Reviewerly/exHarmony huggingface dataset:
Management of Digital Transformation in Education: Modern Scientific Discourse

The article considers the scientific method of implementation and management of digital transformation of higher education, including technological, methodological and economic aspects. The problem field of introduction of new digital models and introduction of managerial experience is considered.Recent research proves the separation of digital transformation as a new area of scientific interest for management development researchers, as well as a new research paradigm. This transformation is undoubtedly interesting and promising, as it contains many aspects and components. The main reasons for slow implementation are the complexity of the problem, the lack of data for decision-making and the uncertainty of economic impact in the future. In the educational environment, this process is slower, perhaps due to fears that such transformation processes will disrupt the traditional academic model that has developed over many years and has been relevant and effective in an era of industrial growth for many years.The relevance of further research is confirmed by the fact that the process of forming a digital market for educational services is not complete. On the one hand, transformation significantly depends on the systematic process of education reform, the availability of clear institutional and public policies. On the other hand, the success of the continued existence and development of educational institutions in the post-pandemic environment will depend on changes in the standard academic paradigm and the introduction of new digital services in combination with quality academic education. Building and implementing new forms and models of education will require managers to take a comprehensive approach and rethink existing norms and standards of governance. JEL classification: O32, O33
Analysis of Arithmetic and Huffman Compression Techniques by Using DWT-DCT

In the recent era, digital contents are exchanging over the internet and it has increased exponentially.Sometimes, we need small sizes to share the real world, because of narrow bandwidth.Hence, the data compression concept came in limelight to utilize the storage capacity and available bandwidth efficiently.This paper presents an analysis of Arithmetic and Huffman compression techniques based on a hybrid combination of the DWT-DCT techniques.The input image is decomposed up to the 3 rd level by using the DWT and then Arithmetic & Huffman coding is applied separately on quantized sub-bands on 2 nd as well as 3 rd level coefficients from approximation subbands to get a high compression ratio and high peak signal-to-noise ratio values.On the third level approximation subband, the DCT method is applied to reduce the blocking effect.Simulation results show that the Arithmetic coding exhibits higher CR than Huffman coding, but smaller PSNR values.
Efficient and reconfigurable reservoir computing to realize alphabet pronunciation recognition based on processing-in-memory

With its high energy efficiency and ultra-high speed, processing-in-memory (PIM) technology is promising to enable high performance in Reservoir Computing (RC) systems. In this work, we demonstrate an RC system based on an as-fabricated PIM chip platform. The RC system extracts input into a high-dimensional space through the nonlinear characteristic and randomly connected reservoir states inside the PIM-based RC. To examine the system, nonlinear dynamic system predictions, including nonlinear auto-regressive moving average equation of order 10 driven time series, isolated spoken digit recognition task, and recognition of alphabet pronunciation, are carried out. The system saves about 50% energy and requires much fewer operations as compared with the RC system implemented with digital logic. This paves a pathway for the RC algorithm application in PIM with lower power consumption and less hardware resource required.
Personalizing Type-Based Facet Ranking Using BERT Embeddings

In Faceted Search Systems (FSS), users navigate the information space through facets, which are attributes or meta-data that describe the underlying content of the collection. Type-based facets (aka t-facets) help explore the categories associated with the searched objects in structured information space. This work investigates how personalizing t-facet ranking can minimize user effort to reach the intended search target. We propose a lightweight personalisation method based on Vector Space Model (VSM) for ranking the t-facet hierarchy in two steps. The first step scores each individual leaf-node t-facet by computing the similarity between the t-facet BERT embedding and the user profile vector. In this model, the user’s profile is expressed in a category space through vectors that capture the users’ past preferences. In the second step, this score is used to re-order and select the sub-tree to present to the user. The final ranked tree reflects the t-facet relevance both to the query and the user profile. Through the use of embeddings, the proposed method effectively handles unseen facets without adding extra processing to the FSS. The effectiveness of the proposed approach is measured by the user effort required to retrieve the sought item when using the ranked facets. The approach outperformed existing personalization baselines.
Super-Resolution Model Quantized in Multi-Precision

Deep learning has achieved outstanding results in various tasks in machine learning under the background of rapid increase in equipment’s computing capacity. However, while achieving higher performance and effects, model size is larger, training and inference time longer, the memory and storage occupancy increasing, the computing efficiency shrinking, and the energy consumption augmenting. Consequently, it’s difficult to let these models run on edge devices such as micro and mobile devices. Model compression technology is gradually emerging and researched, for instance, model quantization. Quantization aware training can take more accuracy loss resulting from data mapping in model training into account, which clamps and approximates the data when updating parameters, and introduces quantization errors into the model loss function. In quantization, we found that some stages of the two super-resolution model networks, SRGAN and ESRGAN, showed sensitivity to quantization, which greatly reduced the performance. Therefore, we use higher-bits integer quantization for the sensitive stage, and train the model together in quantization aware training. Although model size was sacrificed a little, the accuracy approaching the original model was achieved. The ESRGAN model was still reduced by nearly 67.14% and SRGAN model was reduced by nearly 68.48%, and the inference time was reduced by nearly 30.48% and 39.85% respectively. What’s more, the PI values of SRGAN and ESRGAN are 2.1049 and 2.2075 respectively.
A TPACK-in Practice Model for Enhancing EFL Students’ Readiness to Teach with Ed-Tech Apps

&lt;p class="0abstract"&gt;In Indonesia, educational technology courses have been offered in teacher preparation programs to equip students with theoretical knowledge and practical skill of utilizing technology in skill-based language teaching. Despite this, empirical studies focused on online training for EFL teacher candidates on using digital tools appropriately for pedagogical purposes and adjusting to traditional learning processes remain unexplored. Therefore, to address this gap, the present study attempts to investigate the design and implementation of a TPACK-in practice model to enhance students’ preparedness to face the rapidly-growing digital world and, more importantly, understand post-pandemic pedagogy. For this purpose, a qualitative case study was adopted. The data were obtained from multiple sources, including field notes, video-recorded classroom observation, one-on-one online interviews, and task documentation. The population of this research included 163 students in the 5&lt;sup&gt;th&lt;/sup&gt; semester of the English Language Education Study Program of a university in &lt;em&gt;Yogyakarta&lt;/em&gt;, Indonesia. We selected one class randomly, consisting of 40 pre-service English teachers during the odd semester of the 2020/2021 Academic Year. Findings demonstrate: 1) lesson activity-specific model in the educational technology course, 2) students’ reports on their hands-on learning experiences in designing teaching-learning activities with a variety of digital tools, 3) the perceived benefits of reviewing ed-tech apps, and 4) challenges encountered by students in completing their project. Finally, pedagogical implications are presented with proposed suggestions offered for future research directions in replicating the study.&lt;/p&gt;
A Low Redundancy Wavelet Entropy Edge Detection Algorithm

Fast edge detection of images can be useful for many real-world applications. Edge detection is not an end application but often the first step of a computer vision application. Therefore, fast and simple edge detection techniques are important for efficient image processing. In this work, we propose a new edge detection algorithm using a combination of the wavelet transform, Shannon entropy and thresholding. The new algorithm is based on the concept that each Wavelet decomposition level has an assumed level of structure that enables the use of Shannon entropy as a measure of global image structure. The proposed algorithm is developed mathematically and compared to five popular edge detection algorithms. The results show that our solution is low redundancy, noise resilient, and well suited to real-time image processing applications.
IOT based Smart Healthcare Wheelchair for Independent Elderly

Notwithstanding adding to the presentation of imaginative innovations and arising business openings in numerous business sectors, improvements in innovation have since brought down labor force requests and working expenses. Likewise, the nature of administrations offered has been upgraded. In this way, as a key empowering innovation for a wide assortment of medical care applications, particularly for the older and handicapped, the Internet of Things (IoT) has as of late acquired extraordinary footing. While IoT-based advancements have started to help the old and incapacitated in numerous parts of their lives and occupations, and IoT helps support the nature of living for the old and crippled, the volume of IoT information produced has risen essentially and surpassed assumptions. An expected option for following their wellbeing status is to make a brilliant wheelchair-based wellbeing checking framework, since it is satisfactory for a more extensive assortment of watchers and, in contrast to wearable gadgets, doesn't need a great deal of support. Shrewd wheelchairs center on the versatility of the unit, yet in addition on observing the patient's prosperity. The point of the current work is to fabricate a shrewd detecting wheelchair through the establishment of sensors inside its structures. The innovation grasped is the Internet of Things, where sensors screen the heartbeat and blood oxygen levels, measure them through implanted frameworks, and move them to the cloud to initiate a trigger in case of any anomaly. The made trigger can be as a SMS or email.
The Construction of Basketball Training System Based on Motion Capture Technology

Motion capture is a cross-cutting application field developed in recent years, which comprises electronics, communications, control, computer graphics, ergonomics, navigation, and other disciplines. The accurate application of basketball technical movements in the basketball game is very important. Therefore, it is of great significance to capture and standardize athletes' movements and improve their training. Unfortunately, there are numerous issues in traditional classroom teaching that largely helps to train the athletes. To solve the issues of traditional basketball classroom teaching, a virtual simulation system for students' sports training is designed in this paper. Firstly, the information of basketball dribbling movement is captured and simulated in three dimensions. Secondly, we compare it with the standard database to judge the irregularities of athletes' movements, and carry out digital processing on athletes' movements and skill improvements statistics in combination with system functions. Thirdly, we set up a gradual training cycle. Finally, the Kinect-based capture technology is adopted to obtain the activity information of different joints of the human body. Through processing the motion data, relevant motion analysis data are fed to the established motion model, to realize the comparative analysis of motion pictures. In our experiments, we observed better training of the physical education.
Partial Differential Equations-Based Iterative Denoising Algorithm for Movie Images

Film video noise can usually be defined as the error information visible on the video image, caused by the digital signal system. This distortion is inevitably present in the video obtained by various camera equipment. Noise reduction techniques are important preprocessing processes in many video processing applications, and its main goal is to reduce the noise contained in a video image while preserving as much of its edge and texture information as possible. In this paper, we describe in detail the principles of the space-time noise reduction filter, propose a 3D-filter algorithm for Gaussian noise, an improved 3D-filter algorithm based on the 3D-BDP (bloom-deep-split) filter for mixed noise, and a filter algorithm for luminance and color noise in low-brightness scenes. By dissecting the partial differential equation (PDE) denoising process, we establish a new iterative denoising algorithm. The partial differential equation method can be considered as the iterative denoising of the filter, and the first stage of the new algorithm uses wavelet-domain adaptive Wiener filter as the filtering base and achieves good results by adjusting the parameters. The proposed model in this paper is compared with the existing denoising model, and the analysis results show that the model proposed in this section can effectively remove multiplicative noise. The experimental report shows that the parameters set by the algorithm have some stability and can achieve good processing results for multiple images, which is an advantage over the partial differential equation method for denoising. The second stage of the algorithm uses the appropriate partial differential equation method to remove the pseudo-Gibbs in the first stage, which further improves the performance of the algorithm. After the image containing Gaussian noise is processed by the new algorithm, the pseudo-Gibbs effect, which often occurs in wavelet denoising, is eliminated, and the step effect, which occurs in partial differential equation denoising, is avoided; the details are better preserved, and the peak signal-to-noise ratio is improved, and a large number of experiments show that it is an effective denoising method.
Sentiment Analysis in Poems in Misurata Sub-dialect

Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.
Web Crawler and Web Crawler Algorithms: A Perspective

A web crawler is also called spider. For the intention of web indexing it automatically searches on the WWW. As the W3 is increasing day by day, globally the number of web pages grown massively. To make the search sociable for users, searching engine are mandatory. So to discover the particular data from the WWW search engines are operated. It would be almost challenging for mankind devoid of search engines to find anything from the web unless and until he identifies a particular URL address. A central depository of HTML documents in indexed form is sustained by every search Engine. Every time an operator gives the inquiry, searching is done at the database of indexed web pages. The size of a database of every search engine depends on the existing page on the internet. So to increase the proficiency of search engines, it is permitted to store only the most relevant and significant pages in the database.
I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system

Gaze estimation problem can be addressed using either model-based or appearance-based approaches. Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. Recently, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. However, many appearance- based gaze estimation systems perform well in within-dataset validation but fail to provide the same degree of accuracy in cross-dataset evaluation. Hence, it is still unclear how well the current state-of-the-art approaches perform in real-time in an interactive setting on unseen users. This paper proposes I2DNet, a novel architecture aimed to improve subject- independent gaze estimation accuracy that achieved a state-of-the-art 4.3 and 8.4 degree mean angle error on the MPIIGaze and RT-Gene datasets respectively. We have evaluated the proposed system as a gaze-controlled interface in real-time for a 9-block pointing and selection task and compared it with Webgazer.js and OpenFace 2.0. We have conducted a user study with 16 participants, and our proposed system reduces selection time and the number of missed selections statistically significantly compared to other two systems.
Semi-Supervised Video Object Segmentation Based on Local and Global Consistency Learning

Due to the variety of video types and different quality in the Internet, it brings more challenges to video processing algorithms such as video object segmentation. Most existing video object segmentation methods rely on modules in other fields as an additional structure of the segmentation model. The combination of modules can improve the accuracy of the model, but it will also reduce the algorithm speed. This paper proposes a semi-supervised video object segmentation method based on local and global consistency learning, which does not rely on additional structures to achieve fast segmentation. First, we extract the embedding features of the image based on GhostNet which is the lightweight network. By using the embedded features of pixels, the graph model is established based on the similarity between pixels. Second, we adopt the local-global consistency learning framework to construct the label conduction model. Third, to optimize the memory occupation and inference speed of the model, we propose a sampling strategy for reference frames by considering local and global information. Finally, we establish a high-speed monitoring video dataset to verify the practical application effect of the method. Ours method achieves a result of 69.5% J&F mean with 46 FPS on DAVIS 2017 dataset. At the same time, this paper constructed a high-speed monitoring video dataset. The algorithm obtained 68.2% J&F on this dataset, indicating that the method has good generalization and robust performance in practical applications.
A Novel Luminance-Based Algorithm for Classification of Semi-Dark Images

Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?

Warning: this paper contains content that may be offensive or upsetting.In this paper, we investigate what types of stereotypical information are captured by pretrained language models.We present the first dataset comprising stereotypical attributes of a range of social groups and propose a method to elicit stereotypes encoded by pretrained language models in an unsupervised fashion.Moreover, we link the emergent stereotypes to their manifestation as basic emotions as a means to study their emotional effects in a more generalized manner.To demonstrate how our methods can be used to analyze emotion and stereotype shifts due to linguistic experience, we use fine-tuning on news sources as a case study.Our experiments expose how attitudes towards different social groups vary across models and how quickly emotions and stereotypes can shift at the fine-tuning stage.
Viola: a structural variant signature extractor with user-defined classifications

Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data.Viola is available on pip (https://pypi.org/project/Viola-SV/) and the source code is on GitHub (https://github.com/dermasugita/Viola-SV).Supplementary data are available at Bioinformatics online.