Reviewerly/exHarmony huggingface dataset:
Students’ Projects Based on Web Engineering

The main purpose of a university have for decades been teaching, project, research and service. And then the university must produce the perfected skills and knowledge web engineers. The students engage with the industry, community and business to solve their problems. Students that involved the projects gain project’s experience and easy to understand the theory and method of web engineering. So, Final year students construct a project based on Web Engineering. This paper presents how to support the courses, Web engineering from learned previous academic years and how to create the students’ Web Application projects.
Theme-aware generation model for chinese lyrics

With rapid development of neural networks, deep-learning has been extended to various natural language generation fields, such as machine translation, dialogue generation and even literature creation. In this paper, we propose a theme-aware language generation model for Chinese music lyrics, which improves the theme-connectivity and coherence of generated paragraphs greatly. A multi-channel sequence-to-sequence (seq2seq) model encodes themes and previous sentences as global and local contextual information. Moreover, attention mechanism is incorporated for sequence decoding, enabling to fuse context into predicted next texts. To prepare appropriate train corpus, LDA (Latent Dirichlet Allocation) is applied for theme extraction. Generated lyrics is grammatically correct and semantically coherent with selected themes, which offers a valuable modelling method in other fields including multi-turn chatbots, long paragraph generation and etc.
Lattices from tight frames and vertex transitive graphs

We show that real tight frames that generate lattices must be rational, and use this observation to describe a construction of lattices from vertex transitive graphs. In the case of irreducible group frames, we show that the corresponding lattice is always strongly eutactic. This is the case for the more restrictive class of distance transitive graphs. We show that such lattices exist in arbitrarily large dimensions and demonstrate examples arising from some notable families of graphs. In particular, some well-known root lattices and those related to them can be recovered this way. We discuss various properties of this construction and also mention some potential applications of lattices generated by incoherent systems of vectors.
A Group-Oriented Strong Designated Verifier Signature Scheme with Constant-Size Signatures

A strong designated verifier signature (SDVS) scheme only permits an intended verifier to validate the signature by employing his/her private key. Meanwhile, for the sake of signer anonymity, the designated verifier is also able to generate a computationally indistinguishable transcript, which prevents the designated verifier from arbitrarily transferring his conviction to any third party. To extend the applications of conventional SDVS schemes, in this paper, we propose a group-oriented strong designated verifier signature (GO-SDVS) scheme from bilinear pairings. In particular, our scheme allows a group of signers to cooperatively generate a signature for a designated verifier. A significant property of our mechanism is constant-size signatures, i.e., the signature length remains constant when the number of involved signers increases. We also prove that the proposed GO-SDVS scheme is secure against adaptive chosen-message attacks in the random oracle model and fulfills the essential properties of signer ambiguity and non-transferability.
Study On Different Image Quality Assessment Techniques For Gray Scale Images

Quality is a very important parameter for all objects and their functionalities. For authentic quality evaluation ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, SSIM (Structured Similarity Indexing Method) is proposed which compares the structural measure between obtained and original images. This paper is mainly stressed on SSIM and compares the finding with MSE and PSNR. To measure the image quality we have done a simulation work by adding noise to bench-marked original images and then calculated MSE, PSNR and SSIM of the corresponding image. We have found the superiority of the SSIM in comparison to MSE and PSNR.
PERCEPTION OF CLIENTS OF GYMNASTICS ACADEMIES IN RELATION TO THE PHYSICAL EDUCATION PROFESSIONAL

The physical education professional has become a service provider, considering that in the current society the cultureof the body is expressed increasingly strong in the multiple issues inherent to it, thus being extremely important that theprofessionals are Qualified to provide a quality service, since this product will return customer satisfaction, consequently its futureprofessional in this area depended on this relationship. The objective of this work was to establish the level of customersatisfaction of gym academies in relation to physical education professionals. The present study presents a transversal characterwith a quantitative approach. 60 volunteers participated in this study. To obtain the results was used an online questionnairedeveloped through a tool of Google Docs. The questionnaire was elaborated with the purpose of addressing issues inherent inthe practice of physical exercises in gym academies and satisfaction with the physical education professional. The group inquestion expressed a higher percentage satisfied with the results achieved, the largest percentiles were also established for theaspects inherent to the performance of the physical education professional. It is concluded that the clients are satisfied with theperformance of the physical education professional, a reflection of satisfaction in relation to the successful goals.
Making frequency distributions tangible

This article discusses about how to produce clear and effective data graphics and how to portray sets of numbers in a concrete and intuitive way.
Particle Physics at 6am

This is a story about waking up early...My brain has been buzzing this week. There has been enormous pressure on me to deliver a complex
GoDaddy Website Builder Review: is GoCentral Good or Bad?

It’s time to expand your operation. You can no longer function as a simple blog. Your brand needs some backup for all that momentum, so you need to build a website. With so many fantastic options available, it can be extremely difficult to choose the right website builder. How do you know which ones are great? Can you trust them? In this article, we give you a GoDaddy website builder review (the GoCentral website builder), that GoDaddy promotes.
Multi Party Computation Motivated by the Birthday Problem

Suppose there are n people in a classroom and we want to decide if there are two of them who were born on the same day of the year. The well-known birthday paradox is concerned with the probability of this event and is discussed in many textbooks on probability. In this paper we focus on cryptographic aspects of the problem: how can we decide if there is a collision of birthdays without the participants disclosing their respective date of birth. We propose several procedures for solving this in a privacy-preserving way and compare them according to their computational and communication complexity.
TRENDS IN THE DEVELOPMENT OF ARCHITECTURE SOLUTIONS FOR WEB PUBLISHING SYSTEMS

This article deals with issues related to uploading contents on the Internet. Within this context, the specific attributes of information systems for web publishing and solutions for their architecture are reviewed. The paper analyses some conceptual problems related to those systems in terms of the opportunities provided to users to create, publish, share or comment on the content on the world wide web. It also presents the typical architecture of web publishing systems. Based on the conducted research, the paper presents findings and hypotheses about trends in the development of architecture solutions for web publishing systems. The focus of the paper is on the growing significance of social networks nowadays, and especially of social networks with interactive content. The conclusion summarizes the advantages of applying existing trends in the development of architecture solutions for web publishing systems to the design of software solutions in the sphere.
A First Approach to Distance Education for the Teaching of Business English in the Superior School of Energy and Mining

This paper aims at giving a general overview of the steps followed to implement the distance course for business English teaching in this school.There is an ever-increasing demand for English teaching and learning activities in Cuba as worldwide.The directors, managers, buyers and specialist from different branches of the Ministry of Energy and Mining need to prepare in this language, but there is a shortage of time for studying face to face and here it is where the distance education begins to play its important role in the preparation of the Ministry staff.This course is an alternative for higher rank officers at the Ministry to improve their business English skills.The course is set up in Moodle e-learning platform which eases the teaching process not only for the students but also the professor.The article provides with the course structure as well as an explanation of each one.It socializes some advantages and disadvantages found while implementing this course.
apricot: Submodular selection for data summarization in Python

We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greedy selection algorithm that offers strong theoretical guarantees on the quality of the selected set. Two submodular set functions are implemented in apricot: facility location, which is broadly applicable but requires memory quadratic in the number of examples in the data set, and a feature-based function that is less broadly applicable but can scale to millions of examples. Apricot is extremely efficient, using both algorithmic speedups such as the lazy greedy algorithm and code optimizers such as numba. We demonstrate the use of subset selection by training machine learning models to comparable accuracy using either the full data set or a representative subset thereof. This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets. The code and tutorial Jupyter notebooks are available at https://github.com/jmschrei/apricot
Unsupervised Pivot Translation for Distant Languages

Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not work well for distant languages. In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation. We propose a learning to route (LTR) method to choose the translation path between the source and target languages. LTR is trained on language pairs whose best translation path is available and is applied on the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the advantages of the unsupervised pivot translation for distant languages, as well as the effectiveness of the proposed LTR for path selection. Specifically, in the best case, LTR achieves an improvement of 5.58 BLEU points over the conventional direct unsupervised method.
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis

In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context word on the given aspect. However, such a mechanism tends to excessively focus on a few frequent words with sentiment polarities, while ignoring infrequent ones. In this paper, we propose a progressive self-supervised attention learning approach for neural ASC models, which automatically mines useful attention supervision information from a training corpus to refine attention mechanisms. Specifically, we iteratively conduct sentiment predictions on all training instances. Particularly, at each iteration, the context word with the maximum attention weight is extracted as the one with active/misleading influence on the correct/incorrect prediction of every instance, and then the word itself is masked for subsequent iterations. Finally, we augment the conventional training objective with a regularization term, which enables ASC models to continue equally focusing on the extracted active context words while decreasing weights of those misleading ones. Experimental results on multiple datasets show that our proposed approach yields better attention mechanisms, leading to substantial improvements over the two state-of-the-art neural ASC models. Source code and trained models are available at https://github.com/DeepLearnXMU/PSSAttention.
Online Food Ordering System

As Sweegy, Zomato, Food panda are limited with only restaurants and cafe where we can place orders.All the time it is not affordable and healthy for people to order the food from the restaurants, so there is need of an application which will provide the facility of home -made food and mess too.So from this application, we are filling the gap of availability of mess and chef which is not in other food delivery apps.First Highlighting feature of our application is chef-at-home.And second is Free Food in which user can earn through performing the tasks and same can be used for ordering the food from app only.This app will deal with the real time data as data of food posted on each day will be invalid after one day.Various messes can register through this app and can start their own business.The App will also provide the facility of booking the caterers for various events.
조지아어와 한국어의 증거성에 대한 대조언어학적 연구

Evidentiality is an independent grammatical category to express the information source of the statement. This paper aims to provide a contrastive analysis of evidentiality in Georgian and Korean. In Georgian, perfect represents inference as an evidential strategy, and the quotative particles are used as a quotative evidential. Korean has five evidential choices instead of the previously suggested three choices. Based on universal semantic parameters of evidential systems and the cross-linguistic comparison of the evidential strategies of Georgian, this paper argues that the Korean periphrastic construction -ul kesi- is an assumption evidential marker and the particle -tako is a quotative evidential maker.
Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line Enhancing

In this paper, our goal is salient building outline enhancement and extraction from images taken from consumer cameras using L0 smoothing. We address weak outlines and over-smoothing problem. Weak outlines are often undetected by edge extractors or easily smoothed out. We propose an iterative method, including the smoothing cell and sharpening cell. In the smoothing cell, we iteratively enlarge the smoothing level of the L0 smoothing. In the sharpening cell, we use Hough Transform to extract lines, based on the assumption that salient outlines for buildings are usually straight, and enhance those extracted lines. Our goal is to enhance line structures and do the L0 smoothing simultaneously. Also, we propose to create building masks from semantic segmentation using an encoder-decoder network. The masks filter out irrelevant edges. We also provide an evaluation dataset on this task.
Primo Usability with Diverse Populations in Mind

Cal State East Bay is consistently ranked as one of the most diverse campuses in the United States. The Cal State East Bay Library prides itself on supporting our diverse student body with their research needs. Finding inspiration from a previous ELUNA presentation regarding opportunities for equity, librarians at Cal State East Bay took the opportunity to work with our diverse student body to understand their experience with and reaction to Primo. While most articles about usability testing on Primo relied on students completing tasks, we were interested in learning about the user’s perspective of the tool in terms of inclusivity and approachability. In order to understand student reactions to Primo, the presenters conducted focus groups of students. We plan to share what our students told us about their experiences, as well as what changes we implemented to improve their experience. Hopefully, you will walk away inspired to listen to your students experiences with Primo!
METRIC-BASED FRAMEWORK FOR TESTING &amp; EVALUATION OF SERVICE-ORIENTED SYSTEM

The increase in the significance of service orientation in system development is accelerating with an increase in demand for qualitative and cost-effective systems.Service-Oriented Architecture (SOA) is one of the established structural designs used for developing and implementing flexible, reusable, rapid and low-cost service-oriented systems.The established testing and evaluation methods don't work well for systems that are made-up of services (service-oriented system).As a result, several testing and evaluation metrics for service-oriented systems were proposed.However, these metrics were created based on preceding software development approaches that offer insufficient focus to service-oriented systems thereby lacking the efficiency to evaluate these systems.Furthermore, Lack of access to source code also frustrates classical mutation-testing approaches, which require seeding the code with errors.This paper discusses different testing and evaluation metrics available for SOS and proposed a theory-grounded framework for testing and evaluation of service-oriented systems with the aim of decreasing cost and increasing the quality of the SOS.Then, the proposed framework is validated theoretically to check its usability and applicability for testing and evaluation of SOS.The results show that the proposed framework is able to decrease cost and increasing the quality of the SOS.
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future

Common language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does not require any linguistic annotation of phrase segmentation. Instead, we define syntactic heights and phrase segmentation rules, enabling the model to automatically induce phrases, recognize their task-specific heads, and generate phrase embeddings in an unsupervised learning manner. Our method can easily be applied to language models with different network architectures since an independent module is used for phrase induction and context-phrase alignment, and no change is required in the underlying language modeling network. Experiments have shown that our model outperformed several strong baseline models on different data sets. We achieved a new state-of-the-art performance of 17.4 perplexity on the Wikitext-103 dataset. Additionally, visualizing the outputs of the phrase induction module showed that our model is able to learn approximate phrase-level structural knowledge without any annotation.
Study and Analysis of the Religious Issues and Concepts of the Poetry Books for Children and Adolescents among 2011-2014

In recent years, religious subjects have received much attention in the field of children and adolescent's literature, and poets and writers also have shown great interest in writing story and composing poetry about the religious themes. Children and adolescent's literature especially in the form of poetry, which for its song, rhyme and short duration is more impressive than the story, can be considered as an attractive and  low-cost media for every society’s culture. The content and the concept of the poetry used for children are so important since it can play a significant role in the children education and upbringing, so each adult person as a father, mother, teacher, and etc. should pay attention to the content and everything else which can poems convey to the children and adolescents. So for the importance of this issue, this study aims to study children and adolescent's’ poetry books among 2011-2014 to find out to what extent poets use variety of religious issues and concepts and also to investigate the quality of the poetry concepts. The results of the present study show that in some religious concepts, a few poems have been written by the poets and also some of the content of the poems has not been able to provide religious concepts well. Therefor the results of this study can pave the way for the poets to write poetry in these cases.
A Calculus of Interfaces for Guarded Attribute Grammars

We address the problem of component reuse in the context of service-oriented programming and more specifically for the design of user-centric distributed collaborative systems modelled by Guarded Attribute Grammars. Following the contract-based specification of components we devel-opp an approach to an interface theory for the components of a collaborative system in three stages: we define a composition of interfaces that specifies how the component behaves with respect to its environement, we introduce an implementation order on interfaces and finally a residual operation on interfaces characterizing the systems that, when composed with a given component, can complement it in order to realize a global specification.
A Closed-Form Learned Pooling for Deep Classification Networks

In modern computer vision tasks, convolutional neural networks (CNNs) are indispensable for image classification tasks due to their efficiency and effectiveness. Part of their superiority compared to other architectures, comes from the fact that a single, local filter is shared across the entire image. However, there are scenarios where we may need to treat spatial locations in non-uniform manner. We see this in nature when considering how humans have evolved foveation to process different areas in their field of vision with varying levels of detail. In this paper we propose a way to enable CNNs to learn different pooling weights for each pixel location. We do so by introducing an extended definition of a pooling operator. This operator can learn a strict super-set of what can be learned by average pooling or convolutions. It has the benefit of being shared across feature maps and can be encouraged to be local or diffuse depending on the data. We show that for fixed network weights, our pooling operator can be computed in closed-form by spectral decomposition of matrices associated with class separability. Through experiments, we show that this operator benefits generalization for ResNets and CNNs on the CIFAR-10, CIFAR-100 and SVHN datasets and improves robustness to geometric corruptions and perturbations on the CIFAR-10-C and CIFAR-10-P test sets.
Best WordPress Image Optimizer Plugin: Top Compression Options

Most website owners either don't know about the WordPress image optimizer options available to them, or they ignore the fact that images add to the size of their site because they feel that the visual element images add is important for user engagement. This is true of course, but the site’s performance suffers. Thanks to some of these WordPress image optimizer plugins however, you can get the best of both worlds!