Reviewerly/exHarmony huggingface dataset:
Metric-Cauchy sequence for wheeled mobile robot navigation

In this paper, the path planning approach for the wheeled mobile robot has been presented using Metric-Cauchy Sequence for static environment. The Euclidean metric associated with the distance measurement and the Cauchy sequence optimizes the navigational decisions. This dual approach applies to study navigational challenges such as path complexity, path safety, path optimization, obstacle avoidance, map generation, and goal tracking. The developed navigational model has been tested in a simulation environment using MATLAB software, and real-time environment using the Khepera robot and in-house developed robot on a defined arena for single-robot and multi-robot system. The observations made from both the platforms are compared in terms of navigational time and path length, and obtained results are found to be in close proximity to each other. Furthermore, the developed controller is also compared with the other existing work for validating the performance.
Method of internet service easy login application based on RFSIM

Proposed a login authentication system and realization to a service website based on RFSIM, so that users can easily manage the daily use of the internet user account and password, ensure that the internet user account and password will not be forgotten because of an increasing number of network services. By secure and reliable network authentication, We can easily login on the service website.
A New Approach for Processing Natural-Language Queries to Semantic Web Triplestores

Natural Language Query Interfaces (NLQIs) have once again captured the public imagination, but developing them for the Semantic Web has proven to be non-trivial. This is unfortunate, because the Semantic Web offers many opportunities for interacting with smart devices, including those connected to the Internet of Things. In this paper, we present an NLQI to the Semantic Web based on a Compositional Semantics (CS) that can accommodate many particularly tricky aspects of the English language, including nested n-ary transitive verbs, superlatives, and chained prepositional phrases, and even ambiguity. Key to our approach is a new data structure which has proven to be useful in answering NL queries. As a consequence of this, our system is able to handle NL features that are often considered to be non-compositional. We also present a novel method to memoize sub-expressions of a query formed from CS, drastically improving query execution times with respect to large triplestores. Our approach is agnostic to any particular database query language. A live demonstration of our NLQI is available online.
Autonomous virtual vehicles with FNN-GA and Q-learning in a video game environment

Driving autonomous virtual vehicles have been developed by the use of several artificial intelligence (AI) algorithms. However, it is possible to improve their results and find other applications through the use of new algorithms or combinations of them. Taking into account this, in this work, firstly Feed-forward Neural Network (FNN) and Genetic Algorithm (GA) were selected, and then, these algorithms were combined to have the new FNN-GA algorithm. In order to evaluate its performance, the most popular circuits, such as ovals and tracks with different types of curves, were selected from auto racing, which were implemented in the Unity 2D video game engine. After, as a way to compare the performance of the "FNN-GA" algorithm, the Q-learning algorithm was selected, and the agents to learn with them were placed on said tracks. As a result, after executing 100 generations of the agents, per algorithm, it is found that taking into account the better use of CPU and RAM resources, Q-learning is the best, on the contrary, taking into account the quantity of in learning generations, FNN-GA is the best since it learns in fewer generations. On the other hand, both algorithms learn easier on ovals than on circuits with different types of curves.
Feature Selection and Classification of Texture Images Based on Local Structure and Low-Rank Constraints

Texture description is a challenging problem in computer vision and pattern recognition. The task of texture classification is to classify texture into the class it belongs to, which is influenced by variations in scale, illumination, and changes in perspective. There are many texture descriptors in the literature. In this paper, we combine five texture descriptors for texture classification, which obtained better performance than the single descriptor at the price of high dimensionality. To solve this problem, we proposed a novel unsupervised feature selection method based on local structure and low-rank constraints, which can not only reduce the dimensions but also further improve the classification accuracy. To evaluate the performance of combing multiple descriptors and the proposed feature selection method, we design a variety of experiments in two typical texture datasets, namely KTH-TIPS-2a and CURET. Finally, the result shows the proposed method outperforms the state-of-the-art methods.
Principal component analysis of climatological impact on rooftop solar power plant installed in Western India

India is home to one of the largest solar photovoltaic (SPV) plant installation expansion programmes. After an astonishing leap in the installations of ground-mounted solar photovoltaic plants, India is moving towards the rooftop SPV plant installations to increase the economic viability. This study has been conducted to assess the climatological impact on the electricity generation of 50 kWp SPV plant installed in Western India using Principal Component Analysis (PCA). Upon applying PCA to the 16 climatological parameters, three Principal Components have been retained with a cumulative percentage of the variance of 88.886%. PC1 (Air Quality Factor), PC2 (Irradiance Index), and PC3 (Wind Factor) are identified with 53.385%, 27.719%, and 7.781% cumulative percentage of the variance. The findings might be useful for policymakers who can develop policies that favour the growth of such plant installations considering all important factors affecting the generation and the future climate change scenarios for the region.
Comparison Between Original Texts and Their Revisions to Improve Foreign Language Writing

A text written by non-native speakers makes readers feel unusual, even if there are no evident errors in the sense of basic grammar. We discuss such an intangible error in this paper. Therefore, the goal is to discover the similarities of writing among foreign languages by Japanese native speakers. We compare the original texts written in a foreign language and their revisions by a native speaker to identify the parts which s/he feels unusual and gives the options. To not rely on the subjective context and the uniqueness of a language, we replace a word into a part-of-speech by the morphological analysis. Some parts-of-speech are deleted from the original text by revising a text, and some parts-of-speech are added instead. We consider a set of different parts-of-speech, which is added or deleted by the revision, and find common trends with multiple languages from the numbers and ratios of sentences. These findings are not always familiar to the experts, and on the other hand, they may give new viewpoints to language learners and teachers to improve their activities.
Using Different RNN Variants to Generate Realist Reviews

This project aims to implement LSTM and GRU to generate realistic text. First, some basic background information will be introduced. After that, the design of models will be presented, then the performance of different RNN variants will be compared. In addition, the time consumption also as a factor of performance to compare. At last, the reason for the difference in performance will be discussed.
Reform of Database and Data Warehouse Course Based on OBE-CDIO Model

In the era of big data, many traditional courses had to keep pace with the times. The Database and Data Warehouse course is a new course derived from the course of Database Principles. Aiming at the problems of emphasis on theory, light practice and light process in traditional teaching of Database course, this paper proposes a teaching design model based on the combination of OBE and CDIO. It aims at the teaching goals, teaching content, teaching links, teaching of Database and Data Warehouse courses Methods and course evaluations were designed in all directions. At the same time, the course design is practiced through a semester of real course teaching. According to the evaluation system, analyze the teaching effect and draw teaching reflection.
EWIDL: Single-Source Web API Documentation Management System

This paper is an overview of the EWIDL documentation management system for Web APIs. We designed EWIDL based on our experience with the development of Tizen Web API. Over the eight years of our work, two requirements have proven to be the most important for successful documentation management. Firstly, the documentation should be machine-readable to support automatic validation tools development. EWIDL achieves this by combining Doxygen documentation comments' format with Web IDL language. Secondly, the system should support the maintenance of multiple variants of documentation. Tizen Web API behavior varies between different target devices, depending on the software version and hardware profile (mobile, wearable, and TV). Managing documentation for all configurations simultaneously is a challenging task with ever-increasing complexity as new versions are released. We chose a single-sourcing approach by including conditional configuration preprocessor syntax in EWIDL documents.
Training Multiple Object Awareness (MOA)

In classic Multiple Object Tracking and Multiple Identity Tracking (MOT, MIT), observers attempt to track N of M identical or unique items respectively as they move around a display. Typical tracking capacity is 2-4 items. Noting that we usually seem to know about the location and identity of more than 2-4 items in the world, Wu and Wolfe (2018) introduced the Multiple Object Awareness (MOA) paradigm. In MOA, observers are asked to track all unique items on screen. Like MIT, when items are occluded, observers are asked to localize one randomly selected item. Unlike MIT, if the first answer is wrong, observers keep clicking on locations until they find the target. If they were guessing, observers would need to click on half of the locations on average. However, they are not guessing. They have imperfect, but real knowledge about multiple items. From the distribution of clicks, it is possible to derive a capacity, based on this partial knowledge. This yields much larger estimates of 8-10 items. Is MOA capacity trainable and, if so, is any improvement distinct from improvements in classic tracking capacity? Five observers were trained for two hours per day for 10 days within three weeks. Observers tracked 16 unique items. Items were from three categories: animals, tools and food. After 7-20 seconds all items were occluded. Observers were asked to locate one specific item. Data consists of the number of clicks required to uncover the target. Three observers significantly improved from capacity of 8.3 to 11.5 items (averaged over final three sessions). Two observers started poorly and got worse (capacity 0-3 items). MIT capacity improved proportionally suggesting that this improvement may not be MOA specific training. Future research will determine if the stark differences between learners and non-learners reflect “real” individual differences or merely badly behaved observers.
Importance Weighted Adversarial Variational Bayes

Adversarial variational Bayes (AVB) can infer the parameters of a generative model from the data using approximate maximum likelihood. The likelihood of deep generative models model is intractable. However, it can be approximated by a lower bound obtained in terms of an approximate posterior distribution of the latent variables of the data q. The closer q is to the actual posterior, the tighter the lower bound is. Therefore, by maximizing the lower bound one should expect to also maximize the likelihood. Traditionally, the approximate distribution q is Gaussian. AVB relaxes this limitation and allows for flexible distributions that may lack a closed-form probability density function. Implicit distributions obtained by letting a source of Gaussian noise go through a deep neural network are examples of these distributions. Here, we combine AVB with the importance weighted autoencoder, a technique that has been shown to provide a tighter lower bound on the marginal likelihood. This is expected to lead to a more accurate parameter estimation of the generative model via approximate maximum likelihood. We have evaluated the proposed method on three datasets, MNIST, Fashion MNIST, and Omniglot. The experiments show that the proposed method improves the test log-likelihood of a generative model trained using AVB.
The Complexities of Random-Turn Hex, Square, and Triangle Games

In random-turn games, players toss a coin to decide who moves.This paper studies the complexities of the algorithms for playing random-turn connection games perfectly on regular tessellations.Our study theoretically shows that there are algorithms playing random-turn Hex, Square and Triangle perfectly in O(n 9 • 2.618 n ), O(n 9 • 2.746 n ) and O(n 9 • 3.645 n ) time for each move respectively, where n is the board size.We then implement the perfect-playing algorithm for randomturn Square and measure the actual running time it costs for each move.We then compute and analyze the game lengths on random-turn Square, Hex and Triangle and conjecture that the asymptotic complexity of their game lengths are the same.We finally compare the perfect-playing algorithm with the sampling algorithm by competing against each other, and the numbers of their wins and loses are reported.
Proposing a System to Supporting for Kitchen Knife Skill Improvement using Acceleration Sensor

We propose a system to supporting kitchen knife skill improvement using an acceleration sensor attached to the handle of the kitchen knife. This system is capable to judge for the user's cooking skills. We conducted an experiment to judge kitchen knife skills using the proposed system to clarify the amount kitchen knife skill features by masters and beginners. As a result, we were confirmed that three indices such as the mean value of the acceleration for the horizontal direction, the SD value of the acceleration for the anteroposterior direction, and the SD value of the acceleration for the vertical direction were effective as indices to improve the kitchen knife skills of beginners.
Semantic Web: Graphs, Imprecision and Knowledge Generation

Growing interests in Artificial Intelligence generate expectations for constructing intelligent systems supporting humans in a variety of activities. Such systems would require the ability to analyze data and information, as well as to synthesize new elements of knowledge. To make it possible, there is a need to develop techniques and algorithms capable of extracting logic structures from data, processing and modifying these structures, and creating new ones. In this context, a graph-based representation of data—in particular a knowledge graph proposed by the Semantic Web initiative—is of a special interest. Graphs enable representing and defining semantics of data via utilization of multiple types of relations. Additionally, imperfections associated with processes of collecting and aggregating data and the imprecise nature of knowledge in variety of domains require data representation formats that can handle vagueness and uncertainty. In our opinion, knowledge graphs combined with elements of fuzzy set theory represent important building blocks of knowledge generation procedures. This chapter contains a brief overview of a framework enabling construction of systems capable of extracting logic structures from data and synthesizing knowledge. Utilization of fuzziness enables representation of imprecision and its inclusion in knowledge generation processes.
EdgeAugment: Data Augmentation by Fusing and Filling Edge Maps

Data augmentation is an effective technique for improving the accuracy of network. However, current data augmentation can not generate more diverse training data. In this article, we overcome this problem by proposing a novel form of data augmentation to fuse and fill different edge maps. The edge fusion augmentation pipeline consists of four parts. We first use the Sobel operator to extract the edge maps from the training images. Then a simple integrated strategy is used to integrate the edge maps extracted from different images. After that we use an edge fuse GAN (Generative Adversarial Network) to fuse the integrated edge maps to synthesize new edge maps. Finally, an edge filling GAN is used to fill the edge maps to generate new training images. This augmentation pipeline can augment data effectively by making full use of the features from training set. We verified our edge fusion augmentation pipeline on different datasets combining with different edge integrated strategies. Experimental results illustrate a superior performance of our pipeline comparing to the existing work. Moreover, as far as we know, we are the first using GAN to augment data by fusing and filling feature from multiple edge maps.
A Novel S-box Optimization Method Based on Immune Genetic Algorithm

S-box is the only nonlinear component in block cipher, and its performance is the key factor to determine the data security of cipher algorithm. Our paper proposes a novel S-box optimization method based on immune genetic algorithm. Firstly, the S-boxes population is generated by chaotic system, then the S-boxes with excellent performance are optimized by a series of operators, including extracting the anti-agent and immune selection. The nonlinear degree criterion, differential uniformity criterion and strict avalanche effect criterion of S-boxes are analyzed. The experimental results show that the optimized S-box has strong characteristics. Compared with the traditional genetic algorithm, this method has faster convergence speed and better resistance to linear attacks and differential attacks. The optimized S-box has a good application in the field of information security prospects.
An Alternative to Power Measure for Fuzzy Rule-Based Classification Systems

An effective way to deal with classification problems, among other approaches, is using Fuzzy Rule-Based Classification Systems (FRBCSs). These classification systems are mainly composed of two modules, the Knowledge Base (KB) and the Fuzzy Reasoning Method (FRM). The KB is responsible for storing information related to the problem, while the FRM performs the classification of new examples based on the KB. A key point in the FRM is how the information given by the triggered fuzzy rules is aggregated. Several FRMs have been proposed in the literature employing generalizations of the Choquet Integral as the aggregation function. The usage of this function to perform the aggregation is efficient because it uses fuzzy measures to model the interrelationships between the data. Indeed, the performance of the classification system is strongly related to the underlying fuzzy measure. However, many of those generalizations have used fuzzy measures that do not properly model the interaction between the data. In this context, we intend to enhance the way how the relationships between the rules are modeled in the FRM of an FRBCS. In order to accomplish that, we propose the use of a well known fuzzy measure, the Sugeno lambda, as an alternative to the Power Measure, which is widely used in many generalizations of the Choquet Integral. The experimental evaluation shows statistical equivalent results comparing our method with the state-of-the-art fuzzy classifier.
A Solution of Text Based CAPTCHA without Network Flow Consumption

With the widespread application of distributed information processing, information processing security issues have become one of the important research topics; CAPTCHA technology is often used as the first security barrier for distributed information processing and it prevents the client malicious programs to attack the server. The experiment proves that the existing “request / response” mode of CAPTCHA has great security risks. “The text-based CAPTCHA solution without network flow consumption” proposed in this paper avoids the “request / response” mode and the verification logic of the text-based CAPTCHA is migrated to the client in this solution, which fundamentally cuts off the client's attack facing to the server during the verification of the CAPTCHA and it is a high-security text-based CAPTCHA solution without network flow consumption.
Designing a Synthesized Content Feed System for Community Radio

The use of text-to-speech to generate radio content is largely unexplored, despite the importance of radio in remote parts of the world, where TTS offers a robust means of transforming data into media for low-literate audiences and those without regular internet access. How suitable are TTS voices for meeting the expectations of radio listeners and what type of content are these voices best suited to deliver? We present an application for generating automated daily synthesized weather forecasts for selected locations and language varieties, based on the provision of a regularly updated weather data service. We present results from a pilot listener study aimed at exploring people’s reactions to this and other synthesized audio content, as we begin to explore best practices around the design of a synthesized content feed system for community radio.
Single target tracking algorithm based on multi-feature fusion

Aiming at the problems of target scale change, color similarity and occlusion during target tracking, this paper proposes a single target tracking algorithm based on the fusion feature of color feature (CN) and direction gradient histogram (HOG). Under the relevant filtering and tracking framework, the original RGB color space is mapped to the color attribute space to reduce the target color from being affected by environmental changes during the tracking process. The adaptive component dimensionality reduction through principal component analysis (PCA) method, features The number of channels drops from 10 to 2, and the cost of crossing different feature subspaces is increased by smoothing constraints. At the same time, the direction gradient histogram is extracted, and the feature map is calculated by kernel correlation filtering to obtain the correlation response map, and the maximum response value is found from the response map to determine the target position. 36 groups of color video sequences were selected on the OTB standard data set for experiments. The popular correlation filter tracking algorithm was compared. The experimental results show that the algorithm has high recognition accuracy and can be used in complex environments such as illumination changes, target occlusion and deformation. Stable tracking target.