Network Revolution Research Articles

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Technological Transformation of Telco Operators towards Seamless IoT Edge-Cloud Continuum.

This article investigates and discusses challenges in the telecommunication field from multiple perspectives, both academic and industry sides are catered for, surveying the main points of technological transformation toward edge-cloud continuum from the view of a telco operator to show the complete picture, including the evolution of cloud-native computing, Software-Defined Networking (SDN), and network automation platforms. The cultural shift in software development and management with DevOps enabled the development of significant technologies in the telecommunication world, including network equipment, application development, and system orchestration. The effect of the aforementioned cultural shift to the application area, especially from the IoT point of view, is investigated. The enormous change in service diversity and delivery capabilities to mass devices are also discussed. During the last two decades, desktop and server virtualization has played an active role in the Information Technology (IT) world. With the use of OpenFlow, SDN, and Network Functions Virtualization (NFV), the network revolution has got underway. The shift from monolithic application development and deployment to micro-services changed the whole picture. On the other hand, the data centers evolved in several generations where the control plane cannot cope with all the networks without an intelligent decision-making process, benefiting from the AI/ML techniques. AI also enables operators to forecast demand more accurately, anticipate network load, and adjust capacity and throughput automatically. Going one step further, zero-touch networking and service management (ZSM) is proposed to get high-level human intents to generate a low-level configuration for network elements with validated results, minimizing the ratio of faults caused by human intervention. Harmonizing all signs of progress in different communication technologies enabled the use of edge computing successfully. Low-powered (from both energy and processing perspectives) IoT networks have disrupted the customer and end-point demands within the sector, as such paved the path towards devising the edge computing concept, which finalized the whole picture of the edge-cloud continuum.

DIGITALIZATION OF THE NATIONAL AGRO-INDUSTRIAL COMPLEX: NEW CHALLENGES, REALITIES AND PROSPECTS

The purpose of this scientific article is to diagnose the processes of digital transformation of the national agro-industrial complex in order to substantiate the actualization of digitization of the activities of agro-industrial enterprises for conducting "smart" management" based on the adoption of a set of high-tech solutions in the direction of maximum automation of the industry.To achieve this purpose, a wide range of research methods was used, the main of which were the methods of generalization and synthesis, scientific abstraction, analytical diagnostics, regulatory and statistical. The application of the regulatory and legal method made it possible to identify the main elements of the legal field for the use of digital technologies in the activities of enterprises of the agro-industrial complex. The dialectical method of cognition of socio-economic processes, the formal-logical method and the method of system analysis were applied in order to better understand the processes of digitalization, to identify the patterns of their course, as well as the level of influence on the formation and modern development of the national agro-industrial complex.It is determined that national agro-industrial production is characterized by a huge potential for future development by increasing the efficiency of land, labor and biological resources, the full use of which requires the improvement of high-production technologies and the formation of an information management system.A two-factor model of the genesis of innovative information technologies and innovative business ideas in the agroindustry is built, where there is a clearly pronounced dependence on the influence of the human factor, relatively low yields at a relatively high production cost, and flexible price conditions. The global challenges of digitalization of the agroindustry are defined, and the importance of the network revolution is identified in the process of radical changes in the cost structure of agribusiness and the mechanism of profit formationThe importance of the formation of ecosystems of agricultural market participants for making effective digital decisions, cooperation and effective collaboration of entire digital platforms of market participants is substantiated. The key tools and methods of digital development of agro-industrial enterprises and the national agro-industrial complex as a whole are considered.It is determined that an important indicator of the development of digitalization of the agroindustry is the development of the global landscape of startups and technology companies, and the analysis of the investment volumes in the main digital projects in the agro-industrial complex is conducted. The optimal directions for successful digitalization in the agro-industrial complex have been specified, which should be considered in the form of a three-level development for agricultural holdings and a two-level development for relatively small agricultural producers.

The effect of digital technology on students' reading Behavior

The study discusses the effect that technology can have on a student's reading behavior. Where students now a day spend a lot of time with cell phones. Rapid advances in telecommunications technology, media and information technologies and the widespread development of global information infrastructure transform learning and reading patterns into global paradigms. It was this paradigm that became dominant and effectively became involved in the revolution of information networks, which would later determine the future of national welfare. Technology, however, remains the initial hypothesis before research is carried out. In this method is use qualitative method. Study using qualitative method provided a comprehensive and clear picture of the social situations studied, comparative events from one social to another or from one time to another; or be able to find patterns of connections between certain aspects and others, and be able to find hypotheses and theories. In a study that described the work done by 12 respondents. Based on Moleong, the aim of the writer is to examine how the student's behavior, habits, and perceptions of how technology affects reading habits for them. The purpose of this study is to know whether technology has a major impact on student reading behavior. The results of this study indicate that technology had a positive impact on the interest in reading students, but most students prefer reading nonfiction rather than reading lessons. And technology can has a negative impact on the student as student interest is distracted and they are sedated with the modern technological sophistication of gadgets.

The effect of digital technology on students' reading Behavior

The study discusses the effect that technology can have on a student's reading behavior. Where students now a day spend a lot of time with cell phones. Rapid advances in telecommunications technology, media and information technologies and the widespread development of global information infrastructure transform learning and reading patterns into global paradigms. It was this paradigm that became dominant and effectively became involved in the revolution of information networks, which would later determine the future of national welfare. Technology, however, remains the initial hypothesis before research is carried out. In this method is use qualitative method. Study using qualitative method provided a comprehensive and clear picture of the social situations studied, comparative events from one social to another or from one time to another; or be able to find patterns of connections between certain aspects and others, and be able to find hypotheses and theories. In a study that described the work done by 12 respondents. Based on Moleong, the aim of the writer is to examine how the student's behavior, habits, and perceptions of how technology affects reading habits for them. The purpose of this study is to know whether technology has a major impact on student reading behavior. The results of this study indicate that technology had a positive impact on the interest in reading students, but most students prefer reading nonfiction rather than reading lessons. And technology can has a negative impact on the student as student interest is distracted and they are sedated with the modern technological sophistication of gadgets.

Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks

Silicon photonics is promising for artificial neural networks computing owing to its superior interconnect bandwidth, low energy consumption and scalable fabrication. However, the lack of silicon-integrated and monitorable optical neurons limits its revolution in large-scale artificial neural networks. Here, we highlight nonlinear germanium-silicon photodiodes to construct on-chip optical neurons and a self-monitored all-optical neural network. With specifically engineered optical-to-optical and optical-to-electrical responses, the proposed neuron merges the all-optical activation and non-intrusive monitoring functions in a compact footprint of 4.3 × 8 μm2. Experimentally, a scalable three-layer photonic neural network enables in situ training and learning in object classification and semantic segmentation tasks. The performance of this neuron implemented in a deep-scale neural network is further confirmed via handwriting recognition, achieving a high accuracy of 97.3%. We believe this work will enable future large-scale photonic intelligent processors with more functionalities but simplified architecture.

An Efficient Method for Pricing Analysis Based on Neural Networks

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data.

The Chattering Mind: A Conceptual History of Everyday Talk

The Chattering Mind: A Conceptual History of Everyday Talk

To the absent reader, to those who are going to arrive

The essay engages in conversation with the work of Pedro Lemebel, and attempts to articulate through its close reading a reflection on the stakes of epistemological care, language, and translation in contemporary globalised academia. This reflection is organized as a response to a series of texts, and stages a conversation between them: Pedro Lemebel’s Canción para un niño boliviano que nunca vio la mar (2004), the letter “White Colleagues Listen! An open letter to UK Theatre, Dance and Performance Studies” by the Network Revolution or Nothing (2020), Pedro Lemebel’s manifesto Hablo por mi diferencia (1986). Engaging in particular with the idea of “aesthetic education” advanced by Gayatri Spivak and with the reflections on language and decolonization articulated by Silvia Rivera Cusicanqui, the essay meditates on the political capacity that teachers and scholars, working in globalised neoliberal academia, can build together to attend to the urgent task of vigorously questioning the system of epistemological thinking that make possible the identification of any marker of difference.

Activity of Opposition Telegram Channels and Behavioral Factor of Google Users as a Research Method for Analyzing the Protests in Belarus in 2020

The protests in the Republic of Belarus in August-November 2020 have been studied in article on the basis of data from Telegram Analytics and Google Trends, based on structural and functional analysis and a network approach, have been studied. A method of determining the cycles of protest activity is proposed, according to which Belarusian events can be divided into weekly periods. Specified protests are denoted as a network revolution - a state coup technology based on the concept of isolating the pillars of support for the political regime - he consistent paralysis of the states protection and management bodies, as well as the use of Internet resources for the mobilization, communication and coordination of protesters. The organizers of the network revolution have planned two options for the development of the state coup. The first one is the creation of a permanent hotbed of protests in the center of the capital and the gradual isolation of the pillars of support of the regime. The second one is holding a series of decentralized actions followed by consolidation and formation of a permanent hotbed of protests in the center of the capital and major cities. The experience of the events in Belarus showed that if the pillars of support for the regime are maintained, namely the bodies of state management and the institutions of internal protection of the Interior Ministry, the KGB, protests subside on the second week of protests.

Enhancing 5G SDN/NFV Edge with P4 Data Plane Programmability

The 5G network revolution will be enabled by deep integration of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to support multi-tenancy, per-user and per-application quality of service and experience. However, full softwarization and current SDN platforms may not be able to sustain the complexity and the heterogeneity of different requirements, for example, strict latency, jitter, high precision traffic and advanced monitoring. For such services, SDN/NFV needs to be boosted not only considering orchestration and control plane, but also data plane programmability. In this article, the potential of the P4 language is illustrated with the aim to show its disruptive novel functionalities at the data plane level currently not available in a SDN/NFV network, opening the way to new orchestration frameworks and enabling a novel autonomic and flexible network at the edge. Use cases, assessments and softwarized performance results are proposed and discussed in the edge and IoT scenario, targeting advanced traffic engineering, cyber security, multi-tenancy, 5G offloading, and telemetry, to demonstrate the feasibility of such an approach.

The Role of the Security Forces in Times of political Turbulence

The power structures are an indispensable tool, the state power system’s resource and a significant factor determining the relationship and outcome of the conflict between the government and society in a period of internal political turbulence. Periods of instability and uncertainty are traditionally accompanied by security officials’ presence in the bowels of power and participation in the struggle for it. At present, the world is faced with choosing one of several possible trajectories for further development. World globalisation and the network revolution increase an individual’s capabilities and importance, and a “man with a gun” a priori, in the historical process. Modern realities of international relations stimulate the existence and strengthening of power structures as state power instruments. Domestic challenges and events in several countries of the world, which actively declared themselves in 2020–2021, force them to rethink the possibility of using law enforcement agencies in resolving internal political conflict situations, as well as the need to conduct an analytical assessment of possible options for the actions of personnel associated with the use of armed violence during periods of intense political turbulence within states. The article substantiates the need for a prompt response of state authorities to changes in the country’s political situation. The author believes that the tendency to an increase in the importance of power structures, as a force exerting a decisive influence at the point of bifurcation on the choice of the direction of further development, requires not only awareness but adjustment of communication processes in the connection “state — power structures — civil society”.

Guest Editorial: 5G for Internet of Things

Tremendous efforts have been devoted to 5G and Internet of Things (IoT) over the last decade due to their potential applications, such as smart city, home robotics, and Augmented Reality (AR). 5G represents a complete revolution in mobile networks for accommodating the ever-growing demands of users, services and applications. These applications have drawn the attention and increased aspirations of researchers from the fields of computer vision, computer graphics, and machine vision.

A user collaboration privacy protection scheme with threshold scheme and smart contract

Recently, the revolution of 5G networks has promoted a wide application of wireless technology, and then followed with the prosperity of Location-Based Service (LBS). However, an intricate problem called privacy violation still restricts the development of this type of service. To preserve users’ privacy, some privacy preservation strategies had been proposed. As an efficient framework used for protecting personal privacy, users’ collaboration is mainly proposed for dealing with the issue of service bottleneck and the issue of attack focus. However, there are still several challenges far from being perfectly solved, such as the collusion attacks as well as the willingness of collaborative users. Therefore, to address these challenges, in this paper, based on the conception of information division and user’s incentive, a (t,n) smart contract privacy preservation scheme short for (t,n) SCPPS is proposed. In this scheme, the query information is encrypted and split by (t, n) threshold scheme, so the collaborative users will be difficult to learn anything about the initiator, and the collusion will be difficult to constitute the integral query without t users colluding with each other. On the other hand, the combination of (t, n) threshold scheme and smart contract further provide incentive competition, as just the earliest collaborative users that submit the partition information and get the corresponding feedback will obtain the incentive. At last, the security analyses were given, and then tests of users’ granularity and locations difference in the continuous query in the real environment. Furthermore, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme, and the experimental verification is executed using two different types of datasets. Thus, compared with other similar schemes, the results with brief explanations about reasons can further demonstrate the superiority of the proposed scheme.

An Improved Artificial Neural Network Model for Effective Diabetes Prediction

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data‐driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP‐SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP‐SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.

О развитии прикладной математики, искусственного интеллекта и компьютерных вычислений

A transition from the industrial to the post-industrial phase of the development of civilization takes place currently. In the traditional phase of development (until the twentieth century), the researchers and thinkers focus was on the nature study. In the industrial times (XX century) - the machines world was comprehended and developed. The most important result of this period was the creation of computers. Now the world is entering to the post-industrial phase, where the focus is on the person. This is changing applied mathematics, computer reality. There is a bifurcation in which a significant part of applied mathematics is becoming an industry. A number of promising innovative projects draw attention in this context. There is a transition from "continuous" to "discrete" mathematics, largely associated with the development of cryptography. Another feature of the moment is the neural network revolution in machine learning, which gave a new powerful impetus to the artificial intelligence (AI) development progress. It led to the widespread use of devices and systems that solve "intelligent" problems at the human level and above. The possibilities for solving very complex problems are limited by means that contradict the established views that thinking and reason are based on the soul, which has limitless possibilities. The strong AI (general AI, AGI) creating problem is not in building devices with magical capabilities, but in creating limited systems that can learn to solve many of the problems available. An important AGI property should be cooperation with people, taking into account civilizational norms. This property has not only a technical side, it depends on social relations. Sustainable development requires an agency approach to AGI. Modern applied mathematics has faced fundamental difficulties in complex problems. Multiple increases in the performance of computing systems did not lead to breakthrough results. As new paradigms, "analog" computing systems working with the use of new principles are neural networks and quantum computers. It is shown that these approaches, at best, can help to solve the "past tasks", and not the "future tasks" associated with the post-industrial phase of the development of civilization. The domestic applied mathematics efforts should be used to solve the central problems of the post-industrial society development in Russia using interdisciplinary approaches. This can make it possible to return to the number of countries with advanced technologies in the most important modern production areas. The alternative directions of the science development related to the human capabilities expansion are considered. The key one is related to AI, which can change reality. The article pays special attention to alternative approaches to AI development.

Applications of Deep Learning in Molecule Generation and Molecular Property Prediction.

Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the scope of a single Account. In this Account, we will focus on two key areas where deep learning has impacted molecular design: the prediction of molecular properties and the de novo generation of suggestions for new molecules.One of the most significant advances in the development of quantitative structure-activity relationships (QSARs) has come from the application of deep learning methods to the prediction of the biological activity and physical properties of molecules in drug discovery programs. Rather than employing the expert-derived chemical features typically used to build predictive models, researchers are now using deep learning to develop novel molecular representations. These representations, coupled with the ability of deep neural networks to uncover complex, nonlinear relationships, have led to state-of-the-art performance. While deep learning has changed the way that many researchers approach QSARs, it is not a panacea. As with any other machine learning task, the design of predictive models is dependent on the quality, quantity, and relevance of available data. Seemingly fundamental issues, such as optimal methods for creating a training set, are still open questions for the field. Another critical area that is still the subject of multiple research efforts is the development of methods for assessing the confidence in a model.Deep learning has also contributed to a renaissance in the application of de novo molecule generation. Rather than relying on manually defined heuristics, deep learning methods learn to generate new molecules based on sets of existing molecules. Techniques that were originally developed for areas such as image generation and language translation have been adapted to the generation of molecules. These deep learning methods have been coupled with the predictive models described above and are being used to generate new molecules with specific predicted biological activity profiles. While these generative algorithms appear promising, there have been only a few reports on the synthesis and testing of molecules based on designs proposed by generative models. The evaluation of the diversity, quality, and ultimate value of molecules produced by generative models is still an open question. While the field has produced a number of benchmarks, it has yet to agree on how one should ultimately assess molecules "invented" by an algorithm.

AI-Enabled Next-Generation Communication Networks: Intelligent Agent and AI Router

Nowadays, network users' demands for low latency services and individual data security grow rapidly. In this article, we conceive a new communication network architecture based on the concept of intelligent agents. The proposed intelligent agent serves as a virtual secretary of the corresponding user to request desired service from Internet companies or neighboring agents, which can reduce Internet surfing latency and prevent privacy leakage. Moreover, to improve the forwarding efficiency of data packages, a new mixed routing structure and the AI router are also introduced, to support various on-demand services with low latency. Overall, the proposed communication network architecture is expected to be more efficient than the existing one, and inspire readers to deliberate the revolution of future communications networks.

A Role of Routing, Transport and Security Mechanisms in Information Centric Network

Legacy networks which were discovered in 1970’s suited better for olden day’s communication model where the network is host centric, but nowadays expectations from communication networks is changing and it is shifting from host centric to data centric. To suit recent expectations there is need of revolution in communication networks; Information Centric Network (ICN) is a new kind of network architecture evolving for future internet. This article introduces ICN and its prominent features and reviews the work done post architectural approaches in the areas of routing, transport and security specifically.

Investigating an adequate level of modelling for retrofit decision-making: A case study of a British semi-detached house

This paper investigates what level of modelling (zoning or internal load scheduling) is required to support heating related retrofit decision-making. First, this paper tests the effect of thermal zoning by incrementally reducing the number of thermal zones from modelling every room as a separate zone to modelling the house as a single zone. Second, this paper examines the influence of internal load schedules (occupancy, lighting and equipment schedules) on prediction accuracy. Actual internal load schedules were derived from the smart meter data of 666 households collected by the Customer-Led Network Revolution project. Cluster analysis was applied to extract a set of prototypical schedules to capture major variations across all households. Last, this paper evaluates the effects of the zoning and internal load scheduling modelling assumptions in the context of thermal retrofit decision-making.For the specific parameters studied and the specific building design, the use of different zoning strategies and different internal load schedules yielded the same ranking of top retrofit options. For the specific climate and the baseline assumptions for the retrofits, different cluster schedules resulted in different magnitudes of energy savings, but the ranking of top retrofit options was not impacted by the choice of household internal load schedules. However, the actual internal load schedules affected the energy-saving potentials achievable by the same set of retrofit options. The case study highlights that the optimal set of retrofit options selected given the specific physical characteristics of a house is the same regardless of differences in the input of internal load schedules. However, it was found that energy-saving potentials achievable by the same retrofit option substantially vary according to the actual internal load schedules. This finding implies that energy retrofit policies can be tailored to target certain groups of households selected by clustering their actual energy use profiles to cost-effectively maximise energy savings from the domestic sector.