Reviewerly/exHarmony huggingface dataset:
Research on Image Rotation Recognition Based on Histogram Feature Description

Abstract Image recognition is a statistical pattern recognition problem in pattern recognition, which mainly includes four steps: image acquisition, preprocessing, feature extraction and selection, and pattern classification. In this paper, an omni-directional sequential feature description method based on F histogram is proposed. In this method, shapes are segmented by directional lines in all directions, the sequential feature description quantity between object segmentation segments in each direction line is calculated, the omni-directional sequential feature description quantity of shapes is constructed, and the similarity between shapes is calculated. In this method, the normal vector histogram is used as the point cloud feature descriptor, which can optimize the search of the corresponding point set, thus improving the ICP algorithm. By selecting the appropriate expansion center and expansion order, the recognition ability of the filter is improved. At the same time, using the concept of image moment, the characteristic moment vector of the target is constructed to identify the target with in-plane rotation and scale change. Based on the optimization strategy based on histogram feature description, the recognition speed of the algorithm is significantly improved.
Neural keyword confidence estimation for open‐vocabulary keyword spotting

Despite the recent prevalence of keyword spotting (KWS) in smart home, open-vocabulary KWS remains a keen but unmet need among the users. Conventional open-vocabulary KWS systems are difficult to obtain a high wake-up rate and low false alarms simultaneously due to the lack of specific data for model optimisation. In this letter, a light-weight neural keyword confidence estimation module (KCEM) for the second detection part in the open-vocabulary KWS system is proposed, which utilises the transformer structure to calculate the confidence by fusing the keyword embedding and the acoustic feature obtained from the KWS model. KCEM method is evaluated on a self-collected open-vocabulary KWS test set, yielding equally efficient performance compared with typical confidence estimation methods, a reduction in false reject rate by 34% and 29% relative under clean and noisy conditions, respectively, at 0.04 false alarms per hour.
Color Compensation Based on Color Background Shadow for Pill Identification

Pill color is one of important features for automatic pill identification; however, it's difficult to clarify an exact color. Pill color is changed when luminance changes. Moreover, its color is changed because of white balance function in taken camera too. In this report, we propose a color compensation based on color background shadow for pill identification. To compensate pill color, “RGB” component of pill image is converted in term of “YIQ” component to separate between luminance intensity (“Y” value) and color component (“I” and “Q” values). “Y” value of the selected pill is compensated by “Y” value of its background, whereas “I” and “Q” values of the selected pill are compensated by “I” and “Q” values of its shadow. Experimental results confirm an effectiveness of our proposed compensation method.
Usability and Applications of Virtual and Augmented Reality in Older Adults

Virtual reality (VR) and augmented reality (AR) have been explored to be an effective alternative to integrating mental health intervention proposals, particularly in eldercare. The objective is to map the usability and applications of VR and AR technologies in interventions for the elderly population. The main areas of interventions in AR and VR applied to the elderly are stimulation and cognitive rehabilitation, physical rehabilitation, treatment of mental diseases, and promotion of quality of life. Despite the need for further studies, VR and AR have strong adherence among the elderly and demonstrate promising potential in interventions that seek to promote mental health and improve the quality of life.
Blockchain-based Maritime Monitoring System

In the last decade, blockchain technology consolidated as an extremely versatile security tool for decentralized systems, becoming the backbone technology of IT systems in many different sectors. Aiming to develop mechanisms for naval systems cybersecurity, this work proposes a blockchain-based Maritime Monitoring System (MMS) to protect the integrity, authenticity, and availability of relevant navigation data. It proposes a permissioned blockchain solution using HyperLedger Fabric – a robust, modular, and efficient open-source blockchain platform. We evaluate this solution’s performance and feasibility through an experiment where the blockchain receives sensing data from an SDR-based low-cost Automatic Identification System receiver. Smart contracts process this data in a distributed way and write it into the blockchain ledger. The results provide quantitative analysis on the blockchain technology overhead and its impact in terms of Quality of Service, demonstrating the feasibility and effectiveness of our solution in the scope of an MMS.
Promote the Reform of Analytical Chemistry Teaching Mode with “Internet + Discipline Competition” as the Guide

Based on years of course teaching practice in analytical chemistry, this paper takes “Internet + discipline competition” as a guidance, refers to the teaching modes and methods of famous schools at home and abroad, and establishes a complete analytical teaching model, including the creation of COME teaching model, exploratory experimental teaching, establishment of echelon training mechanism. By constructing an analytical chemistry teaching model guided by “Internet + discipline competition”, teaching activities are truly based on students' independent learning and active exploration, so that students transform from passive learning to active and inquiry teaching. In this way, a teaching system conducive to healthy development of innovative consciousness and innovative ability is formed.
Object-Process Methodology for Intelligent System Development

Development of the new artificial systems with unique characteristics is very challenging task. In this paper the application of the hybrid super intelligence concept with object-process methodology to develop unique high-performance computational systems is considered. The methodological approach how to design new intelligent components for existing high-performance computing development systems is proposed on the example of system requirements creation for “MicroAI” and “Artificial Electronic” systems. The prototype code in Python is available on GitHub: https://github.com/dorofeevv/electronic .
Hybrid genetic model with ANOVA for predicting breast neoplasm using METABRIC gene data

In 2020, 2.3 million females identified with breast cancer, and it is the second most frequent cancer among females. As a result, there is severe requirement of an accurate procedure to recognize breast cancer. In accordance with breast cancer organization the breast cancer is divided into two types one is invasive and another one is non-invasive. In Indian cities like Delhi, Kolkata and Mumbai have the highest death rate for female due to breast cancer. Unusual alteration in genes is the prime reason for breast cancer. Our paper contains metabric dataset related to 1905 patients along with 31 clinical characteristics and 331 genes. The novelty of this paper is to find out genes which are highly responsible for death from breast cancer with foremost clinical features and implementation of a hybrid method which is a combination of genetic algorithm and artificial neural network to get the best accuracy with the help of machine learning as this can preserve life of females. For attribute scaling standard scaler is used which range data in between 0 and 1. Attribute selection is performed with the help of ANOVA which is a statistical appliance. With this tool 6 leading clinical attributes are examined. In our research 12 genes gsk3b, kmt2c, map4, tsc2, tnk2, pdgfb, ncoa3, akt1, afdn, sdc4,map2k2 and tubb4b which are extremely responsible for death in our dataset are recognized and our hybrid method implementation gives the perfection of 90.57%.
Improvement of learning method of multi-agent system by sharing learning data

Deep reinforcement learning algorithms have been employed to solve the problems of multi-agent learning, especially the non-stationarity problem that reduces the learning efficiency. In this study, an architecture of improving the learning efficiency by using an adaptation of Actor-Critic method is proposed. It was clear that the convergence of learning became faster when the agents shared their experience data with the other agents at some rate. In two kinds of maze benchmarks, some numerical experiments was conducted to show the performance of the learning architecture. And the result of this numerical experiments showed that the agents achieved their purpose in the mazes by the proposed method. Even though the agents did not share all their data, they could be trained to take the optimal policies.
Mathematical Modeling of a Virtual Platform for Training Specialists in the Oil and Gas Industry

Abstract A relevant task in the organization of comprehensive training of specialists in various fields is the development of the necessary theoretical and practical competencies. In the conditions of pandemic restrictions and the need to conduct classes in a distance mode, there is a need to develop new educational resources and means of representing knowledge, functioning on the basis of modern information technologies. One of the promising approaches is the transfer of laboratory and practical exercises to a virtual platform that allows you to master the necessary practical competencies on a computer. However, the functioning and visualization of physical or chemical processes in such virtual platforms must correspond to real processes. The article discusses the mathematical modeling of a virtual platform for training specialists in the oil and gas industry. An approach to the construction of a mathematical model based on the collected experimental data, the study of the laws of equilibrium of liquid and gas, and hydrostatic equations is considered. On the basis of the obtained model, an algorithm for the functioning of a virtual stand on the Unity platform is built. The presented approach can be used in the implementation of various virtual platforms for organizing the learning process in a distance form.
Research on the Application of Virtual Reality Technology in the Integrated Design of Architectural Landscape

Due to the variability and complexity of landscape design in space and time, it is difficult to accurately express landscape design only by relying on individual hand-drawn effects and text descriptions. Based on this, the application research of virtual reality technology in the integrated design of architectural landscape is carried out. First, through the analysis of virtual reality technology, the advantages of virtual reality technology are explored in depth, and then the advantages of virtual reality technology are applied to the 3D landscape modeling design. Finally, application examples are analyzed. The results show that the entire roaming scene more truly reflects The local topography and landforms realistically simulate the local architectural landscape and restore the damaged characteristic town. The average FPS of the user roaming scene on an ordinary computer is not less than 18 frames/s, and the browsing is basically smooth.
Personalized Smart Recommendation System for Industrial Internet of Things

The Internet of things services emerge recommendation system as an integral component providing a more reliable service for users and supporting users to get data anyplace. Nevertheless, the conventional recommendation prototype cannot satisfy users’ precise and fast recommended conditions in the (IoT) Internet of things ecosystem. Finding a community by comparing actual user data results against a low recommendation ability during large-volume data features. Besides, the conventional recommendation method neglects the essential link between the user's time and choice. Over time, user importance fluctuates in actuality. With the variation of time, the smart recommendation scheme should accommodate users’ precise and quick service. We propose an innovative smart recommendation system design that relied on the time correlation coefficient (TCC) to approach this prompt service. This smart system also integrates with a developed K-means by cuckoo search (CSK-means), named TCCF. The clustering technique can gather related users collectively for an additional specific and quick reference. Furthermore, a personalized and efficient recommendation paradigm that relied on the (PTCCF) preference pattern model develops the TCCF's quality better than the traditional one. Through examining the user's responses, it can present a more robust feature recommendation. We conducted two entire information sets of Douban & MovieLens through the vast experiments. Our design's accuracy has increased by approximately 5.2 in percentage. Precise test outcomes have revealed that for the IoT scenario, PTCCF and TCCF are adequate.
The Construction and Application of New Media Technology in Higher Vocational Education under the Background of "Internet +"

With the information technology development, many new things related to the Internet have emerged. Supported by digital technology, new media that provides personalized information content to a broad audience through various information delivery platforms are emerging media that accompany the Internet. . This type of media not only has high timeliness, breaks the boundaries of space and time, possesses massive amounts of information, satisfies the needs of resource sharing, and allows audiences (readers) to change their reading habits, but also realizes the communication and interaction between the audience, the media and the audience. , To transform the disseminator of information into those who actively disseminate information, increase the audience's enthusiasm in information dissemination, and further enhance the audience's enthusiasm for new media. All of these have had a huge impact on traditional media, especially traditional paper media. In order to achieve sustainable development, and to obtain survival opportunities in the present and future, paper media must be reformed and transformed. The most important of these is to achieve integration with new media to enhance the vitality of paper media development. This article analyzes the construction and application of new media technology in higher vocational education under the background of "Internet +" in order to guide practical work.
Web-Based Programmer Performance Recording and Measurement Information System (Case Study: PT. Bits Miliartha)

Every company must always strive for all employees to work effectively, efficiently and productively so that they can achieve the company's vision. Like employees in general, programmers need to be measured for their performance and productivity so that the company does not lose out on paying them. On the other hand, a lot of tasks also confuse programmers if they get piled up and go unrecorded. Therefore, the authors make research on information systems for recording and measuring the performance of web-based programmers (case study: PT. Bits Miliartha). The research method used is a qualitative method by means of observation and interviews. The results obtained from this study is an information system that is devoted to recording programmer tasks and efficiently helping companies to assess programmer performance. Information system made by golang and vuejs
A Dual Self-Attention based Network for Image Captioning

Image captioning technology has become an important solution for intelligent robots to understand image content. How to extract image information effectively is the key to generate accurate and reliable captions. In this paper, we propose a dual self-attention based network (DSAN) for image captioning. Specifically, we design a Dual Self-Attention Module (DSAM) embedded into an encoding-decoding architecture to capture the contextual information in the image, which can adaptively integrate local features with global dependencies. The DSAM can significantly improve the caption results by modeling rich contextual dependencies over local features. Experimental results on the MS COCO dataset show that the proposed DSAN can achieve better performance than existing methods.
Absolute Phase Unwrapping with Deep Neural Network for Structured Light 3D Reconstruction

Phase-coding structured light is an important technique in 3D reconstruction. However, a great challenge is the wrapped phase that causes geometry ambiguity. Conventional unwrapping methods such as spatial and temporal approaches face the problem of error propagation and low efficiency. In this paper, we propose to solve the phase unwrapping problem with a deep neural network. To be specific, the phase unwrapping problem is cast to a semantic segmentation task, where the wrapped phase is the input and the fringe index for every pixel is the output. An encoder-decoder architecture, which is like U-net, is adopted as the network. We further propose a combined loss function by considering cross entropy loss, phase consistency loss and edge consistency loss. With 10000 artificially synthesized samples, the proposed method converges well. Experimental results demonstrate that the trained model well predicts fringe orders on both simulation data and real captured data. In addition, it unwrapps every pixel independently and avoids phase error propagation, and further achieves accurate 3D reconstruction.
Intelligent Educational Evaluation

This chapter introduces the application of AI in the comprehensive evaluation of students. Student problem-solving skills reflect their academic and practical performance. Psychological data analysis suggests their mental health. Physical test indicates their health status; their growth and development plan reflect their personality development.
Issues and Framework of Rules for Resolving Anaphora in Marathi Text

Understanding the natural language is difficult for the computers because natural language is coherently ambiguous. This paper presents the different types of issues occurred during the resolution of the anaphora in Marathi text. The work on anaphora resolution is performed on many Indian languages like, Hindi, Tamil, Telugu, Bengali, and Urdu, etc. but very less work has been done in the Marathi language. Anaphora resolution is a complex problem for the researcher. Difficult issues like unavailability of the Marathi corpus as well as same gender antecedent is the main issue in the anaphora resolution. To overcome those types of issues we have framed some rule to resolving an anaphora. This paper mainly focused on the issues in Marathi text and anaphora in Marathi text as well as the framework for resolving that issue.
Auditorium and non-auditorium academic interaction – a tool for improving the professional and practical training of pedagogical students

The paper presents academic interaction characteristics in the context of competence-oriented higher pedagogical education. Options are analysed for stimulating subjective activity in communication by implementing innovative models for cooperation between teachers and students. The developing effects of the synergetic functioning of academic subjects in both auditorium and non-auditorium interaction are systematized. Conceptual solutions are substantiated for optimizing the professional and practical training of pedagogical students in the context of modern educational paradigms. Trends are presented and perspectives are specified for achieving sustainable quality of academic interaction by transforming and renewing higher pedagogical education with necessary innovations.
Particle Filtering for Nonlinear Systems with Round-Robin Protocol and Uniform Quantization

In this paper, the particle filter problem is investigated for a class of nonlinear systems with communication constraints. The communication between sensor and remote filter is subject to uniform quantization effects. Moreover, in order to solve the problem of limited network capacity, the Round-Robin protocol is introduced in this paper, which can effectively avoid communication conflicts and reduce packet loss. Through mathematical hypothesis and stochastic analysis, an explicit expression of likelihood function combined with the probability information of networked phenomenon is established under the recursive Bayesian estimation framework. A novel particle filter algorithm is proposed for nonlinear state estimation in the simultaneous presence of uniform quantization and Round-Robin protocol. Finally, a simulation example is given to illustrate the effectiveness of the proposed particle filtering method.
Design of Sports Training Correction System Based on Virtual Reality Technology

Aiming at the problem of serious insufficiency of the exercise correction system in the personal exercise scene, this paper proposes and designs a prototype system of a virtual personal exercise training device (PAT). Unlike exercise videotapes or live TV broadcasts, this system allows users to independently select the type of exercise, and the guided program will create a virtual trainer that meets the user's settings. Based on the virtual reality technology of computer vision, it can recognize and interpret the motion posture in real time, so as to effectively evaluate the user's motion state and give appropriate feedback at the same time. In this system, the central behavior recognition is designed separately from the user incentive and user feedback modules, and the main line of the system is synchronized, which can realize the seamless connection of recognition, interpretation, background music incentives, etc. This system has the advantages of stable operation and small real-time delay in the test, which can basically meet the needs of personal exercise correction.
Construction and Exploration of Digital Talent Ability Quality Model in Cold Chain

Digital transformation has become an urgent issue for Chinese enterprises, and cold chain logistics industry is inevitable. In recent years, as long as people mention the enabling of cold chain logistics technology, they will inevitably mention the digital transformation. The cultivation of digital talents is not only for an enterprise, but also for the development of the whole society. Education will become a key force in reconstructing the world pattern in the digital era. It is the new mission of education in all countries to grasp the global digital development trend, find out the breakthrough and main direction, and cultivate talents to adapt to the development of science and technology and society in the digital era. At present, no scholars have studied the transformation of cold chain logistics industry from the perspectives of "digital cold chain logistics", "cold chain digital talent training" and "cold chain digital talent team construction". This paper will take the cold chain industry as an example to explore the ability and quality of digital talents and provide a reference for the cultivation of digital talents in colleges and enterprises.