Reviewerly/exHarmony huggingface dataset:
RSS Using Social Networks User Profile

E-commerce application in recent days are gaining more importance due to its worldwide services and user-friendly applications. Some of the social networking applications are being backbone of e-commerce applications help in its functioning. With this, the number of e-commerce applications are being associated with social networking site and their user profiles. E-commerce sites use this social networking sites as service providers to their customers associated with their social networking profiles. In these online transactions, social networking sites act as third-party service providers to its users with respect to e-commerce interactions. Social networking sites acting as third-party service providers should be careful about users type, their behaviour and services that they provide. Users choosing their services should also be careful about what to choose and what not to choose. In this three-tier interactions, trust plays a very important role. Trust on any service providers should be evaluated to stay away from online fraudulent activities. User interacting through these applications should always be able to decide between the trusted services and fraud services. Hence, service selection in social networking is proposed with the help of Refined Service Selection (RSS) algorithm using social networks user profiles. Implementation is done with the help of real data set and found that the accuracy of RSS is more when compared with the existing algorithms.
Interpretation of Handwritten Documents Using ML Algorithms

Handwritten character recognition is a continuing field of research which covers artificial intelligence, computer vision, neural networks, and pattern recognition. An algorithm that executes handwriting recognition can acquire and detect characteristics from given handwritten document/image as input and convert them to a machine-readable form. It is also known as the task to convert the input text to extracting the features with the help of symbol representation and icons of each letter. The main goal of this handwritten text recognition is to identify an input character and text on a scanned image which are written in cursive writing, and these features are extracted with each input character pixels. Each character dataset contains 26 alphabets. IAM datasets are used for training the characters and for classification and recognition. The output is generated in the form of human-readable text. In this paper, the handwritten documents has been interpreted using machine learning algorithms such as connectionist temporal classification (CTC), long short-term memory networks (LSTMs), and generative adversarial networks (GANs). The results show the comparison of feature extraction based on proposed approach with convolutional neural networks (CNN) and recurrent neural network (RNN) with real-time datasets gain in better performance when using these algorithms.
ПРОБЛЕМЫ ОНЛАЙН ОБУЧЕНИЯ ИНОСТРАННОМУ ЯЗЫКУ: ИЗ ОПЫТА ПРЕПОДАВАНИЯ ЯПОНСКОГО ЯЗЫКА

This article reported on online tools for online classes and the advantages and problems of online classes using these tools. The purpose of this report is to help learners and teachers find the right tool for their teaching style and educational environment. This article reported on the strengths and weaknesses of two online meeting tools and seven other online tools that can be used inside and outside the classroom as tools to support online classes. In this article, online classes are defined as synchronous classes in which the teacher and several learners interact simultaneously through a screen by using the Internet to access a web communication tool that enables face-to-face communication.
Status of Technical Sciences in Higher Education

The purpose of the article is to substantiate the prospects and timeliness of strengthening and accelerating for the process of technization of higher education in Russia. The status of technical knowledge in higher education is changing. During the period of the new industrial revolution, the status of engineering sciences in higher education is steadily rising. It is shown that one of the leading trends of the new industrial revolution is the complication of the structure of technical knowledge. Modern studies of the structure of technical knowledge have shown its complex and heterogeneous nature. In the article, based on the latest data, a case study of the structure of technical knowledge is carried out using the example of medical masks. The coronavirus pandemic in the context of the new industrial revolution has revealed the low level of technization of higher education in Russia. In this article it is justified that the expediency of giving for a students, at their request and choice, a technical and non-technical specialties when they study within the framework of one educational program. It is explained that the transition to a higher level of technization is based on a rational and democratic didactic principle of maximum diversity in the combination of two or more specialties during a five-year study within the framework of one educational program сonsidering the structure of technical knowledge. It is noted that the process of strengthening and accelerating the technization of higher education in Russia should not violate the human rights and discard the democratic values of modern society.
Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

Semantic web technology is adapted to the internet of things (IoT) for web - based applications to globally connect the services. Web ontology language (OWL) domain ontology is a powerful machine - readable language for domain knowledge representation. The developer stored the IoT application relevant ontology in a repository or catalogue. Hence, IoT application - related ontology files are available for reus e, but many of the IoT application - relevant ontology files are publicly not available or inaccessible. The proposed idea is to extract the contextual knowledge of IoT applications that contain inaccessible ontology files. The context - wise specific domain I oT applications are not obtainable, hence respective ontology - based research papers are identified and their frequent terms are computed. The selected contextual dominant frequent terms from the transport domain are passed into the skip - gram flavour of wor d2vector modelled n atural language processing ( NLP ) corpus which produces most similar terms. The domain experts select the appropriate terms to annotate in OWL ontology for contextual knowledge augmentation. Finally, 1422 contextual terms were generated b ased on dominant terms of selected IoT applications.
Speech-Driven Web News Application using Artificial Intelligence

The proposed idea of the Speech-driven Web Application provides a simplistic approach and ease to the user. The system provides a very user-friendly, easy to use, dynamic, and informative user experience. A voice-controlled system embedded in a web application can enhance user experience and can provide voice as a means to control the functionality of e-commerce websites. The system also offers the ability to search news on the basis of preferred location, source, theme, and interest. The system will reduce the amount of human effort required by the user to perform previously and will offer a more exciting way of getting informed. In this paper, a very fascinating proposition is presented, a new web-based service that is a fusion of the revolutionary new Alan Studio, News API and React. The system enables the user to have the ability to not only stay informed and updated but that too by using one of the most sought-after technologies in the world right now. The service provides all the components required for a user to be able to use voice and speech as a medium to find and look for news about his/her choice and the option to go through the news in a very concise or in very detailed manner.
Image Processing: Convert a Sketch into Coloures Image using cGAN

Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or rely on retrieval of existing photographs. In this work, we propose a novGenerative Adversarial Network(GAN) approach that synthesizes plausible images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new network building block suitable for both the generator and discriminator which improves the information flow by injecting the input image at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.
A Semantic Feature Extraction Method for Images Based on M-NetVLAD

The essence of image semantic research is to extract low-level information from image attributes in order to form a transfer and mapping of low-level information to high-level semantics. In this paper, an image semantic feature extraction method is customized based on the extension of CNN features and NetVLAD signature. And it is combined with CNN features to obtain a matching function for image semantic matching. The correspondence of visual and semantic features makes the accuracy of image retrieval effectively improved. By comparing with the original NetVLAD pipeline, our method performs better.
Learning an Artificial Neural Network to Discover Combinations of Bit-Quads to Compute the Euler Characteristic of a 2-D Binary Image

The Image Analysis community has widely used so-called bit-quads to propose formulations for computing the Euler characteristic of a 2-D binary image. Reported works have manually proposed different combinations of bit-quads to provide one or more formulations to calculate this important topological feature. This paper empirically shows how an Artificial Neural Network can be trained to find an optimal combination of bit-quads to compute the Euler characteristic of any binary image. We present results with binary images of different complexities and sizes and compare them with state-of-the-art machine learning algorithms.
I2-Net: Intra- and Inter-scale Collaborative Learning Network for Abdominal Multi-organ Segmentation

Efficient and accurate abdominal multi-organ segmentation is the key to clinical applications such as computer-aided diagnosis and computer-aided surgery, but this task is extremely challenging due to blurred organ boundaries, complex backgrounds, and different organ sizes. Although existing segmentation methods have achieved good segmentation results, we found that the segmentation performance of abdominal small and medium organs is often unsatisfactory, but the accurate location and segmentation of abdominal small and medium organs plays an important role in the diagnosis and screening of clinical diseases. To address this problem, in this paper we propose an intra- and inter-scale collaborative learning network (I2-Net) for the abdominal multi-organ segmentation task. Firstly, we design a Feature Complementary Module (FCM) to adaptively complement the local and global features extracted by CNN and Transformer. Secondly, we propose a Feature Aggregation Module (FAM) to aggregate multi-scale semantic information. Finally, we employ a Focus Module (FM) for collaborative learning of intra- and inter-scale features. Extensive experiments on the Synapse dataset show that our method outperforms the state-of-the-art approaches and achieve accurate segmentation of abdominal multi-organs, especially for small and medium organs.
Prefix sum (scan)

This chapter introduces parallel scan (prefix sum), an important parallel computation pattern and the concept of work efficiency for parallel algorithms. It introduces three styles of kernels: Kogge-Stone, Brent-Kung, and two-phase hybrid. These kernels each present a different tradeoff in terms of work efficiency, speed, and complexity. The chapter then introduces two hierarchical parallel scan algorithms that are designed to process arbitrarily long input lists while maintaining work efficiency.
Algorithmic translation and emotional outrage

During this first quarter of the twenty-first century, political developments around the world have punctuated the reality that our digital media play a discernible role in stoking the flames of moral outrage among the public writ large. Indeed, the ways in which moral outrage is expressed and experienced within contemporary existence are intrinsically shaped by the translative processes of a-signifying digital machinery as it circulates mediated representations of our political reality. Borrowing from Susan Petrilli’s theory of translative semiotics, this chapter presents a semiotic examination of the assertion made above, arguing that as digital algorithms assemble and position signs among an arbitrary and precarious assemblage of interpretants within the Internet of things, the meanings and experiences of morality and moral outrage are instantiated collaboratively in a dialogical-dialectic choreography between human minds and their built environments. In the process, the meaning of morality takes on the impermanent character of a floating signifier among an undulating assemblage of equally impermanent interpretants. For better or worse, the human Umwelt is caught in the dialogically dialectic web of cybersemiosic translations, which has transformed the traditional order of political norms and customs, distorting the expression and experience of moral outrage within our contemporary world.
Metric Comparison For Text Classification

Text classifications have been popular in recent years. To classify the text, the first step that needs to be done is to convert the text into some value. Some values that can be used, such as Term Frequencies, Inverse Document Frequencies, Term Frequencies – Inverse Document Frequencies, and Frequency of the word itself. This study aims to get which metric value is best in text classification. The method used is Naïve Bayes, Logistic Regression, and Random Forest. The evaluation score that is used is accuracy and Area Under Curve value. It comes out that some metric values produce similar evaluation scores. Another finding is that Random Forest is the best method among others, also the best metric for text classification is Term Frequencies – Inverse Document Frequencies.
PERMAVOST '22

Modern software engineering is getting increasingly complicated. Especially in the HPC field, we are dealing with cutting edge infrastructure and a novel problem with unprecedented scale. The ability to monitor and analyze the performance of such applications and infrastructure is imperative for the future of improvement, design, and maintenance. In the current era, the writing and maintenance of these applications have ceased to be the job solely of computer scientists and has grown to encompass a wide variety of experts in mathematics, science, and other engineering disciplines. The fact that many developers from these disciplines have not received a formal education in computer science and rely increasingly on the tools created by computer scientists to analyze and optimize their code shows that there's a need for a forum to work together.
A Novel Hybrid Voting System Using Machine Learning

In this proposed system a facial and fingerprint recognition-based technique is implemented. This technique uses computer vision and machine learning algorithms for the recognition of faces. The goal is to make the voting process safe, secure and easy to use. This system will eliminate the need for voting booths and electoral votes. The system will also be much more helpful for the voters because they don’t need to stand in a long queue to cast their vote. The proposed system uses an Android mobile phone to capture the user photo and the python server in the backend performs facial recognition. The system is much safer than the traditional system and also saves money which is usually spent during the election process. It also eliminates the need for human resources and time for managing the voting system.
ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus

Part-of-speech (POS) tagging is a classical natural language processing (NLP) task. Although many tools and corpora have been proposed, especially for the most widely spoken languages, these suffer from limitations concerning their user license, the size of their tagset, or even approaches no longer in the state-of-the-art. In this article, we propose ANTILLES, an extended version of an existing French corpus (UD French-GSD) comprising an original set of labels obtained with the aid of morphological characteristics (gender, number, tense, etc.). This extended version includes a set of 65 labels, against 16 in the initial version. We also implemented several POS tools for French from this corpus, incorporating the latest advances in the state-of-the-art in this area. The corpus as well as the POS labeling tools are fully open and freely available.
Poster Abstract: Explainable Sensor Data-Driven Anomaly Detection in Internet of Things Systems

Deep learning or black-box models are widely used for anomaly detection in Internet of Things (IoT) data streams. We propose a technique to explain the output of a deep learning model used to detect anomalies in an IoT based industrial process. The proposed technique employs dual surrogate models to deliver black box model explanation. We have also developed an interactive dashboard to give further insights into the detected anomaly. The dashboard integrates our proposed deep learning explanation technique with historical logs to explain the detected anomaly for personas with different backgrounds.
Car Damage Mask RCNN

The most challenging task is to detect the vehicle damage using a picture. In this proposed project vehicle damage detection using mask zonal convolutional neural network is to estimate of the damaged area of the car. Using higher technology like computer vision and machine learning the car damage detection with masking can be performed. Consider the situation of the accident place, the user can just send the photo of their damaged car and the company will upload the image to the computer, the computer will show the picture of damaged region with masking. Using higher mechanization like sighting system exp and machine learning will help to noticing the car damage detection with masking.
Hand Gesture Detection and Recognition

The design introduces an operation using computer vision for Hand gesture recognition. A camera records a live videotape sluice, from which a shot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least formerly. After that a test gesture is given to it and the system tries to fete it. A exploration was carried out on a number of algorithms that could best separate a hand gesture. It was set up that the slant sum algorithm gave the loftiest delicacy rate. In the preprocessing phase, a tone- developed algorithm removes the background of each training gesture. After that the image is converted into a double image and the totalities of all slant rudiments of the picture are taken. This sum helps us in secerning and classifying different hand gestures. former systems have used data gloves or labels for input in the system. I've no similar constraints for using the system. The stoner can give hand gestures in view of the camera naturally. A fully robust hand gesture recognition system is still under heavy exploration and development; the enforced system serves as an extendible foundation for unborn work.
Verbal Aggressions Detection in Mexican Tweets

Verbal aggressions are a struggle that a great number of social media users have to face daily. Some users take advantage of the anonymity that social media give them and offend a person, a group of people, or a concept. The majority of proposals which pretend to detect aggressive comments on social media handle it as a classification problem. Although there are a lot of techniques to face this problem in English, there is a lack of proposals in Spanish. In this work, we propose using several Spanish lexicons which have a collection of words that have been weighted according to different criteria like affective, dimensional, and emotional values. In addition to them, estructural values, word-embeddings and one-hot codification were taken into account.
Cyclic Existence, Iteration, and Digital Transcendence

This article explores MetaObjects’ ongoing collaboration with Lu Yang to develop a live motion capture performance. As a studio facilitating digital production with artists, the knowledge acquired delves into the worldview of the artist reflected in works and in practice. Lu Yang’s work is inherently collaborative and evolves in increasing complexity with each iteration. Similarly, reincarnation and repetition are present in Buddhist conceptions of cyclic existence and the wheel of life. Lu Yang connects an interest in folk beliefs and Chinese medicine to neuroscience presenting a multi-layering of temporalities in contemporary culture. During the COVID-19 pandemic, the performance was transformed into an online experience, deepening Lu Yang’s interest in digital reincarnation. The work presents an interest in digital reincarnation where identities are fluid and open to reinvention in the virtual realm.
Distributive Dictionary of the Historical Corpus “Manuscript”: Problem Statement, Material, Methods

Characteristics of linguistic materials and methods used to create an electronic distributive dictionary based on the historical corpus “Manuscript” (http://manuscripts.ru/mns/mns_evp.vec.main ), containing marked–up machine-readable transcriptions of extant Slavonic manuscripts and excerpts of the X-XV centuries, are given. The conditions for the use of statistical methods for the distributive analysis of the words of ancient Slavonic texts are discussed, the requirements for specialized tools and demonstration of the forms of visualization of the prototype of the dictionary are formulated. Examples of methods of automatic extraction of words with similar lexical environment from a large array of text data are given. The procedures and tools for preparing linguistic data are described (in particular, the formation of subcorps based on metadata and the methods implemented in the n-gram module for extracting the most frequent combinations of linguistic units from the corpus), the use of the k-skip-n-gram method for calculating word vectors and the method of finding the cosine distance between vectors is justified. The parameters of the dictionary query form are demonstrated, it allows specifying the type of the analyzed linguistic unit (lemma or text precedent), its mask and the cosine distance threshold. An example of a sample for the lemma лѣто ‘summer’ is given, it includes a list of words that have the closest contextual compatibility, the values of the cosine distances of the analyzed and the words close in distribution, as well as a list of words that occur next to the analyzed and the searched words. A sampling graph is shown, it demonstrates not only semantic, thematic, associative analogues of the word лѣто ‘summer’, but also some groups of associates. The analysis of the material, methods and results allowed us to draw conclusions about the need to use statistical measures when assessing the proximity of their components for the formation of vectors and about some other conditions for preprocessing linguistic material.