Reviewerly/exHarmony huggingface dataset:
Continuous Recognition of 3D Space Handwriting Using Deep Learning

In this paper, we attempt to present novel input methods that help enable byzantine free of hands interface through recognition of 3D handwriting. The motion is detected wirelessly by the use of the inertial measurement unit (IMU) of the Arduino 101 board. Two different approaches are discussed. One approach is to use the pattern matching engine (PME) of the Intel® Curie™ module on Arduino 101 mounted on the back of the hand. The second approach uses the IMU input to a well-structured recurrent neural network. The spotting of handwriting segments is done by a support vector machine. The former approach, being indigent of memory, is not preferred over the latter. The deep learning approach can continuously recognize random sentences. The model was trained on 1000 freely definable vocabulary and was tested by only one person, achieving the lowest possible word error rate of 2%.
Efficient knowledge distillation of teacher model to multiple student models

Deep learning models are proven to deliver satisfactory results on training a complex non-linear relationship between the set of input features and different task outputs. However, they are memory intensive and require good computational power for both training as well as inferencing. In literature one can find different model compression techniques which enables easy deployment on edge devices. Knowledge distillation is one such approach where the knowledge of complex teacher model is transferred to a lower parameter student model. However, the limitation is that the architecture of the student model should be comparable to the complex teacher model for better knowledge transfer. Due to this limitation, we cannot deploy this student model that learns from a complex and huge teacher on edge devices. In this work, we propose to use a combined student approach wherein different student models learn from a common teacher model. Further, we propose a unique loss function which will train multiple student models simultaneously. An advantage of this approach is that these student models can be as simple as possible when compared with traditional single student model and also the complex teacher model. Finally, we provide an extensive evaluation to prove that our approach can improve the overall accuracy significantly and allow a further compression by 10% when compared with generic model.
Cold Start Recommendation Algorithm Based on Latent Factor Prediction

At present, recommendation algorithms have been widely used in online shopping, commercial services and entertainment. Although the recommended methods are endless, they are generally recommended based on the user's historical behavior or project information. Since the emergence of collaborative filtering in the 20th century, it become a very popular method in recommendation. But on the issue of cold start, collaborative filtering is not effective due to the lack of information. In this paper, we focus on solving the user side cold start problem. We propose a cold start recommendation algorithm based on latent factor prediction by combining collaborative filtering algorithm and matrix factorization. When it is just an ordinary user, we predict the rating by matrix factorization, and during the training iteration, we propose an improved optimization algorithm based on Alternate Least Squares (ALS). When the user is a new user, we predicted the latent factor by feature prediction. And then, the final outcome is calculated. The experiment uses the movielens data set. The experiment results show that our proposed recommendation algorithm has approximately 20.4% improvement over the baseline cold start algorithm. In addition, this paper design experiment to validate the proposed optimization algorithm effect, and the experiment results prove that the proposed algorithm has good precision and scalability.
Two-phase Switching Optimization Strategy in LSTM Model For Predictive Maintenance

Predictive maintenance has emerged as an important strategy in the industrial operations due to the current advancement on machine learning approaches. The use of Long Short Term Memory (LSTM) neural network can infer sequential data in time domain with an easy deployment for predictive maintenance. An efficient optimization is required in training a LSTM model to update the weights. In this paper, the Stochastic Diagonal Approximate Greatest Descent algorithm (SDAGD) with momentum is proposed to optimize the learning parameters of LSTM cell using a two-phase switching optimization strategy. First, an iterative step of SDAGD algorithm is defined at the boundary of local search region if the local search region does not contain a global solution. This optimizer switches to the Newton method using the controlling parameter of relative step- length when an optimal solution does exist in the search region. The implementation of momentum in the SDAGD algorithm is performed on LSTM model optimization to accelerate the convergence. As a result, this optimizer has reduced both the testing and training error rate with the acceleration aspects of momentum parameter.
Learning Multi-Graph Neural Network for Data-Driven Job Skill Prediction

Specifying an appropriate skill set for a job position is critical for talent recruitment. However, it is often more difficult than people think since it needs a great understanding of the role of the position, related technologies, and even the global situation of the job market. To this end, we propose to learn the mapping between the job position description and its required skills in a data-driven manner. This task is challenging due to the complex mapping relationships between job descriptions and skills, which is caused by complex influencing factors. In this paper, we propose a novel Multi-Graph Neural Network based Skill Prediction model (MGNSP) to make skill prediction by learning effective deep semantics matching of job positions and skills. Specifically, to capture the complex heterogeneous relations among the job positions, skills, and meta information, we devise a joint learning approach of graph neural networks for multiple information networks, which are J-Net, S-Net and JS-Net, respectively. After jointly learning complementary semantics of job positions and skills with three multi-layer graph neural networks from these information networks, the skills are predicted by learning to match their representations. Extensive experimental results on a real-world dataset validate the effectiveness of our model.
Intrinsic Motivation Model Based on Reward Gating

Intrinsic motivation (IM) research is a promising part of reinforcement learning which can push artificial agents to completely new frontiers. Namely, from agents with a simple action repertoire, driven by the human engineered reward, to more autonomous agents with their own goals and skill development, able to act successfully in the environments which are unknown to their human designers. In this paper, we introduce an IM model, which combines via gating two different motivational signals: a prediction error estimated by the forward model and a predictive surprise estimated by the meta-critic. This approach accelerates the exploration of the environment and hence the agent is able to find sources of an external reward in a shorter time than the baseline agents, especially in case of sparse reward. We test this prediction using two environments with dense reward (HalfCheetah and Ant) and two with sparse reward (MountainCar and AerisNavigate), and show the superior performance of an agent with a gated reward in most cases as expected. The models are also compared using reliability measures related to dispersion and risk, calculated during learning. The source code is available at https://github.com/Iskandor/MotivationModels.
Deep learning based handwritten digit recognition in Android

Abstract Handwritten digit recognition refers to the recognition of handwritten digits written and printed on paper, and the recognition results are stored in the computing machine in the form of text. According to the characteristics of a handwritten digital image, based on the multi-layer perceptron method, this paper introduces the source and principle of handwritten digital image recognition in detail, and analyzes the steps, related functions and theories of handwritten digital image recognition in sequence. The proposed algorithm was validated on the handwritten dataset MNIST, and the model was evaluated and adjusted. Eventually, the trained model was migrated to Android.
An Optimized Process of Multiscale Retinex Algorithm Based on Hardware System

This paper introduces an optimized process of Multiscale Retinex (MSR) algorithm that apply to the hardware system. Retinex theory is a constant color model derived from the Human Visual System (HVS) and can obtain more stable result for image enhancement than classic algorithms. Generally, the MSR model in Retinex is more suitable for enhancing images on hardware since it has less computation than other models. On the basis of ensuring the stability of the MSR algorithm results, we further optimize its calculation process. And the key issue in the restructuring model process is how to use the simplest Gaussian Convolution Kernel (GCK) to accomplish the Multiscale Gaussian Convolution (MSGC) assignment. Moreover, we briefly introduce the video display and the algorithm processing mechanisms in the hardware application, which includes a sophisticated Image Processing IP (IP-IP) based on the rearranged calculation process we designed. Under the real-time condition, the hardware system based on upgraded MSR can enhance smoothly color image and embrace a perfect performance in terms of visual intuition.
OJ Intelligent Training System based on Artificial Intelligence for Hierarchical Personalized Teaching

This paper introduces a new feature of the OJ system at Donghua University: intelligent training. Compared to other OJ systems, our OJ has the following features: 1) A clustering algorithm is used to classify the problems. When a student completes a category of problems, he or she passes a level and is awarded the corresponding score. 2) The system intelligently determines whether a student can pass the level or not. Generally, students with higher abilities need to complete fewer problems but receive higher scores. 3) The system uses a classification algorithm to determine whether plagiarism has occurred based on the code submitted by the student and the behavioral characteristics of the student at the time of submission, thus preventing plagiarism as much as possible. 4) The system uses a BP neural network to predict students' final exam scores, and then uses that predicted value as a point for reflecting ability to enhance students' sense of achievement in completing the practices. The OJ intelligent training model, based on the above features, allows students to train themselves at different levels according to their ability. Weaker students can practice more at lower levels to strengthen their foundation, while stronger students can practice more at higher levels to compete in competitions. This achieves the goal of personalized teaching at different levels and achieves better teaching results.
An efficient approach to distributions fitting with R

The practical step by step guide on how to run a distributional analysis in R programming is being proposed in this research. The existing derived properties of Generalized Rayleigh and Rayleigh distributions were used to demonstrate the steps in coding the distributions as well as applied the codes to real life datasets. Likewise, we demonstrated how to interpret the results gotten from the application for intending users who want to run a distributional analysis in R programing.
Using visual thinking for computer support in teaching the design of technological equipment

The aim of the work is to substantiate the possibility of using multidisciplinary visualization tools in the educational process based on a logical-semantic scheme (LSS), which acts as a methodological platform for building a system of automated support for engineering decisions in designing technological equipment.&#x0D; An interactive learning environment is examined, which, by actively interacting with students, significantly increases the effectiveness, being able to accommodate and coordinate together more multidisciplinary information.&#x0D; In a visual version, the characteristics of the technologization means are presented, in conditions of computerization having logical and semantic schemes about the studied subject area, and an example of a multidisciplinary presentation of a course for students on designing heater plates for injection molding of plastics is considered.&#x0D; A diagram is presented that illustrates the current state of theoretical knowledge. It allows visualizing the basic concepts, formation, structure and arrangement of the stated knowledge for students. It clearly shows the cause-and-effect relationship. This, on the one hand, makes it easier for a young specialist to understand the knowledge being studied, and on the other hand, it allows having deeper understanding of the design of technological equipment.
Modeling and searching technology of distribution network data resources based on knowledge mapping

In order to realize the explicit expression of the distribution network data relationship of the power grid enterprises and the efficient search of data resources, a method for modeling and intelligent searching of distribution network data resources based on knowledge mapping is proposed. This method firstly identifies the core ontology of the distribution network based on the ontology construction process and the distribution network business, then maps and extracts the identified ontology model and the relational database physical model to form a knowledge mapping of distribution network data resources. On this basis, combining the BiLSTM-CRF-based distribution network named entity recognition algorithm and the BERT-based Chinese semantic matching algorithm, a distribution network data resource search engine technology is proposed to realize the search text feature extraction and result matching. Finally, the application verification data of a power grid company shows that the distribution network knowledge mapping has a good effect on the expression and visualization of data resource relationships. At the same time, the accuracy rate of the proposed distribution network data resource search engine technology method reaches 72.2%.
Action recognition based on element-level fine-grained multi-modal fusion

Abstract Traditional action recognition algorithms often only pay attention to video RGB features or optical flow features. These methods do not make good use of the audio information in the video. Based on RGB and optical flow characteristics, this paper introduces the processing of audio information, and classifies videos based on element-level fine-grained multi-modal fusion. Through experimental comparison, the accuracy of the multi-modal fusion algorithm proposed in this paper is improved by 7.38% on the HMDB51 dataset and 3.18% on the UCF101 dataset compared to the simple modal splicing. At the same time, it is proved that the introduction of audio modes can effectively improve the performance of the model.
Reinforcement Learning in Deep Web Crawling: Survey

Context: Reinforcement learning (RL) can help in solving various challenges of deep web crawling. Deep web content can be accessed by filling the search forms rather than hyperlinks. Understanding the search form and proper selection of queries are necessary steps to retrieve the deep web content successfully. Thus, crawling the deep web is a very challenging task. The reinforcement learning-based technique helps in filling the search form and retrieving the deep web content successfully. RL selects the action based on the given state, and the environment assigns reward/penalty to the selected action. Objective: This study reports a survey of RL-based techniques applied in the domain of deep web crawling. Method: Existing literature survey is based on 31 articles from 77 articles published in various reputed journals, conferences, and workshops. Results: Challenges related to various crawling steps of deep web crawling are presented. RL-based techniques are being used in multiple research papers, which solves deep web crawling challenges. Comparative analysis of RL techniques used in deep web crawling is done based on the strength, metrics, dataset, and research gaps. Conclusion: Various RL-based techniques can be applied to deep web crawling, which has not been explored yet. Open challenges and research directions are also recommended.
DCEN: A Decoupled Context Enhanced Network For Few-shot Slot Tagging

Few-shot slot tagging is an important task in developing dialogue system. Most previous few-shot slot tagging models classify an item according to its similarity to the representation of each class. These models leverage context information implicitly through each words' contextual embedding. However, the entangled language features of words may interfere with context information, misleading the utilization of crucial slot features in few-shot scenario. To tackle these problems, we propose the Decoupled Context Enhanced Network (DCEN) for few-shot slot tagging. Different from previous models, we extract decoupled context explicitly to make full use of slot features contained in the context. Decoupled context includes two parts, local and global decoupled context information. We introduce a local extractor to extract local decoupled context by integrating information from adjacent words, and a global extractor based on transformer to extract global decoupled information by orthogonalization. Experimental results on SNIPS show that our model achieves the state-of-the-art performance with considerable improvements.
Redefining Dictionaries for Mobile Learning: A Study of the Learner-Friendly Features of an Online Dictionary

Amidst a plethora of materials available for learning English as a second or foreign language, the place of dictionary has not been defined adequately. The potential of dictionaries as source of information about language pertaining to vocabulary, grammar, pronunciation, and spellings is generally underestimated. While the use of primary teaching materials like textbooks may be maximized or minimized depending on the context, the scant use of a dictionary is common to most of the language classrooms around the world. The ‘taken-for-granted’ approach towards dictionaries has made its use recede to the background in the language curriculum. Reasons like time constraints, the teacher’s lack of training or expertise, non-affordability or lack of resources etc. could also be cited in favour of the usual neglect of this teaching-learning aid. However, with the recent splurge in the use of mobile devices for language learning, the place of dictionaries is being redefined in the language learning process due to factors like portability, freedom of time and place, flexibility of usage, and social connectivity. The present article is an attempt to explore and uncover a range of features offered by mobile dictionaries that could potentially foster language learning. The focus of study and observations will be the online version of Oxford Learner’s Dictionary since it incorporates the printed version and provides additional learner-centred resources. The use of this dictionary is looked at as an opportunity to be utilized in order to strengthen linguistic and communicative competence of the learners.
Methodology of institutional changes in university education

The article analyses the problem of institutional changes at the university as an academic system in a pandemic. The academic values of the modern university professional community include the social responsibility of the university. In the new conditions, this value dimension of the role of the university in the life of society determines the main directions of research on transformations of the university as the most important social institution. The factors of institutional sustainability of university education are determined. The author examines the role of the university in overcoming global and local risks.
A privacy preserving and public verifiable signature scheme for consortium blockchain

From the perspective of privacy, blockchain can be divided into three categories: private blockchain, consortium blockchain and public blockchain respectively. Unlike private blockchain and public blockchain, consortium blockchain is only available for users holding the same access licenses to the blockchain system even they may belong to different organizations. Consequently, authentication in the consortium blockchain is of great importance since participants in the same consortium are within the same trust domain. In this paper, we propose a privacy preserving and public verifiable signature scheme for consortium blockchain. In our scheme, all the members in the consortium have the privilege to generate a signature on a file without leaking his sensitive identity. What’s more, the generated signature can be verified to the consortium instead of a single member. The presented scheme is suitable to be deployed for providing data authentication in consortium blockchain due to its high efficiency and security level.
An Effective Algorithm for Object Detection Based on Deep Learning

Intersection over Union (IoU) is an important function in object detection based on deep learning. But, there is a gap between the used distance losses and this metric value of maximizing. This paper improves the IoU function based on IoU. Redesigned the neural network structure, and used the PASCAL VOC2012 dataset. The structure model of the neural network is compare with others in the accuracy of object detection. Experimental results show that our method can achieve good results in object detection. The approach provides a new idea for object detection.
Implementation of the electronic model of the thematic and typological acquisition plan in the scientific library

Automation of library processes is an important topic in modern conditions of digitalization and transition of human activities to the latest computer technologies. Acquisition of literature to library collections forms the document base for the subsequent work of the library, so this process needs continuous improvement and optimization of its constituent operations. The article is devoted to the application of computer technologies in the preparation and use of the scientific library thematic-typological plan of acquisition as the scientific base of building collections. The object of the article is to present the experience of creating and using the electronic model of the Thematic and Typological Acquisition Plan in the scientific library, to define the perspectives of implementing the data base for information and library servicing UB RAS users. The article presents the results of content analysis of subject headings and indices of the State Rubricator of Scientific and Technical Information for bibliographic descriptions of Russian journals received in 2015–2017, in the Consolidated electronic catalog of periodicals of the Central Scientific Library of the Ural Branch of the Russian Academy of Sciences; of books, published by the institutes of the Ural Branch of the Russian Academy of Sciences in 2015–2017, according to the Electronic catalog of Russian books and continuing publications of the Central Scientific Library of the Ural Branch of the Russian Academy of Sciences; of publications (published in 2015–2019) of the institutes of the Ural Branch of the Russian Academy of Sciences, included in the database of the Russian Science Citation Index on the portal «eLIBRARY.ru» – are presented in the article. It also describes the stages of creation, analyzes the characteristics and capabilities of the database «Thematic-typological acquisition plan» of the Central Scientific Library of the Urals Branch of the Russian Academy of Sciences.