Reviewerly/exHarmony huggingface dataset:
Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.
Human–Computer Communication Using Recognition and Synthesis of Facial Expression

To develop a complex computer system such as a robot that can communicate smoothly with humans, it is necessary to equip the system with a function for both understanding human emotions and expressing emotional signals.From both perspectives, facial expression is a promising research area.In our research, we have explored both aspects of facial expression using infrared-ray images and visible-ray images and have developed a personified agent for expressing emotional signals to humans.A human-computer-human communication system using facial expression analysis and a music recommendation system using a personified agent with facial expression synthesis are reported in this paper.
The Improvement of the CurveCP Cryptography for enhancing the secure of Internet of Things

With the development of Information Technology, Internet of Things (IoT) has been applied widely in human civilization, but it leads the increase of the risk about information security. The traditional security solutions has revealed the lack of compatibility with IoT because of the difference about complex communication protocol, low energy, processing ability and limited memory. Therefore, the suitable IoT security solutions needs the balance between energy and costs as well as wide adaption for the various network protocols. Our research team, after had been under a long process of analyzing theoretical documents and operating simulated experiments, recognized that the Lightweight cryptography is the most optimal solution in new network platform generation. Our team also improved and implemented CurveCP which is one of these Lightweight cryptographies in the Wireless sensor Networks (WSN) so as to enhance data secure and information security of IoT System. This study describes briefly the improvement of CurveCP Lightweight cryptography by reducing length of cryptographic key as well as implement in IoT System. It also includes the simulation experiment, solutions evaluation, conclusion and future development.
Different Methods Review for Speech to Text and Text to Speech Conversion

In the instant corporation, transmission is the primary fundamental to momentum.Transitory information, to the correct person with the correct aspect is very essential, not just on an industry level, but also on an individual position.Nature is inspiring in the direction of digitization and the mechanisms of intercommunication.Telephone calling, emails, text memorandums belong to a fundamental element of signal communication in this tech-intellect nature.In procedures to distribute the intention of adequate transmission intervening two endpoints without obstacles, numerous utilizations have shown up the impression, which operates as an intermediary and helps in efficiently transmitting signals in the scheme of text or speech messages accomplished huge structure of webs.Most of these implementations discover the Usage of tasks essentially articulatory and acoustic-positioned speech recognition, reorganization from audio messages to text, and then text to artificial speech signals, vocabulary interpretation amidst individual leftovers.Researchers will be penetrating distinct algorithms and techniques that are enforced to obtain the specified utilitarian.
Integrating PAD Class to College English Reading Teaching

Reading is an important way for people to obtain information.The improvement of reading ability is the key to English learning.In order to improve English reading teaching effectiveness, a new teaching mode called PAD (Presentation-Assimilation-Discussion) class is adopted.In this paper, the author focuses on the application of PAD class in college English reading instruction: reconstruction of teaching material, fostering analytical reading competence and formative assessment in college English reading context.
NGO-GM: Natural Gradient Optimization for Graphical Models

This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent strategy that incorporates additional meta parameters. We show that our approach is a strong alternative to the celebrated EM approach for learning in graphical models. Actually, our natural gradient based strategy leads to learning optimal parameters for the final objective function without artificially trying to fit a distribution that may not correspond to the real one. We support our theoretical findings with the question of trend detection in financial markets and show that the learned model performs better than traditional practitioner methods and is less prone to overfitting.
AN APPLICATION OF LINEAR DIOPHANTINE EQUATIONS TO CRYPTOGRAPHY

In this chapter we propose a Key exchange protocol based on a random solution of linear Diophantine equation in n variables, where the considered linear Diophantine equation satisfies the condition for existence of infinitely many solutions. Also the crypt analysis of the protocol is analysed.
Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model

Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist.We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model.Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data.From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence.We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation.
Dynamic Evaluation and System Coordination Degree of the Integration of Artificial Intelligence and Real Economy

Artificial intelligence (AI) is an important driving force of the new round of technological revolution and industrial change, and the development of a new generation of AI can help improve comprehensive national power and promote healthy and sustainable economic development. AI can promote economic development through four ways. First, AI replaces labor, expands labor connotation, increases labor supply, and enriches labor wealth; AI empowers laborers and improves labor productivity. Second, AI empowers the three industries and improves production efficiency. Third, AI creates consumer surplus and improves social welfare. Fourth, AI empowers government to correct government failure and improve government efficiency, which in turn corrects market failure and improves economic efficiency. The economic subsystem covers both quantitative and qualitative aspects of economic growth, economic structure, economic efficiency, and economic support. Environmental subsystems are divided into environmental quality, environmental pollution, and environmental protection. While using AI to promote economic development, it is also important to strengthen the research and prevention of potential risks of AI development to ensure that AI is safe, reliable, and controllable.
Using Personas to Promote Inclusive Education in an Online Course

Inclusion and diversity are themes at the forefront of education development and the subject of much institutional level policy.However, there are differences that can be made at the course or even module level by local teams that will both compliment institutional policy and have an immediate impact on students.This paper aims to describe the process taken to embed diversity and inclusion into an online career development short course.We will explain how user experience personas, created in the design phase of the course, developed into an integral part of the course curriculum and how they are used to show diversity to a fully asynchronous online cohort.We discuss how theories of pedagogy and design can be combined to have a positive impact on the sense of belong for all students.Finally, we explore how these theories can be applied to wider practice and offer suggestions for how other courses can use our process to embed elements of diversity into their programmes, and hopefully increase a sense of student belonging.
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation

Superpixels serve as a powerful preprocessing tool in numerous computer vision tasks. By using superpixel representation, the number of image primitives can be largely reduced by orders of magnitudes. With the rise of deep learning in recent years, a few works have attempted to feed deeply learned features / graphs into existing classical superpixel techniques. However, none of them are able to produce superpixels in near real-time, which is crucial to the applicability of superpixels in practice. In this work, we propose a two-stage graph-based framework for superpixel segmentation. In the first stage, we introduce an efficient Deep Affinity Learning (DAL) network that learns pairwise pixel affinities by aggregating multi-scale information. In the second stage, we propose a highly efficient superpixel method called Hierarchical Entropy Rate Segmentation (HERS). Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously. We demonstrate, through visual and numerical experiments, the effectiveness and efficiency of our method compared to various state-of-the-art superpixel methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance

Sentiment analysis is a process that is a very popular concept nowadays because of the high volume of reviews, micro blogs, comments etc., generated in different sites like e-commerce and social networking sites.The main problem in the current system is, for users to know the polarity result of bulk data, which is very tough because users need to study and understand each review in terms of the polarity.Users are expecting a onetime result of the polarity of bulk reviews, comments, micro blogs etc.In social networking sites, users post their status or opinions to share to the world.In this category, Twitter is the most popular one.In twitter, users post many micro blogs related to a topic or crisis etc, and the topic may be linked with a greater number of micro blogs based on the keywords or hash tags used.In twitter, we can search for any topic with keywords or hash tags, and we get a bulk of responses of the users world-wide.If we want to know the exact opinions of the users, we need to analyze the data with sentiment analysis.Sentiment analysis is a concept of defining a statement as positive, neutral, or negative by analyzing words of the statement.Many concepts have been proposed for this requirement, and many sentidatasets have been prepared for this requirement.But by taking the advantages of Machine Learning we are proposing a concept of sentiment analysis in twitter using ML techniques.In this we use multiple ML techniques such as Random Forest, Naïve Bayes and Support Vector Machine for evaluation and comparison of the results.
Global trends in the reproduction of human capital in the tertiary education system

This article assesses global trends in higher education. Two tasks are central to this effort:1) research into higher as performing the macro-function of producing human capital;2) a comparison of the organization and financing of higher education in different countries.Our hypothesis is as follows. Along with the influence of the mental development of the population and the level of productive forces, we believe that the main difference in the effectiveness of higher education systems is a consequence of the regulatory influences of governments.Study of the contradictions accumulated in the course of continuous reform allows us to propose measures to improve systemic interaction. The article compares the organization of higher education in the USA, Great Britain, Japan, and Russia at various stages, from admission to universities, to employment of graduates, and the corresponding organization and funding of the educational process. Research results are these. Comparing the indicators of applicant selection suggests replacing the Unified State Exam in Russia with an indicator of the weighted average score of electronic diaries. The study of the learning process showed a tendency to replace pure sciences in curricula with applied sciences. Variants of increasing the share of education in pure sciences are proposed to extend the life of basic competencies of graduates in conditions of local backwardness and uncertainty in the development of regional labor markets. For organization and financing of higher education, the analysis suggests an incompatibility between the Bologna system as introduced in Russia, and the preserved course system of education, with its fixed structure of curricula and expulsion for academic failure.The article shows ways to eliminate this incompatibility, such as the transition to a subjectstatus system of education and re-teaching in the subject. This will eliminate the current situation of fining universities for each expelled student. Conclusions are provided about the need for an integrated approach to subsequent transformations based on the study of global trends in the development of higher education and the preservation of the advantages of the development of Russian higher education.
A NEW SOYBEAN NDVI DATA-BASED PARTITIONING ALGORITHM FOR FERTILIZATION MANAGEMENT ZONING

With the broad application of management zoning, various partitioning algorithms are used for this purpose.The K-means algorithm is the most widely used method with the best performance.We proposed a model-based partitioning algorithm based on the K-means algorithm that does not need to process all the data in each computational iteration and thus is able to improve the management zoning speed.We constructed a calculation model for management zone partitioning using 2000 normalized difference vegetation index (NDVI) values.This model was then used to partition the next 2000 points of NDVI data acquired, and the model was updated after each 2000 NDVI values until the management zoning computation was completed for the entire field.Based on both internal evaluation indicators (the sum of squared errors (SSE) and silhouette coefficient (SC)) and external evaluation indicators (the Rand index (RI) and homogeneity), we compared the clustering performance of the two algorithms in management zone partitioning and found that when the amount of NDVI data reaches 8000 values, the proposed method achieves a management zone partition result similar to that of the conventional K-means algorithm but is faster.This advantage becomes increasingly profound as the data volume increases.
Self-Contextualized Attention for Abusive Language Identification

The use of attention mechanisms in deep learning approaches has become popular in natural language processing due to its outstanding performance.The use of these mechanisms allows one managing the importance of the elements of a sequence in accordance to their context, however, this importance has been observed independently between the pairs of elements of a sequence (self-attention) and between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences.To tackle these particular issues we propose the self-contextualized attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of a sequence.The proposed mechanism was evaluated in four standard collections for the abusive language identification task achieving encouraging results.It outperformed the current attention mechanisms and showed a competitive performance with respect to state-of-the-art approaches.
Research on Cross-Session and Cross-Device Search: A Systematic Literature Review

The complex process of online information search has been of great interest to academics and practitioners, regarding its motivation, triggers, phases, and various aspects of user search behavior. Web search spanning multiple sessions has been studied, including users switching between devices. The study presented in this paper aims to critically analyze two decades (2000 - 2020) of research on cross-session and cross-device search. A systematic literature review of 29 journal papers and peer reviewed academic conference proceedings was conducted. The main results were extracted, synthesized, and presented. Topical (focus and terminology), methodological (data collection, sample size, sample type, and countries studied) and bibliometric (publication outlet and citations) aspects of online information search were evaluated and described. The paper identifies keywords for effective literature searches and adds new insights about effective research methods and sampling, the most cited publications and most influential authors in this field and the possibility of conducting interdisciplinary research. Both scholars and practitioners can use this study to gain a deeper understanding of the cross-session and cross-device search research agenda. Future research suggestions inform researchers about issues that warrant further attention.
A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

The known algorithms for planning the trajectory of movement of mobile robots in an unknown environment have high computational complexity or do not allow finding the trajectory that is optimal along the length of the path, while maintaining a safe distance from obstacles. The aim of the work is to increase the efficiency of solving the problem of planning the trajectory of movement of mobile robots from the initial position to the final position in an unknown environment with obstacles, taking into account the limited capabilities (sensory and computational) of mobile robots. The solution to this problem was carried out on the basis of step-by-step optimization of the current position of the robot relative to a given target. The proposed method analyzes the possibility of a robot moving in directions determined by means of analytical geometry based on measurements of on-board distance sensors. An element of scientific novelty is the procedure for calculating trajectory segments based on the choice of an intermediate state and correcting the trajectory taking into account the measurements of the on-board distance sensors of the mobile robot. The proposed method makes it possible to search for the trajectory of a mobile robot in an unknown environment while ensuring a given distance to obstacles. The use of the presented algorithm allows the robot to maintain a high efficiency of the task while functioning in conditions of information deficiency. The reliability of the results was confirmed in the course of software simulation. The solution of the problem, taking into account these features, made it possible to reduce the computational complexity of the method, as well as to remove restrictions on the use of trajectory planning algorithms for mobile robots with low-performance on-board sensors and computing devices. The presented algorithm is implemented in the form of software in the Python programming language, which can be used to simulate autonomous control systems for mobile robots.
A Real-Time Image Semantic Segmentation Method Based on Multilabel Classification

Image semantic segmentation as a kind of technology has been playing a crucial part in intelligent driving, medical image analysis, video surveillance, and AR. However, since the scene needs to infer more semantics from video and audio clips and the request for real-time performance becomes stricter, whetherthe single-label classification method that was usually used before or the regular manual labeling cannot meet this end. Given the excellent performance of deep learning algorithms in extensive applications, the image semantic segmentation algorithm based on deep learning framework has been brought under the spotlight of development. This paper attempts to improve the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) based on the multilabel classification method by the following steps. First, the standard convolution is replaced by applying Receptive Field in Deep Convolutional Neural Network in the convolution layer, to the extent that every pixel in the covered area would facilitate the ultimate feature response. Second, the ASPP (Atrous Spatial Pyramid Pooling) module is improved based on the atrous convolution, and the DB-ASPP (Delate Batch Normalization-ASPP) is proposed as a way to reducing gridding artifacts due to the multilayer atrous convolution, acquiring multiscale information, and integrating the feature information in relation to the image set. Finally, the proposed model and regular models are subject to extensive tests and comparisons on a plurality of multiple data sets. Results show that the proposed model demonstrates a good accuracy of segmentation, the smallest network parameter at 0.3 M and the fastest speed of segmentation at 25 FPS.