Reviewerly/exHarmony huggingface dataset:
Acoustical Analysis of the Thai Duct Flute, Khlui

The purpose of this paper is to study the acoustical model of the Thai traditional duct flute, khlui. The relation between physical characteristics of the flute, such as acoustic length, playing hole, temperature effect, etc., and the frequency of each musical note of khluis is investigated. First, the fundamental frequencies of each finger hole of 10 khluis are measured. Then, data between the ideal musical notes and measured notes of khlui are analysed. The results indicate that the production process of khlui still has limits in accuracy. Finally, in order to develop a production standard of the Thai duct flute, a model of khlui is designed. The musical notes of the designed flute are measured. The accuracy of the designed flute is investigated to support the understanding of its acoustical model.
The Marlo Diagram in the Classroom

In this paper we present some results obtained using the Marlo diagram for the didactic of logic in a High School in Huelva, Spain, between 2014 and 2019. This tool was created by the author to improve the bad results obtained with traditional methods of propositional calculus, excessively complex, abstract and far from common sense. To avoid the most frequent fallacies in the classroom, we developed a simple and intuitive diagrammatic deduction system that allows us to decompose and visualize the simplest and most elementary steps of reasoning. Our goal was reinforcing valid inference patterns by synchronizing natural, formal and diagrammatic languages. Although in 2014 we could only solve syllogisms in our diagram, since 2016 we can also solve propositional logic exercises. This is possible by expressing all logical connectives by quantifying the predicate. Proceeding this way allows us to solve any inference of the propositional calculus in our diagrams through the same processes of analysis and synthesis that take place in the syllogism. The fact of recovering the middle term as the basis for any inference allows us to teach logic from common sense and, consequently, obtain better results in performance tests. The first comparisons we have made of our teaching method with the conventional method encourage us to continue developing a tool that, according to our experience, allows us to improve abstract thinking from an earlier age than conventional methods.
Noises Adaptive Filtering Structure for Robotic Positioning Using Monocular Vision

This paper has developed a new monocular vision feedback control strategy based on the adaptive filtering structure, for robotic positioning without noise parameters. In real robotic workshops, the perfect statistic knowledge of the the system noise and observation noise are not easy to be derived, thus a noise adaptive filtering structure is firstly studied for the nonlinear mapping on-line identification between robotic vision and motor spaces. In our finding, the Kalman recursive filtering gain is improved by a feed-forward neural network, in which the neural estimator dynamic adjustment its weights to minimization the error of vision-motor mapping estimation, do need not the knowledge of noise variances. Finally, the proposed vision servoing control based on adaptive filtering has been successful implemented in robotic positioning tasks, and the experimental results demonstrate its validity and practicality for a six-degree-of-freedom (DOF) robotic system.
An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.
Emotional Control of Wideband Mobile Communication

To avoid overloading in Wideband Mobile Communication Systems, a new approach is proposed, using Elastic Power Control that was studied in the frames of a mathematical model of WMCS. Both concepts, the model of collective radio stations and the elasticity, are theoretical ones. They have been studied mostly with mathematical means on the base of theory of collective behavior of learning automata. However, in present paper it is proposed a practical implementation of an elasticity to guarantee stability of mobile communication. In this new approach the Natural Language Base, consisting from emotive phrases, is proposed to be used in a feedback for Power Control to avoid the crossing the edge of instability, namely the area of admissible vectors of communication qualities.
Multi-target tracking based on target detection and mutual information

The key to achieving target tracking is to completely segment the target, reasonably extract features and accurately identify the target. At the same time, it is necessary to consider the time of algorithm implementation to ensure real-time and accuracy. This paper uses the Faster RCNN network structure to unify the extraction and classification of candidate detection areas into a single convolutional network architecture, and uses multi-person learning mechanisms to complete the learning of network parameters. while improving the efficiency and accuracy of physical detection. By using mutual information matching, in image registration, when two images are registered in a spatial position, the mutual information of the gray levels of the pixel pairs corresponding to the overlapped parts reaches the maximum value, and the best matching point of the image can be determined accordingly To achieve the effect of accurately tracking multiple targets. Combining three indicators of target tracking, average overlap expectation (EAO), tracking accuracy and robustness, the tracking algorithm proposed in this paper has the best effect on target tracking and has broad application prospects.
Distributed Image Retrieval Base on LSH Indexing on Spark

With the advent of the era of big data, how to process massive image, video and other multimedia data timely and accurately has become a new challenge in related fields. Aiming at the computational bottleneck and inefficiency of traditional image content retrieval system, in this paper, a distributed image retrieval framework base on Location Sensitive Hash (LSH) indexing is proposed on Spark, which combines with the distributed storage and computing characteristics of Spark big data platform. Then, distributed K-means based Bag of Visual Word (BoVW) algorithm and LSH algorithm are proposed to build LSH index vectors on Spark in parallel. The experiment shows that the retrieval time of our framework can be reduced significantly, and both retrieval recall and precision are high compared with the traditional retrieval method.
A Novel Contour Model with Local and Global Segmentation Property

In this paper, a novel region-based active contour model is presented which can selectively implement the local or global segmentation. A novel force field which uses the mean information inside and outside the contour is proposed to control the curve evolution. An efficient numerical schema is utilized to implement the proposed model in order to reduce the computational cost. It is validated on numerous synthetic images and real images, and the promising experimental results demonstrate that the proposed method has better performance than some traditional methods.
A Blockchain Based Terminal Security of IoT

With the rapid development of the IoTs, the distributed access of a large number of terminals poses a serious challenge to the credibility of the system. To solve the above problems, based on the decentralized block chain technology, a terminal authentication scheme suitable for the IoT is proposed. The scheme uses block chain node to record information of terminals, the consensus mechanism is improved through key sharing algorithm, and efficient distributed authentication is realized. The simulation results show that the proposed scheme is feasible.
A Dynamic Recommender System for Online Judges Based on Autoencoder Neural Networks

In recent years, we have witnessed the raising popularity of programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC). In order to train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In this paper we present a dynamic Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN).
Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation

In this letter, we study an active learning problem for maximizing an unknown linear function with high-dimensional binary features. This problem is notoriously complex but arises in many important contexts. When the sampling budget, that is, the number of possible function evaluations, is smaller than the number of dimensions, it tends to be impossible to identify all of the optimal binary features. Therefore, in practice, only a small number of such features are considered, with the majority kept fixed at certain default values, which we call the working set heuristic. The main contribution of this letter is to formally study the working set heuristic and present a suite of theoretically robust algorithms for more efficient use of the sampling budget. Technically, we introduce a novel method for estimating the confidence regions of model parameters that is tailored to active learning with high-dimensional binary features. We provide a rigorous theoretical analysis of these algorithms and prove that a commonly used working set heuristic can identify optimal binary features with favorable sample complexity. We explore the performance of the proposed approach through numerical simulations and an application to a functional protein design problem.
Chinese Named Entity Recognition for Hazard And Operability Analysis Text

To solve the problem that it is difficult to identify the key Chinese entity information in the hazard and operability analysis text, a deep neural network model based on bidirectional long short-term memory and conditional random field (BiLSTM-CRF) is proposed to identify key named entities in the text. In the word vector pre-training process, bidirectional encoder representation from transformers (BERT) model is used to pre-train word vectors instead of the static word vectors in the traditional word2vec model, and then obtain context-related dynamic word vectors, to improve the representational ability of word vectors, and solve the problem of word boundary division when word vectors are used in Chinese corpus training. This model has the ability of complete the task of Chinese named entity recognition. The F1 value on the test corpus reaches 93.31%, which is 21.82% higher than conditional random field (CRF) Baseline, and 4.49% higher than the traditional BiLSTM-CRF model. The experimental results show that the BERT-BiLSTM-CRF model is effective for the named entity recognition (NER) task of the hazard and operability analysis text, and it is helpful to automatically extract the relationship between the entities in the hazard and operability analysis text and build safety analysis knowledge graph.
Quality Improvement of Chicken Eggs Through the Vaccine Schedule Using Database Program System

Eggs are the output of poultry, which generally have a high quality sources of protein and nutrients. Poultry industry produces products such as eggs, chicken meat, and compost. Consumers often complain about the low quality of eggs, such as eggshells that are broken, coarse, white, mushy, and increasingly smaller eggs. The current vaccine scheduling systems on the farm still operate conventionally and the vaccine scheduling is often inaccurate. In this case, the researchers designed and implemented an Early Warning Systems in the scheduling vaccination to improve the quality of chicken eggs. In the development of an early warning, this management system can provide information prior to vaccination. After going through the implementation testing of the system, it was discovered that there was a decrease in egg defects from 4% to 2.2%. From these results, it can be concluded that by using an early warning management system in Vaccine Scheduling, it can significantly reduce the level of defect that can improve egg quality.
The Design of Multiuser BGN Encryption with Customized Multiple Pollard’s Lambda Search Instances to Solve ECDLP in Finite Time

Data privacy breach is an important concern for data owners, in today’s online world. Data science applications, smart city applications and smart grid applications generate precious knowledge through analyzing user’s data. But many a times, it is experienced by the users that their sensitive personal data is exposed by these applications, without their consent. Due to the serious issues of data privacy preservation problems, public cloud-based data centers have failed in achieving popularity in third-party computation. To solve this privacy preservation problem, data can be collected and uploaded in unidentifiable format, such a way that meaningful computation can be performed easily. Homomorphic encryption is one of the favorite techniques, wherein direct computation on encrypted data is permitted and its results are equivalent to their plaintext format. BGN ‘doubly’ homomorphic encryption can execute infinite additions and single multiplication over encrypted data values at a time. BGN is based on the elliptic curve. Constant size of ciphertext is an attractive feature of BGN encryption. User has to solve elliptic curve discrete logarithm problem (ECDLP) to receive the desired results of addition and multiplication in plaintext format. Solving ECDLP is most time-consuming operation. Hence, BGN encryption can be used in small message space only. Customize Pollard’s lambda method can increase the message space of BGN encryption and solve the problem. Multiple instances of Pollard’s lambda attack can be executed with different range of intervals to solve ECDLP in finite time. This customization will make narrow search intervals to Pollard’s lambda to solve even 78-bit ECDLP for 88-bit prime field in finite times.
Application of Monte Carlo Tree Optimization Algorithm on Hex Chess

Hex Chess attracts more and more people because of the simple rules, but also because of the simple rules, it brings complex algorithms. The simple Monte Carlo tree search process is often very slow due to the large amount of computation. In the double game, the Monte Carlo algorithm cannot converge to the best decision strategy of the double game. This paper proposes an improved algorithm of Monte Carlo tree search combined with MTD(f) algorithm, so that the search results are not distorted by the randomness of Monte Carlo algorithm. In order to further improve the computational efficiency of the search algorithm in the two-player game, the TCL and the connected domain strategy are adopted. Compared with the improved algorithm, not only shortens the time of Monte Carlo tree search, but also improves the performance.
Vehicle-identification Based on Complementarity Feature

Vehicle re-identification is one of the important tasks in the field of intelligent transportation, it is indispensable in intelligent traffic police, intelligent security and other application situation. In this paper, we proposed a novel complementarity feature for re-identification. The algorithm is divided into two stages: online and offline. A view discriminator is built to determine whether the view of each vehicle image is front-view or back-view. Then each vehicle image is assigned a reverse sample based on the vehicle's view information. In the online phase, we train a Complementarity Feature generator by using the features of the reverse sample as the supervised information. Experimental results show that our algorithm is superior to existing methods.
Creating a learner corpus infrastructure: Experiences from making learner corpora available

With language resources being collected in many - also small - projects in learner corpus research with considerate amounts of time and ef- fort spent in this activity, making these types of data available in a FAIR way, with standardized and reasoned methods, would contribute substan- tially to the advancement of the field. Additionally, it would answer current demands in transparency, replicability and reusability. In this article, we dis- cuss some of the challenges when making learner corpora FAIR and report from experiences in fulfilling this aim while creating a learner corpus infra- structure at a research institution hosting five different learner corpora.
Distributed Public Cloud on Low Power Devices

The current cloud providers run their cloud platforms on big private data centers with huge power demands and resource consumptions. This paper presents a model for a distributed, free, public cloud on low power devices. The main idea is to use peoples home low power devices and their resources to build a free distributed cloud platform. The attempt has been made to use existing open source technologies. The research will allow building public distributed cloud platforms offering different services like storage, computation etc. as an alternative to the proprietary cloud platforms running on huge powerful centralized private data centers.
Correspondence as the Primary Measure of Quality for Web Archives: A Grounded Theory Study

Creating an archived website that is as close as possible to the original, live website remains one of the most difficult challenges in the field of web archiving. Failing to adequately capture a website might mean an incomplete historical record or, worse, no evidence that the site ever even existed. This paper presents a grounded theory of quality for web archives created using data from web archivists. In order to achieve this, I analysed support tickets submitted by clients of the Internet Archive's Archive-It (AIT), a subscription-based web archiving service that helps organisations build and manage their own web archives. Overall, 305 tickets were analysed, comprising 2544 interactions. The resulting theory is comprised of three dimensions of quality in a web archive: correspondence, relevance, and archivability. The dimension of correspondence, defined as the degree of similarity or resemblance between the original website and the archived website, is the most important facet of quality in web archives, and it is the main focus of this work. This paper's contribution is that it presents the first theory created specifically for web archives and lays the groundwork for future theoretical developments in the field. Furthermore, the theory is human-centred and grounded in how users and creators of web archives perceive their quality. By clarifying the notion of quality in a web archive, this research will be of benefit to web archivists and cultural heritage institutions.
3D modeling of an object located in the underwater robot’s manipulator workspace using a point cloud

Operations involving direct contact of the robot's gripping device with surrounding objects are among the hardest in the field of robotics engineering. Therefore, minimizing the risk of tool damage and processing disruption is important. This can be achieved by using the information about the position of the object in question relative to the grip and its geometric parameters. In this paper, a method of describing an object located in the workspace of an underwater robot with solids of revolution is proposed. It is done using a noisy and incomplete point could, which is obtained with a stereoscopic system located on the terminal link of the underwater robot's manipulator.
An Improved Performance Simulated Annealing Based On Evolution Strategies for Single Objective Optimization Problems

This paper presents solutions for single objective optimization problems with developed algorithm from simulated annealing based on a simple (μ + λ) -ES, It is divided into two algorithms, separated mutation (SM1) and survival mutation (SM2). After that, compared with randomized local search and simulated annealing. The test function is part of the IEEE WCCI 2020 on the topic of CEC-C2 single objective bound constrained optimization. This research has chosen the basic functions in the test such as Bent cigar function, rastrigin function, high conditioned elliptic function, HGBat function, rosenbrock's function, griewank's function, discus function, expanded schaffer's function, weierstrass function, sphere function, natyas function, lévi function N.13, himmelblau's function, and three-hump camel function. These functions are attract attention and competition. A results of SM1 and SM2 can solve single objective optimization problems better than RLS and SA. In high conditioned elliptic, The fitness value of RLS is equal to 3.96E-11, The fitness value of SA is equal to 8.12E-10, The fitness value of SM1 is equal to 5.39E-14 and The fitness value of SM2 is equal to 9.70E-15, It let us show the efficiency of SM2 that can get better results than SM1.
An Improved Wolf Pack Algorithm

This paper aims to overcome the shortcomings of original wolf pack algorithm (WPA). The shortcomings of original WPA include fixed scouting directions, scouting step length that cannot be adjusted dynamically, lack of information exchange between each other in artificial wolves and lead wolf does not participate in intelligent behavior. An improved wolf pack algorithm is proposed in this paper. The improved algorithm provides scout wolves variable scouting directions and scouting step length could adjust dynamically based on the Fibonacci Sequence by the improved algorithm. Furthermore, ferocious wolves could exchange information with scout wolves during raiding. Finally, the hunting principle of lead wolf is provided in the improved algorithm. Thus, WPA is improved from four aspects: global optimization, convergence speed, convergence accuracy and robustness. The simulation results show that compared with original WPA, the improved wolf pack algorithm not only increase the global search capability, but also avoids early-maturing of the algorithm. Furthermore, it has strong robustness and significant improvements in the search accuracy and rate of convergence.