Reviewerly/exHarmony huggingface dataset:
Reflection the modern ideas about the content of general education in the school physics textbooks

Abstract The article considers the evolution of the general education goals in particular physics education, changes through this process the understanding of the subject «Physics» content. Soviet education aimed at preparing a comprehensively harmonically developed personality through the knowledge paradigm and the content of education was fully consistent with this goal. Later it takes place a rethinking approaches of understanding the content of education and the content of a subject. At the present goals of education are represented in the form of educational results: personal, subject and metasubject, which implies a change in the content of the subject «Physics» in the direction of increasing the importance of methodological, interdisciplinary, evaluative knowledge and strengthening the procedural component. This trend reflects in the textbooks: the procedural component is expanded, there are problems and tasks that involve conducting experimental research in the classroom and at home, solving creative tasks, and completing educational projects. The article substantiates the role of the physics textbook in improving educational outcomes, provides examples of the organization of educational content in school textbooks of physics in Russia and Singapore.
Towards Environmental-Adaptive and Performance-Resilient Consensus in Distributed Ledger Technology

Many recent research works have proposed distributed ledger technology (DLT) that employs Byzantine fault-tolerant (BFT) consensus protocols as the underlying core primitive to create a total order among all transactions. Compared to many Proof-of-Work (PoW) blockchains, this design typically benefits from increased performance, energy efficiency and proven liveness and safety characteristics. While BFT protocols have the potential to create highly resilient infrastructures, some questions yet remain to be answered. This paper sketches our current and future research on how DLTs can benefit from making the underlying BFT protocol adaptive towards the system's environment (e.g., geographic decentralization or system scale) and resilient against attacks of malicious replicas that are targeted at degrading the overall system performance.
Beauty tech nails

There has been a significant increase in the use of mobile devices and their possibilities for interaction. Currently several actions in people's surrounds, both face-to-face and in virtual spaces can be performed by using these devices. Some examples are the control of some home appliances, location sharing and facial, voice and gesture recognition without direct contact. Based on that, Beauty Technology proposes an interaction approach embedded in beauty products, so that the user can interact with the world in a discreet and comfortable way. In this context, we present, in this article, an exploratory study on the utilities and limitations of beauty tech nails for remote interaction actions on mobile devices.
Swarm ascending: Swarm intelligence-based exemplar group detection for robust clustering

An exemplar is a representative observation for each cluster. Exemplar-based clustering algorithms, which find the exemplars and assign data points to the nearest exemplar, have exhibited promising performance. However, the single- and multi-exemplar methods become inadequate for clustering data points with nonlinear and local patterns because one exemplar (or a set of sparse exemplars for a nonlinear cluster) is insufficient to represent the cluster. In this paper, we propose a swarm intelligence-based exemplar group detection method that ascends data points to local high-density points and groups the merged points. The proposed method is robust to nonlinear and local patterns because it detects the intrinsic structure of each cluster more sufficiently than sparse exemplars. We use simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy. The comparison results demonstrate that the proposed method outperforms the alternatives.
Controllable photon extraction based on a single-photon Raman interaction

Abstract A target whispering-gallery-mode microresonator (WGMM) directly coupled to a waveguide with an auxiliary side-coupled WGMM is proposed to deterministically extract both the resonant and non-resonant single incident photons from a waveguide. Based on the single-photon Raman interaction (SPRINT) between an Λ-type three-level atom and the target WGMM, a full quantum theory in real space is adopted to calculate the extraction efficiencies at the single-photon level. The results show that the extraction efficiencies can be significantly improved by appropriately tuning the frequencies of the auxiliary WGMM and the coupling strength between the two WGMMs, even when the atom and WGMMs have dissipations. Since mode redistribution is only externally imposed on the auxiliary WGMM, the population and phase of the atom are not directly affected. The nonlocal control, which ensures that the SPRINT takes place, results in high extraction efficiencies. We also find that the transmission probabilities of both the resonant and non-resonant incident photons can be controlled in a range from 0 to 100%, so that the proposed double-WGMM system has the potential to be used as a single-photon switch.
Optimal system design of language training strategy based on artificial intelligence

With the rapid development of China’s economy and the rapid increase in the number of Chinese learners in recent years, Chinese fever has become a common phenomenon in the global language exchange system. However, in the absence of foreign Chinese teachers at present, the development of Chinese new technology, this document uses the new computer technology to establish a Chinese teaching platform. The system is based on speech recognition technology to help foreigners learn spoken language, manuscript recognition technology and Chinese letters. foreign students in artificial intelligence technology, simulation of Chinese foreign education and training. The establishment of this system will not only help foreign students to solve the problem of Chinese learning successfully, but also make important contributions to the learning of Chinese students. Some new technologies, such as speech recognition, will be recognized by more and more Chinese students with the development of new technologies and the renewal of the system, and will make the greatest contribution to the promotion of Chinese culture.
Chatbots for CRM and Dialogue Management

In this chapter, we learn how to manage a dialogue relying on the discourse of its utterances. We show how a dialogue structure can be built from an initial utterance. After that, we introduce an imaginary discourse tree to address the problem of involving background knowledge on demand, answering questions. An approach to dialogue management based on a lattice walk is described. We also propose Doc2Dialogue algorithm of converting a paragraph of text into a hypothetical dialogue based on an analysis of a discourse tree for this paragraph. This technique allows for a substantial extension of chatbot training datasets in an arbitrary domain. We evaluate constructed dialogues and conclude that deploying the proposed algorithm is a key in successful chatbot development in a broad range of domains where manual coding for dialogue management and providing relevant content is not practical.
Introduction to Supervised Machine Learning for Data Science

We present an introduction to supervised machine learning methods with emphasis on neural networks, kernel support vector machines, and decision trees. These methods are representative methods of supervised learning. Recently, there has been a boom in artificial intelligence research. Neural networks are a key concept of deep learning and are the origin of the current boom in artificial intelligence research. Support vector machines are one of the most sophisticated learning methods from the perspective of prediction performance. Its high performance is primarily owing to the use of the kernel method, which is an important concept not only for support vector machines but also for other machine learning methods. Although these methods are the so-called black-box methods, the decision tree is a white-box method, where the judgment criteria of prediction by the predictor can be easily interpreted. Decision trees are used as the base method of ensemble learning, which is a refined learning technique to improve prediction performance. We review the theory of supervised machine learning methods and illustrate their applications. We also discuss nonlinear optimization methods for the machine to learn the training dataset.
Successive Japanese Lyrics Generation Based on Encoder-Decoder Model

In this paper, we propose lyrics writing based on the Encoder-Decoder model. Because recent lyrics writing support systems writes lyrics based on an N-gram language model, they cannot consider a long-term context of lyrics. In the proposed method, we consider a context of 3 words and over by writing lyrics with the Encoder-Decoder model. By using relative mora positions and an algorithm of generating several sentences, we accomplish generating the number of morae that specified and plural candidate sentences.
Study on viewing completion ratio of video streaming

In this paper, a model is investigated for optimizing the encoding of adaptive bitrate video streaming. To this end, the relationship between quality, content duration, and acceptability measured by using the completion ratio is studied. This work is based on intensive subjective testing performed in a laboratory environment and shows the importance of stimulus duration in acceptance studies. A model to predict the completion ratio of videos is provided and shows good accuracy. By using this model, quality requirements can be derived on the basis of the target abandonment rate and content duration. This work will help video streaming providers to define suitable coding conditions when preparing content to be broadcast on their platform that will maintain user engagement.
Business process management approach for improving agile software process and agile maturity

Abstract Continuous improvement of agile software processes is inevitable in order to reach sustainable and high‐quality project outcomes. In this sense, organizations need guidance on how to apply agile software process improvement effectively; comprehensive approaches with in‐depth analysis and guidelines are required to keep software processes in balance and to continuously improve them, as in a typical business process, over time. This research aims to explore the implementation of Business Process Management approach for improving an agile software process and accordingly agile maturity level. Many organizations utilize from Business Process Management approach in order to improve workflows and to discover organization‐specific weaknesses. For this purpose, the study presents a practical, flexible model based on Business Process Management approach for agile software process improvement, which also incorporates maturity assessment for measuring the agile maturity level. Furthermore, an exploratory case study with a triangulation methodology of mixed methods was carried out for implementation of the model. The research puts forward that Business Process Management approach can be used for in‐depth analysis of agile software process, discovery of problems, and redesign of the process. The research roadmap introduces worthwhile insights on agile software process improvement for researchers and practitioners.
Facial Age Estimation by Curriculum Learning

Curriculum learning has been widely used in training neural networks because of its significant improvements in generalization capability. However, it has not been applied to age estimation tasks. In this paper, we incorporate curriculum learning into age estimation. Experimental result of the proposed method on AFAD database for age prediction shows a substantial reduction of the prediction error compared to the traditional training strategy.
Quality of Word Vectors and its Impact on Named Entity Recognition in Czech

Named Entity Recognition (NER) focuses on finding named entities in text and classifying them into one of the entity types.Modern state-of-the-art NER approaches avoid using handcrafted features and rely on feature-inferring neural network systems based on word embeddings.The paper analyzes the impact of different aspects related to word embeddings on the process and results of the named entity recognition task in Czech, which has not been investigated so far.Various aspects of word vectors preparation were experimentally examined to draw useful conclusions.The suitable settings in different steps were determined, including the used corpus, number of word vectors dimensions, used text preprocessing techniques, context window size, number of training epochs, and word vectors inferring algorithms and their specific parameters.The paper demonstrates that focusing on the process of word vectors preparation can bring a significant improvement for NER in Czech even without using additional language independent and dependent resources.
Design of Automatic Key Finder for Search Engine Optimization in Internet of Everything

Technology is advancing day by day. Automation is playing an important role. Internet of Everything (IoE) has emerged as a key technology that enables everything to be connected through the internet. In every field Internet of everything is generating a vast amount of data like health system, traffic system, smart city monitoring system, education system, social networking sites, government organizations, etc. So to handle this huge amount of data, a system is required through which it becomes easier to track, analyze, handle, and apply that data in a better way. A unique system is proposed in this article through which important keys are identified. From those keys, desired data can be retrieved quickly and efficiently. The proposed system uses the entire new process to get the desired output. The k-means clustering with a modified entire process is used. By using natural language processing the data is accessed by the voice command. Through simulation results, it is proved that the proposed system has given better results and the searching process becomes smoother and efficient.
Supervised Transfer Learning for Sequence Tagging of User-Generated-Content in Social Media

Neural-networks based approaches for Natural Language Processing (NLP) are effective when dealing with learning from large amounts of annotated data. However, these are only available for a limited number of languages and domains due to the cost of the manual annotation. Particularly, despite the valuable importance of Social Media’s content for a variety of applications (e.g., public security, health monitoring, or trends highlight), this important domain is still poor in terms of annotated data. In this work, we analyse the impact of supervised sequential transfer learning to overcome the sparse data problem in the Tweets-domain by leveraging the huge annotated data available for the Newswire-domain. We experiment our approach on three NLP tasks: part-of-speech tagging, chunking and named entity recognition.
Mining Larger Class Activation Map with Common Attribute Labels

Class Activation Map (CAM) is the visualization of target regions generated from classification networks. However, classification network trained by class-level labels only has high responses to a few features of objects and thus the network cannot discriminate the whole target. We think that original labels used in classification tasks are not enough to describe all features of the objects. If we annotate more detailed labels like class-agnostic attribute labels for each image, the network may be able to mine larger CAM. Motivated by this idea, we propose and design common attribute labels, which are lower-level labels summarized from original image-level categories to describe more details of the target. Moreover, it should be emphasized that our proposed labels have good generalization on unknown categories since attributes (such as head, body, etc.) in some categories (such as dog, cat, etc.) are common and class-agnostic. That is why we call our proposed labels as common attribute labels, which are lower-level and more general compared with traditional labels. We finish the annotation work based on the PASCAL VOC2012 dataset and design a new architecture to successfully classify these common attribute labels. Then after fusing features of attribute labels into original categories, our network can mine larger CAMs of objects. Our method achieves better CAM results in visual and higher evaluation scores compared with traditional methods.
Graph Regularized Subspace Clustering via Low-Rank Decomposition

Subspace clustering (SC) is able to identify low-dimensional subspace structures embedded in high-dimensional data. Recently, graph-regularized approaches aim to tackle this problem by learning a linear representation of data samples and also a graph structure in a unified framework. However, previous approaches exploit a graph embedding term based on representation matrix, which could over-smooth the graph structure and thus adversely affect the clustering performance. In this paper, we present a novel algorithm based on joint low-rank decomposition and graph learning from data samples. In graph learning, only a low-rank component of the representation matrix is employed to construct the graph embedding term. An alternating direction method of multipliers (ADMM) is further developed to tackle the resulting nonconvex problem. Experimental results on both synthetic data and real benchmark databases validate the effectiveness of the proposed SC algorithm.
Identifying Improvement Opportunities in Software Engineering Education at the Maranhão State

The teaching of Software Engineering (SE) has become challenging due to the large amount of content taught and the constant evolution of the software industry, directly impacting the requirements necessary to apply for a position in the area of Information Technology (IT). In this context, it is clear that students of computer courses still find it difficult to know how to prepare for the job market, and Higher Education Institutions (HEIs) may have difficulties in aligning themselves with market expectations with their syllabus in SE. This paper aims to provide a holistic view of the teaching-learning process, involving the views of students, HEIs and IT companies in the context of the state of Maranhão; and to propose reflections on the current state of the education to complement the contents and methodologies applied in the classroom with the needs of the local industry. To do so, three activities were carried out: (1) mapping expectations and evaluating higher education from the students' point of view; (2) analysis of higher education disciplines in higher education institutions in the state of Maranhão in Brazil; and (3) survey of expectations of the IT market considering the vacancies disclosed in relation to the SE area in the state of Maranhão. The results indicate a partial fulfillment of the demand regarding the taught topics related to SE from the local industry by the HEIs. In addition, there is a need to improve the teaching-learning methodological processes, focusing on practical activities and the learning of teamwork skills, in addition to knowledge in software development.
An Improved Weighted-Removal Sentence Embedding Based Approach for Service Recommendation

Currently, there is a large amount of information about user requirements and service in natural language. How to measure the semantic similarity between user requirements and service description is a critical issue in service recommendation and service solution construction. In this paper, we propose a service recommendation method based on the improved Weighted-Removal(WR) sentence embedding to solve the shortcomings of traditional information retrieval methods. After data preprocessing, we use the GloVe method to obtain the word vectors and use the improved WR sentence embedding method to obtain the sentence vectors. The similarity between the vectors can be better measured. The experimental results show that the proposed improved WR method is significantly better than the traditional methods in terms of recommendation accuracy, richness, and ranking.
Improving Energy Compaction of Adaptive Fourier Decomposition

Adaptive Fourier decomposition (AFD) provides an expansion of an analytic function into a sum of basic signals, called mono-components. Unlike the Fourier series decomposition, the AFD is based on an adaptive rational orthogonal system, hence it is better suited for analyzing non-stationary data. The most popular algorithm for the AFD decomposes any signal in such a way that the energy of the low-frequency components is maximized. Unfortunately, this results in poor energy compaction of high-frequency components. In this paper, we develop a novel algorithm for the AFD. The key idea is to maximize the energy of any components no matter how big or small the corresponding frequencies are. A comparative evaluation was conducted of the signal reconstruction efficiency of the proposed approach and several conventional algorithms by using speech recordings. The experimental results show that with the new algorithm, it is possible to get a better performance in terms of the reconstruction quality and energy compaction property.
Vulnerable Road Users Detection based on Convolutional Neural Networks

The roads support many different types of users. With geared efforts to advance connected and autonomous vehicles (CAVs), smart mobility systems, and advanced driver assistance-based vehicles, the safety of road users becomes a growing concern. Some users may require more cautious interactions and support to ensure safe usage of the road infrastructures. While considerable effort has been done to detect different types of road users or objects from a vehicle's viewpoint, there are certain classes of vulnerable road users which have been overlooked in prior works. The objective of this work is to detect vulnerable road users (e.g., Strollers, Motorbikes, and Bicycles) in order to aid in reduction of collisions. We investigate the performance of one-stage and two-stage deep object detection methods in detection of said vulnerable users. Since there is a lack of publicly accessible datasets containing objects of our interest from an infrastructure viewpoint, we introduce our own dataset collected from a road side. We highlight the benefits and shortcomings of the studied methods in the context of vulnerable road users detection under challenging conditions such as occlusions.
Light-weighted Network based Human Pose Estimation for Mobile AR Service

Recent advances in deep learning algorithms and hardware have led to the rapid development of computer vision. Based on the development of computer vision field, many deep learning algorithms are applied to augmented reality(AR) technology. However, in order to increase the accessibility of users, the majority of AR services are provided in mobile devices. In addition, many deep learning algorithm techniques require high computing resources to be applied directly to AR services. Therefore, in order to apply deep learning technology to mobile AR services, it is necessary to implement a lightweight network. In this paper, we propose a light-weighted network model to estimate the single human pose for the mobile AR service. The contribution of this paper is as following. i) The proposed light-weighted network is applied to the commercial mobile devices. ii) A method for integrating Unity 3D rendering tool and TensorFlow Lite library is proposed. iii) The body skeleton of human is extracted and analyzed in real time. Our results suggest that it will be useful to provide a way for new interactions in mobile AR services.