In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...) of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

Network Security Secure Network has now become a need of any organization. The security threats are increasing day by day and making high speed wired/wireless network and internet services, insecure and unreliable. The need is also induced in to the areas like defence, where secure and authenticated access of resources are the key issues related to information security. The security measures should be designed and provided, first a company should know its need of security on the different levels of the (...) organization and then it should be implemented for different levels. Security policies should be designed first before its implementation in such a way, so that future alteration and adoption can be acceptable and easily manageable. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...) about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water (...) quality monitoring and decision-making for stakeholders and policymakers. (shrink)

This paper presents a phenomenological analysis of shame in social networks. Initially, I examine Sartre’s (1956) account of the look and shame along with Dolezal’s (2017) reinterpretation. I then explore how shame is negotiated in online interactions arguing that, in social networking systems (SNSs), shame is banned. Since subjects are constantly visible when posting content, they tend to share material that minimizes the risk of shame’s thunderstruck. Yet, this shameless self-presentation raises complex phenomenological intricacies regarding personal identity and self-identification: (...) in SNSs individuals strive for self-completion in posts, attempting to solve their otherwise irresolvable self-individuation process. (shrink)

This chapter introduces empirical network analysis (ENA) as a toolbox to complement other methods in philosophy of science. It aims to provide a hands-on introduction to ENA for philosophers of science and to discuss the usefulness of ENA for addressing questions of interest to philosophy of science. We accompany our account by an in-depth consideration of two examples of ENA to reflect not only on the potentials but also on the challenges of ENA. The chapter concludes by outlining skills required (...) for concrete applications and pointing to several resources that can be consulted to acquire those skills. (shrink)

The increasing complexity of modern networks has amplified the challenges associated with ensuring robust and scalable security. With the rapid evolution of cyber threats, traditional methods of network security management are often inadequate, leading to inefficiencies and vulnerabilities. Automation has emerged as a transformative approach to streamline network operations, enhance security postures, and reduce the margin of human error. This study explores the integration of Ansible, a powerful open-source automation tool, into network security workflows to deliver a comprehensive framework (...) for secure network automation. This research begins by examining the limitations of conventional network security management, emphasizing the time-intensive and error-prone nature of manual configurations. It highlights how automation, specifically using Ansible, addresses these issues by enabling consistent, repeatable, and scalable processes. Ansible’s ability to seamlessly integrate with diverse networking devices and platforms makes it an ideal choice for automating complex network environments. The methodology involves the development of automated playbooks for various security tasks, including firewall rule management, vulnerability scanning, configuration compliance checks, and incident response. By leveraging Ansible's declarative language, these playbooks are designed to be user-friendly and adaptable, ensuring ease of deployment across networks of varying sizes and complexities. The study also incorporates real-world use cases to demonstrate the tool's effectiveness in mitigating security risks while significantly reducing administrative overhead. Furthermore, the paper evaluates the impact of automation on key performance metrics such as response time to security incidents, accuracy of configurations, and overall system uptime. The findings indicate that integrating Ansible into network security workflows not only enhances operational efficiency but also fortifies security by minimizing misconfigurations and improving threat response times. These results underscore the importance of adopting automation as a critical component of modern network security strategies. In addition to technical insights, the study addresses potential challenges, such as initial implementation costs, skill gaps, and the need for continuous updates to automation scripts to align with evolving security requirements. It provides recommendations for organizations to overcome these obstacles, including investing in employee training and adopting iterative implementation strategies. The research concludes by affirming the transformative potential of automation tools like Ansible in revolutionizing network security management. By reducing reliance on manual processes, organizations can focus their efforts on proactive threat hunting and strategic planning, thereby fostering a more resilient security infrastructure. This paper serves as a comprehensive guide for network administrators and IT professionals seeking to leverage Ansible for secure network automation, providing both theoretical insights and practical solutions to enhance network security in an increasingly dynamic cyber landscape. (shrink)

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in (...) order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a knowing community. And (...) another part of this thinking is centered around the new, and the newly empowered, learner, the member of the net generation, who is thinking and interacting in new ways. These trends combine to form what is sometimes called 'e-learning 2.0' - an approach to learning that is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather, as embedded in meaningful activities such as games or workflows. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel capabilities (...) of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

Neural networks and deep learning have significantly advanced the field of artificial intelligence, offering solutions to complex problems such as image recognition, natural language processing, and decision-making tasks. This paper explores the principles of neural networks, particularly deep learning models, their evolution, applications, challenges, and potential future directions. A comprehensive analysis of key algorithms, architectures, and advancements is provided, with an emphasis on the practical implications of deep learning in various domains. By understanding the foundations of neural (...) class='Hi'>networks, we can better address issues of scalability, interpretability, and computational efficiency. (shrink)

In this chapter, I consider three ‘promises’ of bringing phenomenology into dialogue with networked learning. First, a ‘conceptual promise’, which draws attention to conceptual resources in phenomenology that can inspire and inform how we understand, conceive of, and uncover experiences of participants in networked learning activities and environments. Second, a ‘methodological promise’, which outlines a variety of ways that phenomenological methodologies and concepts can be put to use in empirical research in networked learning. And third, a ‘critical promise’, which suggests (...) how work done in the realm of critical phenomenology is complementary to and useful for exploring the social justice and emancipatory aims of networked learning researchers. Through this framework, I reflect upon multiple ways in which phenomenology has and might continue to fruitfully inform and shape networked learning research. In doing so, I emphasise that deploying phenomenology in the context of networked learning not only enriches our understanding of networked learning but that phenomenology itself is enriched through testing, finetuning, and expanding its methods, concepts, and understanding, as well as unveiling its own limitations and constraints. (shrink)

Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description logic (...) as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.” . (shrink)

We consider how an epistemic network might self-assemble from the ritualization of the individual decisions of simple heterogeneous agents. In such evolved social networks, inquirers may be significantly more successful than they could be investigating nature on their own. The evolved network may also dramatically lower the epistemic risk faced by even the most talented inquirers. We consider networks that self-assemble in the context of both perfect and imperfect communication and compare the behaviour of inquirers in each. This (...) provides a step in bringing together two new and developing research programs, the theory of self-assembling games and the theory of network epistemology. (shrink)

This essay inquires into the possibility of extending Randall Collins' analysis (as it is presented in The Sociology of Philosophies) of the process of innovation within intellectual networks.

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of (...) three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Intelligent Tutoring Systems (ITS) has a wide influence on the exchange rate, education, health, training, and educational programs. In this paper we describe an intelligent tutoring system that helps student study computer networks. The current ITS provides intelligent presentation of educational content appropriate for students, such as the degree of knowledge, the desired level of detail, assessment, student level, and familiarity with the subject. Our Intelligent tutoring system was developed using ITSB authoring tool for building ITS. A preliminary evaluation (...) of the ITS was done by a group of students and teachers. The results were acceptable. (shrink)

Network Virtualization (NV) and Software-Defined Networking (SDN) are two pivotal innovations that transform the way networks are designed, managed, and scaled. SDN provides a centralized control layer to manage and optimize network resources, while Network Virtualization allows for the creation of multiple virtual networks over a shared physical infrastructure. Together, these technologies enable a more flexible, scalable, and efficient network infrastructure that can meet the demands of modern applications and services. This paper explores the synergy between SDN and (...) Network Virtualization, emphasizing how SDN enhances the capabilities of NV by providing centralized control, automation, and programmability. It also addresses the challenges of integrating these two technologies, such as network security, performance optimization, and the need for robust orchestration mechanisms. The paper concludes by highlighting the benefits of combining SDN and NV and suggests potential research directions for further exploration. (shrink)

This article brings together two research fields in applied ethics - namely, information ethics and business ethics- which deal with the ethical impact of information and communication technologies but that, so far, have remained largely independent. Its goal is to articulate and defend an informational approach to the conceptual foundation of business ethics, by using ideas and methods developed in information ethics, in view of the convergence of the two fields in an increasingly networked society.

Categorization and memory for specific items are fundamental processes that allow us to apply knowledge to novel stimuli. This study directly compares categorization and memory using delay match to category (DMC) and delay match to sample (DMS) tasks. In DMC participants view and categorize a stimulus, maintain the category across a delay, and at the probe phase view another stimulus and indicate whether it is in the same category or not. In DMS, a standard item working memory task, participants encode (...) and maintain a specific individual item, and at probe decide if the stimulus is an exact match or not. Constrained Principal Components Analysis was used to identify and compare activity within neural networks associated with these tasks, and we relate these networks to those that have been identified with resting state-fMRI. We found that two frontoparietal networks of particular interest. The first network included regions associated with the dorsal attention network and frontoparietal salience network; this network showed patterns of activity consistent with a role in rapid orienting to and processing of complex stimuli. The second uniquely involved regions of the frontoparietal central executive network; this network responded more slowly following each stimulus and showed a pattern of activity consistent with a general role in role in decision-making across tasks. Additional components were identified that were associated with visual, somatomotor and default mode networks. (shrink)

In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we can make more explicit (...) what kind of questions personalized network models can address in PCC, given their representational and explanatory boundaries, using perspectival reasoning. (shrink)

I explored the association between components constituting the basis for moral and optimal human functioning, i.e., moral reasoning, moral identity, empathy, and purpose, via network analysis. I employed factor scores instead of composite scores that most previous studies used for better accuracy in score estimation in this study. Then, I estimated the network structure among collected variables and centrality indicators. For additional information, the structure and indicators were compared between two groups, participants who engaged in civic activities highly versus lowly. (...) The results demonstrated significant intra- and inter-scale associations in the network. The network structure was invariant across the two groups. Despite the network invariance, I found that the global connectivity between and centrality indicators of examined factors were higher among the high civic engagement group in general. I discussed the implications of the findings in research on moral functioning based on moral psychology and virtue ethics. (shrink)

This research paper delves into the realm of Financial Network Analytics, unraveling the opportunities and challenges it presents in the domains of systemic risk management and investment analysis. The study navigates the intricate web of interconnected financial entities, employing advanced analytics techniques to uncover meaningful insights. Through a comprehensive exploration, the paper identifies opportunities for leveraging financial network data to enhance systemic risk detection mechanisms and refine investment strategies. Simultaneously, it addresses the inherent challenges, such as data complexity and dynamic (...) network structures, offering nuanced perspectives on their implications. The findings contribute to the evolving landscape of financial analytics, providing a foundation for the development of robust tools and methodologies that can aid financial institutions and investors in navigating the complexities of modern financial networks. (shrink)

Cyber espionage campaigns pose significant challenges to global security, exploiting vulnerabilities in network infrastructures. This research paper explores advanced network traffic analysis models tailored for detecting sophisticated cyber espionage operations. The study focuses on leveraging machine learning algorithms, anomaly detection systems, and hybrid threat detection frameworks to identify subtle yet malicious activities within network traffic. Through a review of research, this paper synthesizes key findings and outlines practical applications, offering a roadmap for enhancing cybersecurity frameworks. Findings highlight the efficacy of (...) deep learning and unsupervised models in mitigating espionage-related risks, paving the way for more robust detection systems. (shrink)

Depression is a common and devastating instance of ill-being which deserves an account. Moreover, the ill-being of depression is impacted by digital technology: some uses of digital technology increase such ill-being while other uses of digital technology increase well-being. So a good account of ill-being would explicate the antecedents of depressive symptoms and their relief, digitally and otherwise. This paper borrows a causal network account of well-being and applies it to ill-being, particularly depression. Causal networks are found to provide (...) a principled, coherent, intuitively plausible, and empirically adequate account of cases of depression in every-day and digital contexts. Causal network accounts of ill-being also offer philosophical, scientific, and practical utility. Insofar as other accounts of ill-being cannot offer these advantages, we should prefer causal network accounts of ill-being. (shrink)

A small consortium of philosophers has begun work on the implications of epistemic networks (Zollman 2008 and forthcoming; Grim 2006, 2007; Weisberg and Muldoon forthcoming), building on theoretical work in economics, computer science, and engineering (Bala and Goyal 1998, Kleinberg 2001; Amaral et. al., 2004) and on some experimental work in social psychology (Mason, Jones, and Goldstone, 2008). This paper outlines core philosophical results and extends those results to the specific question of thresholds. Epistemic maximization of certain types does (...) show clear threshold effects. Intriguingly, however, those effects appear to be importantly independent from more familiar threshold effects in networks. (shrink)

In this paper I discuss the ontological status of actants. Actants are argued as being the basic constituting entities of networks in the framework of Actor Network Theory (Latour, 2007). I introduce two problems concerning actants that have been pointed out by Collin (2010). The first problem concerns the explanatory role of actants. According to Collin, actants cannot play the role of explanans of networks and products of the same newtork at the same time, at pain of circularity. (...) The second problem is that if actants are, as suggested by Latour, fundamentally propertyless, then it is unclear how they combine into networks. This makes the nature of actants inexplicable. -/- I suggest that both problems rest on the assumption of a form of object ontology, i.e. the assumption that the ontological basis of reality consists in discrete individual entities that have intrinsic properties. I argue that the solution to this problem consists in the assumption of an ontology of relations, as suggested within the framework of Ontic Structural Realism (Ladyman & Ross, 2007). Ontic Structural Realism is a theory concerning the ontology of science that claims that scientific theories represent a reality consisting on only relation, and no individual entities. -/- Furthermore I argue that the employment of OSR can, at the price of little modification for both theories, solve both of the two problems identified by Collin concerning ANT. -/- Throughout the text I seek support for my claims by referring to examples of application of ANT to the context of networked learning. As I argue, the complexity of the phenomenon of networked learning gives us a convenient vantage point from which we can clearly understand many important aspects of both ANT and OSR. -/- While my proposal can be considered as an attempt to solve Collin's problems, it is also an experiment of reconciliation between analytic and constructivist philosophy of science. -/- In fact I point out that on the one hand Actor Network Theory and Ontic Structural Realism show an interesting number of points of agreement, such as the naturalistic character and the focus on relationality. On the other hand, I argue that all the intuitive discrepancies that originates from the Science and Technology Studies’ criticism against analytic philosophy of science are at a closer look only apparent. (shrink)

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In (...) particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks. (shrink)

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from (...) a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro‐societies that varied in their network structure. We contrasted three types of social networks: fully connected, small‐world, and scale‐free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small‐world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift). (shrink)

We propose a percolation based M2M networking architecture and its data transmission method. The proposed network architecture can be server free and router free, which allows us to operate routing efficiently with percolations based on six degrees of separation theory in small world network modeling. The data transmission can be divided into two phases routing and data transmission phase. In the routing phase, probe packets will be transmitted and forwarded in the network thus path selections are performed based on small (...) world strategy. In the second phase, the information will be encoded, say, with the fountain codes, and transmitted using the paths selected at the first phase. In such a way, an efficient routing and data transmission mechanism can be built, allowing us to construct a low cost, flexible and ubiquitous network. Such a networking architecture and data transmission can be used in many M2M communications, such as the stub network of internet of things, and deep space networking, and so on. Soujanya Ambala | Dr. Srinivas Ambala | Sreedhar Ambala "M2M Networking Architecture for Data Transmission and Routing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1 , December 2016. (shrink)

In the current era dominated by cloud computing, networking infrastructure serves as the backbone of digital operations. Among the top cloud service providers, Microsoft Azure offers a robust suite of networking solutions tailored to meet the evolving needs of modern businesses. This document aims to provide a comprehensive overview of Azure networking, examining its key components, deployment models, best practices, and practical applications. By exploring Azure Virtual Network, Load Balancer, VPN Gateway, ExpressRoute, Firewall, and other services in detail, the goal (...) is to equip organizations with the necessary knowledge and insights to effectively leverage Azure networking for seamless cloud connectivity and enhanced security. (shrink)

This draft investigates Genetic Networks as a special case with comparisons to other networks. The intention is to discover the mapping between generic and abstract network properties and specific case studies.

By 2016, telecom providers began embracing Network Function Virtualization (NFV) to replace proprietary hardware appliances with software-based services. When combined with Software-Defined Networking (SDN), NFV offered a flexible and programmable approach to service chaining and resource allocation. This research investigates the architectural integration of SDN and NFV for carrier-grade network services, focusing on orchestration, interoperability, and performance isolation. The study analyzes the role of management and orchestration (MANO) frameworks, such as ETSI NFV, and evaluates open-source implementations like OpenStack and OpenDaylight. (...) Through performance benchmarks and service deployment scenarios, the paper demonstrates how SDN/NFV convergence enhances agility, reduces operational costs, and meets SLA requirements in telecom environments. (shrink)

Latour’s actor-network ‘theory’ (ANT), and more generally Latour’s constructivist and relativistic work, has since long been debunked. (1) It does not make any sense, mixing all conceptual categories together (humans and non-humans, facts and moral prescriptions, science and politics); (2) nevertheless, it pretends to explain important issues such as our current environmental crisis and what to do to overcome it; (3) consequently, it can have extremely damaging political consequences. Latour’s ANT may perhaps be considered as a work of art but (...) certainly not as a work of science. Unfortunately, I find that Anders Blok and Casper Bruun Jensen’s attempt to salvage it is unconvincing and inherits the same problems. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are pivotal (...) for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

The article addresses the problem of how semantic information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for semantic information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising the target (...) semantic information t by default, is correctly satisfied by the information flow of correct answers provided by an informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an informational analysis of knowledge, according to which knowledge is accounted semantic information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained. (shrink)

: In the digital age, cybersecurity and network engineering are two integral disciplines that must operate in unison to safeguard information and ensure the resilience of modern systems. While network engineering focuses on designing, implementing, and maintaining infrastructure, cybersecurity emphasizes protecting this infrastructure and its data from threats. This research explores the intricate relationship between these domains, advocating for a unified approach to achieve optimal protection in an increasingly interconnected world. The study highlights the evolving threat landscape characterized by sophisticated (...) cyberattacks, including ransomware, distributed denial-of-service (DDoS) attacks, and advanced persistent threats (APTs). Traditional security measures often fail to address the dynamic nature of these threats, necessitating a deeper integration of cybersecurity principles into network design. By embedding security at every layer of the network architecture, organizations can create systems that are not only efficient but also inherently resilient to attacks. Central to this paper is the concept of security-by-design, a methodology that integrates cybersecurity considerations into the initial stages of network engineering. This approach ensures that vulnerabilities are minimized from the outset rather than being addressed reactively. Techniques such as zero-trust architecture, micro-segmentation, and software-defined networking (SDN) are discussed as pivotal strategies for achieving a secure network environment. Additionally, the role of artificial intelligence (AI) and machine learning (ML) in detecting anomalies and predicting potential breaches is examined, underscoring the importance of innovation in maintaining robust cybersecurity defenses. The research also explores the human factor, emphasizing the need for cross-disciplinary training that equips network engineers with cybersecurity expertise and vice versa. Collaborative frameworks, such as DevSecOps, are presented as effective means of fostering communication and cooperation between teams. These frameworks not only streamline operations but also enhance the overall security posture of organizations. Case studies from sectors such as healthcare, finance, and critical infrastructure provide practical insights into the successful integration of cybersecurity and network engineering. These examples illustrate how proactive measures have mitigated risks, minimized downtime, and ensured business continuity. However, the study also identifies challenges, including the high cost of implementation, skill shortages, and resistance to change within organizations. This research concludes that bridging the gap between cybersecurity and network engineering is no longer optional but a necessity in an era of relentless cyber threats. By adopting a holistic approach that prioritizes collaboration, innovation, and adaptability, organizations can build networks that are both secure and efficient. Future work should focus on developing standardized protocols and frameworks that facilitate seamless integration and address emerging challenges. This researchcontributes to the broader discourse on cybersecurity and network engineering by providing actionable insights and advocating for a paradigm shift that aligns these disciplines for optimal protection. The findings serve as a call to action for stakeholders to embrace a collaborative mindset, ensuring that the digital infrastructure of tomorrow is resilient, adaptive, and secure. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an average error (...) of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

The Generalized Empirical Method as outlined by Henman initially seems a cogent approach that should be adopted by cognitive neuroscientists. However, some weaknesses in the presumptions of this method in light of modern neuroscience research may challenge its validity. As I am currently working on mapping cerebral-cerebellar networks using fMRI, I am intrigued by the practical utility of the GEM in experimental work.

Abstract: Networking is one of the most important areas currently used for data transfer and enterprise management. It also includes the security aspect that enables us to protect our network to prevent hackers from accessing the organization's data. In this paper, we would like to learn what the network is and how it works. And what are the basics of the network since its emergence and know the mechanism of action components. After reading this paper - even if you do (...) not have a general background on networking - you will be able to manage your own network and be able to distribute and control your ip. (shrink)

In 2017, the widespread adoption of container orchestration platforms such as Kubernetes introduced new complexities in managing network communication across microservices. Unlike traditional virtual machines, containers demanded lightweight, dynamic, and scalable networking solutions. This research explores the challenges of virtual networking in containerized environments, including service discovery, overlay network performance, east-west traffic bottlenecks, and multi-host communication. It evaluates leading network plugins (e.g., Flannel, Calico, Weave) and their trade-offs in terms of latency, security, and scalability. The study proposes an adaptive networking (...) architecture that incorporates policy-based routing and service mesh integration to enhance microservice resilience and observability. (shrink)

—Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and Internet background. Most of the industries use wireless networks for communicating information and data, due to high cable cost. Since, the wireless networks are insecure, it is essential to secure the critical information and data during transmission. The data that transmitted is intercepted by eavesdropper can be predicted by secrecy capacity. The secrecy capacity is the difference between channel (...) capacity of main link and wiretap link of wireless transmission. When the secrecy capacity gets fades, then it is known that transmitted data is intercepted. In the event, of applying optimal sensor scheduling scheme a sensor with highest secrecy capacity is chosen and data is transmitted. It is followed with cooperative localization algorithm to enhance nodes, predict and maintain power of each sensor nodes. Through localization algorithm best sensor nodes are get predicted for transmission. Moreover, RC4 encryption and decryption algorithm is enhanced for data while transmission. This will enable protection to data to an extent. Also, an asymptotic intercept probability analysis is performed to provide an imminent into the impact of the sensor scheduling on the wireless security. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. (...) It involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

This study assessed administrative networking strategies and principals’ supervisory effectiveness in assessing teachers’ notes of lessons, teachers’ instructional delivery, students’ records, and non-academic activities in Cross River State, Nigeria. Three null hypotheses were formulated accordingly to direct the study. The study adopted a descriptive survey design. Census technique was adopted in selecting the entire population of 667 secondary school administrators in Cross River State. The instruments used for data collection were two set of questionnaires designed by the researchers including: Administrative (...) Networking Strategies Questionnaire (ANSQ)”, and “Principals’ Supervisory Effectiveness Questionnaire (PSEQ)” respectively. The reliability of the instruments was established through Cronbach Alpha, and reliability estimates of .89 and .92 were obtained for the two instruments respectively. The null hypotheses were all tested at .05 level of significance using Pearson Product Moment Correlation Analysis with the aid of SPSS v21. Findings from the study revealed that; there is a significant relationship between principals’ effective communication, school-community relationship, and teachers’ involvement in decision-making, with their supervisory effectiveness respectively. Based on the findings of the study, it was recommended amongst others that; secondary school principals should endeavour to communicate relevant ideas, messages, and information to both teaching and non-academic staff of the school; and ensure that appropriate feedback mechanisms are provided based on such information. (shrink)

It is common for social theorists to utilize the metaphors of ‘flow’, ‘fluidity’, and ‘liquidity’ in order to substantiate the ways in which speed and mobility form the basis for a new kind of information or network society. Yet rarely have these concepts been sufficiently theorized in order to establish their relevance or appropriateness. This article contends that the notion of flow as utilized in social theory is profoundly metaphysical in nature, and needs to be judged as such. Beginning with (...) a discussion of the accelerating timescape that characterizes the network society, it will then move on to examine three main issues with this ‘metaphysics of flux’. First, that the concept of flows unjustly privileges the process of becoming and, as a result, is unable to account for the materiality, substantiality, and agency of the objects being mobilized, and the contingency of their mediation. Second, that it posits the accelerating tendencies of capital as an ontological inevitability, thus discounting resistance to such forces. Finally, that it ignores the human faculty for reason and speculative thought in developing alternative means of political praxis. The solution, it will be argued, is not to abandon metaphysical accounts of the network society, but rather to challenge those accounts that, in exhibiting a crude empiricism, work to justify the status quo. (shrink)

A comparison of six alternative definitions of the term 'cellular pathway' against the background of ontological realism.

In this paper, we explain and showcase the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. Our use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, we show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discussion of vaccine safety on Twitter. Roughly half the (...) accounts engaging with this topic are pro-vaccine, while the other half are con-vaccine. We also show that these two camps rarely engage with one another, and that the con-vaccine camp has greater epistemic reach and receptivity than the pro-vaccine camp. In light of these findings, we question whether testimonial networks as they are currently constituted on popular fora such as Twitter are living up to their promise of delivering the wisdom of crowds. We conclude by pointing to directions for further research in digital social epistemology. (shrink)

Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better of results (...) of winnings or getting draw. (shrink)