Reviewerly/exHarmony huggingface dataset:
Sentimental Analysis of Arabic Tweets related to COVID-19 using AraBERT model

Coronavirus has affected the lives of almost everyonebelonging from every aspect of life. COVID-19 has caused depressions, anxiety, stress, fear among the people. Every countryaround the world is facing various challenges amid COVID-19 and trying to implement some measure in order to control the spread of COVID-19. Now-a-days, social media is a powerful tool and people use it for sharing their feelings and thoughts. Like people of all other counties and cultures, Arab people also sharedtheir thoughts on social media during COVID-19. However, the Arabic language is rich in morphology and has a large number of dialects. Arabic Sentimental Analysis has attracted many researchers but still the research work on Arabic Language is limited. In this research, we used AraCOV-19 data, an Arabic language dataset related to COVID, and assigned the sentiments labels to each tweets using Textblob-ar, after doing necessary pre-processing. We classify the negative and positive tweets using AraBERT model and compared the results with state-of-the- art models. The results showed that our model outperformedall other state-of-the-art models by achieving 93% and 92% accuracy for positive and negative classification respectively. Hence, such methods of sentimental analysis are very helpful in devising new policies for the control and prevention of epidemics like COVID-19.
Experimental Investigation of the Pix2pixHD Model for the Improvement of the Fairly Substantial Quality of Low-Light Images

In image synthesis, conditional generative adversarial networks (cGANs) have been investigated numerous times and are used for diverse image generation applications. In this work, the domain of low-light image enhancement that involves transforming low-light images into realistic, bright images employing a cGAN is explored. The pix2pixHD model, a cGAN, has been tested, examined, and improved qualitatively and quantitatively for low-light image enhancement. The employed quantitative metrics include both a no-reference Image Quality Assessment (NIQA) metric such as entropy and full-reference IQA (FIQA) metrics such as MSE, PSNR, SSIM, and LPIPS. Additionally, the pix2pixHD model with different combinations of loss functions is being investigated. Furthermore, the efficacy of histogram-equalized versions of input images in generating well-illuminated images is investigated by employing the lighter pix2pixHD model. The qualitative and quantitative simulation results are also compared with state-of-the-art deep learning models, and this analysis showed that pix2pixHD can challenge the most recent deep learning models for low-light image enhancement, namely MIRNet, LLFlow, and MIRNetv2, with a minimum inference time of approximately 0. 01s on GPU.
A Comparative Analysis of Visual Object Tracking Approaches &amp; Impact on Benchmarks

Visual Tracking is an approach through which a moving object can be tracked at distinct pixel points and coordinates. It is a part of machine vision technology where a machine can recognize the objects as per the patterns and track in the whole frame accordingly. There are so many challenges to recognize the object and track without any overflow. Conventional model is not able to detect the object in the entire frame with high level of accuracy. Machine learning is in trending phase where a dataset can be trained to obtain the better result in tracking the object with more preciseness. There are several researches have been done in this field but certain flaws present in the existing systems for few challenges. Each and every system has its own flaws where accuracy may degrade and system encounters more overlap. There are various benchmarks are available to test the system and obtain the result accordingly to pertain the precision rate. The intention of this paper is to find out the flaws present in the existing algorithms and their results as per the benchmark selected. There are various challenges present in the benchmarks which need to be revised with better accuracy.
Detection of payload injection in Firewall Using Machine Learning

The modern web application processes a huge amount of data to deliver customized services to the users, lack of security makes this application to becomes a victim of cyberattacks. The injection attacks line SQLI and XSS are the attacks that commonly take place. The firewall is implemented in the various layers of the OSI model to provide security for the assets. Each layer firewall uses different techniques and provides the functionality to prevent the attack. One of the important firewall designs is the Application layers firewall, which is also known as the web application firewall WAF, which is used to examine the external request, and protocols to prevent the application layer attacks such as SQLI and XSS. The XSS is a critical vulnerability of web applications. With the XSS the attacker can Steal confidential information in an unauthorized way. With the XSS attack, the attacker set up phishing sites on the websites. In this paper, we are discussing how the application layer firewall is detecting the malicious request patterns of the payloads using the application of machine learning algorithms. Keywords- Supervision, Firewall, XSS, Machine learning, SQLI
Research Progress of Machine Learning in Software Defect Prediction

Software defect prediction is of great significance to ensuring software security. Appropriate metrics are an effective way to predict whether a program contains software flaws. In recent years, machine learning techniques have been widely used to measure programs to predict software defects and have achieved good results. In order to clarify the research progress of software defect prediction based on machine learning, this paper reviews the research results in this field in the past three years from two aspects: within-project defect prediction and cross-project defect prediction. Firstly, the software defect prediction framework based on machine learning and its development are introduced. Secondly, this paper summarizes the research results in the field of defect prediction in the past three years from two aspects: within-project defect prediction and cross-project defect prediction. Finally, based on the research results so far, the status and hot spots of machine learning in software defect prediction are discussed, which provides possible help for the development direction of machine learning in software defect prediction.
Performance Analysis of Large Scale Machine Learning Optimization Algorithms

optimization methods effectively solve challenging machine learning problems. optimization is the numerical computation of the system-designed parameter to make predictions on big data. Machine learning algorithms train the machine to make high-quality predictions. optimization algorithms are used to find the optimal solution for a given objective function. This article provides a review of some of the optimization algorithms. The article also highlights the need for optimization algorithms on large-scale data. We provide a Comparative analysis of the Stochastic and Batch Gradient optimization method. Simulation results verify that both methods can provide an optimal solution. The simulation result also shows that the stochastic gradient method achieves an initial fast convergence, but its performance slows down after some iteration. We also investigate the effect of learning rate on both methods and conclude that both methods with variable learning rates achieve faster convergence than the fixed learning rate. The stochastic gradient method is also computationally more efficient as it computes the gradient on a single sample instead of the whole training data set.
Comparative Analysis on Median Filter Categories for Accurate Noise Removal

The study of how to filter out noise in images is gaining popularity in the field of image processing. Images may become distorted because of a shift in pixels, a change in lighting, or a lack of contrast, rendering them useless. For this reason, eliminating impulse noise is a crucial step in any image processing workflow. Image details are obscured by noise. In order to improve image quality, it is essential to filter out background noise without damaging the original data. Preprocessing in image editing often includes removing noise. Filters based on the median are commonly used to get rid of noise at such high frequencies. In this analysis, we compare the effectiveness of the standard median filter, median filtering filters, and trying to switch adaptive median filters at eradicating noise from digital images. Finally, we provide a comprehensive comparison of these algorithms and discuss many exciting avenues for further study. The effectiveness of SSIM and PSNR was tested and evaluated extensively.
Construction and Implementation of Hierarchical Teaching Model based on Fast Clustering Algorithm

In view of the individual differences in student groups, the traditional teaching model cannot meet the needs of students’ all-round development. Stratified teaching focuses on individual differences and learning needs of students, and makes targeted teaching plans and objectives, which is conducive to the improvement of the overall teaching effect. By establishing the difference model in stratified teaching and taking the computer network course as an example, the hierarchical teaching model based on the fast-clustering algorithm (K-means) is designed to achieve the hierarchical learning of students. The simulation experiment shows that the hierarchical teaching model based on the fast-clustering algorithm is more scientific and reasonable than the hierarchical method of expert classification algorithm and student total score ranking, and can provide reference for other disciplines.
Digital learning and public policy in schools: A transformative paradigm for a changing world

This study explores models of digital learning in public educational institutions, focusing on good practices aimed at bringing about instrumental and methodological change, and encouraging the adoption of new values and greater social justice. It identifies challenges and opportunities to propose a dynamic model of public policy on digital education. Eighteen public educational institutions associated with good digital practices in Andalusia (Spain) participated in the study. Using a descriptive method with a survey design, three questionnaires were administered at the institutions. The results confirm the importance of collective practice in encouraging the introduction of digital technologies in educational institutions. The conclusions show that a participatory culture and innovative teaching initiatives can facilitate the digital integration, on which the proposed policy model is based. The study proposes a policy model based on how participatory culture and innovative teaching initiatives can facilitate digital integration in schools. This dynamic, reflexive policy model proposed can be adapted to emerging social changes and used by policymakers, management staff and teachers in primary and secondary education.
Climate System Ontology: A Formal Specification of the Complex Climate System

Modeling the climate system requires a formal representation of the characteristics of the system elements and the processes that change them. The Climate System Ontology (CSO) represents the semantics of the processes that continuously cause change at component and system levels. The CSO domain ontology logically represents various links that relate the nodes in this complex network. It models changes in the radiative balance caused by human activities and other forcings as solar energy flows through the system. CSO formally expresses various processes, including non-linear feedbacks and cycles, that change the compositional, structural, and behavioral characteristics of system components. By reusing the foundational logic of a set of top- and mid-level ontologies, we have modeled complex concepts such as hydrological cycle, forcing, greenhouse effect, feedback, and climate change in the ontology. This coherent, publicly available ontology can be queried to reveal the input and output of processes that directly impact the system elements and causal chains that bring change to the whole system. Our description of best practices in ontology development and explanation of the logics that underlie the extended upper-level ontologies help climate scientists to design interoperable domain and application ontologies, and share and reuse semantically rich climate data.
Arbutus Biopharma Corp. v. ModernaTX, Inc. <i>United States Court of Appeals of the Federal Circuit, 2023 65 F.4th 656</i>

Biotechnology Law ReportVol. 42, No. 4 Arbutus Biopharma Corp. v. ModernaTX, Inc. United States Court of Appeals of the Federal Circuit, 2023 65 F.4th 656Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.Search for more papers by this authorPublished Online:7 Aug 2023https://doi.org/10.1089/blr.2023.29311.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 4Aug 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Arbutus Biopharma Corp. v. ModernaTX, Inc. United States Court of Appeals of the Federal Circuit, 2023 65 F.4th 656.Biotechnology Law Report.Aug 2023.221-226.http://doi.org/10.1089/blr.2023.29311.cipPublished in Volume: 42 Issue 4: August 7, 2023Online Ahead of Print:May 30, 2023PDF download
Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT

Knowledge graphs play an important role in the field of knowledge management by providing a simple and clear way of expressing complex data relationships. Injection molding is a highly knowledge-intensive technology, and in our previous research, we have used knowledge graphs to manage and express relevant knowledge, gradually establishing an injection molding industrial knowledge graph. However, the current way of retrieving knowledge graphs is still mainly through programming, which results in many difficulties for users without programming backgrounds when it comes to searching a graph. This study will utilize the previously established injection molding industrial knowledge graph and employ a BERT (Bidirectional Encoder Representations from Transformers) fine-tuning model to analyze the semantics of user questions. A knowledge graph will be retrieved through a search engine built on the Transformer Encoder, which can reason based on the structure of the graph to find relevant knowledge that satisfies a user’s questions. The experimental results show that both the BERT fine-tuned model and the search engine achieve an excellent performance. This approach can help engineers who do not have a knowledge graph background to retrieve information from the graph by inputting natural language queries, thereby improving the usability of the graph.
Composing Cagean silence

In this article I model John Cage's pragmatics of silence using the mathematics of category theory with the framework of ontological logs. I use this approach in order to represent knowledge within Cage's so-called silent compositions – 4′33′′, 0′00′′ (4′33′′ No. 2), and One3 = 4′33′′ (0′00′′) + . I first generate a categorical schema F and an accompanying database that describes an instance of 4′33′′ from its premiere in 1952 which is translated via two functors into the categorical schema Z (0′00′′) and O (One3). A pushout of Z and O along F allows for the presentation of the categorical schema S and its accompanying ontological log as an enfolding of Cage's pragmatics of silence. I then introduce the score Listening to John Cage listening in order to conceptualize the category of instances S-Set, and further utilize it to investigate the language of S through its fibre order. I conclude by reporting on persistent spatio-temporal structures of Cagean silence embodied in S as meta-work.
Optimize to generalize in Gaussian processes: An alternative objective based on the Rényi divergence

We introduce an alternative closed form lower bound on the Gaussian process ($\mathcal{GP}$) likelihood based on the R\'enyi $\alpha$-divergence. This new lower bound can be viewed as a convex combination of the Nystr\"om approximation and the exact $\mathcal{GP}$. The key advantage of this bound, is its capability to control and tune the enforced regularization on the model and thus is a generalization of the traditional variational $\mathcal{GP}$ regression. From a theoretical perspective, we provide the convergence rate and risk bound for inference using our proposed approach. Experiments on real data show that the proposed algorithm may be able to deliver improvement over several $\mathcal{GP}$ inference methods.
Amgen Inc. v. Sandoz Inc. <i>United States Court of Appeals of the Federal Circuit, 2023 No. 2022-1147, 2023 WL 2994166</i>

Biotechnology Law ReportVol. 42, No. 4 Case in PointAmgen Inc. v. Sandoz Inc. United States Court of Appeals of the Federal Circuit, 2023 No. 2022-1147, 2023 WL 2994166Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.Search for more papers by this authorPublished Online:7 Aug 2023https://doi.org/10.1089/blr.2023.29310.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 4Aug 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Amgen Inc. v. Sandoz Inc. United States Court of Appeals of the Federal Circuit, 2023 No. 2022-1147, 2023 WL 2994166.Biotechnology Law Report.Aug 2023.210-220.http://doi.org/10.1089/blr.2023.29310.cipPublished in Volume: 42 Issue 4: August 7, 2023Online Ahead of Print:May 30, 2023PDF download
Zarubezhnyi opyt professional'noi podgotovki budushchego pedagoga s pozitsii prosotsial'nogo podkhoda

The author's understanding of the essence, basic ideas and principles of the pro-social approach in pedagogical science is clarified, the international experience in organizing the professional training of future teachers and practicing teachers in different countries is analyzed from the standpoint of the methodology of the pro-social approach. The experience of the USA, Canada, Finland, Sweden and China in the context under consideration is studied, conclusions are drawn about the variability, flexibility of goals, content, organization and development prospects of professional teacher education, taking into account its pro-social orientation.
Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research

Bitcoin and other cryptocurrencies are the alternative and speculative digital financial assets in today's growing fintech economy. Blockchain technology is essential for ensuring ownership of bitcoin, a decentralized technology. These coins display high volatility and bubble-like behavior. The widespread acceptance of cryptocurrencies poses new challenges to the corporate community and the general public. Currency market traders and fintech researchers have classified cryptocurrencies as speculative bubbles. The study has identified the bitcoin bubble and its breaks during the COVID-19 pandemic. From 1st April 2018 to 31st March 2021, we used high-frequency data to calculate the daily closing price of bitcoin. The prophet model and Arima forecasting methods have both been taken. We also examined the explosive bubble and found structural cracks in the bitcoin using the ADF, RADF, and SADF tests. It found five multiple breaks detected from 2018 to 2021 in bitcoin prices. ARIMA(1,1,0) fitted the best model for price prediction. The ARIMA and Facebook Prophet model is applied in the forecasting, and found that the Prophet model is best in forecasting prices.
A Novel Approach for Classification of Real Time Data Stream to Reduce Query Processing Time

The difficulty of data analysis and decision-making becomes more difficult as the volume of data grows. To put it another way, processing big amounts of data necessitates many resources to analyze and provide a result. Big data is a term that refers to an environment that is used to process enormous amounts of data and analyze them. However, the query answer is generated with a significant delay if the traffic is sluggish, and the data block size is large. To optimize the delayed response, considerable effort must be made to improve the performance of large data systems. This paper proposes a method based on streaming data mining for overcoming this delayed data response. The proposed technique contributes to live twitter stream collection, data pre-processing and translation of unstructured data into structured data features, and data stream classification utilizing the group learning concept for streamed text data. Even when a single pattern appears for query processing, this method optimizes query processing speed and generates responses in less time.
Sampling Imbalanced Data for Multilingual Machine Translation: An Overview of Techniques

This work presents an overview of methods which alleviate data imbalance problem in Multilingual Neural Machine Translation (MNMT). The idea of MNMT is to build a single model for many translation directions instead of building separate models for each direction. In this work, the methods are divided into two groups (static and dynamic methods), compared with each other, and analyzed in terms of performance. Static methods are defined as those which have a fixed sampling strategy, whereas dynamic methods adjust the sampling strategy based on the current state of model training. Analysis shows that both static and dynamic methods are able to improve translation quality, especially for low-resource directions. Static methods are simpler but sensitive to the hyperparameter choice. As the overview demonstrates, dynamic methods in general outperform static methods.
Euler Transformation Axis Method for Online Virtual Trail Room Using Fusion of Images

In recent days, most people prefer online shopping rather than going to the shop and buying goods. While buying clothes on E-commerce sites, customers don’t have an overview of how the clothing looks on them, but trail rooms are available in shops. A new software application was developed using OpenCV named Online Virtual Trail Room (OVTR) to explain how the dress looks on the customer who purchases online. This paper aims to create an OVTR proposing a new Euler Transformation axis algorithm that doesn’t require a web camera or front camera. The proposed methodology uses the Viola and Jones method for face detection and eyes detected in the ROI of the detected face. The proposed virtual trail room fusion method can be implemented in any web based platform using ordinary mobile camera and avoids the need of complex systems like 3D rendering or Augmented reality.