Reviewerly/exHarmony huggingface dataset:
Education and Modern Technologies, Their Positive and Negative Impact

Education vicinity unit generally touchy inside the course of the range in our culturally philosophy kingdom. Why, then, do they have to take part in every other philosophical machine education workshop? Even analysis indicates that several philosophical gadget education efforts area unit ineffective. A good way to range the negative attitudes that educators have in the direction of philosophical device schooling workshops, a teacher has to be creative. He/she has to hinge at the contributors' backgrounds and beliefs because of the truth simply introducing a variety of philosophical device strategies might not deliver the desired effects. Technology is probably a present of god. While the present of lifestyles it is perhaps the exceptional of god's gifts. It’s the mother of civilizations, of arts and of sciences. Technology has surely modified the method we have a propensity to live. It’s far wedged completely one-of-a-type elements of life and redefined residing. Past query, technology plays a totally vital function in each sphere of lifestyles. Many manual responsibilities is machine-controlled, due to era. Additionally, numerous complex and worrying strategies is implemented truly and larger efficiency with the assist of present day technology. Because of the equipment of technology, living has modified and it is modified for higher. Generation has revolutionized the sector of training. The significance of era in schools cannot be unheeded. In truth, with the onset of computers in education, it is grow to be less difficult for teachers to impart records and for college students to acquire it. The utilization of era has created the approach of coaching and mastering all of the more satisfying.
Iterative Methods for Determining Derivatives of Stationary Distributions of Finite Markov Chains

We present algorithms for obtaining derivatives of steady-state probability distributions of finite Markov chains. Our focus is on a conceptually simple approach for developing such algorithms from established methods of computing steady state probability distributions. We briefly discuss our prototype (an application of the approach to the power method) and present the results of applying the approach to LOPSI (lopsided iteration) and Neut’s matrix geometric methods.
The Vast and the Focused: On the need for domain-focused web corpora

As the Web ought to be considered as a series of sources rather than as a source in itself, a problem facing corpus construction resides in meta-information and categorization. In addition, we need focused data to shed light on particular subfields of the digital public sphere. Blogs are relevant to that end, especially if the resulting web texts can be extracted along with metadata and made available in coherent and clearly describable collections.
Challenges in the Management of Large Corpora (CMLC-7) 2019

Large textual datasets require careful design, collection, cleaning, encoding, annotation, storage, retrieval, and curation to be of use for a wide range of research questions and to users across a number of disciplines. A growing number of national and other very large corpora are being made available, many historical archives are being digitised, numerous publishing houses are opening their textual assets for text mining, and many billions of words can be quickly sourced from the web and online social media. A number of key themes and questions emerge that are of interest to the contributing research communities: (a) what can be done to deal with IPR and data protection issues? (b) what sampling techniques can we apply? (c) what quality issues should we be aware of? (d) what infrastructures and frameworks are being developed for the efficient storage, annotation, analysis and retrieval of large datasets? (e) what affordances do visualisation techniques offer for the exploratory analysis approaches of corpora? (f) what kinds of APIs or other means of access would make the corpus data as widely usable as possible without interfering with legal restrictions? (g) how to guarantee that corpus data remain available and usable in a sustainable way? This year’s event focused primarily on huge and complex datasets, across the entire spectrum of their life cycle: from the selection of data (including organizational and legal issues) and modelling of the eventual resources, through curation and all the way to analysis and visualisation. Attention was also paid to the ecosystem in which datasets thrive and interact – with interoperability being one of the meeting’s leitmotifs.
A Lexicon Weighted Sentiment Analysis Approach on Twitter

Sentiment analysis in social media has grabbed more considerable attention because the results of such studies are highly applicable in social, economic, and political contexts. This study aimed to present an approach for collecting data from Twitter while storing and analysing the data using the hadoop as a big data platform as well as a hybrid trial and error model using the Bayes theorem plus a dictionary of words for the sentiment analysis. This method classifies tweets in two positive and negative classes based on the probability of positive words and negative words. According to the results, the accuracy of the proposed approach boosted from 67% to 71%. Then a new idea was employed in form of a weighted dictionary to achieve a higher accuracy. As such. the accuracy of the proposed approach reached a rate of 78% according to the results of another analysis conducted on the same data.
COVID-19 Health Information-seeking Behavior in Timorese Society: A Phenomenological Study

This study aims to determine the media used in obtaining health information about COVID-19 and the reasons for choosing the media. The theory used is Information-seeking Behavior. This research is qualitative with the phenomenological method. The informants were 38 people who were selected based on the purposive sampling technique. Data collection was carried out utilizing in-depth interviews, observation, and documentation. The data analysis technique is Creswell’s data analysis in phenomenological research. The results showed that the media used to find health information about COVID-19 were printed mass media (newspapers, magazines, and tabloids), electronic media (television, radio), and internet media. Meanwhile, choosing the media was due to the ease of accessing existing media and the diversity of information obtained.
Reading Medium with Pictorial Content: An Approach towards Communication Analysis

This study is dedicated to the possibilities offered by reading mediums without textual information, letterless books and picture books. The purpose of the present study is to expand the subject of the science of reading to include alternative reading modalities, i.e. reading beyond the lettered text. The author believes that this is a constructive approach to a broader and more contemporary definition of reading, which corresponds to the present multimodal and transmedia environment. The method of collecting primary data has been applied to 11 empirical objects. The historical examination identified four paradigmatic examples of reading media without textual content: “Les songes drolatiques de Pantagruel” (“The Drolatic Dreams of Pantagruel”), “Mutus Liber”, “Wordless Book” and “Codex Seraphinianus”. In the present study, “mute”, or “silent”, books, eccentric books “for watching”, visual, or picture, books have all been brought together under the umbrella term “silent books”. The cinematographic and storyboard approaches in the “optical” book-making are examples of vindicating the visual communication modality in the art of book publishing. A parallel process takes place in media sciences in which iconicity is emancipated as the object of reading. Just like narratives of lettered text, pictorial narratives too require reading literacy. This is a new, holistic type of literacy, or transliteracy, for communication using texts in multiple modality, with prevalent imagery and iconicity. The review performed has proved that the process of defining a new kind of transliteracy for coping in a polyglotic and transmedia environment must obligatorily extend in the direction of non-letter perception and competence in visual reading. A definition of reading as a receptive process of obtaining information in an indirect (medium-based) way which does not depend on letter symbols or the reader’s visual analyzer has been proposed.
Attention-Based SRGAN for Super Resolution of Satellite Images

Single image super resolution plays a vital role in satellite image processing as the observed satellite image generally has low resolution due to the bottleneck in imaging sensor equipment and the communication bandwidth. Deep learning provides a better solution to improve its resolution compared to many sophisticated algorithms; hence, a deep attention-based SRGAN network is proposed. The GAN network consists of an attention-based SR generator to hallucinate the missing fine texture detail, a discriminator to guess how realistic is the generated image. The SR generator consists of a feature reconstruction network and attention mechanism. Feature reconstruction network consists of residually connected RDB blocks to reconstruct HR feature. The attention mechanism acts as a feature selector to enhance high-frequency details and suppress undesirable components in uniform region. The reconstructed HR feature and enhanced high-frequency information are fused together for better visual perception. The experiment is conducted on WorldView-2 satellite data using Googles free cloud computing GPU, Google colab. The proposed deep network performs better than the other conventional methods.
SPECIFICS OF SOCIAL AND CULTURAL PROJECTS REALIZATION IN MODERN RUSSIA

In Russia, state cultural institutions play a crucial role in forming and meeting the cultural needs of society, creating conditions and technologies for the comprehensive development and creative selfrealization of personality, ensuring active cultural leisure activity, and maintaining the proper level of culture as a material and spiritual environment for human existence. Various socio-cultural projects that have spread widely in Russia in recent decades are developed and implemented for these purposes. The goal of the present study is to identify the specific characteristics of the realization of socio-cultural projects in the cultural institutions of Moscow. Socio-cultural projects that had been realized in the cultural institutions of Moscow in the 2000s serve as the object of the study. The study methods comprise theoretical analysis of project documents, the practices of formation and realization of socio-cultural projects in the cultural institutions of Moscow, as well as practical participation in the measures and projects of cultural institutions and the development of sociocultural programs. The study concludes that the projects and programs of district cultural institutions are actualized by specific social problems of individual social groups (children, adolescents, adults, and pensioners). The main direction of socio-cultural activity aimed at resolving social problems is the organization of psychological support for families in solving family problems. Further improvement of sociocultural activity can be achieved through restructuring the content and forms of said activity in the direction of strengthening its social targeting.
A​ ​ Review on Sentiment Analysis

In a current scenario, we have become an integrated part of the internet as our daily life activities are fully dependent on it​ . Social media such as Twitter, Facebook connect with friends, family, and the main source of information about the world. ​ Today we buy our daily essential items from the internet (Amazon, Flipkart), we get education using online blogs and videos. Lots of emotions are generated on the internet. Sentimental Information on the internet is very helpful for businesses, customers, and the government. We have a bulk of content on the internet but we have lots of challenges for sentiment analysis and the evaluation process. Natural language processing helps to find sentiment information from the text. This paper surveys sentiment analysis techniques and challenges.
Automated Discovery of Real-Time Network Camera Data From Heterogeneous Web Pages.

Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks. Network cameras offer real-time visual data that can be used for studying traffic patterns, emergency response, security, and other applications. Although many sources of Network Camera data are available, collecting the data remains difficult due to variations in programming interface and website structures. Previous solutions rely on manually parsing the target website, taking many hours to complete. We create a general and automated solution for aggregating Network Camera data spread across thousands of uniquely structured web pages. We analyze heterogeneous web page structures and identify common characteristics among 73 sample Network Camera websites (each website has multiple web pages). These characteristics are then used to build an automated camera discovery module that crawls and aggregates Network Camera data. Our system successfully extracts 57,364 Network Cameras from 237,257 unique web pages.
Close2U- An Android assistant to show nearby places by using various API service providers

With the immense use of android operating systems in today’s technology, Android apps became part of our daily lives and we use different apps for different purposes. The motivation behind every location-based application is to give the user a live experience with real data in real-time. To ease the searching of nearby places for the user, this android application provides the details of nearby locations like restaurants, hospitals, emergency services, travel services and also COVID vaccination centres like an assistant that answers the user’s query based on different queries. The apps that are available already use location and show the google maps inside the application which makes the application heavy and the cost of API request is high, but in this application, the user interface will be simple and it will fetch only the required data from different API Service providers and the data adapters for the application change based on different API service providers.
Efficient spherical harmonic transforms on GPU and its use in planetary core dynamics simulations

&amp;lt;p&amp;gt;Most of the new supercomputers now use acceleration technology such as GPUs. They promise much higher performance than traditional CPU-only servers, both in terms of floating point operation throughput and memory bandwidth. Furthermore, the electric consumption is significantly reduced, resulting in lower carbon emissions.&amp;lt;br&amp;gt;However, such high computation speeds can only be achieved if a set of more or less stringent rules are followed with respect to memory access and program flow. As a consequence some algorithms more easily approach peak performance.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we present the results of an effort to achieve high performance on recent nvidia GPU accelerators for the spherical harmonic transform. The spherical harmonic transform can be split into a Legendre transform (which is compute bound) and a Fourier transform (which is memory bound).&amp;lt;br&amp;gt;By taking advantage of recent algorithmic improvements as well as by tuning the Fourier transform, the can now compute a full forward or backward spherical harmonic transform up to degree 8191 on a single 16GB Volta GPU in less than 0.35 seconds.&amp;lt;br&amp;gt;For lower resolution (up to degree 1023), a single Volta GPU performs a full transform more than 3 times faster than a 48-cores dual socket Skylake Xeon Platinum server.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We also present results of an ongoing effort to port the (simulation of planetary core fluid and magnetic field dynamics) to GPU-accelerated computers.&amp;lt;/p&amp;gt;
Incremental text categorization based on hybrid optimization-based deep belief neural network

One of the effective text categorization methods for learning the large-scale data and the accumulated data is incremental learning. The major challenge in the incremental learning is improving the accuracy as the text document consists of numerous terms. In this research, a incremental text categorization method is developed using the proposed Spider Grasshopper Crow Optimization Algorithm based Deep Belief Neural network (SGrC-based DBN) for providing optimal text categorization results. The proposed text categorization method has four processes, such as are pre-processing, feature extraction, feature selection, text categorization, and incremental learning. Initially, the database is pre-processed and fed into vector space model for the extraction of features. Once the features are extracted, the feature selection is carried out based on mutual information. Then, the text categorization is performed using the proposed SGrC-based DBN method, which is developed by the integration of the spider monkey optimization (SMO) with the Grasshopper Crow Optimization Algorithm (GCOA) algorithm. Finally, the incremental text categorization is performed based on the hybrid weight bounding model that includes the SGrC and Range degree and particularly, the optimal weights of the Range degree model is selected based on SGrC. The experimental result of the proposed text categorization method is performed by considering the data from the Reuter database and 20 Newsgroups database. The comparative analysis of the text categorization method is based on the performance metrics, such as precision, recall and accuracy. The proposed SGrC algorithm obtained a maximum accuracy of 0.9626, maximum precision of 0.9681 and maximum recall of 0.9600, respectively when compared with the existing incremental text categorization methods.
Correlation between spiritual-moral and intellectual problems of education humanization in modern times

Modifying current education system into spiritual, moral and humanitarian requirements of today's world has become an absolutely controversial problem, and because of the understanding it has become a key feature of the state, society and citizens' attitude towards education system, the notion that the education system was a system only serving national or political interests has been over. In today's developed countries, education is, on the one hand, the main productive force of society - the source of the formation of a human outlook, which, on the other hand, develops man spiritually, morally and intellectually; it is no coincidence that the tendency of society's becoming a large network-an indicator of the level of humanity, is manifested in a peculiar way. The article below scrutinizes the problems and shortcomings of the education humanization.
Episodic earthquake swarms in the Mineral Mountains, Utah driven by the Roosevelt hydrothermal system

&amp;lt;p&amp;gt; The Mineral Mountains are located in south-central Utah within the transition zone from the Basin and Range to Colorado Plateau physiographic provinces, near the Roosevelt Hot Springs. First evidence of swarm-like activity in the area was found in 1981, when a six-station temporary network detected a very energetic swarm of ~1,000 earthquakes. More recently, in mid-2016, a dense local broadband seismic network was installed around the Frontier Observatory for Research in Geothermal Energy (FORGE) in southcentral Utah, ~10 km west of the Mineral Mountains. Beginning in 2016, the University of Utah Seismograph Stations detected, located, and characterized 75 earthquakes beneath the Mineral Mountains. In this study, we build an enhanced earthquake catalog to confirm the episodic swarm-like nature of seismicity in the Mineral Mountains. We use the 75 cataloged earthquakes as templates and detect 1,000 earthquakes by applying a matched-filter technique. The augmented catalog reveals that seismicity in the Mineral Mountains occurs as short-lived earthquake swarms followed by periods of quiescence. Earthquake relocation of ~800 earthquakes shows that activity is concentrated in a &amp;lt;2 km long E-W striking narrow zone, ~4 km east of the Roosevelt hydrothermal system. Two fault orientations, both N-S and E-W parallel to the Opal Mound and Mag Lee faults, respectively, are observed after computing composite focal mechanisms of highly similar earthquakes. After examining the spatial and temporal patterns of the best recorded earthquake swarm in October 2019, we find that a complex mechanism of fluid diffusion and aseismic slip is responsible for the swarm evolution with migration velocities reaching 10 km/day. We hypothesize that these episodic swarms in the Mineral Mountains are primarily driven by migrating fluids that originate within the Roosevelt hydrothermal system.&amp;lt;/p&amp;gt;
Why Do HSS Master Students Publish Papers in Chinese-Language Predatory Journals: A Qualitative Research Based on Grounded Theory

In China, HSS master students are becoming the main target of Chinese-language predatory journals. Existing research has not paid attention to why the HSS master students publish papers in Chinese-language predatory journals. This research interviewed 13 HSS (Humanities and Social Sciences) master students with different majors using the semi-structured interview method, then used GT to analyze the data and found that research discrimination, research context, self-awareness and individual demand are the main reasons why HSS master students publish papers in Chinese-language predatory journals. This research suggests that universities, colleges, and research institutions establish mechanisms for evaluating the quality of researcher’s papers, and no longer value the number of papers.
Enhancing the Transferability of Adversarial Attacks with Input Transformation

Abstract Deep neural networks (DNNs) are challenged by their vulnerability to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Adversarial attacks can thus be an important tool to evaluate and select robust models before they are deployed. However, under the challenging black-box setting, most of existing adversarial attacks can only fool a model with a low success rate. Based on image augmentation methods, we found that random transformation of image size can eliminate overfitting in the generation of adversarial examples and improve their transferability. To this end, we propose an adversarial example generation method based on this phenomenon, which can be integrated with Fast Gradient Sign Method (FGSM)-related methods to build a stronger gradient-based attack and generate adversarial examples with better transferability. Extensive experiments on the ImageNet dataset demonstrate that our methods attack both normally trained models and adversarially trained models with higher attack success rates than existing baseline attacks. We hope that our proposed attack method can serve as a benchmark for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods.
Agnostic Proper Learning of Halfspaces under Gaussian Marginals

We study the problem of agnostically learning halfspaces under the Gaussian distribution. Our main result is the {\em first proper} learning algorithm for this problem whose running time qualitatively matches that of the best known improper agnostic learner. Building on this result, we also obtain the first proper polynomial time approximation scheme (PTAS) for agnostically learning homogeneous halfspaces. Our techniques naturally extend to agnostically learning linear models with respect to other activation functions, yielding the first proper agnostic algorithmfor ReLU regression.