Reviewerly/exHarmony huggingface dataset:
A Deep Learning Method for Salient Object Detection

Salient object detection is a traditional topic in computer vision that has attracted a lot of interest recent years. It detects the most salient object in an image and then segments the whole region of the salient object from the image. Since salient object detection can extract salient foreground object from the background, it is useful for several applications, such like image segmentation, object recognition, and target tracking. In this paper, we propose a novel deep learning method for salient detection based on Fully Convolutional Neural Networks (FCNs). The proposed scheme merges different level of features: to combines the side outputs of different layers to get both details and region information and generates global features and local features by using different size of pixel matrices. We tested our method on several different widely used benchmark data sets and received state-of-the-art results by comparing other methods with advantages of high efficiency and high accuracy rate.
Virtual and augmented reality as the most popular direction of cultural and leisure activities of scientific libraries

The demand for innovations in libraries stipulates transformation of every library process, library user services, and cultural activities. The author examines the popular vector of cultural and recreational activities of scientific (academic) libraries, i.e. virtual reality (VR) and augmented reality (AR). These technologies have become commonplace in public libraries: virtual tours, educational films, instructional films with the possibility of “live” participation. This type of projects is described in detail, the examples in Russian and world practice, including academic libraries, are cited. The author concludes on the viability of these technologies in academic libraries and provides recommendations for applying VR- and AR-technologies in libraries.
Research on local trajectory planning of autonomous mobile robots

In the process of autonomous motion, the mobile robot needs real-time local trajectory planning based on the global smooth path and nominal local trajectory tracking. In this paper, the local trajectory planning problem of mobile robot is studied, and the local path represented by quintic polynomial is optimized in Frenet coordinate system with the global smooth path as reference path, then, according to the information of the robot’s pose, velocity and angular velocity, the robot’s velocity is planned according to all kinds of constraints. A LQR closed loop control law is designed to realize the precise tracking of local optimal trajectory, which makes the robot move safely and stably in the dynamic environment. In the “maze” simulation environment, using the Stageros simulator the local trajectory planning algorithm is tested, and the validity and correctness of the algorithm are verified.
Enriched global horizontal irradiance prediction using novel ensemble improved backpropagation neural network

Penetration of solar energy into the power grid and smart grid is becoming an urge because of the continuous progress in industrialization and advancement. Requires a high accurate Global Horizontal Irradiance (GHI) prediction to achieve effective penetration of solar energy. This paper proposes a novel Ensemble Improved Backpropagation Neural Network (EIBPNN) with enhanced generalization ability because it is developed based on the various inputs’ individual improved backpropagation neural networks. Hence, the variance of individual IBPNN and input parameters based uncertainty are overcome and has the generic performance capability. The comparative analysis imparts the proposed prediction model results improved GHI prediction than the existing models. The proposed model has enriched GHI prediction with better generalization.
An Highly Robust Image Forgery Detection Using STPPL-HBCNN and Region Detection Using DBSCAN-ACYOLOv2 Technique

In an assortment of fields, like cyber forensics, newspapers, magazines, courts, medical field, and numerous more, digital images play a major role. Thus, the trustfulness of an image’s contents is extremely vital. Because of the development of higher-resolution digital cameras, computers, and many image editing softwares, image manipulation has turned out to be very much simple and easy. It has become an extremely challenging task to detect forged images as the falsification of images is not at all restricted to experts. The work has presented an effective image forgery detection (IFD) utilizing spatiotemporal pyramid pooling layer based on hierarchical batch convolutional neural network (STPPL-HBCNN) as well as region localization utilizing density-based spatial clustering of application with noise-based Atrous convolution Yolov2 (DBSCAN-ACYolov2). For higher-quality visual perception, the input images are transmuted as of RGB to YCbCr, and the informative aspects are extracted as of the image utilizing bilinear interpolation-based spatial–temporal discrete wavelet transform (BI-STDWT). The methods render time–frequency-centered features and also does denoising. Next, the feature (extracted) is inputted to STPPL-HBCNN. It renders optimal convergent of loss and evades the loss of image data for detecting whether the image is forged or not. There is no requirement for additional processing if the image is normal. However, if it is forged, then DBSCAN-ACYolov2 carries out the region localization or segmentation. In general, the approach attains better detection accuracy (DA) in addition to region DA, evades false prediction, and relatively remains to be robust than the prevailing top-notch methods.
Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection

Feature selection is a very important preprocessing step in machine learning tasks. Selecting the most informative features provides several advantages like removing redundancy, picking up important domain features, and improving algorithm performance. Recently, the black hole algorithm mimicking the real-life behavior of stars and black holes was proposed in literature for solving several optimization tasks which includes feature selection. In this novel feature selection algorithm, each star represents a distinct subset, and the black hole represents the subset having the best fitness. The iterative movement of stars toward the black hole facilitates discovering the best subset. In this work, we have presented a hybrid feature selection algorithm coupling the existing binary black hole algorithm with an existing binary genetic algorithm. In this new algorithm, the control switches between the black hole and genetic algorithms. We have introduced the concept of switching probability parameters to facilitate the switching between the black hole and genetic algorithms. Our optimally tuned hybrid algorithm in terms of the switching probability improves the algorithm performance considerably. We have compared the results of the new algorithm with the existing algorithms with the help of nine publicly available benchmarking datasets. The results indicate that the synergistic coupling apart from improving accuracy selects smaller subsets. The coupled algorithm also has been found to have smaller a variance in accuracies.
A Fair Blockchain Transaction Based on Commitment

Aiming at the problems of the correctness of transaction information and the fairness between participants, a FBT (fair blockchain transaction) scheme based on commitment is proposed. Firstly, a FOC-EC (FO commitment scheme based on elliptic discrete logarithm) scheme is designed to hide the transaction amount to ensure its correctness. At the same time, the scheme improves the computational efficiency and security of the FBT scheme. Secondly, use the FOC-EC and the SM9 signature technology, the new scheme realizes the binding between the commitment value and the participants’ identity information to prevent participants’ denial behaviors. Finally, through the smart contract, the scheme verifies the signatures of both parties and commitment values in order to prevent participants’ cheating behaviors, and punished them in cash. Which ensure fairness between participants in the scheme. The analyses of security and performance show that the scheme not only ensures the correctness of transaction information, but also realizes the punishment of dishonest participants. Moreover, the scheme has better the computational efficiency.
An Examination of How the Interaction Between Senior IT Managers and C-Level Executives Impacts on Cyber Resilience When Undertaking a Digital Transformation Project

Digital Transformation is a ubiquitous phrase that has become even more prolific in use following a recent world-wide pandemic that has forced many organisations to reconsider their processes, their business model, and their ways of working. However, whilst many organisations have embarked on a digital transformation, cyber resilience is not always considered, causing many transformations to lead to greater vulnerabilities and a higher exposure to cyber security breaches. The aim of this research is to develop a deeper understanding of the phrase ‘digital transformations’, who predominantly leads on them and if Cyber Resilience is a vital part of these transformation programmes. The research also considers whether the relationship between the C-Level and the IT Team has any bearing on whether Cyber Resilience is included in these transformation projects. The researcher has established that strong leadership was required to make certain that cyber resilience was included in digital transformation projects. In addition to this it must be seen and accepted that Cyber Resilience is an organizational issue for there to be a real impact. The relationship between the board and their view of Cyber Resilience was key to any sort of successful culture acceptance within their organisations. This acceptance also made a difference as to whether it was included in the business strategy. Even where the relationship between the board and the IT team was good, cyber resilience could be missed out. This was due to the lack of real understanding at board level. Therefore, cyber can never just be led by the IT function and should not be seen just as a technical issue.
Research on LZW Algorithm Based on AES in Data Backup Under Data Block Compression and Encryption

With the rapid development of network information technology and digital storage of information, there are more and more data leakage, loss, damage by virus, illegal string change and other security issues, so data security is increasingly valued by people. In order to ensure the security of data, the method of data backup is often used to restore the problem data, but the backup data may also be leaked, damaged and other problems. On the basis of lossless compression LZW data compression algorithm, AES encryption algorithm is used to compress and encrypt data in blocks. This scheme can reduce the storage space of backup data and encrypt the data in blocks. Using the characteristics of LZW block compression, part of the value in the previous compressed block is used as the encryption key of the next data block. Even if the encryption key is leaked for the first time, all encrypted backup data will not be leaked and the security of backup data will be enhanced.
A Contrastive Study of Intonation Features of Information Focus in English and Chinese

Information Focus; Contrastive Study; Intonation Features; Halliday
Multi-varied Cumulative Alignment for Domain Adaptation

Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains' probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca ).
Bias Field Based Active Contour Model Driven by Pre-Fitting Piecewise Constants for Image Segmentation

How to locate object boundaries precisely and efficiently is one of the most arduous work in image segmentation. In order to address this problem, this paper presents a novel active contour model based on bias field through pre-fitting functions. In the proposed model, an image is decomposed into a bias field part and a structure part based on image model theory. Firstly, the structure part, which is deemed as piecewise constants, is calculated through local mean algorithm before curve evolution. Secondly, using pre-fitting piecewise constant as prior knowledge, we define a novel energy functional by incorporating the image model into clustering criterion function. Therefore, bias field estimation and image segmentation can be simultaneously realized during energy minimization. Moreover, proposed regularization method ensures the segmentation accuracy and efficiency. We validate the proposed model on some challenging images, and experimental results suggest that our model outperforms some classic and latest representative models.
Quantum Approach for Vertex Separator Problem in Directed Graphs

The Vertex Separator Problem of a directed graph consists in finding all combinations of vertices which can disconnect the source and the terminal of the graph, these combinations are minimal if they contain only the minimal number of vertices. In this paper, we introduce a new quantum algorithm based on a movement strategy to find these separators in a quantum superposition with linear complexity. Our algorithm has been tested on small directed graphs using a real Quantum Computer made by IBM .
Compositing Foreground and Background Using Variational Autoencoders

We consider the problem of composing images by combining an arbitrary foreground object to some background. To achieve this we use a factorized latent space. Thus we introduce a model called the “Background and Foreground VAE” (BFVAE) that can combine arbitrary foreground and background from an image dataset to generate unseen images. To enhance the quality of the generated images we also propose a VAE-GAN mixed model called “Latent Space Renderer-GAN” (LSR-GAN). This substantially reduces the blurriness of BFVAE images.
Comparative Study on Image Feature Extraction Techniques

In image processing, algorithms are used to identify various desired parts or features (Shape, Edge) of the image. Using image processing we can detect shape of the object and extract texture of the objects. There are many methods for performing feature extraction in image processing depends on a domain. This paper discusses the comparison of the different image feature extraction techniques with parameters like image translation, scaling, rotation invariant, noise etc. The motivation is to obtain the best technique to be used in further feature extraction from images. The extracted features are chosen carefully; the feature set should extract the relevant information from the input data to perform the desired task. Feature extraction is about to obtain the relevant information from the original data. It will be useful in image classification and recognition, object detection. Extracting features like edges and corner from an image is useful for many purposes.
Experiment-Driven Quantum Error Reduction

Error correction is wide and well elaborated area of quantum information theory. Those methods, however, demand additional resources, like quantum gates, qubits or time. We have observed, in statistical sense, that the qubit’s error in real quantum computers, once calibrated doesn’t change much until next one. Then being so, for quantum sampling based computations, one can determine the correction experimentally and use it until the next calibration, without a need of utilize additional resources. In this work we present the method of determining such a correction and applying it to practical quantum-sampling algorithms. Quantum sampling is the method, which we deliberately decline to obtain one deterministic result of one-shot computation in. Instead of that, we provide a number of same experiments. Then we observe the probability distribution function (PDF) thus obtained, which is considered as the final result of computation. We have observed and experimentally proved in this work, that error of this probability distribution is correlated with the local quantum phase of qubits involved in computations. Hence we are able to create a Phase Distortion Unraveling (PDU) function for each qubit and for whole system as well, that depends on this phase. Briefly, the final result after correction is the sum of PDF and PDU.
DEVELOPMENT AND EFFECTIVENESS OF AUGMENTED REALITY-BASED LEARNING FOR HEALTH SCIENCE STUDENTS: A SYSTEMATIC REVIEW

Background and Objective. The rapid development of technology makes it easier for teachers to continue to be interactively connected with students, for example, by using Augmented Reality technology. We conducted this review intending to investigate the diffusion and the effectiveness of AR technology as a learning media for students from various health fields. Materials and Method. This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) Checklist. We used some databases including PubMed, Google Scholar, Wiley Online Library, and Sciencedirect to search relevant literature with eligibility criteria, namely articles published in the period 201-2021, and discuss the development of Augmented Reality -based applications for learning students in the field of health Results. The studies included are on the development of AR-based learning applications carried out to improve the clinical skills of health students (Medicine, Nursing, and Midwifery). Various types of application development are carried out including anatomy, Endotracheal Intubation, AR Prototype for Medical Surgery, Intravascular Neurosurgery, injection skills, and Laparoscopic. Conclusion. The use of Augmented Reality as a learning medium really helps improve the understanding and skills of students majoring in health sciences.
Deep Recurrent Neural Networks for OYO Hotels Recommendation

Recommendation Systems at OYO solve a complex personalization problem with scale and sophistication. The authors have focused on the development of the best-in-class recommendation system in the hospitality industry using a deep learning based model. The objective of the work is to develop a recommendation model which uses sequences of user interactions with contents derived from user interactions. The hybrid model described in the paper is a deep recurrent neural network based architecture split into two components: first, an embedding generation model and then a deep prediction and ranking model. The models have shown significant performance improvement both online and offline over existing collaborative filtering based models across geographies irrespective of traffic density and hotel supply density. The success of the deep learning based hybrid recommendation model at OYO across different geographies indicates immense potential of such recommender systems in industries such as travel, hospitality etc.
Design and Implementation of Intelligent Operation and Maintenance of BDS-3 RNSS Service

The RNSS service is one of the most basic services provided by BDS-3. It provides global users with RNSS function similar to GPS. As the core of satellite navigation system, the intelligent operation and maintenance of ground operation management system affects the service support ability to a certain extent. Combined with the satellite ground system composition and operation process status of BDS-3, this paper designs an intelligent operation and maintenance system scheme for RNSS service of BDS-3, which carries out system operation and maintenance monitoring, evaluation and processing from the aspects of satellite ground operation status monitoring and evaluation, system service performance monitoring and evaluation, fault diagnosis and prediction; Integrate satellite ground working condition status, data quality status and service application performance status, and use horizontal correlation, vertical mining and other technologies to conduct big data analysis according to different multi dimensions such as time, space, satellite, ground station, signal and application; The software integrating the functions of data reception, data governance, data storage, satellite ground operation status monitoring and evaluation, system service performance monitoring and evaluation, fault diagnosis and prediction, visualization and so on is developed, which realizes the monitoring, evaluation and analysis of the operation status and application performance of RNSS service of BDS-3, The operation and maintenance capability of RNSS service of BDS-3 is improved.
Validation of Educational Comic Integrated Material of Hidrometeorological Disaster to Improve and Knowledge Environmental Care Attitude

One way to optimize disaster mitigation efforts in the field of education is to provide reading materials related to disasters that usually occur in areas where students live.Comics are fun reading materials for students.To be used as reading material for elementary school students, comic needs to be validated.The purpose of this research is to inform the validation results and what are the validator's suggestions regarding the development of disaster comics for elementary school-aged children.The research instrument used was a validation questionnaire.Validation is carried out on aspects of content, language, graphics, and functionality.The results show that the comics developed are valid.The main suggestions regarding the development of comics for elementary school-aged children are to try to make the characters' faces clear and good-looking, bright colors, and use simple language, create a separate dialogues or monologues to explain the special term.
Multi-trial Neural Architecture Search

And now we come to the most exciting part of this book. As we noted at the end of the last chapter, HPO methods are pretty limited for automating the search for the optimal deep learning models, but Neural Architecture Search (NAS) dispels these limits. This chapter focuses on NAS, one of the most promising areas of automated deep learning. Automatic Neural Architecture Search is increasingly important in finding appropriate deep learning models. Recent researches have proven the NAS effectiveness and found some models that could beat manually tuned ones. NAS is a fairly young discipline in machine learning. It took shape as a separate discipline in 2018. Since then, it has made a significant breakthrough in automating neural network architecture construction that solves a specific problem. The most manual design of neural networks can be replaced by automated architecture search soon, so this area is very up and coming for all data scientists. NAS produced many top computer vision architectures. Architectures like NASNet, EfficientNet, and MobileNet are the result of automated Neural Architecture Search.