Reviewerly/exHarmony huggingface dataset:
Research on the Development of Integration of Neuroscience and Artificial Intelligence

Abstract Since the birth of the subject of artificial intelligence, the long-term goal of this subject is to realize the intelligent system of human level. However, after nearly 60 years of development, no general intelligent system has been able to approach the level of human beings: it has the ability to cooperate with a variety of different cognitive abilities; it has a strong adaptive ability to complex environments; it has the ability of self-learning to new things and new environments. With the development of Brain and Neuroscience and Cognitive Science, it is possible to observe some activities of Brain Neural Networks under various cognitive tasks at different scales and obtain relevant data. Therefore, inspired by brain working mechanism, the development of Brain-like Intelligence has become a hotspot in the field of Artificial Intelligence and Computational Science in recent years. Machine Intelligence Brain-like Intelligence System (MIBS) inspired by cognitive behavior mechanism and realized by hardware and software cooperation is similar to human beings in information processing mechanism, cognitive behavior and intelligence level. The goal is to enable machines to realize various human cognitive abilities and their coordination mechanism, and ultimately reach or surpass the level of human intelligence. In this paper, we will study from the perspective of brain science, cognitive science and artificial intelligence. This paper reviews the history, current situation and research focus of brain-like intelligence research, and looks forward to its development direction, potential application fields and potential far-reaching impact.
An Adaptive Virtual Machine Placement Gaussian Model and Method

Multiple types of resources in data centers are mutually constrained. Once a physical resource of hosts has a large overload risk, the hosts cannot be deployed the living migration VMs even if there are any remaining resources, resulting in waste of resources. To improve the comprehensive resource utilization, a virtual machine placement scheme and method based on adaptive Gaussian model (AGM-VMP) is proposed. First, the AGM-VMP method declares that when multiple resources are in the normal state at the same time in data centers, it facilitates reducing the waste of resources and degrades energy consumption. Next, the adaptive Gaussian model is developed to estimate the normal workload probability of hosts. Finally, the model, scheme and method of virtual machine placement is treated according to the presented normal workload probability of hosts. Experimental results show that the AGM-VMP method improves energy utilization, quality of service and comprehensive resource utilization.
Strategic Management of Cloud Service Portfolio Using Genetic Algorithm

The article proposes a static model of strategic portfolio management of cloud services, a distinctive feature of which is the assessment of the information security of the service portfolio. A dynamic model for the strategic management of the cloud services portfolio has been built, taking into account the dynamics of sales of services. The methods for performing calculations on the proposed models are given. The method of portfolio formation based on the genetic algorithm supported by the MATHLAB package is considered.
The words separation in old Cyrillic texts with fuzzy search method

The article describes a solution to the problem of word separation in old Cyrillic texts after the stage of graphic character recognition on scanned documents. The article proposes an algorithm for fuzzy text search using the grammatical dictionary of the Old Russian language, with the completeness and accuracy of search results evaluate. To assess the relevance and ranking of the search results, a method for calculating the rank of the symbol recognition variant based on the TF-IDF metric is developed. The article also presents a software system of automated word search, presents the results of experiments that prove the effectiveness of the developed algorithms and programs.
Using artificial neural networks to solve text classification problems

The article is devoted to neural network text classification algorithms. The relevance of this topic is due to the ever-growing volume of information on the Internet and the need to navigate it. In this paper, in addition to the classification algorithm, a description is also given of the methods of text preprocessing and vectorization, these steps are the starting point for most NLP tasks and make neural network algorithms efficient on small data sets. In the work, a sampling of 50,000 English IMDB movie reviews will be used as a dataset for training and testing the neural network. To solve this problem, an approach based on the use of a convolutional neural network was used. The maximum achieved accuracy for the test sample was 90.16%.
Smart operator for the human liver automatic segmentation, present in medical images

Abstract The segmentation of the human body organ called liver is a highly challenging problem due to the noise, artifacts and the low contrast exhibited by the anatomical structures located around the liver and that are present in digital images, generated by any modality of medical images. The main modalities are: ultrasound, nuclear emission, magnetic resonance and the gold standard called multi-slice computed tomography. In this paper, with the objective of to address this problem, we consider multi-slice computed tomography images and we propose an automatic strategy based on two phases. In the first phase, a digital filtering bank is used for diminishing the noise effect and the artifacts impact in the quality of images. In the second phase, called liver detection, we use a smart operator based on least squares support vector machines for generating both the morphology and the volume of liver. The application of this strategy allows generating the morphology of the liver in a precise and efficient manner as it was demonstrated by the metrics used to assess its performance. These results are very important in clinical-surgical processes where both the shape and volume of liver are vital for monitoring some liver diseases that can affect the normal liver physiology.
Entanglement classification via integer partitions

In Walter et al. (Science 340:1205, 2013), they gave a sufficient condition for genuinely entangled pure states and discussed SLOCC classification via polytopes and the eigenvalues of the single-particle states. In this paper, for 4n qubits, we show the invariance of algebraic multiplicities (AMs) and geometric multiplicities (GMs) of eigenvalues and the invariance of sizes of Jordan blocks (JBs) of the coefficient matrices under SLOCC. We explore properties of spectra, eigenvectors, generalized eigenvectors, standard Jordan normal forms (SJNFs), and Jordan chains of the coefficient matrices. The properties and invariance permit a reduction in SLOCC classification of 4n qubits to integer partitions (in number theory) of the number $$2^{2n}-k$$ and the AMs.
Face Sketch Synthesis Based on Adaptive Similarity Regularization

Face sketch synthesis plays an important role in public security and digital entertainment. In this paper, we present a novel face sketch synthesis method via local similarity and nonlocal similarity regularization terms. The local similarity can overcome the technological bottlenecks of the patch representation scheme in traditional patch-based face sketch synthesis methods. It improves the quality of synthesized sketches by penalizing the dissimilar training patches (thus have very small weights or are discarded). In addition, taking the redundancy of image patches into account, a global nonlocal similarity regularization is employed to restrain the generation of the noise and maintain primitive facial features during the synthesized process. More robust synthesized results can be obtained. Extensive experiments on the public databases are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm.
Web-Based Academic Information System

Abstract The purpose of this research is to build a web-based system for making the academic information system more effective and efficient in data processing in the field of formal education. The research method used was descriptive using structured and development methods using prototypes. While for programming languages used PHP and MySQL as the database. On implementation of this web-based academic information system in the class distribution process, scheduling, managing receipts, and managing student value data so that they are made temporary final report Value of knowledge and attitude by assessing using index systems (A, B, C, D). Influence The information system is very powerful in facilitating the collection of existing data, just by making it a system, we can reduce energy, time, and Human Resources. The final result of the assessment process is to make temporary report cards automatically. Therefore, this study provides several implications, including implications for planning and development curriculum that is useful for managing student, teacher, grades, scheduling, and so on. So The conclusion is, by analyzing, discussing and restoring the system for academics will produce many benefits that can be obtained and in achieving school goals.
Clustering-Anonymization-Based Differential Location Privacy Preserving Protocol in WSN

Playing a vital role in the period of big data and intelligent life, wireless sensor networks (WSN) transmits a bulk of data. Location information as the vital data in transmission is widely used in detecting and routing for the network. With the big data mining and analysis, the security of location and data privacy in WSN faces great challenges. To the problem of active attacking like node capture in wireless sensor network node location privacy, existing location privacy preserving protocols are analyzed and Differential Location Privacy protocol based on Clustering Anonymization is proposed. By sensor nodes clustering using genetic clustering algorithm, the individual location is hidden in the statistical location information of the group. The Laplace Mechanism is also added to the protocol to realize differential location privacy. Node location privacy in WSN is preserved as well as privacy preserving budget is saved. The result of theoretical analysis and contrastive simulation experience shows that the protocol can be useful.
Secure and Trustworthy Cloud: Need of Digital India, an e-Governance Project

Role of cloud computing in the execution of e-governance services has led to an ever-growing need for secure and trustworthy cloud services. Cloud Computing brings IT services in form of utilities that can be consumed as per demand. Cloud computing uses the internet to deliver differential services through geographically apart data centers. These data centers offer pooled infrastructure resources that can be utilized on pay as per use basis. All these properties of the cloud make it a perfect solution for E-Governance. The only concern that is making current users vigilant and future users dubious about this service is “Security”. Due to augmented security lapse incidences in recent years, consumers are apprehensive about adopting it and continuously losing trust in this computing paradigm. The prime concern of this research to find ways to overcome these challenges. The key intent of this research is to find a framework that can compute the trustworthiness of cloud provider based on security coverage. The framework evaluates trust value for a provider, on the basis of standards & certification attainment related to security components required for the services offered by him.
The Activity of Petr Ivanovich Makushin in the Field of Librarianship and Public Education

The author considers the activity of bookseller and educator P.I. Makushin on the organization of public libraries, open general education courses and the People’s University in Tomsk. The aim of the article is to assess the contribution of the Siberian philanthropist not only to the development of the specific educational institutions, but also to the cultural and educational level of the entire Tomsk region in the late 19th-early 20th century. In 1873, P.I. Makushin left the service in church educational institutions and organized the bookselling enterprise in Tomsk. He provided with books not only shops, but also many educational institutions in the Tomsk province. In 1884, P.I. Makushin was directly involved in the opening in Tomsk of the first free people’s library in Russia, special building for which was built in 1887. The establishment of free rural libraries by P.I. Makushin in Tomsk province was of utmost importance in spreading knowledge in Siberia. For a long time, the philanthropist nurtured the idea of opening the first People’s University in Siberia. In 1905, he appealed with this proposal to the Tomsk city Duma, which supported his initiative and decided to allocate a land plot for the construction of the University on the Salt Square in Tomsk. The University building was constructed in 1912; and the official approval of the Charter of the Institution took place in 1916. The long time from the idea to the implementation of the project was connected with the obstacles arisen from the negligence of local officials and the reluctance of the Minister of public education L.A. Kasso to open another University in Tomsk. The First World War had a devastating impact on the life of the People’s University: from the second half of 1916, its building was used for the barracks of war prisoners; the work of the academic Department was episodic. In 1919, the University was nationalized, then various organizations housed the building, and its founder was almost without means of livelihood. Referring to the historiographical coverage of P.I. Makushin’s charitable activities, the author confidentcy concludes that Soviet historiography deliberately suppressed Makushin's contribution, like the contribution of many Russian entrepreneurs, to the development of intellectual sphere of certain regions; therefore, interest to this problem has largely intensified in recent decades.
Hybird Single-Multiple Frame Super-Resolution Reconstruction of Video Face Image

The goal of super-resolution (SR) face image reconstruction is to generate a high quality and high resolution (HR) image based on low resolution (LR) input face images, to support video face recognition. This paper proposes a novel hybrid SR reconstruction framework of combining a multiframe SR interpolation method and a learning based single frame SR method. The proposed framework first formulates a simple but effective frame selection strategy as a preprocessing step. A weight map based on canonical correlation analysis (CCA) theory is then estimated to blend the results from single frame SR method and multiframe SR approach. Experimental results demonstrate that the proposed method achieves competitive performance against the state-of-the-art both simulated and real LR sequences.
Aplicación de Realidad Aumentada para promoción turística en el Triángulo del Sol del Estado de Guerrero

To show information about the three main tourist destinations in the state of Guerrero: “Acapulco de Juárez”, “Ixtatapa-Zihuatanejo” and “Taxco de Alarcón” to users using an application for devices with Android Operating System with Augmented Reality, a promotional video reproduced by recognizing the target of the city and showing information like activities or attractions of each of these destinations; these images will be on a poster located at different terminals within the Mexican Republic and the state of Guerrero, thus achieving greater impact and interest in national users.
Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

Deep residual networks for action recognition based on skeleton data can avoid the degradation problem, and a 56-layer Res-Net has recently achieved good results. Since a much “shallower” 11-layer model (Res-TCN) with a temporal convolution network and a simplified residual unit achieved almost competitive performance, we investigate deep variants of Res-TCN and compare them to Res-Net architectures. Our results outperform the other approaches in this class of residual networks. Our investigation suggests that the resistance of deep residual networks to degradation is not only determined by the architecture but also by data and task properties.
Managing influence in complex systems to ensure safety of their operation

This article proposes a new methodology for identifying influences to ensure the safety of a complex system operation. The process of identifying influences uses the Algorithm of Effective Controls for graph models presented in the form of a directed weighted signed graph with a causal relationship on the edges. A fuzzy cognitive map (FCM) is used as a graph model. The result of the influence identification process is a set of components of the influence vector and impact vector with the required level of productivity. The result can be validated.
Visualization of information through infographics

The article is devoted to the use of infographics as one of the methods of visualizing information and the possibility of influencing the attention of the audience. The main objective of the study is to systematize the basic rules for compiling infographics and identify additional principles. Analyzed the experience and problems of using visualization by the media. outlines the basic concepts and definitions of the visual transmission of information. Reviewedadvantages and disadvantages of using infographics. Based on the analysis of the sphere of communicative design, various types of visual infographics are considered. The possibility of distorting the broadcast information to influence the perception of the audience is revealed. This article attempts to disclosebasic tools for creating understandable infographics. The main stages of creating infographics are considered. As a result of the study, additional principles were identified and described to create a clear visualization.
Implementation of RSA Cryptography Algorithm in Language C in Exchange of Text Messages

The encryption approach is widely used in mobile applications where there is message exchange and in banking transactions or financial pointers tools. The present research has as a proposal to approach an algorithm developed in C language and based on the RSA encryption methodology using the symmetric and asymmetric key concepts in order to demonstrate its use in the exchange of messages where there is secrecy of information, thus the importance of information security, its use has obtained interesting results with the manipulation of large scale vectors where the number of characters is a factor the process of letter inversion for encryption and decryption.
Multimodal and Multiclass Semi-supervised Image-to-Image Translation

In this paper, we propose a multimodal and multiclass semi-supervised image-to-image translation (MM-SSIT) framework to address the dilemma between expensive labeled work and diversity requirement of image translation. A cross-domain adversarial autoencoder is proposed to learn disentangled latent domain-invariant content codes and domain-specific style codes. The style codes are matched with a prior distribution so that we can generate a series of meaningful samples from the prior space. The content codes are embedded into a multiclass joint data distribution by an adversarial learning between a domain classifier and a category classifier so that we can generate multiclass images at one time. Consequently, multimodal and multiclass cross-domain images are generated by joint decoding the latent content codes and sampled style codes. Finally, the networks for MM-SSIT framework are designed and tested. Semi-supervised experiments with comparisons to state-of-art approach show that the proposed framework has the ability to generate high-quality and diversiform images in case of fewer labeled samples. Further experiments in the unsupervised setting demonstrate that MM-SSIT is superior in learning disentangled representation and domain adaption.
Stability Analysis of a Statistical Model for Cloud Resource Management

In this paper, we presented a comprehensive stability analysis of statistical models derived from the network usage data to design an efficient and optimal resource management in a Cloud data centre. In recent years, it has been noticed that network has a significant impact on the HPC and business critical applications when they are run in a cloud environment. The existing VM placement algorithms lack capabilities to deploy such applications in an effective way and cause performance degradation. As a result, there is an urge for a network-aware VM placement algorithm which will consider the application behaviour and system capability. Our approach uses static models based on simple probability distribution concept and partition (number theory) to characterise and predict the resource usage behaviour of the VMs. However, the stability of those models is a key requirement to ensure a persistent placement of the VMs which can prevent their frequent migration and keep the infrastructure rigid. The paper investigates the stability of the models with respect to time. Sticky HDP-HMM method was proven highly capable to model the monitoring data with a certain accuracy. The refined data was further used to estimate the resource consumption of each VM and physical host running in the infrastructure. A stability parameter has been defined to determine the level of steadiness of the models that gives us a clear indication on whether the models can be used further to derive an optimal placement decision for new VMs. The paper ends with a discussion on instance based stability analysis and future work.
My Gynea Helper

Cellphone becomes an alternative tool for everyone to do something work or make things easier for work. Nowadays to reduce everyone’s forgetfulness is to use a cellphone, because they will always carry the cellphone everywhere. One of the problems of pregnant women is being lazy to bring a record book to go to the doctor to make an appointment. With this pregnancy tracker application pregnant women do not have to bother anymore to bring a record pregnancy book to meet a doctor, because with this application it can record all appointment results with a doctor. One of the advantages of this application is that we combine kick tracker and construction time with one application. This second function is to make it easier for pregnant women to check the status of babies in their womb, whether their babies are in a good and normal state as usual. Without having to meet a doctor to find out their own baby’s development. and this application is very easy to use, usually pregnant women also forget to make an appointment with a doctor, this application will remind 1 day or 1 hour before the appointment takes place.
The Improvement Technique in Assessing Teaching Skills using Fuzzy Weighted Grading Method

Abstract The assessment of education is the process of providing the decision on the performance and quality of student achievement. Many cases indicate that the educational assessment involves many criteria of uncertain value, and therefore fuzzy assessment studies have grown rapidly in recent years. In this paper, a method is proposed to improve the assessment of the basic teaching skills of prospective teachers in a Micro-teaching course by using a fuzzy grading system. This method is also based on group decision-making (GDM) because it involves several decision makers who give preferences in the assessment process and negotiate to determine the criteria and sub-criteria for the assessment. The result of the method is a fuzzy value which is represented by a letter grade for the Micro-teaching course and letter grade for each of the basic teaching skills. By using this method, students may have the same final grade, but their mastering of teaching skills is very different.