Reviewerly/exHarmony huggingface dataset:
Effects of the Vibrating Graphene Membrane and the Driven Classical Field on an Atomic System Coupled to a Cavity Field

This paper studies the dynamic effects of the driven external classical field on a system consists of a single two-level atom that interacted locally with a vibrating graphene membrane and a cavity field. The temporal wave function containing the tripartite interaction statuses via a canonical transformation and new resonance conditions is obtained. The possibility of studying the optimal behaviour of the quantum coherence and non-classicality for the graphene-field, atom-field, and atom-graphene states are investigated. It is found that the upper and lower bounds of the two phenomena depend on the detuning parameter, coupling strength of atom-graphene interaction, and initial atomic state. Based on the support of numerical computations, we demonstrate how to control the quantum system by the vibrating graphene membrane and the driven classical field.
Application of Computer Information input Technology in Automatic Dispensing System of Chinese Herbal Medicine

In recent years, with the increasing use of Chinese herbal medicines, people's requirements for the quality and efficiency of Chinese herbal medicine dispensing have increased, and the demand for automated Chinese herbal medicine dispensing systems has also begun to emerge. The development and application of artificial intelligence and computer technology have made automated Chinese herbal medicine dispensing the function of the system has a wider space for extension. Based on this, the purpose of this article is to study the application of computer information input technology in the automated Chinese herbal medicine dispensing system from the perspective of artificial intelligence. This article first analyzes the requirements of the automated Chinese herbal medicine dispensing system, summarizes the requirements of the system from the requirements of software management, Chinese herbal medicine cutting, and automatic medicine dispensing, and analyzes the system's functional requirements from the perspective of users and doctors. This article describes the operation process of the automated Chinese herbal medicine dispensing system, and finally tests the system. The test results show that among the 1000 test cases, the accuracy rate of prescription case analysis of the automated Chinese herbal medicine dispensing system is 96.2%, the accuracy rate of effective prescription is 97.4%, the correct rate of system medicines is 92.6%, and the correct rate of prescription drugs is 87.4. %. The test data shows that the prescription acquisition rate and the correct analysis rate of prescription use cases are up to the standard, which verifies the reliability of the system.
Tacotron Model and CNN in Virtual Reality for Cancer Diagnosis and Communication between Doctors and Patients

Virtual reality is widely used in various fields, such as military, industrial and medical fields. CNN is prevalent when solving problems about image classification. NLP is the most useful model to realize speech synthesis. However, image classification is seldom combined with speech synthesis to be used in a realistic scene. In order to tackle this issue, this paper proposed a system, which combines image classification with speech synthesis organically. There are three steps used to build this system in this paper. First, this paper devises a model to classify whether the patients have skin cancer. It designs a CNN model to deal with the classification of images of skin, diagnosing whether the patient suffers from Melanoma. Second, the Tacotron model is included in this paper to implement speech synthesis, telling the diagnostic results obtained from image classification to the patients. Finally, a virtual reality environment is built to display a scene when a patient entering the hospital and then being diagnosed and getting treatment.
Conclusion

We have demonstrated two-qubit entangling gates between ions of different species, using a technique that requires only a single laser for both species. We first demonstrated the technique with two isotopes of calcium, $$^{40}\mathrm {Ca}^+\,$$ and $$^{43}\mathrm {Ca}^+$$, and also performed a Bell test with these non-identical particles. We then used the same gate mechanism to perform an entangling gate on two different atomic elements, $$^{43}\mathrm {Ca}^+$$ and $$^{88}\mathrm {Sr}^+$$, in a proof-of-principle experiment.
Research on Human Cognition for Biologically Inspired Developments

Robotic developments are seen as a next level in technology with intelligent machines, which automate tedious tasks and serve our needs without complaints. But nevertheless, they have to be fair and smart enough to be intuitively of use and safe to handle. But how to implement this kind of intelligence, does it need feelings and emotions, should robots perceive the world as we do as a human role model, how far should the implementation of synthetic consciousness lead and actually, what is needed for consciousness in that context? Additionally in Human-Robot-Interaction research, science mainly makes use of the tool phenomenography, which is exclusively subjective, so how to make it qualify for Artificial Intelligence? These are the heading aspects of this chapter for conducting research in the field of social robotics and suggesting a conscious and cognitive model for smart and intuitive interacting robots, guided by biomimetics.
Effects of Team Collaboration on Sharing Information Security Advice

Active sharing of information security advice among the employees has undeniable implications for developing a sustainable security environment. This research examines this topic from the network perspective, and focuses on the work relationships that promote sharing security advice. Exponential random graph modeling technique was employed to evaluate the relationship between team collaborative activities and sharing security advice. The findings revealed that those who share security advice also tend to give work- and IT-related knowledge. Moreover, employees who have similar tenure tend to exchange security advice with each other more. Furthermore, the network of sharing security advice is transitive and has a tendency to form separate clusters. Security managers are suggested to take into account the research findings to identify key employees who frequently share security advice in the workplace and devise appropriate strategies to manage them.
Group Recommendation Robotics Based on External Social-Trust Networks

Recommendation robotics helps users to find similar interests or purposes to those of others. We often provide advice to close friends or similar users, such as sharing favorite dishes, listening to favorite music, etc. In traditional group recommendation robotics, however, users’ personalities have been ignored. In this chapter, a method of group recommendation robotics based on social-trust networks is proposed, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members inside and outside of the group. We employ a collaborative filter to obtain members’ predictions and adjust the final group preference rating by the external social-trust network if the group has a large disagreement. The experimental results show that the proposed method has a lower root mean square error and leads to a satisfactory effect for the group.
Towards Protecting Sensitive Text with Differential Privacy

Natural language processing can often require handling privacy-sensitive text. To avoid revealing confidential information, data owners and practitioners can use differential privacy, which provides a mathematically guaranteeable definition of privacy preservation. In this work, we explore the possibility of applying differential privacy to feature hashing. Feature hashing is a common technique for handling out-of-dictionary vocabulary, and for creating a lookup table to find feature weights in constant time. Traditionally, differential privacy involves adding noise to hide the true value of data points. We show that due to the finite nature of the output space when using feature hashing, a noiseless approach is also theoretically sound. This approach opens up the possibility of applying strong differential privacy protections to NLP models trained with feature hashing. Preliminary experiments show that even common words can be protected with (0.04,10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> )-differential privacy, with only a minor reduction in model utility.
Analysis of Color Image Encryption Using Multidimensional Bogdanov Map

Image encryption is a main concern in digital transmission of data over communication network. As encryption and decryption of image has got considerable attention in the past decades, its effectiveness and compatibility need to be taken care of. The work reported in this chapter is mainly concerned with enhancement of dimension in image encryption technique. The work mainly deals with pixels shuffling of an image using Bogdanov chaotic map for both gray and color image, where encryption and decryption process are associated with the key. In color image, the image is divided into all three planes (RGB) individually. Scrambling is done with all three planes individually. All the three planes are summed up into a single plane which gives us the final result. In Bogdanov map, old pixel position is replaced with new pixel position. Further, the authors analyzed security of image encryption techniques with two parameters called NPCR and UACI. The efficacy of the encryption process can be seen in experimental results.
CytoNet, a Versatile Web-Based System for Accessing Advisory Cytology Services

This article describes how the use of artificial intelligence applications as a consultation tool on a cytological laboratory's daily routine has been suggested for several decades. In addition to the use of high-resolution thyroid ultrasonography and fine-needle aspiration cytology, a further reduction of the number of unnecessary thyroidectomies can be achieved through the access to such techniques. Despite the evident advantages, artificial intelligence applications hardly ever find their way to end-users due to the specialized knowledge necessary for designing and using them, as well as the users' unfamiliarity with the required technology. The authors aimed to design an easy-to-use online platform (CytoNet) that gives access to a learning vector quantizer neural network (LVQ NN) that discriminates benign from malignant thyroid lesions to users (medical doctors) with no specialized technical background on artificial intelligence.
3D feature points calibration method for depth-camera

The depth-camera calibration is the basis of application of the depth camera in 3D vision fields, while the depth-camera calibration still presents numerous challenges, for example accuracy and ease of use. In this paper, we propose a novel depth-camera calibration method: the depth-camera calibration method based on 3D feature points (DCCM-3D), DCCM-3D calibrates the depth-camera by directly using depth-images instead of using 2D images. Firstly, we build the depth information transform model of the world coordinate system to the pixel coordinate system by adding the depth information value to the 2D pixel coordinate system. Secondly, two calibration principles for the depth-camera are putting forward: point calibration principle and image calibration principle, which shows that the depth-camera calibration can be finished by obtaining two depth-images and the coordinates of four 3D feature points in each depth-image.
Outcomes of using Artificial Intelligence-based Computer-Assisted Detection/Diagnosis (AI-CAD) for Mammography: A retrospective simulation study with a screening cohort

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
Automatic Classification of User Requirements Information Based on Convolutional Neural Network

User requirements information is semi-structured or unstructured data with sparse features and short lengths. Since manual labeling methods nowadays are time-consuming, which cannot meet increasing requirements for classifying massive user requirements information, an automatic classification method of user requirements information based on Convolutional Neural Networks (CNN) is proposed. Firstly, NLPIR word segmentation tool is used to preprocess word segmentation and remove stop words in user requirements information. Secondly the word vector is trained by word vector model (word2vec). Thirdly convolutional neural network is used to extract abstract features of text information. And finally these features are used as input,and the softmax classifier is used to achieve automatic classification of user requirements information. Compared with the traditional classification method based on probability and statistics, the classification accuracy of the automatic classification model of user requirements information based on CNN has been improved by 13.54%. The experimental results show that the CNN classification algorithm is more suitable for the automatic classification of the user requirements information.
A Lane Segmentation and Traffic Object Detection Multi-task Neural Network for AR-HUD

AR-HUD combines virtual information and real traffic conditions with augmented reality technology to achieve timely forecasting of road conditions, vehicle and pedestrian information, which is an important research direction in the field of intelligent driving. The environment perception module is an important component of AR-HUD, which usually has functions as object detection and lane segmentation. Currently, existing neural network models are mainly oriented to various single-task vision scenarios, which are not applicable to in-vehicle AR-HUD and can hardly meet the real-time requirements. To address this problem, this paper proposes a lightweight multi-task convolutional neural network DS-Net applied to the AR-HUD environment perception module. We train as well as test on the BDD 100K dataset and deploy the trained model to a mobile embedded platform to achieve real-time simultaneous detection and segmentation. Experimental results show that the multi-task network proposed in this paper achieves the processing speed of 23.7 frames per second on the embedded platform and maintain competitive accuracy.
An evaluation model for the entrepreneurial driving force based on game equilibrium decision-making method and big data

In order to quantitatively analyze the impact of innovation and entrepreneurship cognition on entrepreneurial driving force, this paper puts forward an evaluation model of innovation cognition on entrepreneurial driving force based on game equilibrium decision-making and big data method, and carries out empirical analysis with statistical data, constructs a statistical information analysis model of innovation and entrepreneurship cognition on entrepreneurial driving force, and evaluates the mining results of information according to the impact of innovation and entrepreneurship cognition on entrepreneurial driving force. Cluster the evaluation data of the impact of innovation and entrepreneurship cognition on entrepreneurial driving force, and establish a game model. Quantitative game evaluation of the influence of innovative cognition on entrepreneurial driving force and the explanatory variable and control variable models are constructed. The impact of innovation cognition on entrepreneurial driving force is optimized, and the test statistics are established to test and analyze the effectiveness of the impact evaluation. Through experiments, it can be found that this method has good reliability and adaptability in evaluating the impact of innovation cognition on entrepreneurial driving force, and can improve the evaluation level.
Enhancing Academic Recommendation Regarding Common Coauthors' Publication Records

In this chapter, the authors investigated the feasibility of any improvement in paper recommendation by recommending similar papers to an input paper from the publication record of the first author. Although there are numerous approaches for recommending academic papers, they did not consider intellectually recommending papers based on the publication record of common coauthors. Consequently, they are motivated to introduce a remedy for this shortcoming by recommending scholarly papers based on similarity of textual references to visual features which considers the similarity of text fragments of one's publication record to any of their visual features (i.e., tables and figures). Based on the results of evaluation, the proposed enhancement will increase the mean precision, recall, and accordingly, the F-measure. In addition, it increases the position of the relevant papers in the returned list of documents.
Design Patterns for Social Intelligent Agent Architectures Implementation

Multi-agent systems (MAS) architectures are popular for building open, distributed, and evolving software required by today's business IT applications such as e-business systems, web services, or enterprise knowledge bases. Since the fundamental concepts of MAS are social and intentional rather than object, functional, or implementation-oriented, the design of MAS architectures can be eased by using social patterns. They are detailed agent-oriented design idioms to describe MAS architectures as composed of autonomous agents that interact and coordinate to achieve their intentions like actors in human organizations. This chapter presents social patterns and focuses on a framework aimed to gain insight into these patterns. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. The authors consider the broker social pattern to illustrate the framework. The mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is overviewed with a data integration case study.
A Differential-Privacy-based hybrid collaborative recommendation method with factorization and regression

Recommender systems have been proved to be effective techniques to provide users with better experiences. However, when a recommender knows the user's preference characteristics or gets their sensitive information, then a series of privacy concerns are raised. A amount of solutions in the literature have been proposed to enhance privacy protection degree of recommender systems. Although the existing solutions have enhanced the protection, they led to a decrease in recommendation accuracy simultaneously. In this paper, we propose a security-aware hybrid recommendation method by combining the factorization and regression techniques. Specifically, the differential privacy mechanism is integrated into data pre-processing for data encryption. Firstly data are perturbed to satisfy differential privacy and transported to the recommender. Then the recommender calculates the aggregated data. However, applying differential privacy raises utility issues of low recommendation accuracy, meanwhile the use of a single model may cause overfitting. In order to tackle this challenge, we adopt a fusion prediction model by combining linear regression (LR) and matrix factorization (MF) for collaborative recommendation. With the MovieLens dataset, we evaluate the recommendation accuracy and regression of our recommender system and demonstrate that our system performs better than the existing recommender system under privacy requirement.
A Study on Real Time Face Recognition Using Feature Based Image Processing Frameworks

Face recognition systems are models to recognize human faces on the basis of some computation methods. There are lots of applications (Attendance systems, Criminal identifications, Surveillance systems, Human Computer Interfaces etc.) of such systems. This paper aims to build a system which is capable of providing the user with the ability to first create an image database by face detection and feature extraction, and then this database can be used to perform face recognition by comparing the input image (obtained through live video streaming) with the images stored during the training phase. This method used Viola-Jones algorithm for face detection (to create the training set), and Principal Component Analysis (PCA) for face recognition. We have created our own dataset for validating this automatic face recognition system. Experimental work of proposed method under identical physical conditions on our dataset gives 82% accuracy of face recognition.
Practical Research on Computer Aided Translation

In today's information age of big data, the amount of translation is growing rapidly, and the traditional manual translation can not meet the needs of this large number of translation. With the development of science and technology, the emergence of computer-aided translation technology is just an effective solution to this problem. The scientific and reasonable application of computer-aided translation technology in the process of translation practice can greatly improve the efficiency of translation, ensure the quality of translation, and obtain the expected translation effect.
Segmentation Algorithms for Ground-Based Infrared Cloud Images

The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds. Global Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy dispatch, preventing energy shortages caused by occlusion of the sun. This investigation compares the performances of machine learning algorithms (not requiring labelled images for training) for real-time segmentation of clouds in images acquired using a ground-based infrared sky imager. Real-time segmentation is utilized to extract cloud features using only the pixels in which clouds are detected.