Reviewerly/exHarmony huggingface dataset:
Jointly Learning Salience and Redundancy by Adaptive Sentence Reranking for Extractive Summarization

Extractive text summarization seeks to extract indicative sentences from a source document and assemble them to form a summary. Selecting salient but not redundant sentences has always been the main challenge. Unlike the previous two-stage strategies, this paper presents a unified end-to-end model, learning to rerank the sentences by modeling salience and redundancy simultaneously. Through this ranking mechanism, our method can improve the quality of the overall candidate summary by giving higher scores to sentences that can bring more novel information. We first design a summary-level measure to evaluate the cumulating gain of each candidate summaries. Then we propose an adaptive training objective to rerank the sentences aiming at obtaining a summary with a high summary-level score. The experimental results and evaluation show that our method outperforms the strong baselines on three datasets and further boosts the quality of candidate summaries, which intensely indicate the effectiveness of the proposed framework.
Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method

Neural machine translation (NMT) usually employs beam search to expand the searching space and obtain more translation candidates. However, the increase of the beam size often suffers from plenty of short translations, resulting in dramatical decrease in translation quality. In this paper, we handle the length bias problem through a perspective of causal inference. Specifically, we regard the model generated translation score S as a degraded true translation quality affected by some noise, and one of the confounders is the translation length. We apply a Half-Sibling Regression method to remove the length effect on S, and then we can obtain a debiased translation score without length information. The proposed method is model agnostic and unsupervised, which is adaptive to any NMT model and test dataset. We conduct the experiments on three translation tasks with different scales of datasets. Experimental results and further analyses show that our approaches gain comparable performance with the empirical baseline methods.
Readability Enhancement for PDF Documents

Readability has been studied for decades, ranging from traditional paper reading to digital document reading, Web page reading, etc. Different audiences have different needs and the needs trigger the researchers to investigate innovative solutions. For example, in recent years, researchers have studied readability enhancement of English articles for non-native English readers, either on paper reading or hypertext document reading. Using a variety of methods, researchers were able to enhance the reading comprehension and the users’ satisfaction on hypertext document reading, such as changing content presentation with visual-syntactic text formatting (VSTF) format or Jenga format. In terms of dynamically changing content presentation for reading, one less explored format is Portable Document Format (PDF), which was traditionally viewed within a modern Web browser or Adobe Acrobat reader on the desktop. PDF format was standardized as an open format in 2008 and has been widely used to keep a fixed-layout content. However, a fixed layout document presents a challenge to apply existing transformation methods, not mention on mobile devices. In this paper, we not only present a system that uses a novel algorithm to decode PDF documents and apply content transformation to enhance its readability, but we also generalize it to a framework that allows the users to apply customizations and the developers to customize their needs. Although we used Jenga format as an example to enhance the readability of PDF documents, we envision the proposed framework can be used to adopt different customizations and transformation methods. The current result is promising, and we believe it is worth further investigation to make PDF documents readable and accessible for different populations, such as non-native English readers, people with dyslexia or special needs, etc.
Development of digital mathematics teaching materials in elementary schools using whiteboard animation for primary teacher education students STKIP PGRI Lubuklinggau

Abstract The problems that arise in the world of education today include the low competence of teachers and lecturers in the era of education 4.0, namely developing teaching materials or digital material therefore a teacher or lecturer must have various skills including skills in developing the right digital mathematics teaching materials in elementary schools using whiteboard animation for primary teacher education students. This study aims to develop digital mathematics teaching materials in elementary schools using whiteboard animation for primary teacher education students for elementary school mathematics, whiteboard animation for elementary school teacher education students of STKIP PGRI Lubuklinggau which has been tested valid and practical. This study uses a development research approach from the preliminary analysis stage, the design stage, and the assessment stage. The subjects of this study were students of the fifth semester of elementary school teacher education at STKIP PGRI Lubuklinggau. Data collection techniques using questionnaires, observation, or interviews. The data analysis technique used is a descriptive analysis that describes the validity and practicality. The research has a very valid teaching material product with a score of 95% and practical with an average score of 92.5%. Thus, the teaching materials in this study can be used because they have been declared valid and practical.
Devanagari Handwritten Word Recognition for Extended Character Set Using Segmented Character Strokes

Devanagari Handwritten character recognition is an active and open field of research. It is gaining popularity due to its potential applications in the areas such as Postal Automation, automation of filling forms in Banking and Government office, Schools, colleges, etc. Devanagari script is one of the most adopted writing systems and it is the basis for more than 25 languages such as Marathi, Sanskrit, Hindi, Nepali, etc. Devanagari script used by around 18% of the world’s population. Therefore, it arises the necessity to detect Online Devanagari Handwritten word to reduce the typing tasks and save time in the above-mentioned fields. In this paper, we propose an approach to recognize Devanagari characters written using a mouse, pen input devices, touch-screen devices. In the contrast to optical character recognition systems where typically the input to the system is a scanned image of the character drawn, our system needs online time-series data corresponding to the character. Each Devanagari character is realized as sequence of strokes, each of which can be drawn without lifting the pen. Unlike the existing approaches, our system works with extended character set (which includes all the constituting strokes of the characters). The dataset for our model consists of total 100 constituting strokes of all the characters. It includes half-characters, full-characters, characters with different modifiers like kana, matra, velanti, and ukar. Since we are recognizing individual strokes, it facilitates in recognition of composite characters in Devanagari script. In this approach, we use top 30 dominating frequency components from the spectrum of the stroke as features. DCT is used to get the spectrum of the input stroke. Then the Neural Network is trained on these features.
Syntactic Enhanced Projection Network for Few-Shot Chinese Event Extraction

Few-shot learning event extraction methods gain more and more attention due to their ability to handle new event types. Current few-shot learning studies mainly focus on English event detection, which suffering from error propagation due to the identify-then-classify paradigm. And these methods could not be applied to Chinese event extraction directly, because they suffer from the Chinese word-trigger mismatch problem. In this work, we explore the Chinese event extraction with limited labeled data and reformulate it as a few-shot sequence tagging task. To this end, we propose a novel and practical few-shot syntactic enhanced projection network (SEPN), which exploits a syntactic learner to not only integrate the semantics of the characters and the words by Graph Convolution Networks, but also make the extracted feature more discriminative through a cross attention mechanism. Differing from prototypical networks which may lead to poor performance due to the prototype of each class could be closely distributed in the embedding space, SEPN learns to project embedding to space where different labels are well-separated. Furthermore, we deliberately construct an adaptive max-margin loss to obtain efficient and robust prototype representation. Numerical experiments conducted on the ACE-2005 dataset demonstrate the efficacy of the proposed few-shot Chinese event extraction.
Review of the collection of documents and materials: Under the banner of Komuch: (Samara Region, June-October 1918). &#x0D; Kalyagin A.V. (Ed.). Samara, 2018

A review of the collection of documents and materials Under the banner of Komuch published in 2018 in Samara by a team of employees of the Samara Regional Universal Scientific Library and the Central State Archives of the Samara Region is presented; its structure, content and the array of historical documents introduced into scientific circulation are evaluated. The study of the short but bright period of the summer-autumn of 1918, when the power of the Bolsheviks was overthrown on a large territory of the Volga region and the government of the Committee of Members of the Constituent Assembly (Komuch) was created in Samara, claiming all-Russian status, was studied fragmentarily in Soviet times. For a hundred years after these events, in the 1920-ies, only a few collections of documents related to Komuch were published, when censorship was developing in Soviet Russia. The review emphasizes that a number of documentary publications from the funds of Russian and emigrant archives published in the post-Soviet period do not diminish the importance of the actual meaningfulness of new editions of collections of documents. It is the sources, and not their interpretations, even scientific ones, that are important to both researchers and readers seeking to independently comprehend the dramatic events of that time. It is noted that the collection Under the Banner of Komuch provides such an opportunity. The compilers pay special attention to the previously published collections of orders and decrees, many local materials that usually remain on the periphery, which contain historical information about the people of that time and their, including everyday needs: improvement, supply, law and order, and so on.
Distance-Based Skeletonization on the BCC Grid

Strand proposed a distance-based thinning algorithm for computing surface skeletons on the body-centered cubic (BCC) grid. In this paper, we present two modified versions of this algorithm that are faster than the original one, and less sensitive to the visiting order of points in the sequential thinning phase. In addition, a novel algorithm capable of producing curve skeletons is also reported.
Information modeling and redundant data processing of mechanical product configuration design

With the wide application and deep integration of Internet and other information technology and manufacturing enterprise information system, manufacturing big data with mass, high speed, diversity, value and other characteristics has been accumulated. According to the characteristics of mechanical product configuration design information in the big data environment, this paper studies the construction of product configuration design information model [1] and redundant data processing. The specific research contents are as follows: Based on the analysis of the sources and characteristics of mechanical product configuration design information big data, this paper puts forward a galaxy model suitable for mechanical product configuration design information, and integrates the information in product configuration design information into the galaxy model Structured data, unstructured data and semi-structured data are expressed as JSON [2] (JavaScript object notation) metadata; combined with the sensitivity and semantic similarity of keyword data source, the text similarity calculation method of JSON data file is proposed, and the redundant data processing of mechanical product configuration design information is realized.
Generation of Adaptation Strategies for Dynamic Reconfiguration of a System of Systems

A system of systems (SoS) is comprised of constituent systems that interact with each other to achieve a common goal. An SoS operates in an environment with dynamically changing external conditions. Therefore, supporting the resilience of the SoS is crucial to sustain goal achievement at run-time. One way to support this resilience is to generate an adaptation strategy at run-time that guides the dynamic reconfiguration of the SoS. Therefore, adaptation strategies should be effectively generated within a reasonable time at run-time to conduct the dynamic reconfiguration in time. To satisfy this requirement, we propose an adaptation strategy generation approach that consists of two phases. 1) At design time, our approach constructs a knowledge base that contains adaptation strategies for expected environmental conditions. 2) At/during run-time, it generates adaptation strategies by utilizing the knowledge base. Through a case study, we show that our approach can generate adaptation strategies effectively at run-time.
AN EFFICIENT APPROACH TO REDUCING MEMORY CONSUMPTION FOR STORING THE LARGE NUMBER OF SMALL SIZE FILES IN HDFS

Hadoop is the open Source framework of apache and Hadoop framework is developedfor work with Big-Data. It is also specially design for distributed data management.It is used to data storing and accessing data with respect to large size data files. Hadoop Distributed File System (HDFS) used to store the large size data files in the cluster environment of computer system. Hadoop software implementations and framework which has two different components MAPREDUCE and HDFS. Now day HDFS became very popular to manage and handle large size files with very high performance. But the working with the large number of small size files becomes current challenged in the HDFS. Large number of small files impose more burdens of memory consumption, creationas well as management of metadata on NameNode [10].This paper present on propose approach and analysis for memory consumption to existing HDFS and propose framework to enhance the memory management for processing large numbers of small size files in HDFS.
Comparing SVM and Neural Networks’ performance in Face Detection

There exists an abundance of literature where researchers have attempted to improve security using face detection and recognition. Having studied the diverse models proposed in these works, we have come to appreciate the insightful nature of the current research on security and privacy. Although the current research works are commendable, we argue that a thorough examination of the methods can help address the pitfalls to come forward with even more advanced models. The application of computer vision in augmenting security and privacy captivated our attention the most. Thus, we draw comparisons between Support Vector Machines (SVMs) and Neural Networks (NNs) for face recognition from images. The frequent usage of SVM and NN over the years in face detection applications prompted us to choose these two approaches. Keeping data augmentation and HOG as the feature extraction method, we compare the models developed to find the best approach for facial recognition. We used the combination of F1 Score, Accuracy, Precision, and Recall to compare both models.
Simple and Ingenious Mobile Botnet Covert Network Based on Adjustable Unit (SIMBAIDU)

Various services through smartphones or personal computers have become common nowadays. Accordingly, embedded malware is rapidly increasing. The malware is infiltrated by using short message service (SMS), wireless networks, and random calling and makes smartphones bots in botnets. Therefore, in a system without an appropriate deterrent, smartphones are infiltrated easily. In the security threats by malware, random calling has become serious nowadays. To develop the defensive system against random calling and prevent the infiltration of the malware through random calling, it is required to understand the exact process of how to make bots in the botnet. Thus, this research develops a simple and ingenious mobile botnet covert network based on adjustable ID units (SIMBAIDU) to investigate how a botnet network is established by using phone numbers. Perfect octave coding (P8 coding) turns out to be effective in infiltrating smartphones and executing commands, which is used for botnets. The results provide the basic process of P8 coding which is useful for developing defensive systems of smartphones.
Invariant of AES algorithm implementations against Attacks in IoT Devices

Internet-of-Things (IoT) establish connection between billions of smart devices, performing a diverse range of purposes. These devices embedded with sensors, software and network to exchange and collect data with each other. In IoT hardware security is the main task for researchers because large number of devices connected day by day with each other. As researchers concern, latest research is now focused on seeing attack and defensive site of IoT devices. Side channel attacks are one of the major concerns for researchers who work on IoT. This paper presents an implementation of AES algorithm with proposed false key mechanism and power reduction technique against SCA in IoT devices.
Design and Implementation of a Small-scale Autonomous Vehicle for Autonomous Parking

In this paper, we introduce the design and implementation of a low-cost, small-scale autonomous vehicle equipped with an on-board computer, a camera, a Lidar, and some other accessories. We implement various autonomous driving-related modules including mapping and localization, object detection, obstacle avoidance, and path planning. In order to better test the system, we focus on the autonomous parking scenario. In this scenario, the vehicle is able to move from an appointed start point to a desired parking lot autonomously by following a path planned by the hybrid A* algorithm. The vehicle is able to detect objects and avoid obstacles on its path, and achieve autonomous parking.
Pupil size detection based on convolution neural network applied on portable pupillometer

The pupil response to light is usually used for inspection of a patient's activity. When medical personnel performs light reflection examinations, it depends on the experience of medical staff. Even for the same person, they may induce variant measurement results in different situations. This paper proposes an algorithm that can calculate pupil size based on Convolution Neural Network (CNN). The study aims to measure pupil size on a mobile device convenient for medical staff to measure real-time. Still, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses for the best fitting. Therefore, we use the major and minor axis of an ellipse to represent the size of pupils and use the two parameters as the output of the network. The compares the mean error with changing the depth of the network and the field of view (FOV) of the convolution filter. Finally, the mean error of the pupil length is 7.63%. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. In the operation speed, we use mobile device systems at 36 frames per second to use mobile device systems for pupil size prediction.
Single Image Super-Resolution with U-Net Generative Adversarial Networks

It is very challenging to recover a High Resolution (HR) image with real texture from a single Low Resolution (LR) image. SRGAN[1] first applied GAN(Generative Adversarial Network) in the field of image Super-Resolution, and restored a relatively real texture HR. However, SRGAN's reconstructed HR often contains unreal artifacts and distortions. Subsequently ESRGAN[2] has improved this problem, but it still has the shortcomings of not sharp edges of objects and slow reconstruction speed. To further improve the perception quality and accelerate the reconstruction speed, we proposed an image super-resolution algorithm (SR) based on U-Net GAN [3]. As the basic block, we introduced the Residual-in-Residual Self-calibrated Convolution with Pixel Attention block(RRSCPA) [4]. From [5] to heuristic, we design the discriminator into a U-shaped structure, which can provide per-pixel feedback to the generator and promote the generator to generate a more realistic HR. Finally, we replaced the VGG-based perceptual loss[6] with the LPIPS perceptual loss[7] function. Our proposed U-Net SRGAN achieves consistently better visual quality with more realistic and natural textures than ESRGAN.
Prospects of IoT technology

Introduction. Internet of Things (IoT) is one of the promising innovative technologies. Every year more and more people are involved in the use of smart things. At the same time, a relatively small number of papers are devoted to the study of the social value of technology and the experience of human interaction with this technology. It is important to study the features and prospects of the technology, to analyze the attitude and willingness of people to use it. Materials and Methods. We have conducted an Internet survey, in which special attention is paid to the place of IoT in the life of modern people, their attitude to the concept of devices. The obtained data is processed and systematized. Results. The analysis of the survey results allowed us to draw conclusions regarding the attitude and willingness of young people to apply this technology. In the course of the study, the IoT concept was defined, the conditions required for the existence and functioning of the technology were described, the advantages of IoT technology were generalized, information technologies interacting with this technology were specified, the tasks that require solutions for the successful and effective implementation of IoT into Russian reality were listed. Discussion and Conclusions. The Internet of Things is a technology that, with a consistent and systematic solution to a number of problems, can become a significant factor in the development of both individual spheres of life and activity, and the country as a whole. At the same time, it is important to study and consider the social impact of technology dissemination. This will increase trust in the IoT and eliminate negative impacts. The survey shows that young people tend to use smart things more widely. It is necessary to expand the range of smart things, to more confidently introduce the basics of practical application of IoT technology into educational programs, to discuss issues, ways to solve the tasks and pilot projects related to this technology widely in the media. This will enable to train not only people who are practically interested in IoT, but also qualified personnel who are able to solve problems in a new way.