Reviewerly/exHarmony huggingface dataset:
Roman Amphitheater Classification Using Convolutional Neural Network and Data Augmentation

In this paper, we propose a neural model to classify Roman amphitheater images into six groups corresponding to their locations: Rome, Eljem, Nîmes, Arles, Verona and Pula. The proposed neural structure makes essential use of convolutional neural networks for feature extraction and images classification. To avoid overfitting, data augmentation techniques have been deployed to expand the data set size by a magnitude of 16. This method is applied on the augmented training set and results show a substantial performance and high accuracy of $$98.33\%$$ compared to state-of-the-art methods.
Homodyne detection in quantum optics: deterministic extractors and quantum random number generators on ‘vacuum fluctuations’

Abstract Quantum random number generators with a continuous variable are considered based on a primary randomness of the outcomes of homodyne measurements of a coherent state. A deterministic method of extraction of truly random 0 and 1 from the primary sequence of measurements of the quadrature of the field in homodyne detection is considered. The method, in the case of independence of successive measurement outcomes, in the asymptotic limit of long sequences, allows us to extract with a polynomial complexity all the true randomness contained in the primary sequence. The method does not require knowledge of the probability distribution function of the primary random sequence, and also does not require additional randomness in the extraction of random 0 and 1. The approach with deterministic randomness extractors, unlike other methods, contains fewer assumptions and conditions that need to be satisfied in the experimental implementation of such generators, and is significantly more effective and simple in experimental implementation. The fundamental limitations dictated by nature for achieving statistical independence of successive measurement outcomes are also considered. The statistical independence of the measurement outcomes is the equivalent of true randomness, in the sense that is possible in the case of the independence of the measurement outcomes, provably, with deterministic extractor, to extract a ‘truly random sequence of 0 and 1’. It is shown that in the asymptotic limit it is possible to extract all the true randomness contained in the outcomes of physical measurements.
Ecological and Ethical Contexts of Digital Literacy in the Light of Phenomenographic Studies

The paper is aimed at exploration of new contexts of the concept of digital literacy from the perspective of phenomenographic studies. Concepts and basic models of digital literacy are briefly summarized. Results of phenomenographic studies of information literacy are related to digital literacy concepts. Following the sociocultural background, we frame the concepts of digital literacy into ecological and ethical factors of digital information. We apply a meta-analysis of three selected phenomenographic studies in Slovakia related to information literacy, information ethics and information behavior. Common ecological and ethical factors were found, namely emotions, responsibility, truth and value of information. A new interpretation of digital information literacy is derived, based on social representations of experiencing digital information. Ethical awareness of digital resources and ecological adaptations are emphasized. We recommed including values of information, verification of information, digital creativity and experiences into digital information literacy development and courses.
Deep Photo Classification Based on Geometrical Shape of Principal Object Presents in Photographs via VGG16 DCNN

In this paper, we propose a framework using a deep convolutional neural network (DCNN) to classify the photographs automatically based on different geometrical shapes of principal object exist in a photograph. Classification of photographs based on by identifying the shape of main object is challenging when a photograph contains multiple objects of different shapes. For this purpose, we choose only those images to train and test which contain either only single object as it’s principal object or multiple objects of same geometrical shape, because our system is capable to identify only one type of shape of object in a photograph and classify the photograph based on the shape of main object. Shapes are very important element in photography composition. It has different meaning in different shapes and creates different feelings in human perception. Photograph containing a principal object with a geometrical shape is very visually appealing and achieve aesthetically high score in different photography competitions. Related study reveals that shape detection of objects using DCNN is very rare. Our model is a multiclass classifier which can classify the photographs into three different categories like photographs containing main object of circle, square and triangle shapes. Instead of using manually designed features the model automatically extracts features using a pre-trained DCNN VGG16. The proposed system has been executed on the ground truth dataset of 1700 images and achieve very satisfactory result. Another contribution of this work is to produce an image dataset on different geometrical shape of objects called Shape-Detection dataset which will be useful for future work in this domain.
State transfers in vertex complemented coronas

In this paper, we study the existence of perfect state transfer and pretty good state transfer in vertex complemented coronas. We prove that perfect state transfer in vertex complemented coronas is extremely rare. In contrast, we give sufficient conditions for vertex complemented coronas to have pretty good state transfer.
A Lightweight Model for Human Activity Recognition Based on Two-Level Classifier and Compact CNN Model

Wearable devices for human activity recognition (HAR) provide continuous remote health monitoring, which can offer many benefits for patients in rehabilitation, the elderly, and even the population in general. Those devices handle a big amount of data and run complex algorithms for delivering information with precision to the user. However, to achieve a satisfactory performance in terms of energy consumption, real-time response and privacy, the solution needs to be efficient and suitable for running in resource constrained devices. Many HAR proposals use deep-learning approaches for achieving almost perfect performance, nonetheless those techniques are not convenient for embedded solutions. To meet the demands of high precision and low power, more efficient strategies can be developed and optimization techniques can be applied to the usual deep learning approaches. We present in this paper a lightweight model for HAR based on two-level classifier and using a compact, pruned and quantized convolutional neural network. We obtained accuracy and F1-score above 90% with an extremely lightweight solution and intended for use in embedded systems.
Network Embedding Based Collaborative Filtering Model Equipped With User Purchase Motivation and Potential Interactions

The user-item interactions in recommender systems can be modeled as a bipartite graph. With the bipartite graph, numerous researches have been devoted to learning the representation of the user’s fine-grained preferences to improve the performance of recommendation. However, the existing work cannot fully encode high-order collaborative interaction. To address the above problem, we propose a network embedding based collaborative filtering model equipped with user purchase motivation and potential interaction. Experimental results on three real datasets demonstrate that the proposed model outperforms the competitive baselines and significantly improves the recommending performance.
Innovation of Smart Teaching Mode in Universities Based on Values Cultivation

Smart teaching is the inevitable choice of college education informatization in the era of “Internet +”.In recent years, with the rapid development of artificial intelligence technology, under the promotion of educational informatization 2.0 action plan, smart classroom has developed rapidly relying on artificial intelligence technology, and is applied to many aspects of information teaching in universities, which has attracted wide attention from scholars.Smart teaching means can be used to the cultivation of college students' values. By using modern information technology, building an online network teaching platform and virtual scenes, universities can attract college students to learn traditional culture and campus culture, and thus play a very important role in cultivating their correct values.In the case of Wuhan Donghu University, through the methods of questionnaire survey, interview and statistical analysis, this paper analyzes the practice status of college students' values cultivation and smart teaching. It also puts forward corresponding countermeasures and suggestions, in order to provide some reference for the application of smart teaching mode in the cultivation of College Students' values.
Conceptual Design of the Power Supply for the Mobile Monitoring Unit

Nowadays, various surveillance and security systems are used to detect undesirable violation of the monitored area. The physical principles of information’s gathering, methodology of their processing and subsequent evaluation are the main differences in the operational methods of surveillance and security systems, which allow a wide range of their use in applications with a large variability of potential intruders. Assuming that the surveillance and security systems are used in specific applications where the position and the total number of used individual sensing elements within the monitoring area can change over time, it is necessary, mainly due to the practical and economic reasons, to reduce their dependence on the external galvanic power connections to increase the mobility of system. The technical implementation of a power supply that is independent on the external galvanic connection can be generally in the case of complex surveillance and safety systems considered as an issue with a specific approach of solution, because their individual functional modules may require mutually different power requirements to achieve the proper system operation. In consequence, this article is devoted to the conceptual design of a power supply for a mobile monitoring system, intended to prevent human entry into a defined area to restrain the spread of viral diseases in the groups of the population. In this case, the energy independence of the system is ensured by a suitable configuration of accumulator cells designed to meet the power requirements of the monitoring unit. An additional source of energy is a photovoltaic panel, which, depending on a solar activity, recharges the accumulator cells, and therefore prolong the energy autonomy of the monitoring system. The power supply is also supplemented by a protection circuit extending the accumulators lifetime and power-tracking circuit that increases the efficiency of the photovoltaic panel. The relevant parameters of all considered components of the proposed power supply are summarized in this article, including the analysis of solar irradiance captured by the intended photovoltaic panel during the specific month of the year in a given geographic location.
Towards foggy image optimization: dark channel prior via RGB-splitted processing

The dark channel prior algorithm can reduce the impact of fog from an image, which facilitates a number of different applications such as target detection and target tracking. However, the current dark channel prior algorithm fails to solve the problems such as color restoration and image detail, that is algorithm faces a bottleneck. To solve this, we propose an adaptive color algorithm based on the dark channel prior(ACDCP), where the image is split into R,G,B channels, and then Gaussian filtering is performed on each channel. the transmittance of each channel is taken into account to correct the actual value of the atmospheric light value by the offset coefficient C. We qualify visual perception and quantify index analysis value, involving Laplacian gradient, Brenner gradient, SMD, and Vollath function value. The results show that the proposed ACDCP algorithm can effectively improve the color of the image restoration, and achieve a pronounced defogging effect.
The First Intelligence Layer—Environments Sensing

This chapter explains the first intelligence layer of the machine brain-environments sensing, where the video monitoring system is employed as a practical example. Due to the rapid development of information technology, computer vision and machine cognition are getting closely associated. As an indispensable part of computer vision, the acquisition of videos and images is also a technology for environments sensing. We construct a video monitoring system using the powerful video and image processing toolbox of python. The GUI graphic interface for the intelligent acquisition system is implemented, along with the code of related functions. The system senses environmental changes through video acquisition, video-images conversion, and other functions of the system. This system can be directly integrated with image recognition and objects tracking to improve the efficiency of industrial production and guarantee the personal safety of workers.
In-cloud Migration of a Custom and Automatic Booking System

Nowadays the advancement of technologies such as the Internet of Things and Machine Learning has allowed the development of increasingly complex systems to simplify and automate countless different processes. On the other hand, the affirmation of Cloud Computing, and therefore the delivery of different services through the Internet as tools and applications like data storage, servers, databases, networking, and software has made it possible to reach even higher levels in terms of cost savings, increased productivity, speed and efficiency, performance, and security. This paper illustrates the practical example of the possible On-Cloud migration of an On-premise application, using the services and patterns offered by Amazon. The application in exam has the purpose of automatically managing access and booking of a room, and possibly a parking lot, in a tourist complex. Since the original project did not foresee mechanisms of autoscaling of the computational resources, there is no monitoring mechanism or database redundancy, the usage of a Cloud approach was necessary to make possible the automatic scaling of resources based on the unpredictable load of requests from the users of the platform and to achieve performance, security and availability increase of the whole system.
Study on Feature Extraction Method from 2D Character Illustration based on Human’s Cognitive Characteristics for Automatic Voice Estimation

Humans can imagine an approximate voice from a human face, and some studies estimate and generate a voice from a human face. As research applying this, there is research to create a sound from a 2D illustration character. This study considers how a person imagines a voice from a face and examines a method for generating speech from a 2D illustration character. So far, we have verified which voice actor/actress resembles that character’s voice from an illustration of an unknown character by learning by associating a character with a voice actor. As a result, 2 out of 5 characters were judged correctly. We also conducted a questionnaire on where people look in the character’s illustrations to imagine their voices and found that their eyes and hair are powerful features. In consideration of the above results, this study attempts to acquire eye information from 2D illustrated characters. The system improved the extraction system by rotating the image by detecting landmarks to extract the character’s eyes. As a result, when the detection accuracy was verified, the result was 73.7
Multivariable Fuzzy Rule-Based Models and Their Granular Generalization: A Visual Interpretable Framework

Fuzzy rule-based models have been widely used due to their simplicity and effectiveness. However, they still face the problem of dimensional explosion or rule redundancy for multivariable data. In this study, we first propose a novel way to establish a selective sampling and mapping data reduction method. It focuses on reducing the variables of data while decreasing the number of samples, and an appropriate scaling size can be chosen for different situations. Then a multivariable fuzzy rule-based model is developed based on the processed data. Moreover, data projection approach using the distance metric can preserve the structural characteristics of the original data to a certain extent and is presented in a visual form that facilitates an interpretable description of the subsequent modeling. The established fuzzy model is concise and effective with a strong applicability and interpretability. Furthermore, due to the information loss in the projection process and the limitations in the numeric model itself, we introduce the allocation of information granularity to extend the model to granular form at a higher abstract level. Experimental studies on both synthetic and publicly available datasets demonstrate that the proposed method has superior effectiveness and efficiency compared to the existing state-of-the-art regression algorithms.
Research on Vehicle Parking Aid System Based on Parking Image Enhancement

At present, the visual parking assistance system in intelligent driving generally has the problems of unclear parking image quality and high hardware cost. In order to reduce the difficulty of parking and improve the ability to adapt to the environment, this paper proposes a vehicle assistance system based on parking image enhancement. Firstly, Retinex algorithm is used to balance the image illumination information and enhance the color saturation, so that it can adapt to more complex environmental conditions; secondly, Ackerman steering theorem is used to draw the dynamic parking aid line, and the coordinate transformation technology is used to output it to the vehicle screen. The adaptability and effectiveness of the developed system are verified by the relevant experimental research.
Fuzzy Multilayer Perceptrons for Fuzzy Vector Regression

In this chapter, we introduce the fuzzy multilayer perceptrons with cuckoo search (CS-FMLP). In particular, the fuzzy multilayer perceptrons is an algorithm that can deal with fuzzy inputs and fuzzy outputs. The optimal weights and biases are found by the cuckoo search algorithm. We show how this algorithm can be used in the regression research area using three real data sets, the yacht hydrodynamics data set, the energy efficiency data set, and the upper Ping river data set. To show the goodness of the algorithm, we implement the multilayer perceptrons with cuckoo search (CS-MLP) as well. The comparison results show that our CS-FMLP is comparable to the CS-MLP on some results and better than the CS-MLP on the others. However, the real advantage of this CS-FMLP algorithm is that it can provide the possible range of the predicted value, while CS-MLP can only provide the exact predicted value.
Reform and Development of Private Vocational Education in China

This chapter explores the reform and development of private vocational education in China. The development of private vocational education is a hot issue in the field of vocational education in recent years. Under the demand of labor force in the transformation of economic development, it is of great significance to study the reform and innovation of private vocational education.
Mobile Learning in Project-Based Contexts in the Higher Education Sector

This paper discusses evaluation findings from a digital project-based course in the higher education sector. The evaluated course was developed and taught in the context of the Master’s program for Education at Ludwigsburg University of Education (LUE). It aims at the autonomous project-based planning, execution and critical reflection of educational projects by the participating students. Students in this course work and collaborate with mobile devices in various ways in the course and to carry out their projects in groups and individually. The combination of mobile and project-based learning in the course and the students’ projects offers many options for mobile learning scenarios in the higher education sector. The digital project-based character of the course aims for the interconnection of students with their future work area, their future colleagues and their future students through projects in the field of education. The described interconnections and the resulting collaborations play key roles in the successful realization of the projects. The creative use of mobile learning devices is fundamental for the development of the described interconnections. Thus the empirical study has a special focus on the use of mobile learning devices in the course and the students’ projects. Furthermore, the empirical study focuses on students’ attitudes, practices and preferences towards mobile learning and the use of mobile learning devices in the projects. This focus creates insights, which help to develop new perspectives for mobile learning in the higher education sector.
Enhancing Security by Identifying Facial Check-in using Deep Convolutional Neural Network

Facial recognition is widely being used in many applications. In this research face recognition-based facial identification systems have been implemented to identify the person’s identity whether he/she is an insider or outsider. The system will act as a tool for strengthening the safety of any organization and can also monitor in/out attendance. The proposed system aims to also prevent imperfections of manually taking attendance and allows better accuracy. Solving the problems that arose in the regulation of the old system. A combined OpenCV and deep convolutional neural networks techniques have been used to build the model. The purpose of this research is to create a face database of known people that will help to identify individuals. The developed model validated with known images and resulted accuracy is 90% and for unknown around 60%. The time of facial recognition is also recorded as a person’s time of entry and date for validation and verification.
Sentiment Diagnosis in Text Using Convolutional Neural Network

Web 2.0 has empowered people to voice their opinions, share their familiarities and views on various products, events, services, etc. Nowadays, people are turning toward social networking sites, forums, blogs and e-commerce websites to write opinions and reviews. The opinion mining process is very crucial to the recommender system which predicts product preferences of the user and voice of customer material as text can contain related features or aspects of the item the user’s opinion about them. This process requires the use of natural language processing, text analysis, computational linguistics, biometrics and possibly machine learning for identifying the subjective and objective part of the text. Knowledge-based technique, hybrid, and statistical approaches are the major methods used for sentiment analysis. Depending on the type of text used and the type of result we desire, the methods will differ. Previously, methods like lexicons in corpus or dictionary-based approach, part of speech tagger (POS tagger), and even machine learning techniques have been used. Our work focuses on using a deep learning model of Convolution Neural Network (CNN) built on PyTorch. The model uses a vector model of GloVe and a dataset that contains reviews of various movies in two categories, namely plot and quotes.