Reviewerly/exHarmony huggingface dataset:
Show and Write: Entity-aware News Generation with Image Information

Automatically writing long articles is a complex and challenging language generation task. Prior work has primarily focused on generating these articles using human-written prompt to provide some topical context and some metadata about the article. That said, for many applications, such as generating news stories, these articles are often paired with images and their captions or alt-text, which in turn are based on real-world events and may reference many different named entities that are difficult to be correctly recognized and predicted by language models. To address these two problems, this paper introduces an Entity-aware News Generation method with Image iNformation, Engin, to incorporate news image information into language models. Engin produces news articles conditioned on both metadata and information such as captions and named entities extracted from images. We also propose an Entity-aware mechanism to help our model better recognize and predict the entity names in news. We perform experiments on two public large-scale news datasets, GoodNews and VisualNews. Quantitative results show that our approach improves article perplexity by 4-5 points over the base models. Qualitative results demonstrate the text generated by Engin is more consistent with news images. We also perform article quality annotation experiment on the generated articles to validate that our model produces higher-quality articles. Finally, we investigate the effect Engin has on methods that automatically detect machine-generated articles.
Grappling with the scale of born-digital government publications: Toward pipelines for processing and searching millions of PDFs

Official government publications are key sources for understanding the history of societies. Web publishing has fundamentally changed the scale and processes by which governments produce and disseminate information. Significantly, a range of web archiving programs have captured massive troves of government publications. For example, hundreds of millions of unique U.S. Government documents posted to the web in PDF form have been archived by libraries to date. Yet, these PDFs remain largely unutilized and understudied in part due to the challenges surrounding the development of scalable pipelines for searching and analyzing them. This paper utilizes a Library of Congress dataset of 1,000 government PDFs in order to offer initial approaches for searching and analyzing these PDFs at scale. In addition to demonstrating the utility of PDF metadata, this paper offers computationally-efficient machine learning approaches to search and discovery that utilize the PDFs’ textual and visual features as well. We conclude by detailing how these methods can be operationalized at scale in order to support systems for navigating millions of PDFs.
Value Function Factorisation with Hypergraph Convolution for Cooperative  Multi-agent Reinforcement Learning

Cooperation between agents in a multi-agent system (MAS) has become a hot topic in recent years, and many algorithms based on centralized training with decentralized execution (CTDE), such as VDN and QMIX, have been proposed. However, these methods disregard the information hidden in the individual action values. In this paper, we propose HyperGraph CoNvolution MIX (HGCN-MIX), a method that combines hypergraph convolution with value decomposition. By treating action values as signals, HGCN-MIX aims to explore the relationship between these signals via a self-learning hypergraph. Experimental results present that HGCN-MIX matches or surpasses state-of-the-art techniques in the StarCraft II multi-agent challenge (SMAC) benchmark on various situations, notably those with a number of agents.
Twist detection by using a three axis sensor for reducing low back pain during nursing operation

In recent years in Japan, the problem of aging has become serious, and there is concern about a shortage of care workers and nurses in the future, and low back pain is a problem that can cause them to leave their jobs. In this research, our purpose of this paper is to detect twisting motion using a three-axis sensor in order to develop a smart device that solves the problem of low back pain in the field of long-term care and nursing. An algorithm to detect the twist was formulated from the experiment. We decided to use moving average of X-axis accelaration from the experiment. Then, it was verified and its effectiveness was confirmed. Our future prospects are correspondence to other movements and improved accuracy.
Security Enhancement at Lower Layers for SOA Reference Architecture

SOA is widely accepted for designing the projects because it provides flexible and extensible architecture to solve the business problem effectively. In fact, SOA attracts the consumer because of its important features like reusability, modularity, composability, encapsulation and interoperability but security is always a concern for this architecture. Security implementation at a granular level for service-oriented architecture is an important concern. Service-oriented architecture is based on the distributed architecture wherein different services are the part of the different domain which is used to create an application. This causes security issue when services are composed for the creation of an application. Current security implementation at SOA based projects don’t provide security at lower levels, however higher level is secured. Proposed Model enhances the security and reliability at lower levels of SOA using latest security techniques which also helps in maintaining the SLA after the security implementation.
Detection and Tracking Method for Dynamic Barcodes Based on a Siamese Network

In many screen to camera communication (S2C) systems, the barcode preprocessing method is a significant prerequisite because barcodes may be deformed due to various environmental factors. However, previous studies have focused on barcode detection under static conditions; to date, few studies have been carried out on dynamic conditions (for example, the barcode video stream or the transmitter and receiver are moving). Therefore, we present a detection and tracking method for dynamic barcodes based on a Siamese network. The backbone of the CNN in the Siamese network is improved by SE-ResNet. The detection accuracy achieved 89.5%, which stands out from other classical detection networks. The EAO reaches 0.384, which is better than previous tracking methods. It is also superior to other methods in terms of accuracy and robustness. The SE-ResNet in this paper improved the EAO by 1.3% compared with ResNet in SiamMask. Also, our method is not only applicable to static barcodes but also allows real-time tracking and segmentation of barcodes captured in dynamic situations.
We're following the science (but not if it's inconvenient)

British Journal of Child HealthVol. 2, No. 3 EditorialWe're following the science (but not if it's inconvenient)Caroline VoogdCaroline VoogdEditorSearch for more papers by this authorCaroline VoogdPublished Online:16 Jun 2021https://doi.org/10.12968/chhe.2021.2.3.109AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Department for Education. Week 23: Attendance in education and early years settings during the coronavirus outbreak. June 8, 2021. https://bit.ly/3crDvAc (accessed 9 June 2021) Google ScholarNational Education Union. School Attendance Figures: week before half-term. 2021. Google ScholarNEU Office for National Statistics. Coronavirus Infection Survey, UK, June 4, 2021. https://bit.ly/3gn3mu4 (accessed 9 June 2021) Google ScholarPublic Health England. Research and analysis: Variants: Distribution of case data, update of June 3, 2021a. https://bit.ly/3iqA00A (accessed 9 June 2021) Google ScholarPublic Health England. SARS-CoV-2 variants of concern and variants under investigation in England: Technical briefing 14, June 3, 2021b. https://bit.ly/3w64MzW (accessed 9 June 2021) Google Scholar FiguresReferencesRelatedDetails 2 June 2021Volume 2Issue 3ISSN (print): 2633-5417ISSN (online): 2633-5425 Metrics History Published online 16 June 2021 Published in print 2 June 2021 Information© MA Healthcare LimitedPDF download
Web Elements

You learned about the basic actions of the mouse and keyboard in the previous chapter. Now you need to locate a web element to perform these actions. This chapter explains how a web element can be located. There are several types of web elements that are used to build web applications. To test a web application, you first need to trace the web elements in it. The elements can be anywhere on a web page. These web elements can be located or traced by locators provided by Selenium WebDriver.
Privacy Preserving Analytics in Outsourced Healthcare System

The most data intensive industry today is the healthcare system. The advancement in technology has revolutionized the traditional healthcare practices and led to enhanced E-Healthcare System. Modern healthcare systems generate voluminous amount of digital health data. These E-Health data are shared between patients and among groups of physicians and medical technicians for processing. Due to the demand for continuous availability and handling of these massive E-Health data, mostly these data are outsourced to cloud storage. Being cloud-based computing, the sensitive patient data is stored in a third-party server where data analytics are performed, hence more concern about security raises. This paper proposes a secure analytics system which preserves the privacy of patients’ data. In this system, before outsourcing, the data are encrypted using Paillier homomorphic encryption which allows computations to be performed over encrypted dataset. Then Decision Tree Machine Learning algorithm is used over this encrypted dataset to build the classifier model. This encrypted model is outsourced to cloud server and the predictions about patient’s health status is displayed to the user on request. In this system nowhere the data is decrypted throughout the process which ensures the privacy of patients’ sensitive data.
Ontology-based big data analysis for orchid smart farming

Background.Precision agriculture or smart farming is becoming more and more important in modern orchid farming in Thailand.Sensing and communication technologies have witnessed explosive growth in the recent past.These technologies are empowering information systems from many domains such as health care, environmental monitoring and farming, to collect and store large volume of data.Objectives.The research aims to develop an ontology for big data analysis for the smart farming in Rajamangala University of Technology Srivijaya (RUTS), Nakhon Si Thammarat campus.Methods.The ontology design and development process comprises: (1) Ontology design: the domain ontology provide vocabularies for concepts and relations within the orchid domain, and information ontology which specifies the record structure of databases; (2) Ontology development, which consists of five processes: (i) defining the scope, (ii) investigating the existing ontologies and plan to reuse, (iii) defining terms and its relations, (iv) create instances, and (v) implementation and evaluation.Results.The research outcome is the domain ontology and information ontology wherein 11 concepts of smart farming were identified and classified into classes and sub-classes.Contributions.The system is designed for assisting orchid farmers by giving recommended measures and expected results based on the knowledge extracted from best practices.
Indirect Speech Act Analysis of Indonesian Education Department Students of Serambi Mekkah University

This study raises the issue about the employment of indirect speech acts of Indonesian language education students at FKIP Serambi Mekkah University. The purpose of this study was to determine the indirect speech acts of Indonesian language education students FKIP Serambi Mekkah University. The research method used is descriptive qualitative method. The data in this study are indirect speech acts of Indonesian language education students, FKIP, Serambi Mekkah University. The subjects in this study were all Indonesian language students. Data collection was carried out by using listening techniques which included tapping techniques, free speaking listening techniques, conversational free listening techniques, recording techniques, and notetaking techniques. The data analysis technique was carried out by means of data reduction, data presentation, interpreting the meaning of the data, and drawing conclusions. The results of the study found that the utterances spoken by Indonesian language education students at FKIP Serambi Mekkah University contained indirect speech acts. In general, the indirect speech acts used by Indonesian students are news sentences which have the intention of commanding the speech partner to do something, then requesting sentences and news sentences that function to ask the speech partner to give something. Speakers use indirect speech acts when interacting so that the speech conveyed seems more polite and not pushy. Keywords : Pragmatics, Indirect Speech Act, Student Speech
Text-to-Speech Synthesis: Literature Review with an Emphasis on Malayalam Language

Text-to-Speech Synthesis (TTS) is an active area of research to generate synthetic speech from underlying text. The identified syllables are uttered with proper duration and prosody characteristics to emulate natural speech. It falls under the category of Natural Language Processing (NLP), which aims to bridge the gap in communication between human and machine. So far as Western languages like English are concerned, the research to produce intelligent and natural synthetic speech has advanced considerably. But in a multilingual state like India, many regional languages viz. Malayalam is underexplored when it comes to NLP. In this article, we try to amalgamate the major research works performed in the area of TTS in English and the prominent Indian languages, with a special emphasis on the South Indian language, Malayalam. This review intends to provide right direction to the research activities in the language, in the area of TTS.
Method of Extracting Audio-Visual Induced Brain Signals Based on Deep Neural Network

The study of audio-visual induced brain signal extraction is related to the understanding of the nature of human advanced neural information activities, which is of great significance to the development of various human fields. With the development of deep neural network, it provides more sufficient technical support for the presentation of audio-visual brain signals. The purpose of this study is to promote the further application of deep neural network based on the method of extracting audio-visual induced brain signals in other fields. This paper first introduces the concept of audio-visual evoked brain signal and the deep neural network and its algorithm, then proposes a method of extracting audio-visual evoked brain signal on the basis of the existing research and experiment, and carries out a simulation test for the effectiveness of the method. The experimental results showed that the accuracy of the deep neural network method was 21.88% higher than that of the traditional method. The comprehensive score of extraction method increased by 17.38 points, and the final score was 98.72. In general, the audio-visual induced brain signal extraction method proposed based on deep neural network can achieve accurate extraction of brain signals, showing great advantages.
Arab Scholars’ Perceptions on The Quality, Reliability, and Adequacy of Arabic Digital Content

Despite the availability of millions of information resources on the internet, the Arabic digital content represents a relatively small percentage compared with the information available in other languages. The size of Arabic content, the lack of an adequate number of Arabic databases that organize this content and make it available to the Arab reader, and the lack of novelty and originality are the main issues that feature the Arabic content on the internet. The aim of the current study is to clarify the Arab scholars’ perception regarding the quality, reliability, and suitability of Arabic digital content that is available on the internet. A quantitative approach was adopted in this study in order to answer the research questions. A questionnaire was distributed online among a sample of Arab scholars to determine the quality and reliability of the Arabic digital content. Moreover, the questionnaire tried to identify the extent to which the current Arabic digital content meets the growing information needs, to identify the Arab scholars’ uses of Arabic content, and to discover the criteria that determine the digital content suitability. The findings of this study revealed that Arab scholars believe that Arabic digital content is weak and there is a lack of originality. In addition, the results indicated that Arabic digital content on the internet does not satisfy the scholars' needs which enforce them to use English information resources to compensates for the lack of Arabic resources. The study recommended the necessity of establishing mechanisms to support Arabic digital content and increase the academic institutions' role in enhancing Arabic digital content by encouraging and supporting scholarly research in the Arabic language.
Problems of Formation of System Thinking in Modern Russian Education

The construction of new, market-based economic relations in Russia in the early 90s of the last century was accompanied by a radical transformation of the education system to the Bologna standard. At the same time, Western scientists - teachers drew attention to the work on the cognitive development of the child by L.S. Vygotsky, the theory of developmental education by P.Ya. Galperin, the theory of inventive problem solving by G.S. Altshuller and the results of research by other scientists providing the formation of systems thinking in Soviet education.&#x0D; The article shows that the documents regulating the development of Russian education include the formation of systematic and critical thinking in students, but in practice this is not always implemented. The directions of transformation of the Russian education system since the beginning of building market relations in the 90s of the last century are identified as the cause of the current situation.&#x0D; The formation of systems thinking in higher education should consider the peculiarities of the age dynamics of adults. The most developed figurative and verbal thinking among students makes it possible to use the teaching methods of V.F. Shatalov, which reduces the time for studying educational materials by more than 30 times in comparison with classical teaching methods.
Identifying Gender of Internet Users Based on Access History

The use of activities and internet access differs between men and women. On average, men spend more time on the Internet a day. Men also have some of the same online activities as women. However, there are specific differences such as men’s tendency to access features such as breaking news, football, or games and men’s products. On the contrary, women are more interested in shopping, e-commerce, chatting and participating in social networking sites and blogs. The study aims to identify and predict gender of internet users based on their access history. With SVM method, the correct classification rate is the highest compared to the other two models Accuracy = 87.67%, in addition, the Precision, Recall, and F-Score parameters also give outstanding rates. This result allows us to believe in the ability of the SVM machine learning model to effectively handle the classification and gender identification problem with large-dimensional data.
Text Classification on Tamil

By and large, we don't know to talk and read the territorial dialects that are spoken in our nation. So we have accepted Tamil language as it is our territorial and numerous doesn't get it. In our task, the content in Tamil language is stacked from Wikipedia. It is then sifted through and extraordinary characters are evacuated it is then characterized by the titles like id, title, URL, etc. It is then used to prepare the model utilizing CNN calculation and the dataset is created. Along these lines, you would now be able to test utilizing an irregular Wikipedia page and the content is grouped by the titles and anticipated.
Modelling the Outlier Detection Problem in ASP(Q)

Knowledge discovery techniques had important impact in several relevant application domains. Among the most important knowledge discovery tasks is outlier detection. Outlier detection is the task of identifying anomalous individuals in a given population. This task is very demanding from the computational complexity point of view, being located in the second level of the polynomial hierarchy. Angiulli et al. in 2007 proposed to employ Answer Set Programming (ASP) to compute outliers. Their solution is based on the saturation technique and, as a consequence, it is very hard to evaluate by ASP systems. In this paper we resort to Answer Set Programming with Quantifiers (ASP(Q)) to provide a more declarative, compact and efficient modeling of the outlier detection problem. An experiment on syntetic benchmarks proves that our ASP(Q)-based solution can handle databases that are three order of magnitude larger than the ASP-based one proposed by Angiulli et al.
Show-and-Tell or Hide-and-Seek? Examining Organizational Cybersecurity Incident Notifications

The growing frequency of cybersecurity incidents commonly requires organizations to notify customers of ongoing events.However, the content contained within these notifications varies widely, including differences in the level of detail, apportioning of blame, compensation, and corrective action.This study seeks to identify patterns contained within cybersecurity incident notifications by constructing a typology of organizational responses.Based on a detailed review of 465 global cybersecurity incidents that occurred during the first half of 2020, we obtained and qualitatively analyzed 187 customer notifications.Our results reveal three distinct organizational response types associated with the level of detail contained within the notification (full transparency, guarded, opacity), as well as three additional response types associated with the benefitting party (customer interest, balanced interest, company interest).This work extends past classifications of cybersecurity incident notifications and provides a template of possible notification approaches that could be adopted by organizations.