot carry out their functions due to abnormalities or defects in the muscle, bone, or joint system, resulting in impaired activity and mobilization. Most of the existing devices available are operated by hand, such as joysticks, remotes, menu selection on the LCD, etc. For this reason, solutions are needed that can facilitate and help people with disabilities. The solution offered to solve this problem is to create a system that can select the menu on display only by detecting the direction of head movement. Four directions can be detected, namely up, down, left, and right. Detection is done using a webcam and a facial landmark method based on midpoint distance. The accuracy of the system managed users without accessories is 82.66, and using accessories is 77.91. This is indicated by the average total accuracy obtained and computation time, 80.28 and 0.001s.
Superpixel segmentation algorithm based on local network modularity increment

Superpixel segmentation is a kind of image preprocessing technology and a popular research direction in image processing. The purpose of superpixel segmentation is to reduce the complexity of image processing. The most widely applied Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm has high operating efficiency. However, under-segmentation is prone to occur when the number of given superpixel regions is too small. In order to improve the segmentation accuracy, the superpixel segmentation algorithm based on local network modularity increment (LocalNet) from the perspective of network community detection is proposed here. The adjacency network is constructed according to the colour similarity of image pixels, the local community centre is found by the degree of network nodes, and the local network structure of the community is constructed. The modularity increment is employed as the boundary constraint to improve the segmentation accuracy of superpixel segmentation. Through the experimental comparison with the SLIC algorithm, its improved algorithm, and the algorithm proposed in recent years, the results show that our LocalNet algorithm significantly improves in segmentation accuracy Furthermore, the segmentation effect has obvious advantages under the premise that the segmentation speed is not much different from that of the other five algorithms.
A Combination of Resampling Method and Machine Learning for Text Classification on Imbalanced Data

Imbalanced data will affect the accuracy of text classification, in order to solve this issue, 11 different algorithms are used to resampling the dataset. Results show that, 5 different oversampling method and SmoteTomek method can rebalance the dataset effectively, which can improve the recognition rate of models on the minority class obviously, while undersampling methods decrease the overall accuracy of models on imbalanced dataset. Meanwhile, 7 different machine learning algorithms are used to train the model with datasets resampled by SmoteTomek algorithm, after combination, Naive Bayes and Logistic Regression algorithms performs best, they can improve the predictive ability of models on the minority class significantly without decreasing the overall accuracy of models. So in handling multi-class imbalanced text classification, Naive Bayes or Logistic Regression combined with SmoteTomek resampling method should be a preference.
VLSI implementation using fully connected neural networks for energy consumption over neurons

Increasingly, the capacity of artificial neural networks to identify and express top-level concepts in sets of data has become quite significant. Deep learning models like fully connected layer machine learning have demonstrated outstanding results in range of identification and verification applications. It's technology deployments suffered as from complexity and huge silicone areas with energy consumption. Memory accesses, the bulk from which happens in fully connected systems, comprise the energy usage of machine learning. It has many of the depths of neural network. Throughout this work, researchers suggest densely connected networks by demonstrating that there is indeed a drop in the number of links inside fully connected systems of up to 90% enhancing the quality of three popular sets of data. To decrease the memory needs have suggested low-connected grid networks, researchers present perhaps an effective hardware implementation based on the linear response registers. In comparison to the traditional design of fully connected layer neural network models, the suggested design could save up to 90 percent of storage. Findings of execution further reveal that artificial neurons of suggested weak network connections have a power consumption decrease up to 84 percent compared to one single atom of fully connected layer machine learning.
Protected attribute guided representation learning for bias mitigation in limited data

In many real-world problem settings, the training data may contain certain unwanted correlations or biases. Such biases generally occur due to imbalances/skew in the data with respect to some protected attributes. When a standard deep learning model is trained on such biased data, the model will also learn these biases and produce biased predictions. If the available training data is limited, then the impact of biases on the model predictions is magnified, and the performance of existing bias mitigation strategies is also severely affected. We propose a novel approach for mitigating bias in an image classification model at the representation level in the limited data setting. Specifically, we use the protected attribute to guide our representation learning process in the limited data setting such that the learned feature space becomes robust to the biases occurring due to the protected attribute, thereby reducing the impact of bias in the model predictions. We experimentally demonstrate that our proposed bias mitigation approach significantly reduces the model bias score and improves the model performance on seven benchmark datasets. We also demonstrate that our proposed method is also complementary to existing bias mitigation approaches and significantly reduces the bias score of the baseline, sampling, adversarial, domain discriminative, and domain-independent models by 89.97%, 90.94%, 86.74%, 83.62%, and 62.96%, respectively, on the reduced CIFAR-10S dataset. It also significantly improves the mean accuracy of the baseline, sampling, adversarial, domain discriminative, and domain-independent models by absolute margins of 18.83%, 8.32%, 11.78%, 18.74%, and 7.13%, respectively, on reduced CIFAR-10S.
An Efficient Parallelization Model for Sparse Non-negative Matrix Factorization Using cuSPARSE Library on Multi-GPU Platform

Positive or Non-negative Matrix Factorization (NMF) is an effective technique and has been widely used for Big Data representation. It aims to find two non-negative matrices W and H whose product provides an optimal approximation to the original input data matrix A, such that $$A\approx W*H$$ . Although, NMF plays an important role in several applications, such as machine learning, data analysis and biomedical applications. Due to the sparsity that is caused by missing information in many high-dimension scenes (e.g., social networks, recommender systems and DNA gene expressions), the NMF method cannot mine a more accurate representation from the explicit information. Therefore, the Sparse Non-negative Matrix Factorization (SNMF) can incorporate the intrinsic geometry of the data, which is combined with implicit information. Thus, SNMF can realize a more compact representation for the sparse data. In this paper, we study the Sparse Non-negative Matrix Factorization (SNMF). We use Multiplicative Update Algorithm (MUA) that computes the factorization by applying update on both matrices W and H. Accordingly, to address these issue, we propose a two models to implement a parallel version of SNMF on GPUs using NVIDIA CUDA framework. To optimize SNMF, we use cuSPARSE optimized library to compute the algebraic operations in MUA where sparse matrices A, W and H are stored in Compressed Sparse Row (CSR) format. At last, our contribution is validated through a series of experiments achieved on two input sets i.e. a set of randomly generated matrices and a set of benchmark matrices from real applications with different sizes and densities. We show that our algorithms allow performance improvements compared to baseline implementations. The speedup on multi-GPU platform can exceed $$11\times $$ as well as the Ratio can exceed 91%.
Sentiment Analysis on Chinese Movie Comment with LDA Model

Film has become one of the main means for modern people to relax their life, and it is also an important measure of consumer economic growth. With the continuous decline of the global movie industry, only by understanding the current development of Chinese movies and exploring the actual viewing needs of the audience can we effectively promote the development of the movie industry. This paper crawls to the specific information and reviews of popular movies, through data visualization and LDA theme analysis model, gets the general development trend of movies and audience preferences. The results show that China currently occupies most of the world movie market, but all movie practitioners still need to work hard on the reality of the movie story and create high-quality movies that meet the needs of the audience.
MGFL: Multi-granularity Federated Learning in Edge Computing Systems

As a promising machine learning framework in the big data era, federated learning (FL) allows multiple mobile devices to collaboratively train a model without transmitting raw data, thus has attracted widespread attention in both academia and industry. Considering that heterogeneous mobile devices with limited resources and data diversity are bound to impact the actual performance of some training nodes. However, conventional FL could not support collaborative training with multi-granularity neural networks. To this end, we propose multi-granularity federated learning (MGFL) that contains two mechanisms serving for same-granularity FL and cross-granularity FL. MGFL customizes a personalized model for each device by designing a divergence-based similarity measurement method in same-granularity FL. Further, it adjusts the empirical risk loss function to break the restriction of cross-granularity FL. Experimental evaluations demonstrate the positive guidance of the fine-granularity model to the coarse-granularity model, which significantly improves the performance of the coarse-granularity model. Besides, our method shows superiority on both independently identically distribution (IID) and non-IID data.
Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts

When using different cameras and displays in the shot and display of one subject, different color images are often showed. To solve this problem, we have developed a color chart in which the constituent colors dyed with dyes are evenly distributed in the color space. We have also developed a tool that creates an International Color Consortium (ICC) profile from the captured image of this color chart. In this report, we will describe the color difference correction accuracy when our method is applied to actual stained pathological specimens taken with multiple whole-slide imaging (WSI). We confirmed color correction accuracy of Lab values in major parts such as the cell nucleus. The Lab value of the specimen itself measured by a spectrocolorimeter was compared with that of the captured image. As a result, the color difference ΔE in H&E-stained cell nucleus was improved from 32.2 to 6.4 for Nanozoomer and from 13.5 to 7.2 for ultra-fast scanner (UFS) by our color correction. The results of the evaluations for other areas and other stain methods (PAS, EVG, MT, and PAM) were good. In the future, high-accuracy color correction of teacher data/evaluation data in AI diagnosis using pathological images will be important.
Deep Learning approaches for Image Dehazing

Computer visibility is a multi-sectoral systematic field concerned with how computers assist to improve high-level understanding of digital images and videos. Haze is an atmospheric natural phenomenon that hinders computer vision tasks. In the present era, image degradation is a major issue, and creating a high-quality, haze-free image remains a difficult challenge. As a consequence, the performance of many multimedia processing systems leads to poor object recognition and tracking. More haze removal algorithms have been proposed to counter this issue, which aesthetically enhances the visibility of visuals that have been deteriorated by adverse weather, hence eradicating the haze effect. Since haze is reliant on an uncertain scene depth, it is a significant challenge to resolve. This study investigated existing strategies for eradicating haze from obtained photographs. This review is designed to formulate strategies for researchers to point the appropriate direction for future advancements based on current accomplishments.
Key Information Extraction and Talk Pattern Analysis Based on Big Data Technology: A Case Study on YiXi Talks

In the attempt to extract key information and talk patterns from YiXi talks in China to realize “strategic reading” for readers and newcomers of the speaking field, text mining methods are used by this work. The extraction of key information is realized by keyword extraction using the TF-IDF algorithm to show key information of one talk or one category of talks. Talk pattern recognition is realized by manual labeling (100 transcripts) and rule-based automatic programs (590 transcripts). The labeling accuracy rate of “main narrative angle” recognition is the highest (70.34%), followed by “opening form” (65.25%) and “main narrative object”, and the “ending form” is around 50%, with the overall accuracy of the rule-based automatic recognition program for talk patterns at approximately 60%. The obtained results show that the proposed keyword extraction technology for transcripts can provide “strategic reading” to a certain extent. Mature speech mode can be summarized as follows: speakers tend to adopt a self-introducing opening format. They tell stories and experiences through a first-person narrative angle and express expectations and prospects for the future. This pattern is reasonable and can be referenced by new speakers.
An Empirical Study on the Usage and Evolution of Identifier Styles in Practice

Identifiers play an important role in helping developers comprehend and maintain source code. In practice, developers usually employ two widely-used identifier styles, i.e., snake case and camel case, to format identifiers to make them understandable and informative. Despite researchers have empirically investigated the impacts of identifier styles on code comprehension activities, the usage and evolution of identifier styles, however, have not been fully explored. How are individual identifier styles formed in practice? How would identifier styles change and evolve? What are the potential impacts of identifier style-changes? Questions like these are important but have not been fully answered yet. In this paper, we conducted an empirical study on 9,792 GitHub projects to gain some insights into these problems. Specifically, we first analyzed how different identifier styles were formed in real software projects. Next, we explored the change patterns of identifier styles along with the project evolution. Finally, we investigated the potential impacts as well as categories of identifier style-changes. Our empirical results achieved some interesting findings. For example, we first reported some identifier style-change patterns (e.g., snake case &#x2192;camel case &#x2192; snake case), which could help developers resolve style-change problems in practice. Our study also provided some hints for researchers and developers when they use specific identifier styles in programs. For example, when researchers explore the impacts of identifier styles on code comprehension, they are suggested to consider the imbalanced distribution phenomenon of individual identifier styles. Besides, it is worthwhile for developers to build an identifier style-change prediction and propagation tool to reduce the style-change costs.
Assessing Patterns of Cybercrimes Associated with Online Transactions in Namibia Banking Institutions’ Cyberspace

Advances in technology have provided substantial benefits and innovations to the world in massive ways such as ecommerce, timely business processing, connectivity, easy access to information and fast communication. Such benefits and innovations have been adopted in the banking sector, for example, mobile banking, credit cards, Internet banking as well as agency banking. Although information technology has provided substantial benefits mentioned above it, however, comes with disadvantages such as information security breaches and unsecure Information and Communication Technology environments. As a result, end-users are now connected to the Internet, and this has resulted in several internet users becoming victims of cybercriminals. This study aimed at evaluating patterns of cybercrimes associated with online transactions in the Namibian banking institutions’ cyberspace and proposes mitigation strategies. To do that, the researcher interviewed six Namibian banking institutions to understand cybersecurity issues, trends and existing solutions. The study found that cybercrimes such as phishing, social engineering, vishing, smishing, spear phishing, spoofing and website compromise, distributed denial-of-service (DDos) are rampant. This is worsened by the fact that Namibia does not have a local cybersecurity framework to guide local banks in curbing these crimes.
A Weighting Scheme for One-Shot Federated Learning

This paper focuses on one-shot aggregation of statistical estimations made across disjointed data sources for federated learning, in the context of empirical risk minimization.We exploit the role of each local sample size for this problem to develop a new one-shot weighting scheme for federated learning. First, we provide upper bounds on the local errors and biases from which we derive an upper bound for the plain federated learning parameter. Then, by casting an optimization problem based on the bias-variance decomposition of the MSE, we develop a simple and intuitive weighting scheme based only on the local sample sizes. The proposed procedure can be embedded in a wide variety of algorithms used for federated learning. Finally, we evaluate our procedure in the context of large-scale estimation of linear models with ridge regression and compare it to the typical choice of weights in federated learning. We find that, due to unbalanced sample sizes across the data sources, the proposed weighting scheme outperforms other standard ones, and that it converges faster to the performance of a centralized estimator.
Usability of a virtual reality manual wheelchair simulator

Individuals with impaired mobility often require assistance for getting around. The skilled use of a manual wheelchair (MW) is required in order to gain independence while preventing injuries. Training in a virtual reality (VR) setting allows for safe practice of MW skills in a wide range of environments. We developed a low-cost MW simulator which includes visual and haptic feedback. Our objectives were to assess the usability and fidelity of the VR simulator, by clinicians and expert MW users, and to determine whether the addition of haptic feedback would positively improve the user's experience.This mixed method study investigated the sense of presence, overall experience and ease of use of the experience in six MW users, as well as five clinicians (wheeled mobility experts) who practiced in the simulator.Participants reported a positive perception of usefulness, sense of presence, and immersion during the MiWe simulator experience. The addition of haptic feedback to the simulator significantly enhanced fidelity of the overall experience, compared to the no-feedback condition.Our low-cost simulator was well perceived by clinicians and MW users and was considered as a potentially useful tool to complement MW skill training.IMPLICATIONS FOR REHABILITATIONWe developed a low-cost, virtual reality simulator with visual and haptic feedback, for the practice of manual wheelchair skills.Expert clinicians and wheelchair users reported a positive experience after practicing in the wheelchair simulator, in terms of presence, realism and usability.Participants highlighted the potential usefulness of our low-cost simulator in the training of manual wheelchair skills.
Image Encryption Using Chaotic Map And Related Analysis

The superior breadth of data transmission through the internet is rapidly increasing in the current scenario. The information in the form of images is really critical in the fields of Banking, Military, Medicine, etc, especially, in the medical field as people are unable to travel to different locations, they rely on telemedicine facilities available. All these fields are equally vulnerable to intruders. So, to prevent such an act, encryption of these data in the form of images can be done using chaos encryption. Chaos Encryption has its long way in the field of Secure Communication. Their Unique features offer much more security than any conventional algorithms. There are many simple chaotic maps that could be used for encryption. In this paper, at first Henon chaotic maps is used for the encryption purpose. The comparison of the algorithm with conventional algorithms is also done. Finally, a security analysis for proving the robustness of the algorithm is carried out. Also, different existing and some new versions are compared so as to check whether a new combination could produce a better result. The simulation results show that the proposed algorithm is robust and simple to be used for this application. Also, found a new combination of the map to be used for the application.