Reviewerly/exHarmony huggingface dataset:
Computer Technology Applied to Machine Translation and Speech Recognition

Abstract With the historical development of the new ear, computer technology has brought revolutionary changes to people’s lives. The computer technology is deeply applying to the different kinds of the field. People hope that they can use computers to communicate effectively in the process of communicating by using artificial intelligence technology in daily life, such as machine translation and speech recognition. In this paper, the author introduces how computer technology applies to machine translation and speech recognition.
The Effect of Using Cokriging against Ordinary Kriging When Estimating Phosphate Deposits at Elsebaiya Area, Egypt

Application of geostatistical techniques when evaluating mineral deposits could reflect some geological characteristics which help through the stage of planning and production.In the present study, an attempt has been done on two phosphate deposits at Elsebaiya area on both sides of the River Nile namely: Um Tondoba mine at Elsebaiya East area and Western River Nile mine in Elsebaiya West area.Depending on the available data, statistical analysis illustrated differences in the distribution of P 2 O 5 % and ore thickness within the studied areas.Geostatistics used to start with constructing variograms for P 2 O 5 % and thickness for the two phosphate deposits to be used with ordinary kriging models, also constructing cross variograms between P 2 O 5 % and thickness to be used with cokriging models where the difference in the variogram parameters reflected a specific variation for each deposit horizontally and vertically.The ordinary kriging models and cokriging models illustrated different distribution behavior through both the two kriging techniques.
The Problems and Reflections of Computer-aided English Teaching in Contemporary China

Abstract With the continuous popularization of computer and other information technologies, computer-aided teaching has become one of the effective teaching methods for all ages, which has positive practical significance for English education in China. Compared with traditional teaching methods, computer-aided teaching has the advantages of stimulating students’ interest in learning, creating a learning atmosphere and improving classroom efficiency. This paper first briefly introduces the basic concepts of computer-aided English teaching, then expounds the problems in computer-aided English teaching, and finally puts forward some techniques and strategies for computer-aided English teaching.
A Modified Bacterial Foraging Optimizer with Adaptive Chemotactic Step in Dynamic Search Region

Bacterial foraging optimization (BFO), inspired from the foraging process of bacterium called E.coli, has been applied successfully to a variety of real world optimization problems. However, BFO easily encounters the issue of poor convergence when dealing with complex landscapes of optimization problems due to its inherent fixed chemotactic strategy. Aiming at the above issue, an adaptive bacterial foraging optimizer is presented in this paper, which is able to obtain a good balance between exploration and exploitation during the search. In this approach, the chemotactic step-length is adjusted dynamically, that is a larger chemotactic step is for global search and a smaller chemotactic step is conducive to local search. Moreover, the outstanding swarming pattern is incorporated to perform information sharing in population during the evolution, aiming to maintain diversity and convergence. Simulation results on a set of benchmark functions validate the effectiveness of the proposed algorithm.
Introduction to Probabilistic Ontologies

There is no doubt about it; an accurate representation of a real knowledge domain must be able to capture uncertainty. As the best known formalism for handling uncertainty, probability theory is often called upon this task, giving rise to probabilistic ontologies. Unfortunately, things are not as simple as they might appear, and different choices made can deeply affect the semantics and computational properties of probabilistic ontology languages. In this tutorial, we explore the main design choices available, and the situations in which they may be meaningful or not. We then dive deeper into a specific family of probabilistic ontology languages which can express logical and probabilistic dependencies between axioms.
Tail Entity Recognition and Linking for Knowledge Graphs

This paper works on a new task - Tail Entity Recognition and Linking (TERL) for Knowledge Graphs (KG), i.e., recognizing ambiguous entity mentions from the tails of some relational triples, and linking these mentions to their corresponding KG entities. Although plenty of work has been done on both entity recognition and entity linking, the TERL problem in this specific scenario is untouched. In this paper, we work towards the TERL problem by fully leveraging KG information with two neural models for solving the two sub-problems, i.e., tail entity recognition and tail entity linking respectively. We finally solve the TERL problem end-to-end by proposing a joint learning mechanism with the two proposed neural models, which could further improve both tail entity recognition and linking results. To the best of our knowledge, this is the first effort working towards TERL for KG. Our empirical study conducted on real-world datasets shows that our models can effectively expand KG and improve the quality of KG.
Persian FrameNet: a Novel Approach to build FrameNet in the Persian Language Applicable to Islamic Context

FrameNet is a lexical research project that produces a glossary containing very detailed information about syntax (semantic relationships of specific English words such as verbs, nouns, and adjectives). Both human and computer users can use this glossary, so it has become an essential lexical knowledge base for use in fields such as natural language processing and vocabulary semantics. Due to the importance of FrameNet in natural language processing, there is also a need to create FrameNet in languages other than English. There have been few attempts to produce a FrameNet in Persian so far. On the other hand, the existence of an efficient Persian FrameNet can help with the processing of natural language in Islamic texts. In this paper, we present a novel approach to building our first version of the Persian FrameNet. We will then show that although this version is in its infancy, it can be comparable to the FrameNet of some other important languages in terms of frame coverage.
Towards a tool for visualizing pupil dilation linked with source code artifacts

Recent eye tracking research in the field of software engineering has proposed novel visualizations linking developer's gazes with the source code artifacts to better understand how developers comprehend source code artifacts potentially consisting of several different files. In addition, it is well established that cognitive processes can be monitored by recording the change in pupil dilation. Recent pupillometry studies in the software engineering field have shown that pupil dilation can be used either as an indicator of cognitive load or task difficulty. We envision to create a tool for visualizing pupil dilation linked to source code artifacts that can help to better understand the cognitive processes of a developer during code comprehension tasks in terms of cognitive load. In this paper, we describe a feasibility study we conducted to enable a more fine-grained analysis of pupil dilation and we demonstrate some preliminary results.
Swarm Intelligence for Automatic Color and Contrast Retrieval of Digital Images of Paintings

We address the following problem: given an initial high-quality reference image and a variation of it, how to compute suitable values for color map and contrast such that, when applied to this variation, we get an image very similar visually to the reference image. This problem can be formulated as an optimization problem. Unfortunately, this leads to a continuous nonlinear optimization problem too difficult to handle by classical mathematical optimization techniques. To tackle this issue, we apply a powerful swarm intelligence method called cuckoo search algorithm. The method is tested on an illustrative example of a famous painting by artist Vincent Van Gogh. The experimental results show that the method performs very well, with a similarity error rate between the reference and the reconstructed images of only 5.73%. The method can be applied to any variation of the original painting regardless of its initial color map and contrast.
Factor Analysis of English Language Teacher Learning Scale for Assessing Pharmacy Graduates' Language Competencies

The English Language Teacher Learning Scale (ELTLS) has been tested in different academic fields worldwide. However, there is no evidence about its validation in Pakistan. This study reports on validation of ELTLS in Pakistan. The 49 items scale was tested on a sample of 112 teachers from pharmacy departments. The process of validation consisted of two stages. First, the scale was piloted to check reliability Second. Exploratory factor analysis was used to identify the factor structure of the scale. The four factors are teacher cognition, teacher emotion, teacher motivation, and contextual variables. The Principal Component Analysis was applied with orthogonal Varimox rotation method to test the four-factor scale. The results provided evidence for the reliability and validity of the scale. Hence, the scale may be used for assessing language teaching and learning among teachers and students in the field of pharmacy. It is recommended that ELTLS may be tested in other contexts.
Pedagogical project and design of the application of cloud technologies for mastering information competencies by medical university students

The article substantiates the relevance of using cloud technologies in higher education and the interest of researchers in the design of distance learning to the possibilities of such use. The article discusses the pedagogical experience of teaching information methods of data processing through the inclusion of cloud technologies (services) in the methodology of the distance teaching of the course “Logic and Statistics” to future pediatricians at medical university in the aggregate of methods for organizing, monitoring and motivating students’ educational and cognitive activities. It’s shown how, starting from the requirements set by the Federal State Educational Standard of Higher Education in the specialty 05.31.02 “Pediatrics” (specialty level) for the results of development (competencies) and the conditions for the implementation of the program, it is possible to include an initiative pedagogical project in the electronic information and educational environment of the organization. The article describes the design of the application of cloud technologies for using programs, services and a Google Drive as a tool for creating a virtual classroom and laboratory for students to collaborate with each other and with the teacher. The goals and means of a pedagogical project based on the use of methods for simulating distance interaction in professional practice are defined. The article describes the algorithm for the work of a teacher and students in the project “Statistical analysis and interpretation of the simulation game “Clinical trial””.
Intelligent Vehicle Path Planning Based on Hybrid Algorithm

Aiming at the path planning problem of smart cars in complex environments, a hybrid algorithm was proposed for path planning. In the hybrid algorithm, the A * algorithm was combined with the artificial potential field method. The hybrid algorithm fully absorbed the advantages of the two algorithms and made up for the shortcomings of their respective algorithms. The existing problems of traditional A * algorithm and artificial potential field method were improved respectively. Then, the two improved algorithms were merged. And the shortest path was obtained by using A * algorithm. The improved artificial potential field method could effectively avoid various obstacles on the road. The hybrid algorithm appearing above made up for the shortcomings that the A * algorithm couldn't be applied to dynamic environments, and the artificial potential field method couldn't plan the shortest path. The effectiveness of the improved algorithm and hybrid algorithm was verified by MATLAB software.
GTFNet: Ground Truth Fitting Network for Crowd Counting

Crowd counting aims to estimate the number of pedestrians in a single image. Current crowd counting methods usually obtain counting results by integrating density maps. However, the label density map generated by the Gaussian kernel cannot accurately map the ground truth in the corresponding crowd image, thereby affecting the final counting result. In this paper, a ground truth fitting network called GTFNet was proposed, which aims to generate estimated density maps which can fit the ground truth better. Firstly, the VGG network combined with the dilated convolutional layers was used as the backbone network of GTFNet to extract hierarchical features. The multi-level features were concatenated to achieve compensation for information loss caused by pooling operations, which may assist the network to obtain texture information and spatial information. Secondly, the regional consistency loss function was designed to obtain the mapping results of the estimated density map and the label density map at different region levels. During the training process, the region-level dynamic weights were designed to assign a suitable region fitting range for the network, which can effectively reduce the impact of label errors on the estimated density maps. Finally, our proposed GTFNet was evaluated upon three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QRNF). The experimental results demonstrated that the proposed GTFNet achieved excellent overall performance on all these datasets.
Research on Mobile Robot Path Planning Based on Deep Reinforcement Learning

Path planning based on deep reinforcement learning has been a hot topic in the field of mobile robots in recent years, but there are still many shortcomings in its application. Such as the lack of agents' generalization ability, the loss of targets in the process of exploration and the inefficiency of data exploration. In this paper, we propose an improved path planning algorithm to solve the above three problems. We take the Depth Deterministic Policy Gradient (DDPG) algorithm as the basic algorithm, and increase generalization ability of agents by adding adaptive gaussian noise, then, in order to solve the problem of losing target in exploration process, we redesign the reward functions based on the curiosity mechanism. We also add the memory units for agents to make the exploration process more efficient. Finally, we verify that the improved algorithm has good adaptability in different obstacle avoidance environments through simulation experiments.
Author Contributed Representation for Scholarly Network

Scholarly network analysis is a fundamental topic in academia domain, which is beneficial for estimating the contribution of researchers and the quality of academic outputs. Recently, a popular fashion takes advantage of network embedding techniques, which aims to learn the scholarly information into vectorial representations for the task. Though great progress has been made, existing studies only consider the text information of papers for scholarly network representation, while ignoring the effects of many intrinsic and informative features, especially the different influences and contribution of authors and cooperations. In order to alleviate this problem, in this paper, we propose a novel Author Contributed Representation for Scholarly Network (ACR-SN) framework to learn the unique representation for scholarly networks, which characterizes the different authors’ contribution. Specifically, we first adopt a graph convolutional network (GCN) to capture the structure information in the citation network. Then, we calculate the correlations between authors and each paper, and aggregate each embedding of authors according to their contribution by using the attention mechanism. Extensive experiments on two real world datasets demonstrate the effectiveness of ACR-SN and reveal that authors’ contribution to the paper varies with the corresponding authorities and interested fields.
UAV aerial image defogging based on optimum of quality evaluation

Unmanned aerial vehicles have been widely used in military and civil areas, which requires vision processing in explicit usage scenario. Existence of haze or fog can influence the context awareness capability of the aerial vehicles and makes affectation on target tasks. The captured images in hazy scenes suffer from degradation problems including poor contrast, color distortion, incomplete information, which lead to many difficulties in the follow-up processing. A simple and effective single image dehazing algorithm based on atmospheric scattering model and the optimum of image quality evaluation is proposed in this paper. Three image quality evaluation parameters: image entropy, standard deviation, and Fourier amplitude are combined to establish and the image quality evaluation function. On the basis of quality evaluation function, the image with the optimum of quality evaluation among the potential defogging images is chosen as the best result. Results show that this method has lower computational complexity, simplified operations and improved real-time performance.
Grey Wolf Optimizer with Multi-Strategy Optimization and Its Application on TSP

The Grey Wolf Optimizer (GWO) is an optimized search method inspired by the grey wolf predation activity in the wolf pack. Due to the shortcomings of GWO that are easy to fall into the local minimum and slow convergence speed, a GWO with Multi-Strategy Optimization (MSO-GWO) is proposed, that is, the opposition-based learning strategy method is used in the population initialization stage and the weighted distance is used in the search stage to update the position, so to improve the population diversity and avoid falling into the local minimum. After testing and comparative analysis of various international benchmark functions, the results show that the MSO-GWO algorithm has obvious convergence speed and conversion accuracy. Finally, the MSO-GWO algorithm is used to solve Traveling Salesman Problem (TSP), and its solution effect is remarkable.
On the Impact of Early Stopping in Multiple Kernel Learning

Early stopping is a form of regularization commonly used in machine learning applications to avoid overfitting when dealing with iterative optimization procedures. Despite the widespread of early stopping in neural networks, it has rarely been observed in the context of kernel learning due to different reasons. Firstly, the majority of kernel learning methods optimize a (convex) quality measure computed on the training data bounding the empirical error on new examples, placing the need for iterative evaluation in the background. Secondly, the empirical evaluation of a solution during the kernels optimization can be expensive. In this technical report, we empirically analyze different strategies to early stop the kernel optimization in Multiple Kernel Learning algorithms. Our results suggest that (i) the performance on test examples does not necessarily improve during training, i.e. there is a form of overfitting, and (ii) a simple validation metric, e.g. the AUC score, may be an effective and efficient solution to early stop Multiple Kernel Learning algorithms.
Image Classification Based on Deep Belief Network and YELM

For a large amount of image data, an image classification method based on improved deep belief network (IDBN) and improved extreme learning machine (YELM) was proposed. Firstly, IDBN was used to simplify the complex high-dimensional data to lower dimensions space, and get the main inherent feature of the images with lower dimension. Then YELM was used to classify data after the dimension reduction. The proposed IDBN-YELM method has a significant improvement in classification accuracy. Extensive experiments were performed using challenging dataset and results were compared against the models such as DBN, YELM, IDBN-ELM. Though a lot of comparative experiments on the street view house numbers (SVHN) dataset, the results show that the IDBN-YELM has the high classification accuracy for the problem of large-scale image classification.
Conversion of Chinese and English Bilingual Children’s Ethnographic Codes Based on Web Bilingual Corpus

Abstract Parallel corpus is widely used in machine translation, lexicography, bilingual teaching And so on. Web bilingual resources have the following characteristics: 1) multiple fields. Web The fields covered by the information are very broad, such as bilingual on open curriculum plans.Resources contain resources for the content of each subject. 2) Regularity of page layout. Through After observing a large amount of bilingual data on Wb, I found that the vast majority of sentence pairs are Corresponding and logical locations are adjacent. 3) Noise. Web bilingual pages usually contain large Text content that is not related to bilingual, such as comments, comments, descriptions, etc., here we will Content that is unrelated to a double statement pair is called noise Statistical alignment is a method of high accuracy and efficiency in current alignment methods.one. According to our statistics, traditional statistical length alignment on standard flat text The accuracy and recall rate of the method are both around 90%. On the bilingual page, due to the above characteristics, the accuracy and recall rate of the method drop to About 55%.Much scholarship on code-switching (CS) has focused on the development of children’ bilingual ability (Azlan &amp; Narasuman, 2013). Many of these studies, however, adopt the survey to conduct their study. This study explored how code-switching effects the development of language learning of two Chinese-English bilingual children in the Philippines, employing an ethnographically based approach to observe the participants, which lasted 4 months. The findings of the study revealed that the more progress made in English, the more frequencies children take English as matrix language (ML); both inter-sentential and intra-sentential types are presented as reported in bilinguals; CS is a helpful teaching and learning strategy for low proficiency learners. In view of the results, it is obvious that code-switching is a successful communicative strategy which accounts for the development of bilingualism. It is strongly recommended that we should do more research on the implication of this phenomena has on language acquisition right from childhood [1] .