E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. An (...) established validation and management system could face a single failure point and the system is prone to Distributed Denial of Service Attacks (DDoS), denial of service attacks (DoS), malware and other cyber attacks. The execution of a privacy preserving and a secure decentralized system is enabled by the block chain technology. Here any third-party organizations do not have any control over the transactions of the Government. With the help of block chain technology, new and existing data are encapsulated within ledger or blocks, which are evenly distributed through the network in an enduring and sustainable way. The privacy and security of information are improved with the help of block chain technology, where distribution and encryption of data are performed through the total network. This analytical paper maps out the analysis of the security in the e-government system, utilizing the block chain technology that provides privacy and security of information and thereby enhancing the trust among the public sector. Qualitative and theoretical analysis is made for the proposed topic and implications of privacy and security of the proposed system is made. (shrink)

This article explores the norms that govern regular users’ acts of sharing content on social networking sites. Many debates on how to counteract misinformation on Social Networking Sites focus on the epistemic norms of testimony, implicitly assuming that the users’ acts of sharing should fall under the same norms as those for posting original content. I challenge this assumption by proposing a non-epistemic interpretation of (mis) information sharing on social networking sites which I construe as infrastructures for forms of life (...) found online. Misinformation sharing belongs more in the realm of rumour spreading and gossiping rather than in the information-giving language games. However, the norms for sharing cannot be fixed in advance, as these emerge at the interaction between the platforms’ explicit rules, local norms established by user practices, and a meta-norm of sociality. This unpredictability does not leave us with a normative void as an important user responsibility still remains, namely that of making the context of the sharing gesture explicit. If users will clarify how their gestures of sharing are meant to be interpreted by others, they will implicitly assume responsibility for possible misunderstandings based on omissions, and the harms of shared misinformation can be diminished. (shrink)

Mark Pennington argues political systems should be decentralized in order to facilitate experimental learning about distributive justice. Pointing out the problems with Pennington's Hayekian formulation, I reframe his argument as an extension of the Millian idea of 'experiments in living.' However, the experimental case for decentralization is limited in several ways. Even if decentralization improves our knowledge about justice, it impedes the actual implementation of all conceptions of justice other than libertarianism. I conclude by arguing for the compatibility of (...) egalitarian redistribution with the epistemic virtues of markets pointed out by Hayek. (shrink)

The article examines the role of ethnic favoritism in maldistribution of national resources in Kenya and discusses two broad proposals for attacking such corruption. Evidence drawn from research in Kenya disproves the view of Chabal and Daloz, who argue that Africans prefer to distribute goods according to ethnic ties, and shows that frustration with the lack of alternatives to such a system, rather than enthusiasm for it, drives cooperation with corrupt maldistribution. One solution to the problem is to decentralize government (...) so that resources are retained locally. A second solution is to attack the culture of appropriation and push for a fair evaluation of needs and the equitable distribution of national resources to where they are needed most. Drawing on the ideas of Hannah Arendt, the article proposes a modified federalism where government is small enough to enlist the help and support of locals but powerful enough to provide funds to impoverished sectors of the country. (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)

The concept of distributed moral responsibility (DMR) has a long history. When it is understood as being entirely reducible to the sum of (some) human, individual and already morally loaded actions, then the allocation of DMR, and hence of praise and reward or blame and punishment, may be pragmatically difficult, but not conceptually problematic. However, in distributed environments, it is increasingly possible that a network of agents, some human, some artificial (e.g. a program) and some hybrid (e.g. a (...) group of people working as a team thanks to a software platform), may cause distributed moral actions (DMAs). These are morally good or evil (i.e. morally loaded) actions caused by local interactions that are in themselves neither good nor evil (morally neutral). In this article, I analyse DMRs that are due to DMAs, and argue in favour of the allocation, by default and overridably, of full moral responsibility (faultless responsibility) to all the nodes/agents in the network causally relevant for bringing about the DMA in question, independently of intentionality. The mechanism proposed is inspired by, and adapts, three concepts: back propagation from network theory, strict liability from jurisprudence and common knowledge from epistemic logic. (shrink)

We are quickly passing through the historical moment when people work in front of a single computer, dominated by a small CRT and focused on tasks involving only local information. Networked computers are becoming ubiquitous and are playing increasingly significant roles in our lives and in the basic infrastructure of science, business, and social interaction. For human-computer interaction o advance in the new millennium we need to better understand the emerging dynamic of interaction in which the focus task is no (...) longer confined to the desktop but reaches into a complex networked world of information and computer-mediated interactions. We think the theory of distributed cognition has a special role to play in understanding interactions between people and technologies, for its focus has always been on whole environments: what we really do in them and how we coordinate our activity in them. Distributed cognition provides a radical reorientation of how to think about designing and supporting human-computer interaction. As a theory it is specifically tailored to understanding interactions among people and technologies. In this article we propose distributed cognition as a new foundation for human-computer interaction, sketch an integrated research framework, and use selections from our earlier work to suggest how this framework can provide new opportunities in the design of digital work materials. (shrink)

As Distributed Denial of Service (DDoS) attacks evolve, accurately detecting these threats becomes essential to ensuring network stability. Traditional methods often face challenges in recognizing adaptive DDoS patterns and balancing detection accuracy with false positives. This paper presents a machine learning-based framework leveraging Gaussian Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and an Ensemble Random Forest classifier. Through an in-depth performance analysis using accuracy and AUC-ROC metrics, the hybrid model aims to provide a robust, scalable solution to (...) enhance network security. (shrink)

Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks, such as financial operations, administrative functions, and beyond. As AI agents may eventually delegate tasks among themselves to optimize efficiency, understanding the foundational principles of human value exchange could offer insights into how AI-driven economies might operate. Just as trust and value exchange are central to human interactions in open marketplaces, they may also be critical for enabling secure and efficient interactions among (...) AI agents. While cryptocurrencies could serve as the foundation for monetizing value exchange in a collaboration and delegation dynamic among AI agents, a critical question remains: how can these agents reliably determine whom to trust, and how can humans ensure meaningful oversight and control as an economy of AI agents scales and evolves? This paper is a call for a collective exploration of cryptoeconomic incentives, which can help design decentralized governance systems that allow AI agents to autonomously interact and exchange value while ensuring human oversight via progressive decentralization. Toward this end, I propose a research agenda to address the question of agent-to-agent trust using AgentBound Tokens (ABTs)—non-transferable, non-fungible tokens uniquely tied to individual AI agents, akin to Soulbound tokens for humans in Web3. By staking ABTs as collateral for autonomous actions within an agent-to-agent network via a proof-of-stake mechanism, agents may be incentivized towards ethical behavior, and penalties for misconduct are automatically enforced. (shrink)

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)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network with maximum (...) of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network with maximum (...) of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

This chapter explores the personal and professional obstacles faced by Humanities and Social Science scholars contemplating pre-publication of their scholarly work in an on-line network. Borrowing a theoretical framework from the radical educational theorist Ivan Illich, it also develops the idea that pre-publication networks offer higher education a bottom-up, decentralized alternative to business-modeled e-learning. If learners would only embrace this more anarchical medium, appreciating writing for pre-publication as a process of open-ended discovery rather than product delivery, then the (...) prospect of deinstitutionalizing e-learning could become an exciting new reality. The chapter’s methodology is a combination of normative-theoretical analysis and small sample (n=2) case study based on the author’s personal and professional experiences. While the generalizability (or external validity) of the conclusions is limited, the author hopes to motivate further inquiry into the connection between on-line pre-publication and e-learning. (shrink)

The study aimed to identify the reality of the use of social networks in the technical colleges in Palestine, where the variables of social networks were included. The analytical descriptive method was used in the study. A questionnaire consisting of (12) items was randomly distributed to college workers Technology in the Gaza Strip. The sample of the study consisted of (205) employees of these colleges. The response rate was 74.5%. The results showed a high degree of approval (...) for the dimensions of the social networks and a relative weight (74.15%) according to the perspective of the employees of the technical colleges in the Gaza Strip. The results of the study showed that there is a high level of social networking areas (site management and Website Content) in the technical colleges in the Gaza Strip. The field of site management ranked first with a relative weight of 74.91%, and second and last (Content Site) with a relative weight (73.38%). The results showed that there were differences between colleges in the use of social networks where the results showed that the most common colleges used these networks (UCAS) and the least used is (GTC). The results showed no differences between male and female employees in the use of social networks in technical colleges. The researchers suggest a number of recommendations, including: the need to raise awareness of the importance of Facebook and other social networking sites, through the holding of courses for employees in technical colleges, and to identify the ways to optimize the use of such sites, and the benefits of this use, and reflected positively on technical colleges. And the adoption of dealing with the various social networking sites as a reality, and the Palestinian and Arab technical colleges, use them in accordance with the objectives of technical colleges. Advise the Department of Technical Colleges to devote time to their presence on social networks to follow the public and respond to their queries. There is a need for the attention of decision-makers in technical colleges in social sites, because they are considered an important and effective means of communication, and the link between beneficiaries and decision-makers. There is a need to promote the use of modern electronic means of work and the need to increase the link of customers to the college through electronic services. (shrink)

Alternative Food Networks (AFNs), which include local food and Fair Trade, work to mitigate some of the many shortcomings of mainstream food systems. If AFNs have the potential to make the world’s food systems more just and sustainable (and otherwise virtuous) then we may have good reasons to scale them up. Unfortunately, it may not be possible to increase the market share of AFNs while preserving their current forms. Among other reasons, this is because there are limits to both (...) the productive capacities of small owner-operated farms and to the distribution capacities of Farmers Markets and Community Supported Agriculture (CSA). These limits tell in favor of AFN partnerships with larger producers and distributors. But some advocates of AFNs have worried that these partnerships would sacrifice too much. (shrink)

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old (...) Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

In order to understand the transmission of a disease across a population we will have to understand not only the dynamics of contact infection but the transfer of health-care beliefs and resulting health-care behaviors across that population. This paper is a first step in that direction, focusing on the contrasting role of linkage or isolation between sub-networks in (a) contact infection and (b) belief transfer. Using both analytical tools and agent-based simulations we show that it is the structure of (...) a network that is primary for predicting contact infection—whether the networks or sub-networks at issue are distributed ring networks or total networks (hubs, wheels, small world, random, or scale-free for example). Measured in terms of time to total infection, degree of linkage between sub-networks plays a minor role. The case of belief is importantly different. Using a simplified model of belief reinforcement, and measuring belief transfer in terms of time to community consensus, we show that degree of linkage between sub-networks plays a major role in social communication of beliefs. Here, in contrast to the case of contract infection, network type turns out to be of relatively minor importance. What you believe travels differently. In a final section we show that the pattern of belief transfer exhibits a classic power law regardless of the type of network involved. (shrink)

We present a model of the distribution of labour in science. Such models tend to rely on the mechanism of the invisible hand . Our analysis starts from the necessity of standards in distributed processes and the possibility of multiple standards in science. Invisible hand models turn out to have only limited scope because they are restricted to describing the atypical single-standard case. Our model is a generalisation of these models to J standards; single-standard models such as Kitcher are (...) a limiting case. We introduce and formalise this model, demonstrate its dynamics and conclude that the conclusions commonly derived from invisible hand models about the distribution of labour in science are not robust against changes in the number of standards. (shrink)

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows its (...) assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has an accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naïve Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution. (shrink)

Humans and artificial intelligences (AI) will increasingly participate digitally and physically in conflicts yet there is a lack of trusted communications across agents and platforms. For example, humans in disasters and conflict already use messaging and social media to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. AI may reduce the ‘fog-of-war’ and improve outcomes, however current AI implementations are often brittle, have a narrow scope of application and wide ethical risks. Meanwhile, human error (...) causes significant civilian harms even by combatants committed to complying with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and better deliver humanitarian aid to those who need it. However, to be successful, these systems must be trusted by humans and their information systems, overcoming flawed information flows in conflict and disaster zones that continue to be marked by intermittent communications, poor situation awareness, mistrust and human errors. In this paper, we consider the integration of a communications protocol (the ‘Whiteflag protocol’), distributed ledger technology, and information fusion with artificial intelligence (AI), to improve conflict communications called “Protected Assurance Understanding Situation & Entities” PAUSE. Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure; humanitarian signals and status updates for humans and machines in conflicts. Trust-based information fusion provides resource-efficient use of diverse data sources to increase the reliability of reports. AI can be used to catch human mistakes and complement human decision making, while human judgment can direct and override AI recommendations. We examine several case studies for the integration of these technologies into a trusted human-AI network for humanitarian benefit including mapping a conflict zone with civilians and combatants in real time, preparation to avoid incidents and using the network to manage misinformation. (shrink)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by (...) modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by (...) modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

The book is thought-provoking and informative, wide in scope while also being technically detailed, and still relevant to modem AI [at least as of 1998, the time of writing this review, but probably also at the time of posting this entry here, 2023] even though it was published in 1989. This relevance lies mainly in the book’s advocacy of distributed computation at multiple levels of description, its combining of neural networks and other techniques, its emphasis on the interplay (...) between action and perception, and its particular approach to natural language processing. The criticisms I have occasionally made above are relatively minor, and one of the major shortcomings of schema theory at least at the time of the book’s publication -- namely the lack of a detailed, preferably neural, mechanism for schema instantiation -- is one of which Arbib is well aware. The book is well produced, for the most part clearly written, and has useful summaries at the beginning of each chapter. Both general readers and AI researchers (mainly but not exclusively those working on robotics, vision and neural networks) could benefit from reading the book. (shrink)

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is (...) a combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to (...) raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is (...) a combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay. (shrink)

Recreational Drugs European Network (ReDNet) project aims to use the Psychonaut Web Mapping Project database (Psychonaut Web Mapping Group, 2009) containing novel psychoactive compounds usually not mentioned in the scientific literature and thus unknown to clinicians as a unique source of information. The database will be used to develop an integrated ICT prevention approach targeted at vulnerable individuals and focused on novel synthetic and herbal compounds and combinations. Particular care will be taken in keeping the health professionals working directly with (...) young people showing problematic behaviors regularly updated in terms of novel compounds and combinations as well. A user-friendly project website will be developed aimed primarily at delivering the information/prevention approaches, but will also be a way of communicating with project partners and relevant stakeholders (e.g. thematic forum facilities, instant messages, blogs, video chat, wikiblog, newsletters distributed via mailing list). The website will support various ICT prevention tools, including an SMS alert service. Different areas and sections will be aimed specifically at the different target groups. -/- . (shrink)

Cognitive twins, digital replicas of cognitive processes, have emerged as a transformative approach in artificial intelligence and human-machine collaboration. This paper presents a framework for developing a cognitive twin by integrating a Distributed Cognitive System (DCS) with Evolutionary Strategies (ES). The DCS enables decentralized knowledge processing, while ES optimizes learning and adaptation over time. Our approach is evaluated on real-world datasets, demonstrating its efficiency in cognitive modeling and decision-making. Results highlight improvements in adaptability, scalability, and accuracy compared to (...) traditional AI models. (shrink)

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must (...) thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of Things (...) ecosystem. Our approach is to improve Smart Environments' security and awareness of potential threats. Two IoT gateways were used to examine the effectiveness and performance of a deep neural network (DNN) model. The results were promising: the model caused an average increase of less than 30 kb/s in network bandwidth and a mere 2% rise in CPU usage. Additionally, memory and power consumption were minimal, with 0.42 GB and 0.2 GB of memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device. The machine learning models achieved nearly 93% detection accuracy and a 92% F1 score on the datasets used. Our framework demonstrates an effective and efficient method for detecting malware and attacks in Smart Environments. (shrink)

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must (...) thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

Purpose – The internet creates ample opportunities to start a mobile social commerce business. The literature confirms the issue of customer trust for social commerce businesses is a challenge that must be addressed. Hence, this study aims to examine the antecedents of trust in mobile social commerce by applying linear and non-linear relationships based on partial least squares structural equation modeling and an artificial neural network model. -/- Design/methodology/approach – This study applied a non-linear artificial neural network approach to provide (...) a further understanding of the determinants of trust in mobile social commerce based on a non-linear and non-compensatory model. Besides, a questionnaire was distributed to 340 social commerce customers in Malaysia. -/- Findings – The conceptual framework for investigating trust in mobile social commerce has various advantages and contributions to predicting consumer behaviour. The results of the study showed there is a positive and significant relationship between social support, presence and unified theory of acceptance and use of technology2 (UTAUT2). In addition, UTAUT2 has fully mediated the relationship between social support, presence and trust in social commerce. Finally, the results concluded the relationship between UTAUT2 and trust in social commerce would be stronger when the diffusion of innovation and innovation resistance is high and low, respectively. -/- Research limitations/implications – The current study provides a novel perspective on how customers can trust social m-commerce to provide real solutions to managers of encouraging e-marketing among consumers. -/- Practical implications – This paper shows how businesses can develop trust in social m-commerce in Malaysian markets. The findings of this study probably could be extended to other businesses in Asia or other countries. Because trust in social e-commerce has a dynamic role in consumer behaviour and intention to purchase. -/- Originality/value – This study provided a new perspective on mobile social commerce and paid more attention to an investigation of such emerging commerce. The originality of this study is embodied by investigating an integrated model that included different theories that presented new directions of trust in mobile social commerce through social and behavioural determinants. (shrink)

The article proposes a scientific and methodological approach to strategising the development of a manufacturing enterprise in a decentralised environment based on the principle of feedback. This approach involves taking into account the direct and feedback indicators of the enterprise, aimed at increasing its competitiveness and achieving sustainable development goals. The authors propose to assess the level of development of the production enterprise in decentralisation, taking into account the multiplier effect of interaction with the united territorial community on the basis (...) of the target approach (indicators are distributed according to the goals of the enterprise), taking into account the main directions of sustainable development component, each of which has its own system of evaluation indicators. The proposed methodological approach to assessing the level of development of a production enterprise in decentralisation allowed to assess the development of the enterprise in accordance with its qualitative and quantitative characteristics in each of the areas of sustainable development (social, economic, environmental, budget), taking into account their interaction levels. A scientific and methodological approach to modelling the management system of the dynamic state of the production enterprise in terms of decentralisation, which provides for the formation of a model of development of the production enterprise in cooperation with OTG on indicators of its development. The model of management of development of the industrial enterprise in the conditions of decentralisation for LLC TIS, which is located in the territory of the Vizyr UTC of the Odessa region is constructed. (shrink)

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must (...) thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. (shrink)

Create a novel network model for mobile ad hoc network (MANET) nodes and actors in wireless sensor networks to collaborate on event processing. There are two stages in the development of distributed algorithms: setup and negotiation. The first uses weighted proportional max-min fairness to initially allocate MANET nodes across event zones, whereas the latter uses a market-based method to re-distribute the number of MANET nodes based on existing and new events. A detection technique for malicious packet dropping attacks (...) in MANETs. The mechanism of the suggested approach is Collaborative Convolutional Neural Network (CCNN), which is based on the reputation value computed for that node by its neighbours. A node's reputation value is determined by its network packet forwarding behaviour. The reputation information is collected, saved, and transferred between nodes before being calculated under various scenarios. A network simulator was used to test the proposed protocol. The simulation results demonstrate the effectiveness of its performance. Even in the presence of cryptographic procedures, our method incurs negligible network bandwidth and latency costs. Moreover, we demonstrate that the protection is still effective in the presence of misbehaving nodes and routing changes caused by mobility. While further research is needed to thoroughly evaluate our method, we feel that the concept of collaborative security in MANETs is a potential future area. (shrink)

An ad hoc network is comprised of mobile hosts without any wired infrastructure support. Multipath routing allows the establishment of multiple paths between a source and a destination. It distributes traffic among multiple paths instead of routing all the traffics along a single path. In this paper, we propose a new multipath routing protocol that uses all discovered paths simultaneously for transmitting data, by using this approach data packets are balanced over discovered paths and energy consumption is distributed across (...) many nodes through network. (shrink)

These days, Thin-client devices are continuously accessing the Internet to perform/receive diversity of services in the cloud. However these devices might either has lack in their capacity (e.g., processing, CPU, memory, storage, battery, resource allocation, etc) or in their network resources which is not sufficient to meet users satisfaction in using Thin-client services. Furthermore, transferring big size of Big Data over the network to centralized server might burden the network, cause poor quality of services, cause long respond delay, and inefficient (...) use of network resources. To solve this issue, Thinclient devices such as smart mobile device should be connected to edge computing which is a localized near to user location and more powerful to perform computing or network resources. In this paper, we introduce a new method that constructs its architecture on Thin-client -edge computing collaboration. Furthermore, present our new strategy for optimizing big data distribution in cloud computing. Moreover, we propose algorithm to allocate resources to meet Service Level Agreement (SLA) and Quality of Service (QoS) requirements. Our simulation result shows that our proposed approach can improve resource allocation efficiently and shows better performance than other existing methods. (shrink)

This project, titled Bird Species Identification Using Deep Learning, aims to develop a robust system that can identify bird species from images with high precision. The core of this project involves training a CNN model on a diverse dataset of bird images. This dataset includes species from various geographical locations and environments, capturing a wide range of appearances, postures, and behaviors. By preprocessing and augmenting the dataset, the model is designed to handle challenges such as variations in lighting, background noise, (...) and partial occlusions. The implementation of this project has numerous practical applications. For instance, it can be integrated into mobile or web-based platforms to provide real-time bird identification tools for researchers, birdwatchers, and environmental agencies. It can also be utilized in wildlife monitoring programs to track species distribution and migration patterns. Furthermore, the system can support educational initiatives, allowing individuals to learn about bird species and their significance in the ecosystem. (shrink)

In this chapter, I systematically distinguish a variety of ways to rela- tionalize economics and focus on a certain approach to relationalizing normative economics in the light of communal values salient in the African philosophical tradi- tion. I start by distinguishing four major ways to relationalize empirical economics, viz., in terms of its ontologies, methods, explanations, and predictions and also three major ways to relationalize normative economics, with regard to means taken towards ends, decision-procedures used to specify ends, and ends (...) themselves. Then, in the remainder of the chapter, I address what would be involved in relationalizing the ends of economic choices, given ideals of communal relationship characteristically prized by sub-Saharan philosophers, particularly southern African adherents to ubuntu. I advance communal answers to the two large questions of what an economy should distribute and how to engage with stakeholders, and I suggest that the implications will be found plausible by many open-minded enquirers around the world. Although this chapter is a work of ethical philosophy, it is meant to be of broad interest to scholars in economics. (shrink)

rd species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising (...) diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. (shrink)

Bird species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks.

1. Do models formulated in programming languages use explicit rules where connectionist models do not? 2. Are rules as found in programming languages hard, precise, and exceptionless, where connectionist rules are not? 3. Do connectionist models use rules operating on distributed representations where models formulated in programming languages do not? 4. Do connectionist models fail to use structure sensitive rules of the sort found in "classical" computer architectures? In this chapter we argue that the answer to each of these (...) questions is negative. (shrink)

issues in real-time, allowing authorities to address concerns swiftly. Additionally, the integration of IoT-based monitoring, utilizing ESP32/ARDUINO devices, will enable the collection of real-time data, providing alerts on water supply status and system malfunctions. The technology stack comprises JavaScript, PHP, and PostgreSQL with PostGIS for spatial data management, emphasizing secure, scalable data handling. The platform is designed for continuous improvement through user feedback, ensuring it evolves to meet the dynamic needs of the Jal Jeevan Mission. Through innovative technology and a (...) user-centric approach, this project aims to support equitable access to safe water across rural areas. (shrink)

Since at least 2016, many have worried that social media enables authoritarians to meddle in democratic politics. The concern is that trolls and bots amplify deceptive content. In this chapter I argue that these tactics have a more insidious anti-democratic purpose. Lies implanted in democratic discourse by authoritarians are often intended to be caught. Their primary goal is not to successfully deceive, but rather to undermine the democratic value of testimony. In well-functioning democracies, our mutual reliance on testimony also generates (...) a distinctively democratic value: decentralized testimonial networks evade control by the state or powerful actors. The deliberate exposure of deception in democratic testimonial networks undermines this value by implicating citizens in their own epistemic corruption, weakening the resilience of democratic society against authoritarian pressure. In this chapter I illustrate that danger through a close reading of recent Russian social media interference operations, showing both their epistemic underpinnings and their ongoing political threat. (shrink)

Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical tests that comparative (...) psychologists use to determine whether a behavior was generated by a cognitive or a non-cognitive process. Cognition should be understood as the natural kind of psychological process that non-accidentally exhibits the properties assessed by these tests (as well as others we have not yet discovered). Finally, I review two plausible neural accounts of cognition's underlying mechanisms?one based in localization of function to particular brain regions and another based in the more recent distributed networks approach to neuroscience?which would explain why these properties non-accidentally cluster. While this notion of cognition may be useful for a number of debates, I here focus on its application to a recent crisis over the distinction between cognition and association in comparative psychology. (shrink)

The key assumption behind evolutionary epistemology is that animals are active learners or ‘knowers’. In the present study, I updated the concept of natural learning, developed by Henry Plotkin and John Odling-Smee, by expanding it from the animal-only territory to the biosphere-as-a-whole territory. In the new interpretation of natural learning the concept of biological information, guided by Peter Corning’s concept of “control information”, becomes the ‘glue’ holding the organism–environment interactions together. The control information guides biological systems, from bacteria to ecosystems, (...) in the process of natural learning executed by the universal algorithm. This algorithm, summarized by the acronym IGPT (information-gain-process-translate) incorporates natural cognitive methods including sensing/perception, memory, communication, and decision-making. Finally, the biosphere becomes the distributed network of communicative interactions between biological systems termed the interactome. The concept of interactome is based on Gregory Bateson’s natural epistemology known as the “ecology of mind”. Mimicking Bateson’s approach, the interactome may also be designated “physiology of mind”—the principle behind regulating the biosphere homeostasis. (shrink)

In independent renewable energy systems (RESs), one of the primary concerns needing to be addressed is the maintaining of power balances between supplies and requirements that are cost-optimized in residences linked to these systems. The amount of power generated through RESs has substantially risen, with solar and wind being the two primary sources in RESs. In modern power systems, small-scale distributed networks are growing at a rapid pace and distributed generation (DG) plays an important role. Micro grids (...) are very recent additions to electrical infrastructures. Power management is primarily required for smooth operation, maintaining consistency, and robustness, as well as controlling the actual and reactive power of independent DG. However, the batteries are expensive; moreover, during the charging and discharging process, huge amounts of power are lost, characterizing important problems which have to be averted. This paper introduces the weighted particle swarm optimization (WPSO) method for controlling energy systems and grid hybrid energy systems that comprise photovoltaic (PV), wind turbine, batteries, and diesel generators. By maximizing the power derived from RES and reducing battery power usage, energy is preserved, and the cost of energy consumption (energy of diesel) is reduced. Meteorological data from Spain were used in this study’s simulations. The method depends on the data forecast of renewable energy one day in advance and the everyday load power consumption profile. The results of the simulation show that WPSO outperforms existing algorithms in terms of energies, costs, and battery lives. (shrink)

Bitcoin, as a decentralized digital currency, has undergone extreme price fluctuations over the years. Predicting its future price presents a significant challenge due to its volatile nature and susceptibility to various external factors, including market sentiment, regulations, and technological developments. This research aims to build an advanced forecasting model to predict Bitcoin’s price movements accurately. We leverage historical price data and apply cutting-edge machine learning techniques, including Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM). By comparing (...) these methods with existing models, we evaluate their performance in predicting Bitcoin’s future prices, which is vital for traders, investors, and financial institutions in making informed decisions. (shrink)

Title - Gender in distributed cognitive systems: Possible conceptualizations. Abstract - There is a mismatch between social and biological approaches in the studies on sex and gender. Neurofeminist researchers critically examine gendered impacts of research in neuroscience and cognitive science, as well as develop more adequate and gender‑appropriate neuroscientific studies. However, they still seem to be focused on the brain and its relationship with the environment. Moreover, there are a little ‘science‑phobic’ feminist approaches based on actor‑network theory, and social (...) science and technology studies. In this context, I would like to suggest another account of gender. My account is not centered on individual selves, minds or brains, but it is based on concept of distributed cognitive system and cultural ecosystem developed in the cognitive science. The potential integrational role of the approach seems worthy of attention. On the one hand, there are no contradictions between more important assumptions of distributed cognition theory and social studies. On the other hand, it is not necessarily limited to human beings and their minds. I pay particular attention to distinction between the distributed cognition theory and the extended mind theory. Ones of the most important elements of the ‘gender‑sensitive’ distributed system are artifacts and their affordances, most of all — cultural and canonical ones. In this light, the gender relations seem not only distributed, but also able to be designed. (shrink)