Reviewerly/exHarmony huggingface dataset:
Natural language understanding challenges for sentiment analysis tasks and deep learning solutions

&lt;span&gt;When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user's likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don't have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) or opinion mining (OM) is an algorithmic method for automatically determining the polarity of comments and reviews based on their content. Emotional intelligence relies on text categorization to work. In the age of big data, there are countless ways to use sentiment analysis, yet SA remains a challenge. As a result of its enormous importance, sentiment analysis is a hotly debated topic in the commercial world as well as academic circles. When it comes to sentiment analysis tasks and text categorization, classical machine learning and newer deep learning algorithms are at the cutting edge of current technology.&lt;/span&gt;
REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale

Recommendation systems are increasingly modelled as a sequential decision making process, where the system decides which items to recommend to a given user. Each decision to recommend an item or slate of items has a significant impact on immediate and future user responses, long-term satisfaction or engagement with the system, and possibly valuable exposure for the item provider.
Kajian Estetika Pada Kaligrafi Dan Ornamen Di Masjid Al Ikhlas Lubuk Pakam Deli Serdang

The purpose of this study was to "know the aesthetic value of calligraphy and ornament."The research method used was descriptive qualitative method.The population in this study consisted of 10 works of different sizes.The research sample was taken using a total sampling technique, namely by taking a total sample of 10 works.The data collection techniques used were observation, interviews, and documentation.The main instruments in the research were obtained by themselves by using tools such as cameras and stationery.Data analysis techniques used to collect data, prepare researched data, review interview data, make coding, compile reports or conclusions. The results of this study indicate that (1) Calligraphy The calligraphy work done by the Nun Walqolam team is a very good and very beautiful calligraphy work, writing with very good letters as well as in the preparation of the verse in accordance with the theory of IbnMuqlah who applies 4 pillars to get the order.good layouts are Tarsif, Ta'lif, Tastir, and Tansil.(2) The colors applied to this calligraphy work have shades of green and yellow and orange.The colors in this work are green, yellow and orange, but the colors are varied with the dark and bright colors (value) such as green has 4 color levels, yellow has 2 levels and orange has 3 color levels.
Interactively Providing Explanations for Transformer Language Models

Transformer language models (LMs) are state of the art in a multitude of NLP tasks.Despite these successes, their opaqueness remains problematic, especially as the training data might be unfiltered and contain biases.As a result, ethical concerns about these models arise, which can have a substantial negative impact on society as they get increasingly integrated into our lives [1].Therefore, it is not surprising that a growing body of work aims to provide interpretability and explainability to black-box LMs [2]: Recent evaluations of saliency or attribution methods [3,4] find that, while intriguing, different methods assign importance to different inputs for the same outputs, thus encouraging misinterpretation and reporting bias [5,6].Moreover, these methods primarily focus on post-hoc explanations of (sometimes spurious) input-output correlations.Instead, we emphasize using (interactive) prototype networks directly incorporated into the model architecture and hence explain the reasoning behind the network's decisions.
IMAGE CAPTIONING

Image captioning using artificial intelligence is a growing trend. This is formally known as automatic image annotation- a process by which a computer system will generate an automatic textual description to a corresponding image by making use of NLP (Natural Language Processing) and Computer Vision. In today’s day and age where visual data is abundant, Image Captioning plays a significant role in various applications and in recent years, it has gained a lot of study attention in the field of cognitive computing. This research paper contains the analysis and comparison of a few common and efficient image caption generation methods.
Gesture Control Environment Using Sixth Sense Technology

This study proposes the creation of an interface that combines existing technologies such as the Internet of Things, image processing, and sixth sense etc., reducing the hardware constraints levied on users and improving system responsiveness. Sixth Sense technology is a gadget that uses image processing and hand gesture detection strategies to seize, we can also zoom in and zoom out. The implementation of this type of picture processing and recognition of gesture the usage of MATLAB is almost proven. “Sixth Sense/WUW (wear your world)” makes use of gesture recognition, augmented reality, computer vision, and radio frequency identification to develop Sixth Sense technology. Mapping may be done anywhere using the zoom in and zoom out features. The camera also allows the manipulator to capture images of the division they are observing and then assemble them on any superficial or surface. The device has a wide range of applications whereas it is portable, movable, and may be worn around the neck. This concludes that we are carrying a world which is digital with us and we can catch pictures anytime anywhere by just doing some simple gestures. Key Words: Hand gesture detection, Sixth sense technology, Internet of Things, Image processing, artificial neural networks (ANN), augmented reality.
An undercomplete autoencoder for denoising computational 3D sectional images

We developed a deep stacked undercomplete autoencoder (i.e., supervised) network to denoise the noisy 3D sectional images. Results demonstrate the feasibility of our proposed model in terms of peak-signal-to-noise ratio.
The Effectiveness of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>M</mi> </mrow> <mrow> <mi>G</mi> <mi>K</mi> </mrow> </msub> </math> Measure against the Odds Ratio in the Epidemiological Study

In epidemiology, the rule of association is used to determine the factors at the origin of diseases; implicative statistical analysis is thus a necessary tool in epidemiology too. Epidemiologists have more often chosen the so-called odds ratio measure in their studies of the quantification of the implicit link between an exposure and disease. In order to obtain good results, we need to be sure that the odds ratio measure is really the most relevant measure available. Therefore, it is necessary to study the mathematical properties of the odds ratio. This paper proposes a comparative study of the behaviour and mathematical properties of the odds ratio measure, the measure of Guillaume–Khenchaff (MGK), and the normalised odd-ratio measure. We have chosen the MGK measure because the literature considers it to be a good measure for extracting implicit association rules according to its mathematical properties. The result in this paper concerns only the study of probabilistic data.
Cognitive level classification on information communication technology skills for blog

&lt;span lang="EN-US"&gt;Learners can study and update their knowledge continually due to the rapid growth of online content. The Medium blog is a well-known open platform that encourages authors who want to share their experiences to publish content on various topics in multiple languages. Meanwhile, readers can query interesting content by searching for a related topic. However, finding suitable content is still challenging for learners, especially information communication technology (ICT) content in Thai, and needs to be classified into beginner, intermediate, and advanced cognitive levels. Moreover, ICT blog content is usually a mix of Thai language and technical terms in English. To overcome the challenge of content classification, a deep neural network (DNN) classification model was constructed to classify the ICT content from the Medium blog into three levels based on cognition. We examined and compared the classification results with strong baseline models, including logistic regression, multinomial naïve bayes, support vector machine (SVM), and multilayer perceptron (MLP). The experimental results indicate that the proposed DNN model attained the highest accuracy (0.878), precision (0.882), recall (0.878), and F1-score (0.875). &lt;/span&gt;
Progressive 3D biomedical image registration network based on deep self-calibration

Three dimensional deformable image registration (DIR) is a key enabling technique in building digital neuronal atlases of the brain, which can model the local non-linear deformation between a pair of biomedical images and align the anatomical structures of different samples into one spatial coordinate system. And thus, the DIR is always conducted following a preprocessing of global linear registration to remove the large global deformations. However, imperfect preprocessing may leave some large non-linear deformations that cannot be handled well by existing DIR methods. The recently proposed cascaded registration network gives a primary solution to deal with such large non-linear deformations, but still suffers from loss of image details caused by continuous interpolation (information loss problem). In this article, a progressive image registration strategy based on deep self-calibration is proposed to deal with the large non-linear deformations without causing information loss and introducing additional parameters. More importantly, we also propose a novel hierarchical registration strategy to quickly achieve accurate multi-scale progressive registration. In addition, our method can implicitly and reasonably implement dynamic dataset augmentation. We have evaluated the proposed method on both optical and MRI image datasets with obtaining promising results, which demonstrate the superior performance of the proposed method over several other state-of-the-art approaches for deformable image registration.
VISUAL SPEECH RECOGNITION BY USING MACHINE LEARNING AND DEEP LEARNING A CASE STUDY OF SOME RULES OF TAJWEED

Visual speech information plays an important role in lip-reading under noisy conditions or for listeners with a hearing impairment.Correct utterances to read Quran for beginners, there are rules of utterances to learn Quran and we need a software system to tell us if we utter correctly.For that, we built lip-reading model, the model localizes the lips efficiently.We present in this study a classification model for some al-tajweed rules as we depended on Machine Learning -Cascade Object Detector (Viola-Jones Algorithm), HOG features, a multiclass SVM classifier and Aggregate Channel Features (ACF) object detector for features extraction.We uses Matlab to train a classifiers using a pretrained convolutional neural network (CNN) for classifying images from the video stream of four different Rules of Holy Quran Allah Elevating (mufakhum), Allah Lowering (moureqeq), sunny ‫الم‬ and moony‫.الم‬CNN acquires multiple convolutional filters, used to extract visual features essential for recognizing phoneme.CNNs produce highly accurate recognition results
Easy and Flexible Calibration Approach for Deflectometry-based VR Eye-Tracking systems

We present an easy and flexible calibration approach for small field of view Deflectometry systems to be used in VR headsets. Our method replaces all required calibration targets and mirrors with a small (phone) screen to perform intrinsic and extrinsic camera calibration as well as extrinsic calibration of the whole setup.
Blockchain

The world as we know it was shaped by technological innovation. A new technology, Blockchain, that allows digital records and information to be shared securely and transparently. It can carry out transactions with a high level of security and have a significant impact on trade operations, reducing fraud and improving traceability and strengthening trust in value chains.Paperless trade, blockchain uniqueness, digital documentation, all these have brought huge benefits to many developing countries, as the successful implementation of the single window principle in Singapore, Korea and Mexico. It's a "battle of the technologies" aimed at convincing governments that blockchain is the superior technology. Blockchain technology (like many other technologies) is maturing.
Simulation Model of Enterprise Financial Management Activities Based on the Random Forest Algorithm

In the era of information and data, big data and cloud computing have been widely used in all walks of life, especially for promoting the transformation of business model and the financial administration model of corporations. Building various management systems based on the financial sharing center can realize centralized management of business finance and improve enterprise management efficiency. Enterprises must establish a set of perfect financial administration systems to prevent and resolve financial crisis, reduce financial losses and ensure the normal operation of corporations. Based on situational learning theory and simulation experiment technology, this paper constructs a comprehensive simulation experiment system of financial administration and focuses on three key technologies: case decomposition and task-driven, business simulation expansion, and scientific evaluation. Based on the random forest algorithm, a preparation setup model of accounts receivable and payable audit is constructed, and its application in case corporations is analyzed, which provides new ideas for the modern audit work mode and creates a dynamic and open business simulation environment for the financial administration experiment. The results show that the classification accuracy of the improved K-fold random forest algorithm is 1.54% higher than that of the traditional random forest algorithm, and the accuracy of the crisis preparation setup model in predicting the actual enterprise financial crisis reaches 90.327%, which is feasible and effective in the preparation setup of enterprise financial administration and provides a new research idea for the construction of the preparation setup model of enterprise financial administration. Financial management is the core part of enterprise management activities, and it is also an important factor affecting the comprehensive competitiveness of corporations. Therefore, on the basis of fully studying the objective regularity of financial activities, improving the financial administration algorithm, applying the random forest algorithm to enterprise management, and building a perfect management system can comprehensively control the financial administration activities of corporations and realize the innovation of financial administration activities from the aspects of data collection, fund management, and internal control, which is conducive to promoting the continuous improvement of the efficiency of enterprise financial administration.
Taro Kageyama: <i>Ten to sen no gengogaku: Gengo-ruikē-gaku kara mieta Nihongo no honshitsu</i> [Individuals and Links in Language Typology]

Article Taro Kageyama: Ten to sen no gengogaku: Gengo-ruikē-gaku kara mieta Nihongo no honshitsu [Individuals and Links in Language Typology] was published on November 1, 2022 in the journal Journal of Japanese Linguistics (volume 38, issue 2).
Learning-Based Virtual Machine Selection in Cloud Server Consolidation

In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection, and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection, thereby reducing the generation of SLAV, ensuring the performance, and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption.
Using Student Satisfaction Questionnaire to Manage the Lecturer’s Performance

&lt;p&gt;&lt;em&gt;The concept of student satisfaction is too broad covering facilities, lecturers, and others. This paper focuses only on managing the lecturer’s performance based on student satisfaction towards what normally a lecturer has done in teaching. The research is aimed at evaluating the lecturer performance in teaching English 2 based on the students’ observation during the online course as the effect of COVID 19. The collected data from the questionnaire provided for the students is analyzed by using Importance-Performance Grid. The results reveal that the weakness and strength areas of the lecturer can be mapped in four classifications: first, what the lecturer should improve because the lecturer’s performance perceived by the students is in the low level and the students’ expectation is in the high level; second, what the lecturer should maintain or keep going because the lecturer’s performance and the students’ expectation at the same time are in the high level; third, what the lecturer should maintain but think about the weighting of it because the lecturer’s performance is in the low level and the students’ expectation is also in the low level; fourth, what the lecturer should reduce the emphasis if possible because the lecturer’s performance is in the high level and the students’ expectation is in the low level. This research is one of the first attempts to explore what the lecturer can do to improve the performance accordingly in measurable and manageable way by the lecturer himself/herself.&lt;/em&gt;&lt;/p&gt;
Appropriate learning media for mild mentally impaired students at inclusive vocational schools: A literature review

In inclusive vocational high schools, students with mild mental retardation have limited thinking and communication. This limitation needs to be overcome by teachers with aid. Students with mild mental retardation study using learning media to improve their knowledge. This study aims to determine what learning media are appropriate for mild retarded learners of Inclusive Vocational High School. This study is a literature review of documents. There are four stages in writing this literature review: determining research topics and questions, searching for literature, analyzing literature search results, and writing a literature review. The documents are journals, conference proceedings, books, thesis reports, and website pages on the internet. The number of documents analyzed is 32 documents. Learning media that is suitable for students with mild mental retardation can be seen from 1.) The text: i.e., short readings, common words used, not convoluted, Arial or Tahoma fonts with the size of 14 pt, non-italic sentences and underline, adding an illustration like image or table; 2.) The colors of the media take on the hot colors: i.e., red, orange, and yellow; 3.) The types of tests that can be used: i.e. multiple-choice tests, right-wrong choices, yes-no question, crossword, cause-effect, test descriptions, and essay; 4.) The shape of learning media is 3D; 5.) the types of aromatherapy that can be used: i.e. lavender essence, calm, citrus melablend, and Northwoods blend.
Retraction: The Exploring on the University Student Honor Management System Based on Enterprise Refined Management under the Background of Big Data (J. Phys.: Conf. Ser. 1453 012164)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
Blockchain-based systems for modern energy grid: a detailed view on significant applications of blockchain for the smart grid

Data/information has become the most important asset in modern society. The need for secure data transfer is leading to finding advanced technologies for a sustainable solution for system reliability. One such technology is the blockchain. It is a decentralized database and record-keeping system which gives more security to its applications. It has some excellent features, which makes it a promising application for the smart grid paradigm. A smart grid is a developing technology for electricity and data transmission through lines and controls for on-demand power supplies. By combining the internet of things and advanced sensors, the smart grid could be a feasible solution for uninterrupted power/electricity supplies in the near future. However, many adoption issues in the smart grid, such as data security and privacy concerns, are the biggest challenges in the power supply’s data utilization and operations for energy consumption. In order to eliminate these challenges, newer and novel technology is needed. For that, blockchain technology is one of the possible solutions for enabling the smart grid. In this chapter, we are focusing on and giving detailed treatment to more intriguing ideas and some significant application areas of blockchain in the smart grid.
Retraction: Parametric Design of Quasi-Natural Waterscape Gardens Based on Big Data Technology (J. Phys.: Conf. Ser. 1314 012142)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022