Reviewerly/exHarmony huggingface dataset:
Designing a road tool for blind people using the computer minimum system

Abstract This research aims to design blindness devices namely sticks using a minimum computer system, expected to help blind people in their activities and interact with their surrounding environment. This research uses descriptive method, where in the process of conducting research through the stages of Information System development using a waterfall model which is one of the models in the SDLC (System Development Life Cycle). The stages carried out in this study are comprehensive analysis of development needs and System planning. The results of this study are in the form of blind road aids using the minimum computer system. Where after having this system design, it can proceed to the implementation stage
When Cartoon Meets Anime Distinguishing Animation Styles with Convolutional Neural Networks

There are various myths surrounding the pop culture, though each of them can be quite fascinating at times, one of them in particular, “Are Anime Cartoon?” succeeds to keep our interest while compelling us to venture deep into a typical machine learning algorithm, Convolutional Neural Network (CNN) to unravel the same. We thus seek to provide concrete proofs against the false speculations that claim anime and cartoons are all the same. Moreover, we would like to see if a machine can learn to classify two different types of animation formats. And to top it all, since to the best of our knowledge, this peculiar problem remains almost unaddressed, it becomes more than compelling for us to spend our resources on this endeavor.After much deliberation, we decided to choose Convolutional Neural Networks to base our research on. Moreover, as evident from various researches in the field of image classification, unlike traditional methods like multilayer perceptron, Convolutional Neural Networks extracts successively larger features in a hierarchical set of layers and thus, is best suited for our problem consisting of images with a large number of variations. For our dataset, we collected over 25,000 images from various Cartoon and Anime shows. For training, we chose 12,000 images from both categories, and the remaining images which constitute a mixture of Anime and Cartoon are used to test our model. After running multiple tests, we are quite satisfied with our model, as it has training accuracy of over 97.4% and validation accuracy of over 97.3%. This allows us to prove our initial hypothesis that anime and cartoons are, indeed, different. Moreover, since our problem involves classifying a large number of varying images, we also need to take into consideration computational complexity and discuss how various techniques can be used to optimize our model without increasing the need for computing resources.
Hierarchical Approach Towards High Fidelity Image Generation

The high-fidelity image generation has been a subject of active research in the recent past. It provides benchmark towards image decoder’s performance. The autoregressive image models generate small images successfully but scalability has been a problem. The challenges include vast encoding of previous context and learning distribution which maintains global semantic coherence and exactness. These issues have been addressed through subscale pixel network (SPN) and multidimensional upscaling. To improve the accuracy further, in this work a hierarchical version of image generation model is presented. It disentangles background, object shape and appearance to hierarchically generate images of fine-grained object categories. To achieve this information theory associates a factor to latent code and condition relationships between codes to induce hierarchy. The hierarchical model’s learned features are used to cluster real images. The experimental results on ImageNet and CelebAHQ datasets for different image sizes highlight hierarchical model’s superiority against the benchmarks. The images are generated with better fidelity with respect to large scale samples.
Building an Environment for Agility: A Releasable Inventory Tool

In order to know the real status of all the applications and systems in a company, an inventory of all the elements that could be released can be very useful. This paper presents a Releasable Inventory Tool, which was developed to support Agility. It facilitates the creation of an environment aligned to Lean and Agile principles. The releasable inventory enables strategic, tactical, and operational decisions by providing details on current releasable elements, which are constantly monitored and evaluated to support continual improvement, alignment, and value creation.
Web services selection a perspective of computational physics

Abstract Concerning computational physics, web services are conceived as mathematical units that are experienced in different systems that offer service composition. Due to the exponential growth of web services and their deployment on cloud platforms, quality of service parameters have now become an essential factor when searching for and selecting services that must satisfy specific non-functional requirements of a user application. A variety of service components are highly configurable and are dynamic scenarios because a significant number of services can meet these requirements. This work analyzes the systemic perspective of approaches for the selecting and searching of web services that have specifications of optimization strategies based on the configurable quality of service parameters with test scenarios in cloud environments that have a considerable number of services as input. The study shows that policies based on artificial intelligence and related areas are the ones with the most significant convergence, and the approaches analyzed to give a perspective of future work aimed at strategies based on automatic learning.
CARD: A Congestion-Aware Request Dispatching Scheme for Replicated Metadata Server Cluster

Replicated metadata server cluster (RMSC) is highly efficient to be used in distributed filesystems while facing data-driven scenarios (e.g., massive-scale distributed machine learning tasks). Yet, when considering cost-effectiveness and system utilization, the cluster scale is commonly restricted in practice. Within this context, servers in the cluster start to suffer from load-oscillations at higher system utilization due to clients’ congestion-unaware behaviors and unintelligent selection strategies (i.e., servers in the cluster are preferred then evaded intermittently). The consequences brought by load-oscillations degrade the overall performance of the whole system to some extent. One solution to tackle this problem is having clients share a part of the responsibility and behave more wisely for stability concerns. So in this paper, we present a Congestion-Aware Request Dispatching scheme, CARD, which is mainly conducted at clients and directed by a rate control mechanism. Through extensive experiments, we verify that CARD is highly efficient in resolving load-oscillations in RMSC. Apart from this, our results show that RMSC with our congestion-aware based optimization achieves better scalability compared to previous implementations under targeted workloads, especially in heterogeneous environments.
Investigating Deep Learning Word2vec Model for Sentiment Analysis in Arabic and English languages for User’s reviews

In this paper, we explore natural language processing (NLP) methods to perform sentiment analysis or opinion mining. In addition, we show an application on English and Arabic sentiment analysis by implementing sentiment classification for three datasets which are Booking hotel dataset, Food fine Amazon dataset and Arabic movie review dataset. We applied Word2Vec model followed by Random Forest classifier (RF) for Arabic movie dataset. The results show that the Word2Vec model shows highly effective performance in sentiment analysis for English language datasets but it does not work for Arabic language as Arabic language need different mechanism.
YuvConv: Multi-Scale Non-Uniform Convolution Structure Based on YUV Color Model

Since digital images are able to be encoded through the luminance-bandwidth-chrominance (YUV) mode, and the contribution of luminance information is greater than that of chrominance information for human visual perception, it can be inferred that the appropriate reduction of chrominance information in convolutional neural network does not disturb image object recognition. In this paper, we propose a new multi-scale non-uniform convolution called YuvConv, wherein the output feature map of the convolutional layer is regarded as an image. First, the output channels in the new convolution are divided into three kinds of components: Y, U, and V tensors. Then, the tensor Y is used to process luminance information, which is high-resolution and occupies more output channels. Next, the tensors U and V are low-resolution and use fewer channels to process chrominance information. Finally, the adjacent tensors (Y-U, Y-U-V, and U-V) are fused as the output of YuvConv. Experimental results indicate that the use of the YuvConv instead of the standard convolution can improve the performance of deep learning tasks, and it can also reduce memory consumption and computation cost.
Design and Software Implementation of Heuristic and Suboptimal Strategies for the Mancala/Kalah Game

One of the oldest games worldwide – the Mancala game – is focused on in this preliminary study. Namely, its the most popular version – the Kalah game – is considered. This contribution is aimed at the analysis of Kalah rules first. Further, based on these rules, some novel deterministic and suboptimal strategies are proposed. It is proved that the order of playing has a decisive impact on winning. The proposed strategies have been implemented via a simple C++/Qt application. By experiments, a human player, when playing as the second one, cannot defend the designed strategies in general. However, the same applies in reverse – when a human player begins, he/she can nearly always win. To sum up, the proposed software-based strategies are comparable to human opponents.
Designing Electronic Menu Applications for Restaurant Businesses

Abstract This study aims to facilitate and speed up the process of ordering menus in restaurants by customers and minimize the usual errors when ordering. In designing applications, we used descriptive method to see what processes must and can be done so customers can place orders easily and quickly. From this research, an application is produced which will make it easier for customers to make the order process, besides that the ordering process can be done quickly. This result is obtained because the application is designed to have an easy-to-understand flow for customers in order. The ordering process is faster because customers no longer need to ask and wait for the restaurant waiter to serve the order process. By facilitating and speeding up the ordering process, a restaurant will make visitors from restaurants feel comfortable and well served so that the restaurant business will run better.
Real-Time Dynamic Bokeh Rendering With Efficient Look-Up Table Sampling

This article presents a real-time bokeh rendering technique that splats pre-computed sprites but takes dynamic visibilities and intrinsic appearances into account at runtime. To attain alias-free looks without excessive sampling on a lens, the visibilities of strong highlights are densely sampled using rasterization, while regular objects are sparsely sampled using conventional defocus-blur rendering. The intrinsic appearance is dynamically transformed from a precomputed look-up table, which encodes radial aberrations against image distances in a compact 2D texture. Our solution can render complex bokeh effects without undersampling artifacts in real time, and greatly improve the photorealism of defocus-blur rendering.
The development of premarital mobile learning version 3.0 with jquery technology to be learning flexibility premarital course application

Abstract This research activity aims to develop premarital mobile learning applications v 2.0 to become premarital mobile learning v 3.0 based online with the app store. The focus of the application development is more on the flexibility of the application, namely the ease in the process of editing the application’s animated video and e-book content, which will be adjusted to the applicable regulations in the Ministry of Religion. The software v 3.0 premarital mobile learning application that will be designed to build will also be added to the forum feature as a means of communication between the headman, as well as the process of question and answer between the premarital interviewees with the premarital course participants. The approach method used is object-oriented methodology, by implementing UML diagrams as functional modeling, using Javascript programming language, back server database language with php, and using jquery as an interface developer. The framework of application software engineering development with software Development Life Cycle (SDLC), namely the stages of planning, analysis, design, implementation, testing, and maintenance. The result of the research achieved are (1). Developing premarital mobile learning application software v 2.0 into premarital mobile learning application v.30 with all the ease of editing the material to be adjusted to the applicable provisions, material content stored in the html tag server (2). The development of version 3.0 with the flexibility of e-book learning, multimedia animation and forums as a means of communication between the premarital interviewees and the premarital course participants.
Visually Compensating Eccentric In-plane Rotations for Image Stabilization on a Rotating Platform

For camera systems mounted on a rotating platform, it would be difficult to install the image sensor concentrically with the rotation axis. Free rotors are unmanned aerial vehicles that rotate about its centre of mass for flight. Small changes in mass distribution is sufficient to displace the free rotor's rotation axis from the camera's principal axis. This displacement can be modeled as a trochoid equation and the rotation centre of the trochoid for one arch recovered. The algorithm optimally compensates for the image eccentricity in three steps. Firstly, derotating the images about its principal axis. Secondly, computing the optimal values to translate the images in the image stream. Lastly, to shift the images using an affine transformation matrix. The results show that the algorithm is sufficiently accurate to compensate a time-varying rotation and time-varying translation image stream. It is also possible to visually observe the gyroscopic precession of the free rotor.
Effective speckle reduction method based on a rotating ball lens

A novel design for speckle reduction based on a rotating ball lens is investigated. In addition, a standardized speckle measurement method is also developed and the parameters mainly accord with characteristics of human eye. This method could open up an avenue to the practical speckle measurement application.
tanh Neurons Are Bayesian Decision Makers

The hyperbolic tangent (tanh) is a traditional choice for the activation function of the neurons of an artificial neural network. Here, we go through a simple calculation that shows that this modeling choice is linked to Bayesian decision theory. Our brief, tutorial-like discussion is intended as a reference to an observation rarely mentioned in standard textbooks.
A Novel method for Reviewer Assignment Problem based on Reviewers' Profile and Fuzzy Graph Connectivity Measure

Reviewer Assignment Problem (RAP) is defined as a problem of assigning the most suitable expert to the proposal. which is a very important task for any research organization In literature many solutions have been proposed for solving RAP but most of them do not deal with the imprecision associated with the problem. In this paper, a novel approach has been proposed for assigning proposals to legitimate experts based on their previous history and domain expertise. For the purpose, we have first extracted the most important words from the author's submitted paper and reviewer's published articles by constructing a fuzzy graph and applying fuzzy graph centrality measures on it. We have then created fuzzy sets for the selected keywords and their weights (for both author's papers and reviewer's publications). Afterwards, WordNet have been used to find the diatance beween the fuzzy sets represented for author's papers reviewers' papers. Finally, fuzzy extension principle is applied on the sets to slect the top 3 experts for reviewing the proposal. Experimentation shows the proposed method provides satisfactory results on SemEVAL-2010 dataset.
Range Partitioning Within Sublinear Time in the External Memory Model

Range partitioning is a popular method for processing massive data, whose task is to divide the input N data items into k ranges of the same size. To avoid accessing the whole input, in the RAM model, sampling based $$(\epsilon ,\delta )$$ -approximation algorithms with $$O(\frac{k\log (N/\delta )}{\epsilon ^2})$$ time cost have been well studied. However, massive data may be too large to be maintained in the main memory. Usually, they are stored in the external memory devices and need to design I/O efficient algorithms in the external memory model. Then, a natural question is whether or not there are efficient range partitioning algorithms with $$O(\frac{k\log (N/\delta )}{B\epsilon ^2})$$ I/O cost. To answer the above question, this paper studies the range partitioning problem in the external memory model. Two lower bounds of the sampling cost required by the external sublinear range partitioning algorithms are proved, which show that it needs to make a full scan of the input in the worst case. Motivated by the hard instances utilized in the proof of lower bounds, a model for describing the inputs of the range partitioning problem in practical applications is proposed. Finally, for the special case that input data are generated by the proposed model, a nearly optimal algorithm with $$O(\frac{k\log (N/\delta )}{wB\epsilon ^2})$$ I/O cost is introduced.
Application of Web-Based in Product Distribution using Unified Modelling Language

Product distribution is an activity that facilitates the distribution of products from producers to consumers. The product distribution process using agents has accelerate and simplify the product to reach the consumers. The problem is how to deliver the information to the company with effective and effecient ways. The information contents like inventory stock, purchase order, delivery order and billing system. The purpose of research is to overcome problem of the company by provides the application on web based. We used the prototype method as system development and UML (Unified Modelling Language) as a blueprint design of applications. In this study, we used four diagrams like use-case diagram, class diagram, deployment diagram, and component diagram to figure out the design of application. The software testing step has used a black box method. The results of this research is an application that allows agents to order products to companies, manage the availability of products in warehouse, facilitates warehouse delivery of products, provides accurate financial transactions, and various reports as required. Product distribution information systems can improve the company performance and expand marketing of product by facilitating interaction between companies and agents.
Design of Furniture Production Monitoring Information System

Abstract To maintain good quality furniture, companies need to monitor the production process. The purpose of this research is to design a website-based information system that can assist companies in monitoring the furniture production process following procedures and standards. The system development method used in this research was the prototype method and the system approach method used object-oriented. The results of this research are that companies can see the design of a monitoring system in the form of a prototype that has been adjusted to the user wants. Also, the design of this monitoring information system helps the company to find out the best quality raw material data, estimated production process completion following the specified schedule, and checks at each stage of the production process following company procedures and standards. So that the production of furniture produced by the company has high quality and following the needs of the domestic and foreign markets.
7-Dimensional Optimization Task: PBO-Nature-Inspired Optimizer Versus 10-Years-Old Differential Evolution Based Optimizer 3rd Generation EPSDE

Origins of the branch of numerical optimizations with use of evolutionary optimizers date almost 60 years back. It is the area which does not evolve by big jumps and the advancement sometimes hits stagnation periods. At such stagnation times there is a big hungry for the new optimization methods which would fill up the empty space. Many optimizers have appeared in the last decade. These optimizers are more specialized in comparison to optimizers which were proposed dozens of years back. The new optimizers are very often derived from older optimizers. In this presented paper, a 7-dimensional optimization task is solved which is called persons identification using contour of a human hand. The paper is considered as research and comparative study at the same time. An optimizer called EPSDE is compared to the Polar Bear Optimizer. The EPSDE is a deputy of 3rd generation optimizers derived from algorithm differential evolution. The PBO falls into a group of young optimizers marked as “nature inspired”. The PBO is three times more time-demanding and primarily significantly worse in solving of given task which is very difficult. A comparison of both optimizers was conducted with use of large comparative database.
Four Operations over Extended Intuitionistic Fuzzy Index Matrices and Some of Their Applications

In this paper, four new operations are introduced over extended intuitionistic fuzzy index matrices and over their simpler cases, such as intuitionistic fuzzy index matrices, extended index matrices and index matrices. Some of the properties of the four new operators are discussed and their applications in intercriteria analysis are shown. A theorem for stratification of an index matrix is formulated and proved.
A dog food recommendation system based on nutrient suitability

Abstract The demand for a food recommendation service for dogs has rapidly increased with the increasing number of pet owners, because it is generally difficult for dog owners to find food that is perfectly suitable for their dogs' health condition. The purpose of this study is to develop an algorithm for recommending dog food that contains appropriate nutrients based on the physical and health conditions of the dogs. This study proposes a nutrient profiling‐based recommendation algorithm (NRA) for dog food. The proposed algorithm tries to recommend appropriate or inappropriate dog food by using collective intelligence based on user experience and the prior knowledge of experts. Based on the physical and health status of dogs, this study extracts which nutrients are necessary for the dogs and recommends the most suitable dog food containing these nutrients. A performance evaluation was implemented in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC performance of this NRA is 20% higher than k‐NN and 9.7% higher than the SVD model. In addition, the NRA proved to be an evolving system in which the performance of recommendations improves as users' feedback accumulates.