Reviewerly/exHarmony huggingface dataset:
Image Denoising Using NLM Filtering and Convolutional Neural Network

Image denoising technology is one of the forelands during the field of computer graphic along with computer vision. By comparing different filtering techniques which technique is giving the best results of filtering. Non-local means technique is one of the great performing techniques which arouse tremendous research. During this article, an improved weighted non-local means algorithm designed for image denoising is planned. The non-local means denoising technique replaces each pixel through the weighted average of pixels by the surrounding neighborhoods. Furthermore, explanation of the convolutional neural network also gives the greatest results compared to further filtering techniques. The planned method evaluates under testing images among various levels noise. Experimental results are shown used for different filtering techniques by using PSNR.
Generalized Locally-Linear Embedding: A Neural Network Implementation

Locally-linear embedding (LLE) is a prominent dimension reduction method by exploiting the local symmetries of linear reconstructions. Recently, auto-encoders have achieved great success in learning data representation via the deep neural networks (DNN). It is interesting to get the best of both worlds by implementing LLE with DNN. To this end, we introduce an extra fully-connected layer whose weight works as a reconstruction coefficient (i.e., relation among the samples). Consequently, the latent representation can well preserve the neighborhood structure. Experiments on dimension reduction and classification have validated the superiority of the proposed method.
Representation of Narrative Identity in Post-Postmodern Novels “The Curious Incident of the Dog in the Night-Time” By M. Haddon And “Carry Me Down” by M. J. Hyland

In the paper, we investigate the narrative features of British post-postmodern novels: M.J. Hyland’s “Carry Me Down” and M. Haddon’s “The Curious Incident of the Dog in the Night-Time”. The specific features of interaction between individual and collective identities in the novels have been analyzed. It is proved that the post-postmodern identity of the protagonists in the aspect of self-knowledge and self-realization is not connected with communication with the Other. It was investigated that the communicative interaction between the protagonist and other characters in the post-postmodern novels is often a factor of the unbalance of consciousness of the characters formed as a result of genetic abnormalities. The thematic complex of post-postmodern novels, in which mentally specific characters are represented, possessing a neuralgic specificity influencing the world perception, has been determined. The paradigm of the post-postmodern worldview in contemporary British novels is spotlighted. In post-postmodern novels it is represented a new comparing to postmodern tradition individual identity of characters who exist in an independent state relating collective identity and having the ability to resist the negative communicative intervention of the Other. The protagonists of such narratives do not need a collective identity to crystallize their own self. It is investigated that genetic/neurophysiological features and neuralgic disorders of characters (Christopher, John) give grounds to assert the existence of a special type of narrative identity that avoids emotional influences resisting emotional influences from the side Another and striving to understand the reality as a system of clear laws and mathematical laws. In this case, mathematics is explicated in the novels as an ideal language, which makes it possible to hypothetically analyze the origins and principles of the functioning of matter, the mechanisms of interaction in the universe. The perspective of the research are determined by the necessity to outline the poetic specification of post-postmodern novels and the importance of the investigations of their linguistic design, forms, correlations between literary and scientific, philosophical and non-fiction modes.
Remote Sensing Image Dam Detection Based on Dual Threshold Network

For existing target detection algorithms, the accuracy rate is generally not high, and a large number of false detections exist. This paper proposes a dual-threshold target detection network model. This network model is based on a basic single-stage target detection network, adds a double threshold structure, and makes a secondary decision on the detection results. In a dual-threshold network, a threshold interval is first set, and the position of a target whose category probability falls within the interval is obtained. Using the position coordinates, the features of the corresponding position are extracted in the feature map of the basic network. Because the target sizes are different, the feature needs to be pooled into the region of interest. Then use convolution and fully connected networks to obtain new class probabilities to make correct judgments to achieve positive and negative online separation of negative samples and improve the accuracy of network detection. To verify the effectiveness of the algorithm, a data set of dams in remote sensing images is established. Experiments show that compared with the basic SSD network, the method improves the accuracy and recall by 5 percentage points. The above experimental results show that the method is effective in remote sensing image detection.
The Path Planning Study of Autonomous Greeting Robot based on Ant Colony Algorithm

In order to realize the autonomous greeting and greeting of the field guests, the autonomous greeting robot needs to collect and analyze the environmental data. Through the optimization algorithm, an optimal autonomous moving path from the starting point of greeting to a certain guest is found. In this paper, aiming at the autonomous greeting robot, the path planning problem of the autonomous greeting robot based on ant colony algorithm is proposed, and the related design scheme is given. The simulation example shows that the greeting robot can visit specific guests in the shortest path and time. It can effectively ensure that the robot avoids obstacles and other guests in the process of moving. It verifies the feasibility and validity of the path planning proposed in this paper, and lays a theoretical foundation for the further application of the autonomous greeting robot in non-specific environment.
Response to Discussion on “Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease,”

In the paper Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease, the authors introduced two new methods that address the class overlap problem in imbalanced datasets. The methods involve identification and removal of potentially overlapped majority class instances. Extensive evaluations were carried out using 136 datasets and compared against several state-of-the-art methods. Results showed competitive performance with those methods, and statistical tests proved significant improvement in classification results. The discussion on the paper related to the behavioral analysis of class overlap and method validation was raised by Fernández. In this article, the response to the discussion is delivered. Detailed clarification and supporting evidence to answer all the points raised are provided.
Research of Quality Prediction Based on Extreme Learning Machine

Aiming at the problems of poor stability and generalization performance in quality prediction based on extreme learning machine (ELM), this paper presents an improved method of ELM. It is named as the DAE-P-ELM algorithm, which integrates denoising autoencoder (DAE) with principal component analysis (PCA). First, in order to reflect the characteristics and intrinsic relationship of the modeling data as much as possible, DAE technology is introduced to reconstruct the input data. Therefore, output weight sufficiently containing the input data information is obtained, which is used as input weight of the ELM. Then, the PCA technology is used to reduce the dimension of the hidden layer output matrix to avoid the multicollinear problem in calculation of output weight matrix, which solves the problem of poor stability of the model due to too many hidden layer nodes. Finally, the method is applied to the Tennessee Eastman (TE) process. The simulation results show that the content of components G and H predicted by this method is basically consistent with the real value, which proves that the proposed method has a good prediction effect.
Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.
Entangled State Evolution and Entanglement Transfer in Quantum Mesoscopic Coupled Circuits

A mesoscopic circuit, where field quantization (e.g., coherent states and squeezed states) can be exhibited, is one of the ideal quantum systems for manifesting diverse quantum field theoretical phenomena and thermally statistical characteristics. Because of inevitable electromagnetic coupling (e.g., mutual capacitor coupling and mutual inductor coupling) in two neighboring mesoscopic electric circuits, quantum effects relevant to mesoscopic circuit coupling may play a key role in some devices of quantum information processing. Such a circuit coupling can lead to circuit entangled states, where the Fock number states of energy quanta in different circuits are entangled with each other. Since the time dependence in parameters of coupled-circuit Hamiltonian would result from environmental perturbations (e.g., temperature fluctuations in devices) or from some controllable manipulation (e.g., an incident ultrasonic wave), the circuit entangled states would evolve due to these external conditions. Time evolution of entangled states in coupled mesoscopic circuits is considered within a framework of time-dependent quantum theoretical formalism. Note that an applied photon field can also be coupled to such quantum entangled circuits. Then it is possible that an effect of entanglement transfer (i.e., transportation of superposition coefficients in the entangled state) from the quantum mesoscopic coupled LC circuits to the applied external photon field would also occur under some proper parameter conditions. The present characteristics of both quantized coupled mesoscopic circuits and entanglement transfer can also be found in some alternative microscopic- and mesoscopic-scale devices such as the systems in cavity quantum electrodynamics (QED).
Pseudo Random Number Generation Based on Genetic Algorithm Application

Standard computational tool like random number generator is used to create a sequence of numbers which are apparently unrelated. These numbers are used in different computations and statistics. Cloud computing is an emerging technique in all over the world to access the information via the Internet. In this paper, we proposed the hybrid model to generate a random numbers using linear-feedback shift register (LFSR) and GA to increase the key length. The hybrid model is also used to give security for the cryptographic applications. In this work, we implemented the hybrid model using the simulation tool. Modeling and simulation innovations are reasonable for assessing execution and security issues. This paper gives details about the hybrid model we proposed and the cloud simulation tool that we are using, and also we discussed about the different cloud simulation tools.
Certainty Factor Based Pruning for C4.5 Algorithm

Classification is the process of group data into data classes according to their patterns. One method that can classify and show relationships between attributes is the decision tree method. The classification method using this decision tree is still challenging to improve, especially those related to increasing accuracy. One factor that can reduce accuracy is the pruning process of the branches (nodes) with high information values. Thus, this study aims to optimize the pruning process. The pruning process unexpectedly removes some contributive nodes (nodes with high information). The pruning method uses a certainty factor (CF) introduced to ensure that the trimmed nodes are non-contributing nodes. Improving the performance of the C4.5 algorithm using CF has succeeded in minimizing the level of errors caused by the pruning process.
Optimization and Performance Analysis of Extreme Learning Machine by L2-Norm Regularization

Extreme learning machine is a new feedforward neural network. Compared with the traditional neural network, it has the advantages of simple network construction and fast learning. However, as the least square method is used to solve the optimal output weight, the extreme learning machine has some problems such as weak anti-interference ability, poor stability and over-fitting. For the above problems, L2-norm regularization is adopted to optimize the extreme learning machine model, and minimize the training error and output weight by determining the regular parameters in this paper. Simulation results on standard data sets shows that optimization model by L2-norm regularization can significantly improve the stability and anti-interference of model.
Multi-view classifier based on Probabilistic Collaborative Representation and Latent Representation

Multi-view is quite more effective at improving the training of model than merely using single view. However, most existing multi-view learning algorithms only either pay attention to consistency or complementary principle among views, not making full use of multi-view data. Due to its high complexity, algorithm considering both complementarity and consistency has limited ability to process large-scale data. On the basis of Probabilistic Collaborative Representation based Classifier (ProCRC), we propose Probabilistic Collaborative Representation based Classifier for Multi-View (ProCRC-MV), which jointly maximizes the likelihood that a test example belongs to the co-subspace of each class. Learning subspace in the process of collaborative representation, considering consistency and complementarity concur-rently, ProCRC-MV can achieve promising classification performance. Meanwhile, it has low computational complexity, fast running speed, and can still maintain good performance when dealing with large-scale data. ProCRC-MV has the ability for subspace learning based on self-representation, so we combain latent representation learning for better searching subspace with ProCRC-MV to construct a novel classifier called LProCRC-MV, the ability of LProCRC-MV to process complex data is further enhanced comparing with ProCRC-MV.
Equivalence between entanglement of channel and measurement in quantum teleportation

In this work, we analyze the concept of quantum bridge using Non-Maximally Entangled (NME) states. We propose two schemes for the quantum teleportation of an unknown qubit using an NME channel as a bridge between the sender and the receiver. We show that quantum channels and the measurements are equivalent in achieving the quantum teleportation only when the degree of entanglement is the maximum.
Development of Learning Support Platform as PWA and Its Usability Evaluation

In the modern age of smartphone penetration, the application market is also changing. People who regularly use native only applications are not happy with the operability of WEB applications. In this research, we aim to improve usability by using PWA to solve the web application user's dissatisfaction.
Face Recognition Application with the Complete Kernel Fisher Discriminant (CKFD) Method

Facial recognition applications are currently being developed because they can be applied in various problem areas such as criminal recognition, security systems, attendance, or human-computer interaction. However, to develop a face recognition-computing model is quite difficult, because the human face presents something complex, so to develop an ideal computing model for human face recognition is quite difficult. One of the best facial recognition methods is the Complete Kernel Fisher Discriminant (CKFD) method. The Complete Kernel Fisher Discriminant (CKFD) method has two advantages when compared to the previous Kernel Fisher Discriminant (KFD). First, the implementation of this algorithm can be divided into two phases, namely Kernel Principal Component Analysis (KPCA) plus Fisher Linear Discriminant analysis (FLD) so that the results are more transparent and simpler. Second, CKFD can make two categories of discriminate information so that the results are stronger. This research aims to produce an application with a face recognition algorithm with CKFD Method that is quite accurate. This research will produce a face recognition application with the CKFD method. The existence of this application is expected to help various parties who need a face recognition process. The CKFD method used can provide excellent facial recognition results when compared to other methods. The best result was given by using limit value with a False Acceptance Rate(FAR) value equal to 3.3 and a False Rejected Rate (FRR) value equal to 36.7.
How to Trust the Middle Artificial Intelligence: Uncertainty Oriented Evaluation

These last years have seen a renewed importance in measuring machine intelligence and considerable interest in the human-level machine intelligence. Despite this interest, few to the best of our knowledge who proposed a classification for these evaluations. Hernandez-Orallo’s recent findings regarding the measurements of machine intelligence have led to the need of classifying them into major categories which are the task-oriented evaluation and the ability-oriented evaluation. Although this approach is interesting as a broad classification inspired from Weak/strong AI, it suffers from the same discontinuity pitfall as Narrow/general AI. Our work aims to broaden further current knowledge of the efforts applied to evaluate artificial intelligence. Our main contribution in this paper is to shed light on the gap left behind the existing evaluations and propose a middle AI evaluation based on uncertainties and expectations. Thus, we are optimistic that within the next few years, an agreed-on, machine intelligence measurement is destined to become an important component in evaluating human-level machine intelligence concept.
Exponential Cipher Based on Residue Number System and Its Application to Image Security

Residue number system (RNS) which was inspired by Chinese remainder theorem (CRT) provides a method for using groups of smaller numbers to represent large integers. Although cryptographic algorithms based on decimal arithmetic have their advantages, direct sum of smaller rings properties-based algorithms offers better benefits. This application of RNS number systems successfully utilized in an image security. An exponential cipher based on RNS is discussed in this paper. RNS-based exponential cipher systems exhibit properties that increase computation speed, reduce the time complexity and makes the system immune to known-plaintext attacks, side-channel attacks and algebraic attacks. In addition, the characteristics of exponential cipher systems, the method of key generation and RNS-based encryption and decryption are discussed in this paper.
Pixel-Based Approach for Generating Original and Imitating Evolutionary Art

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.
Anticipating Blockchain as Disruptive Technology

Today, people are going to senior managers in almost all industries pitching about their “I have a new product” thing. Disruptive technology transforms a differentiated product that was so expensive and sometimes complicated or sophisticated into a simplified implementation with the applicability of APIs. APIs provide a platform where startup companies can be nitrated to a giant and established companies. Secondly, it changes the business ecosystem to suit all kinds of players small or big. In previous years, only major companies with a lot of resources had access to such technologies. This selfish access to new technologies would make such giants flourish like Amazon, eBay, Google. Blockchain is a form of distributed ledger technology gaining significant research devotion in numerous areas cutting across e-commerce, cryptocurrency, cryptography, logistics, security, finance, and now it is gaining grounds in e-commerce, big data, and internet of things. This chapter introduces the concept of blockchain, applications, and benefits it possesses in various fields related to e-commerce.
How To Set Up Face ID And How Does Face ID Work

In this blog post, I will thoroughly explain what is Face ID, and I will tell you everything you need to know about it. What Is Face ID? ...
Opportunities to expand the offered administrative services from the career centers

Purpose. An important prerequisite for the successful completion of the graduating students is the good functioning of the career center of each higher school. Given the current tendencies of digitization of the services provided, it is necessary to seek opportunities for their expansion. The main objective of the present study is to build on the experience gained and the practices applied in the higher education institutions providing career services to students to seek opportunities to improve this process. Methods. In order to achieve this goal, various methods are used in the publication, such as the method of empirical analysis and synthesis, the analysis of current regulations and studies among students. Results. The results obtained are in the direction of building and developing modern communication channels and modern digital electronic administrative services for the students. Conclusions. The conclusions outline the main challenges facing the higher education career centers and the opportunities for expanding the provided administrative services for the students.