Phishing, a prevalent form of social engineering attack, continues to threaten enterprises by exploiting human vulnerabilities and targeting sensitive information. With the increasing sophistication of phishing schemes, traditional detection methods often fall short in identifying and mitigating these threats. As attackers employ advanced techniques, such as highly personalized spear-phishing emails and malicious links, enterprises require innovative solutions to safeguard their digital ecosystems. This research explores the application of artificial intelligence (AI) in enhancing phishing detection and response, with a specific (...) focus on addressing advanced social engineering tactics. AIpowered phishing detection systems utilize machine learning (ML) and natural language processing (NLP) to analyze communication patterns, detect anomalies, and identify deceptive content in real-time. Unlike conventional rule-based systems, AI approaches adapt dynamically to evolving attack vectors, offering a robust defense against both known and emerging threats. These systems can scrutinize email headers, content, URLs, and attachments to uncover subtle indicators of malicious intent. By leveraging historical datasets and predictive analytics, AI models identify patterns associated with phishing, such as unusual sender behavior, suspicious linguistic cues, and mismatched domain information. This study examines the critical components of AI-driven phishing detection, including feature extraction, algorithm selection, and system deployment within enterprise environments. It highlights the role of NLP in understanding the contextual and linguistic subtleties of phishing messages and how advanced ML models, such as decision trees, support vector machines, and deep neural networks, enhance detection accuracy. -/- Additionally, the research delves into the challenges of implementing AI in phishing detection, such as ensuring data privacy, mitigating adversarial attacks on AI models, and fostering trust through explainable AI. Real-world case studies are analyzed to demonstrate the practical effectiveness of AI-powered phishing detection systems. These examples showcase significant reductions in phishing incidents, improved detection times, and the ability to thwart highly targeted attacks, such as business email compromise (BEC) schemes. The study also emphasizes the importance of integrating AI solutions with existing cybersecurity frameworks and highlights the role of human oversight in complementing AI capabilities. While AI offers promising advancements, its success depends on robust training datasets, continuous model optimization, and alignment with enterprise security goals. The paper concludes by presenting best practices for deploying AI-based phishing detection, including regular model updates, employee awareness programs, and multi-layered security strategies. By leveraging AI, enterprises can proactively defend against advanced social engineering attacks, safeguarding their systems, data, and stakeholders from evolving cyber threats. This research contributes to the growing field of AI applications in cybersecurity, offering actionable insights for organizations seeking to enhance their resilience against sophisticated phishing campaigns. (shrink)

The rapid adoption of cloud computing has revolutionized the way organizations operate, offering unparalleled flexibility, scalability, and efficiency. However, it also introduces a new set of vulnerabilities and security challenges. This manuscript explores the integration of artificial intelligence (AI) in cybersecurity solutions to address these cloud vulnerabilities. By examining the current landscape, AI methodologies, and practical implementation strategies, we aim to provide a roadmap for enhancing cloud security through AI-powered solutions. -/- .

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, network (...) security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, network (...) security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

: The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems (...) to identify potential cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

Small and medium-sized enterprises (SMEs) face unique challenges in managing their business processes effectively due to limited resources and the need for scalability. Enterprise Resource Planning (ERP) systems, such as SAP’s next-generation solutions, offer SMEs the tools they need to streamline operations, improve efficiency, and foster growth. SAP has developed tailored ERP systems like SAP Business One and SAP S/4HANA Cloud for SMEs, designed to cater specifically to the needs of smaller businesses by providing flexibility, affordability, and ease (...) of integration. These solutions empower SMEs to digitize their operations, improve decision-making, and compete with larger enterprises on a more even playing field. This paper explores SAP's next-generation ERP solutions for SMEs, examining their features, benefits, challenges, and the impact they can have on the future of small and medium-sized businesses. (shrink)

The rapid advancements in cloud computing are transforming enterprise solutions across industries. Among the most promising innovations is quantum computing, which holds the potential to revolutionize how businesses process data, optimize systems, and solve complex problems. Microsoft Azure, a leader in cloud infrastructure, has integrated quantum computing through Azure Quantum to offer scalable quantum solutions. This paper explores the impact of Azure's quantum computing capabilities on enterprise solutions, focusing on scalability, problem-solving efficiency, and future-proofing business (...) operations. By examining Azure’s quantum computing tools, quantum algorithms, and their applications in real-world scenarios, the paper highlights how enterprises can leverage this technology to address computational challenges, enhance performance, and maintain competitive advantage in an increasingly data-driven world. The analysis also addresses the potential hurdles and the importance of preparing cloud infrastructures for the quantum computing era. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems to (...) identify potential cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

The digitization process has brought new opportunities in insurance industry operations and innovations but has also revealed major weaknesses. Since more and more actual insurers use Guidewire to handle claims, policies, and customer data, insurers become targets for cyber threats that target valuable information. The framework of Guidewire, along with cloud computing integrated API and third-party tools, is laden with numerous exposure points. These security gaps are utilized to execute phishing, spread malware and gain unauthorized access to customers and operational (...) data. The profound changes in this environment require an evolution of concepts and methods dedicated to cybersecurity from the traditional, post-incident response to comprehensive, targeted strategies for cybersecurity that are appropriate for the insurance sector. To explore these challenges, this paper looks at current cybersecurity frameworks and methods, specifically in the context of Guidewire. The gaps within the existing system are thus determined through the interaction of empirical analysis and real-life scenarios to arm the research with the ability to formulate a solution to fill the gaps. Including threat-detection suites safe system design, the study aims at the antecedents of building protection-based methodologies that provide real-time protection and cope with new forms of cyber threats. Citing this strategic angle, it becomes possible to corroborate the reality that technology development in the insurance industry should not be pursued at the peril of strong security systems in place to support the new breed of insurance systems. (shrink)

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses (...) by leveraging machine learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies. (shrink)

The human factor in information systems is a large vulnerability when implementing cybersecurity, and many approaches, including technical and policy driven solutions, seek to mitigate this vulnerability. Decisions to apply technical or policy solutions must consider how an individual’s values and moral stance influence their responses to these implementations. Our research aims to evaluate how individuals prioritise different ethical principles when making cybersecurity sensitive decisions and how much perceived choice they have when doing so. Further, we (...) sought to use participants’ responses to cybersecurity scenarios to create profiles that describe their values and individual factors including personality. Participants (n = 193) in our study responded to five different ethically sensitive cybersecurity scenarios in random order, selecting their action in that scenario and rating and ranking of the ethical principles (i.e., Beneficence, Non-Maleficence, Justice, Autonomy, Explicability) behind that action. Using participants’ demographics, personality, values, and cyber hygiene practices, we created profiles using machine learning to predict participants’ choices and the principle of most importance to them across scenarios. Further, we found that, generalising, for our participants Autonomy was the most important ethical principle in our scenarios, followed by Justice. Our study also suggests that participants felt they had some agency in their decision making and they were able to weigh up different ethical principles. (shrink)

The increasing integration of digital technologies into every aspect of modern life has led to a growing concern over cybersecurity, particularly in the context of privacy. As organizations collect vast amounts of personal and sensitive data, the tension between securing this information and preserving individual privacy has become a critical ethical dilemma. This paper explores the ethical challenges faced by cybersecurity professionals, policymakers, and organizations when balancing the need for protection against the potential risks to personal privacy. It (...) discusses the implications of surveillance, data collection, and the ethical boundaries of cybersecurity practices. Additionally, it evaluates existing frameworks, legal regulations, and technological solutions that attempt to balance these concerns. The paper also proposes a set of ethical guidelines for cybersecurity practices, focusing on how organizations can protect data while respecting privacy rights. (shrink)

To evaluate the effectiveness of our approach to enhancing cloud computing network forensics by integrating deep learning techniques with cybersecurity policies. With the increasing complexity and volume of cyber threats targeting cloud environments, traditional forensic methods are becoming inadequate. Deep learning techniques offer promising solutions for analyzing vast amounts of network data and detecting anomalies indicative of security breaches. By integrating deep learning models with cybersecurity policies, organizations can achieve enhanced threat detection, rapid response times, and improved (...) overall security posture. This paper discusses the key steps involved in integrating deep learning models into network forensics, including data collection, model selection, real-time monitoring, and adaptive learning. Additionally, it highlights the importance of collaboration between cybersecurity experts and the cloud. Through case studies and experimental evaluations, we demonstrate the effectiveness and practicality of the proposed approach in enhancing cloud computing network forensics. Leveraging deep learning techniques offers promising solutions for detecting anomalies and identifying malicious activities within cloud networks. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve (...) response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

In the modern enterprise environment, where cybersecurity threats continue to evolve in complexity and sophistication, network segmentation and micro-segmentation have emerged as critical strategies for mitigating risks and reducing attack surfaces. This research paper explores the principles, implementation, and benefits of network segmentation and micro-segmentation as essential components of a comprehensive cybersecurity framework. By dividing networks into smaller, isolated segments, these methodologies aim to limit unauthorized access, minimize lateral movement, and contain potential breaches, ensuring a more secure (...) network infrastructure. Network segmentation focuses on dividing large networks into smaller, more manageable subnetworks. This process enforces boundaries between different areas of a network, reducing exposure and protecting sensitive data. Meanwhile, microsegmentation extends this concept to the individual workload level, offering granular security controls that adapt to dynamic and cloud-based environments. These approaches are particularly relevant in today's context, where hybrid infrastructures and multi-cloud deployments are becoming the norm, posing significant security challenges. The paper examines the technical underpinnings of segmentation techniques, highlighting tools and frameworks that facilitate their deployment. It also addresses key challenges, such as the complexity of configuration, potential performance bottlenecks, and the necessity for alignment with broader organizational policies. Case studies from industries such as healthcare, finance, and government are analyzed to demonstrate the effectiveness of segmentation in reducing the scope and impact of cyberattacks. Additionally, this study delves into the evolving landscape of cyber threats, emphasizing the role of segmentation in countering advanced persistent threats (APTs), ransomware attacks, and insider threats. By adopting a zero-trust architecture that integrates micro-segmentation, organizations can ensure that every access request is verified and confined to the least privileged level necessary. This proactive approach to network defense aligns with industry best practices and regulatory standards, enhancing an organization's security posture. Furthermore, the research highlights the importance of continuous monitoring and automation in maintaining segmented networks. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are explored for their potential to optimize and simplify segmentation processes. These advancements enable organizations to dynamically adapt to evolving threats while maintaining operational efficiency. The findings emphasize that while network segmentation and micro-segmentation are not silver bullets, they represent indispensable layers of defense within a multi-faceted cybersecurity strategy. Organizations that successfully implement these strategies can significantly reduce the likelihood and impact of breaches, protect critical assets, and build resilience against future threats. This paper aims to provide a comprehensive guide for cybersecurity professionals, IT administrators, and policymakers to understand and adopt network segmentation and micro-segmentation. By integrating these strategies into their security frameworks, enterprises can fortify their defenses in the face of a constantly shifting threat landscape, safeguarding their infrastructure, data, and operations. (shrink)

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into data integration techniques, (...) performance optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions. (shrink)

In the modern enterprise environment, where cybersecurity threats continue to evolve in complexity and sophistication, network segmentation and micro-segmentation have emerged as critical strategies for mitigating risks and reducing attack surfaces. This research paper explores the principles, implementation, and benefits of network segmentation and micro-segmentation as essential components of a comprehensive cybersecurity framework. By dividing networks into smaller, isolated segments, these methodologies aim to limit unauthorized access, minimize lateral movement, and contain potential breaches, ensuring a more secure (...) network infrastructure. Network segmentation focuses on dividing large networks into smaller, more manageable subnetworks. This process enforces boundaries between different areas of a network, reducing exposure and protecting sensitive data. Meanwhile, microsegmentation extends this concept to the individual workload level, offering granular security controls that adapt to dynamic and cloud-based environments. These approaches are particularly relevant in today's context, where hybrid infrastructures and multi-cloud deployments are becoming the norm, posing significant security challenges. The paper examines the technical underpinnings of segmentation techniques, highlighting tools and frameworks that facilitate their deployment. It also addresses key challenges, such as the complexity of configuration, potential performance bottlenecks, and the necessity for alignment with broader organizational policies. Case studies from industries such as healthcare, finance, and government are analyzed to demonstrate the effectiveness of segmentation in reducing the scope and impact of cyberattacks. Additionally, this study delves into the evolving landscape of cyber threats, emphasizing the role of segmentation in countering advanced persistent threats (APTs), ransomware attacks, and insider threats. By adopting a zero-trust architecture that integrates micro-segmentation, organizations can ensure that every access request is verified and confined to the least privileged level necessary. This proactive approach to network defense aligns with industry best practices and regulatory standards, enhancing an organization's security posture. Furthermore, the research highlights the importance of continuous monitoring and automation in maintaining segmented networks. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are explored for their potential to optimize and simplify segmentation processes. These advancements enable organizations to dynamically adapt to evolving threats while maintaining operational efficiency. The findings emphasize that while network segmentation and micro-segmentation are not silver bullets, they represent indispensable layers of defense within a multi-faceted cybersecurity strategy. Organizations that successfully implement these strategies can significantly reduce the likelihood and impact of breaches, protect critical assets, and build resilience against future threats. This paper aims to provide a comprehensive guide for cybersecurity professionals, IT administrators, and policymakers to understand and adopt network segmentation and micro-segmentation. By integrating these strategies into their security frameworks, enterprises can fortify their defenses in the face of a constantly shifting threat landscape, safeguarding their infrastructure, data, and operations. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed (...) by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

In today's increasingly digital and interconnected business environment, organizations face significant challenges in managing multiple systems, applications, and data streams. SAP plays a central role in integrating these diverse systems and enabling seamless data flow across various platforms. Through solutions like SAP S/4HANA, SAP Cloud Platform, and SAP Integration Suite, businesses can create a unified digital ecosystem that connects onpremise, cloud-based, and third-party applications. These integration capabilities are critical for enhancing operational efficiency, improving decision-making, and fostering innovation. This paper (...) explores the role of SAP as a foundation for enterprise integration, highlighting its technologies, benefits, challenges, and real-world use cases. Additionally, it discusses how organizations can leverage SAP solutions to bridge the gap between legacy systems, modern applications, and emerging technologies in the digital age. (shrink)

The rise of the Metaverse as a virtual, interconnected world has captured significant attention in recent years. As businesses increasingly recognize the potential of the Metaverse to transform industries, Microsoft Azure stands out as a leading platform for integrating and scaling Metaverse solutions. This paper explores how Azure's cloud infrastructure, advanced computing capabilities, and digital transformation tools enable businesses to integrate with the Metaverse, opening new opportunities for collaboration, customer engagement, and innovation. Additionally, the paper discusses the challenges associated (...) with implementing Metaverse solutions, such as data privacy, interoperability, and infrastructure scalability. Through a comprehensive analysis of Azure's role in supporting the Metaverse, this paper presents key considerations for enterprises looking to build and sustain their ecosystems in this new digital frontier. (shrink)

Ambient assisted living technologies are increasingly presented and sold as essential smart additions to daily life and home environments that will radically transform the healthcare and wellness markets of the future. An ethical approach and a thorough understanding of all ethics in surveillance/monitoring architectures are therefore pressing. AAL poses many ethical challenges raising questions that will affect immediate acceptance and long-term usage. Furthermore, ethical issues emerge from social inequalities and their potential exacerbation by AAL, accentuating the existing access gap between (...) high-income countries and low and middle-income countries. Legal aspects mainly refer to the adherence to existing legal frameworks and cover issues related to product safety, data protection, cybersecurity, intellectual property, and access to data by public, private, and government bodies. Successful privacy-friendly AAL applications are needed, as the pressure to bring Internet of Things devices and ones equipped with artificial intelligence quickly to market cannot overlook the fact that the environments in which AAL will operate are mostly private. The social issues focus on the impact of AAL technologies before and after their adoption. Future AAL technologies need to consider all aspects of equality such as gender, race, age and social disadvantages and avoid increasing loneliness and isolation among, e.g. older and frail people. Finally, the current power asymmetries between the target and general populations should not be underestimated nor should the discrepant needs and motivations of the target group and those developing and deploying AAL systems. Whilst AAL technologies provide promising solutions for the health and social care challenges, they are not exempt from ethical, legal and social issues. A set of ELSI guidelines is needed to integrate these factors at the research and development stage. (shrink)

Cloud security remains a top concern for enterprises. This study introduces AI Security, a risk governance framework integrating AI, DevSecOps, and predictive analytics to enhance compliance automation and threat intelligence in cloud environments.

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus (...) on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

In this Article, I propose a novel law and economics explanation of a deeply puzzling aspect of business organization in market economies. Why are virtually all firms organized as capital-managed and -owned (capitalist) enterprises rather than as labor-managed and -owned cooperatives? Over 150 years ago, J.S. Mill predicted that efficiency and other advantages would eventually make worker cooperatives predominant over capitalist firms. Mill was right about the advantages but wrong about the results. The standard explanation is that capitalist enterprise (...) is more efficient. Empirical research, however, overwhelmingly contradicts this. But employees almost never even attempt to organize worker cooperatives. I critique the explanations of the three leading analysts of the subject (N. Scott Arnold, Henry Hansmann, and Gregory Dow), all of whom offer are different transactions cost accounts, as logically defective and empirically inadequate. I then propose an explanation that has been oddly neglected in the literature, that the rarity of cooperatives is explained by the collective action problem identified by writers such as Mancur Olson. Labor management is a public good that generates the n-person prisoner’s dilemma which gives rational actors the incentive to create it in suboptimal (or no) amounts. I support this by reference to the empirical facts about the origin of existing cooperatives and show that this explanation requires no strong version of a questionable rational choice theory. This explanation is supplemented by the mere exposure or familiarity effect derived from social and cognitive psychology, which turns on the fact that labor managed firms are rare, in part because of the public goods problem, thus unfamiliar, which makes them less attractive and thus more likely to be rare. My account points advocates of labor management towards solutions such as institutional changes in incentives, which, however, themselves involve public goods issues. (shrink)

The internet has made the world more linked than ever before. While taking advantage of this online transition, cybercriminals target flaws in online systems, networks, and infrastructure. Businesses, government organizations, people, and communities all across the world, particularly in African countries, are all severely impacted on an economic and social level. Many African countries focused more on developing secure electricity and internet networks; yet, cybersecurity usually receives less attention than it should. One of Africa's major issues is the lack (...) of adequate digital security infrastructure, which has harmed businesses, governmental institutions, and individual communities more than it has helped. The majority of African countries operate without cybersecurity measures in place to combat cyberattacks. Only a few examples of today's cyber risks include digital extortion, business email intrusion, data breaches, online fraud, ransomware, and phishing, and new types of cybercrime are always developing. Due to the advent of new technology, cybercriminals have become more organized and quicker in their attacks and alliance creation. To maintain a secure digital environment for all internet users, this study focused on the challenges, solutions, and need for African countries to improve their online safety by tackling cybercrime, online fraud, and cybersecurity concerns. The objective of this study is to offer practical and long-term answers to the problems posed by cybercrime, with a continuing emphasis on enhancing online safety. It will assess the effectiveness of cutting-edge cybersecurity measures, legislative frameworks, and cross-border cooperative efforts, as well as potential areas for improvement. Additionally, the study will examine cutting-edge methods like blockchain technology, machine learning, and other cutting-edge methods that could improve our using digital defences to stop cybercrime. (shrink)

The high failure rate of new small enterprises in Angola calls to attention the fact that they are not equipped enough to meet the challenges of sustainable economic growth, equitable distribution of wealth and job creation. The worrying impact on the Angolan landscape, as a consequence, has had economists scrambling for solutions in recent years. Moving from on the assumption that the current business climate has been a contributing factor, this paper attempts to examine the role of external environmental (...) factors in the context of Angola. Adopting a quantitative approach, the questionnaire was utilized as the primary data collection tool with snowballing as the sampling technique. The Statistical Package for the Social Sciences (SPSS) software delivered a result to the quantitative data that indicate a severe lack of economic support and availability of fundamental business resources such as raw material, skilled people and finance, and rigid policy-making regulations. The doomed-to-fail death knell to the Angolan small business comes in the form of a high level of corruption and theft in the country. (shrink)

The rise of remote work has redefined the modern workplace, driven by advancements in technology and the global need for flexible work environments. While remote work offers increased productivity and employee satisfaction, it has also introduced a host of network security challenges. Organizations must contend with securing sensitive data and critical applications across distributed networks, many of which extend beyond traditional corporate perimeters. This research paper explores the multifaceted security threats associated with remote work environments, including phishing attacks, unpatched software (...) vulnerabilities, unsecured home networks, and the misuse of personal devices. These challenges are exacerbated by a lack of centralized control and the increasing sophistication of cybercriminals exploiting these vulnerabilities. Key findings highlight the importance of robust authentication protocols, such as multi-factor authentication (MFA), to ensure secure access to corporate resources. Endpoint security solutions, including encryption and antivirus software, play a critical role in safeguarding data on remote devices. Moreover, the paper emphasizes the significance of implementing virtual private networks (VPNs) and zero-trust security frameworks to reduce attack surfaces and limit unauthorized access. Another critical focus is the role of employee training and awareness programs in mitigating human error, which remains a leading cause of data breaches. This study also underscores the necessity of proactive threat detection and response mechanisms to identify and address breaches in real-time. Advanced technologies, such as artificial intelligence (AI) and machine learning (ML), are pivotal in enhancing these capabilities, enabling organizations to adapt to the rapidly evolving cyber threat landscape. Furthermore, comprehensive network segmentation strategies are discussed to isolate critical systems and minimize the impact of potential breaches. The research incorporates case studies of successful implementations of these solutions, demonstrating their effectiveness in fortifying network security for remote operations. A comparative analysis is presented to evaluate the cost-effectiveness and scalability of various approaches, catering to organizations of different sizes and industries. The findings suggest that a multi-layered defense strategy is essential for achieving resilience in remote work environments. The paper concludes with actionable recommendations for organizations seeking to enhance their security posture. These include prioritizing regular updates and patches, adopting secure cloud-based collaboration tools, and conducting periodic security audits. Additionally, fostering a culture of cybersecurity awareness among employees is paramount to addressing the human factor in cyber risks. By addressing the unique challenges posed by remote work, this study provides a comprehensive roadmap for protecting data and applications in decentralized environments. The insights gained aim to equip organizations with the tools and knowledge necessary to navigate the complexities of securing remote work infrastructures while maintaining business continuity and trust. (shrink)

Organizations are migrating their policy management systems from the ground to the cloud, changing how they manage, secure and scale their operations. This paper analyses how Amazon Web Services (AWS) and Snowflake support robust and efficient cloud-based policy management solutions. This marriage of sort of AWS, which has an almost panoply of cloud services, and Snowflake, which is very well known for its data warehousing and analytics capabilities, makes sense because it is an enterprise in which you will (...) want to modernize your policy management. Ways to explore Amazon’s infrastructural offerings, such as IAM (Identity and Access Management) and Security Hub, are also discussed as key components in the path to governance and compliance in a cloud environment. Snowflake’s ability to merge and process large sets of data worldwide to drive dynamic policy changes, predictive analysis, and real time insight punts is unique. Finally, this paper discusses the critical challenges this paper addresses, ranging from security, high interoperability, and cost optimization, and how these platforms address these hurdles and features, such as Zero Trust, automated scaling, and native integration with enterprise systems. Using case studies and the best practices, this analysis offers actionable insights for organizations that want to use AWS and Snowflake to manage cloud policy. These findings indicate that a combined approach using AWS and Snowflake looks to improve our operational efficiency and ensure we comply with the existing framework to be ready for future advancements in the cloud native policy ecosystems. (shrink)

Enterprise Risk Management involves the process of identification, evaluation, treatment, and communication regarding risks throughout the enterprise. To support the tasks associated with this process, several frameworks and modeling languages have been proposed, such as the Risk and Security Overlay (RSO) of ArchiMate. An ontological investigation of this artifact would reveal its adequacy, capabilities, and limitations w.r.t. the domain of risk and security. Based on that, a language redesign can be proposed as a refinement. Such analysis and redesign (...) have been executed for the risk elements of the RSO grounded in the Common Ontology of Value and Risk. The next step along this line of research is to address the following research problems: What would be the outcome of an ontological analysis of security-related elements of the RSO? That is, can we identify other semantic deficiencies in the RSO through an ontological analysis? Once such an analysis is provided, can we redesign the security elements of the RSO accordingly, in order to produce an improved artifact? Here, with the aid of the Reference Ontology for Security Engineering (ROSE) and the ontological theory of prevention behind it, we address the remaining gap by proceeding with an ontological analysis of the security-related constructs of the RSO. The outcome of this assessment is an ontology-based redesign of the ArchiMate language regarding security modeling. In a nutshell, we report the following contributions: (1) an ontological analysis of the RSO that identifies six limitations concerning security modeling; (2) because of the key role of the notion of prevention in security modeling, the introduction of the ontological theory of prevention in ArchiMate; (3) a well-founded redesign of security elements of ArchiMate; and (4) ontology-based security modeling patterns that are logical consequences of our proposal of redesign due to its underlying ontology of security. As a form of evaluation, we show that our proposal can describe risk treatment options, according to ISO 31000. Finally, besides presenting multiple examples, we proceed with a real-world illustrative application taken from the cybersecurity domain. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while enhancing efficiency, (...) expand the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while enhancing efficiency, (...) expand the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

This paper extensively develops and thoroughly creates a highly centralized and extremely efficient log collection system utilizing and leveraging the robust syslog server and comprehensively collects and gathers log data from the numerous hosts residing within the network, meticulously analyses and scrutinizes the detailed log files and promptly sends and transmits a critical alert message to the central host through secure and reliable SMS. This paper thoroughly and comprehensively addresses and tackles the increasingly growing and rapidly expanding need for extremely (...) robust and highly reliable cybersecurity measures and solutions in the vulnerable and sensitive power sector. (shrink)

This article summarizes the question of finding effective solutions to ensure the excellence of modern business through the introduction of managerial innovations. The main purpose of the study is to improve the theoretical and methodological approach to the implementation of managerial innovations in the system of modern business solutions. Systematization of literature sources and approaches to solving the problem of improving business management through the introduction of managerial innovations indicates the widespread use of methodological approaches and methods of (...) managerial innovation, but much management issues to ensure business excellence, justification of new opportunities for its development remain unnoticed. The urgency of solving this scientific problem is that in conditions of growing uncertainty and risk, accelerating the transformation of business, the importance of ensuring its perfection in the long term economic development of Ukraine is growing. The methodological tools of research methods were a system of methods used to obtain the final results of the study: generalization and comparative analysis – to clarify the essence of the concept of «managerial innovation»; abstract-logical – to reveal the relationship between the use of information and communication technologies, business excellence and implementation of management innovations; system-analytical – in developing a model of business excellence based on the use of modern information and communication technologies. The object of research is the industrial enterprises of the modern economy of Ukraine, because they are the basis of economic formation, a source of replenishment of the country budget and form the export orientation of business. The article presents the results of abstract-logical analysis of the blocks of business excellence, which showed that at the present stage of its development, the introduction of managerial innovations should be associated with the digital transformation of business. The results of the system-analytical analysis are presented, which are the basis for the development of an economic model for business excellence. The results of the study can be useful for business structures (and in particular, business units of the industrial complex of Ukraine), which seek to increase the efficiency of decision-making in today's digital business transformation. (shrink)

"Understanding Scientific Progress constitutes a potentially enormous and revolutionary advancement in philosophy of science. It deserves to be read and studied by everyone with any interest in or connection with physics or the theory of science. Maxwell cites the work of Hume, Kant, J.S. Mill, Ludwig Bolzmann, Pierre Duhem, Einstein, Henri Poincaré, C.S. Peirce, Whitehead, Russell, Carnap, A.J. Ayer, Karl Popper, Thomas Kuhn, Imre Lakatos, Paul Feyerabend, Nelson Goodman, Bas van Fraassen, and numerous others. He lauds Popper for advancing beyond (...) verificationism and Hume’s problem of induction, but faults both Kuhn and Popper for being unable to show that and how their work could lead nearer to the truth." —Dr. LLOYD EBY teaches philosophy at The George Washington University and The Catholic University of America, in Washington, DC "Maxwell's aim-oriented empiricism is in my opinion a very significant contribution to the philosophy of science. I hope that it will be widely discussed and debated." – ALAN SOKAL, Professor of Physics, New York University "Maxwell takes up the philosophical challenge of how natural science makes progress and provides a superb treatment of the problem in terms of the contrast between traditional conceptions and his own scientifically-informed theory—aim-oriented empiricism. This clear and rigorously-argued work deserves the attention of scientists and philosophers alike, especially those who believe that it is the accumulation of knowledge and technology that answers the question."—LEEMON McHENRY, California State University, Northridge "Maxwell has distilled the finest essence of the scientific enterprise. Science is about making the world a better place. Sometimes science loses its way. The future depends on scientists doing the right things for the right reasons. Maxwell's Aim-Oriented Empiricism is a map to put science back on the right track."—TIMOTHY McGETTIGAN, Professor of Sociology, Colorado State University - Pueblo "Maxwell has a great deal to offer with these important ideas, and deserves to be much more widely recognised than he is. Readers with a background in philosophy of science will appreciate the rigour and thoroughness of his argument, while more general readers will find his aim-oriented rationality a promising way forward in terms of a future sustainable and wise social order."—David Lorimer, Paradigm Explorer, 2017/2 "This is a book about the very core problems of the philosophy of science. The idea of replacing Standard Empiricism with Aim-Oriented Empiricism is understood by Maxwell as the key to the solution of these central problems. Maxwell handles his main tool masterfully, producing a fascinating and important reading to his colleagues in the field. However, Nicholas Maxwell is much more than just a philosopher of science. In the closing part of the book he lets the reader know about his deep concern and possible solutions of the biggest problems humanity is facing."—Professor PEETER MŰŰREPP, Tallinn University of Technology, Estonia “For many years, Maxwell has been arguing that fundamental philosophical problems about scientific progress, especially the problem of induction, cannot be solved granted standard empiricism (SE), a doctrine which, he thinks, most scientists and philosophers of science take for granted. A key tenet of SE is that no permanent thesis about the world can be accepted as a part of scientific knowledge independent of evidence. For a number of reasons, Maxwell argues, we need to adopt a rather different conception of science which he calls aim-oriented empiricism (AOE). This holds that we need to construe physics as accepting, as a part of theoretical scientific knowledge, a hierarchy of metaphysical theses about the comprehensibility and knowability of the universe, which become increasingly insubstantial as we go up the hierarchy. In his book “Understanding Scientific Progress: Aim-Oriented Empiricism”, Maxwell gives a concise and excellent illustration of this view and the arguments supporting it… Maxwell’s book is a potentially important contribution to our understanding of scientific progress and philosophy of science more generally. Maybe it is the time for scientists and philosophers to acknowledge that science has to make metaphysical assumptions concerning the knowability and comprehensibility of the universe. Fundamental philosophical problems about scientific progress, which cannot be solved granted SE, may be solved granted AOE.” Professor SHAN GAO, Shanxi University, China . (shrink)

This book gives an account of work that I have done over a period of decades that sets out to solve two fundamental problems of philosophy: the mind-body problem and the problem of induction. Remarkably, these revolutionary contributions to philosophy turn out to have dramatic implications for a wide range of issues outside philosophy itself, most notably for the capacity of humanity to resolve current grave global problems and make progress towards a better, wiser world. A key element of the (...) proposed solution to the first problem is that physics is about only a highly specialized aspect of all that there is – the causally efficacious aspect. Once this is understood, it ceases to be a mystery that natural science says nothing about the experiential aspect of reality, the colours we perceive, the inner experiences we are aware of. That natural science is silent about the experiential aspect of reality is no reason whatsoever to hold that the experiential does not objectively exist. A key element of the proposed solution to the second problem is that physics, in persistently accepting unified theories only, thereby makes a substantial metaphysical assumption about the universe: it is such that a unified pattern of physical law runs through all phenomena. We need a new conception, and kind, of physics that acknowledges, and actively seeks to improve, metaphysical presuppositions inherent in the methods of physics. The problematic aims and methods of physics need to be improved as physics proceeds. These are the ideas that have fruitful implications, I set out to show, for a wide range of issues: for philosophy itself, for physics, for natural science more generally, for the social sciences, for education, for the academic enterprise as a whole and, most important of all, for the capacity of humanity to learn how to solve the grave global problems that menace our future, and thus make progress to a better, wiser world. It is not just science that has problematic aims; in life too our aims, whether personal, social or institutional, are all too often profoundly problematic, and in urgent need of improvement. We need a new kind of academic enterprise which helps humanity put aims-and-methods improving meta-methods into practice in personal and social life, so that we may come to do better at achieving what is of value in life, and make progress towards a saner, wiser world. This body of work of mine has met with critical acclaim. Despite that, astonishingly, it has been ignored by mainstream philosophy. In the book I discuss the recent work of over 100 philosophers on the mind-body problem and the metaphysics of science, and show that my earlier, highly relevant work on these issues is universally ignored, the quality of subsequent work suffering as a result. My hope, in publishing this book, is that my fellow philosophers will come to appreciate the intellectual value of my proposed solutions to the mind-body problem and the problem of induction, and will, as a result, join with me in attempting to convince our fellow academics that we need to bring about an intellectual/institutional revolution in academic inquiry so that it takes up its proper task of helping humanity learn how to solve problems of living, including global problems, and make progress towards as good, as wise and enlightened a world as possible. (shrink)

This dissertation lays the foundation for a new theory of non-relational intentionality. The thesis is divided into an introduction and three main chapters, each of which serves as an essential part of an overarching argument. The argument yields, as its conclusion, a new account of how language and thought can exhibit intentionality intrinsically, so that representation can occur in the absence of some thing that is represented. The overarching argument has two components: first, that intentionality can be profi tably studied (...) through examination of the semantics of intensional transitive verbs (ITVs), and second, that providing intensional transitive verbs with a nonrelational semantics will serve to provide us with (at least the beginnings of) a non-relational theory of intentionality. This approach is a generalization of Anscombe's views on perception. Anscombe held that perceptual verbs such as "see" and "perceive" were ITVs, and that understanding the semantics of their object positions could help us to solve the problems of hallucination and illusion, and provide a theory of perception more generally. I propose to apply this strategy to intentional states and the puzzles of intentionality more generally, and so Anscombe's influence will be felt all through the dissertation. -/- In the first chapter, titled "Semantic Verbs are Intensional Transitives", I argue that semantic verbs such as "refers to", "applies to", and "is true of" have all of the features of intensional transitive verbs, and discuss the consequences of this claim for semantic theory and the philosophy of language. One theoretically enriching consequence of this view is that it allows us to perspicuously express, and partially reconcile two opposing views on the nature and subject-matter of semantics: the Chomskian view, on which semantics is an internalistic enterprise concerning speakers' psychologies, and the Lewisian view, on which semantics is a fully externalistic enterprise issuing in theorems about how the world must look for our natural language sentences to be true. Intensional Transitive Verbs have two readings: a de dicto reading and a de re reading; the de dicto reading of ITVs is plausibly a nonrelational reading, and the intensional features peculiar to this reading make it suitable for expressing a Chomskian, internalist semantic program. On the other hand, the de re reading is fully relational, and make it suitable for expressing the kinds of word-world relations essential to the Lewisian conception of semantics. And since the de dicto and de re readings are plausibly related as two distinct scopal readings of the very same semantic postulates, we can see these two conceptions of semantics as related by two scopal readings of the very same semantic postulates. -/- In chapter two, titled "Hallucination and the New Problem of Empty Names", I argue that the problem of hallucination and the problem of empty names are, at bottom, the same problem. I argue for this by reconstructing the problem of empty names in way that is novel, but implicit in much of the discussion on empty names. I then show how, once recast in this light, the two problems are structurally identical down to an extremely fine level of granularity, and also substantially overlap in terms of their content. If the problems are identical in the way I propose, then we should expect that their spaces of solutions are also identical, and there is signi cant support for this conclusion. However, there are some proposed solutions to the problem of hallucination that have been overlooked as potential solutions to the problem of empty names, and this realization opens new non-relational approaches to the problem of empty names, and to the nature of meaning more generally. -/- In chapter three, titled "Intensionality is Additional Phrasal Unity", I argue for a novel approach to the semantics of intensional contexts. At the heart of my proposal is the Quinean view that intensional contexts should, from the perspective of the semantics, be treated as units, with the material in them contributing to the formation of a single predicate. However, this proposal is subject to a number of objections, including the criticism that taken at face value, this would render intensional contexts, which seem to be fully productive, non-compositional. I begin by discussing the concept of the unity of the phrase, and pointing to various ways that phrases can gain additional unity. I then proposes that the intensionality of intensional transitive verbs is best construed as a form of semantic incorporation; ITVs, on their intensional readings, meet all of the criteria for qualifying as incorporating the nominals in their object positions. I then give a semantics for ITVs that builds on existing views of the semantics of incorporation structures, and gesture at how this can be extended to intensional clausal verbs, including the so-called propositional attitude verbs. (shrink)

Tackling climate change is one of the most demanding challenges of humanity in the 21st century. Still, the efforts to mitigate the current environmental crisis do not seem enough to deal with the increased existential risks for the human and other species. Persson and Savulescu have proposed that our evolutionarily forged moral psychology is one of the impediments to facing as enormous a problem as global warming. They suggested that if we want to address properly some of the most pressing (...) problems that cause catastrophic harm to our existence, we should enhance our moral behavior by biomedical means. The objective of this paper is, precisely, to reflect on whether a Moral Bio-Enhancement (henceforth MBE) program would be a viable option to confront the climate emergency. To meet this goal, I will propose the Ultimate Mostropic (hereafter UM) thought experiment, a hypothetical situation where we have already discovered the UM, an available, safe (without any deleterious secondary effects), extremely cheap and effective pill to enhance our cognitive, affective and motivational abilities related to morality. After briefly presenting the main argument of Persson and Savulescu regarding MBE and climate change, I will point out some of the difficulties that make MBE a daunting but exciting philosophical and scientific debate. In order to overcome these complications, I will describe the UM thought experiment, which involves two scenarios of the MBE program: (a) the state-driven, compulsory and universal enterprise, and (b) the initiative of voluntary individuals. I will show that the shortcomings of MBE programs through the UM in both scenarios make Persson and Savulescu’s proposal a not appealing pathway to mitigate climate change. In the final section, I will suggest that an inaccurate attribution of responsibilities underlies their proposal and that the collective inaction problem should be redirected primarily through a reinforcement of the political nature of the solutions. (shrink)

This paper explores whether it is possible to reformulate or re-interpret Lewis’s theory of fundamental laws of nature—his “best system analysis”—in such a way that it becomes a useful theory for special science laws. One major step in this enterprise is to make plausible how law candidates within best system competitions can tolerate exceptions—this is crucial because we expect special science laws to be so called “ceteris paribus laws ”. I attempt to show how this is possible and also (...) how we can thereby make the first step towards a solution for the infamous difficulties surrounding the troublesome ceteris paribus clause. The paper outlines the general ideas of the theory but also points out some of its difficulties and background assumptions. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine learning (...) techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

An important objection to sententialist theories of attitude reports is that they cannot accommodate the principle that one cannot know that someone believes that p without knowing what it is that he believes. This paper argues that a parallel problem arises for propositionalist accounts that has gone largely unnoticed, and that, furthermore, the usual resources for the propositionalist do not afford an adequate solution. While non-standard solutions are available for the propositionalist, it turns out that there are parallel (...) class='Hi'>solutions that are available for the sententialist. Since the difficulties raised seem to show that the mechanism by which sentential complements serve to inform us about attitudes and about sentence meaning does not depend on their referring to propositions, this casts doubt on whether talk of propositions should retain a significant theoretical role in the enterprise of understanding thought, language and communication. (shrink)

In this special issue we will investigate, from the perspective of agricultural ethics the potential to develop a Responsible Research and Innovation approach to agriculture, and the limitations to such an enterprise. RRI is an emerging field in the European research and innovation policy context that aims to balance economic, socio-cultural and environmental aspects in innovation processes. Because technological innovations can contribute significantly to the solution of societal challenges like climate change or food security, but can also have negative (...) societal consequences, it is assumed that social and ethical aspects should be considered during the R&I process. For this reason, the emerging concept of RRI calls for ethical reflection on the nature, scope and applicability of responsibility and innovation in innovation practices in general, and the way social–ethical issues can be applied and addressed in agriculture. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve the (...) accuracy and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

In order to create a good world, we need to learn how to do it - how to resolve our appalling problems and conflicts in more cooperative ways than at present. And in order to do this, we need traditions and institutions of learning rationally devoted to this end. When viewed from this standpoint, what we have at present - academic inquiry devoted to the pursuit of knowledge and technological know-how - is an intellectual and human disaster. We urgently need (...) a new, more rigorous kind of inquiry that gives intellectual priority to the tasks of articulating our problems of living and proposing and critically assessing possible cooperative solutions. This new kind of inquiry would have as its basic aim to improve, not just knowledge, but also personal and global wisdom - wisdom being understood to be the capacity to realize what is of value in life. To develop this new kind of inquiry we will need to change almost every branch and aspect of the academic enterprise. (shrink)

I will be talking today about the limits of cognitive science. I won’t be talking about contingent shortcomings that could perhaps be remedied with, say, more time, resources, or ingenuity. Rather, I will be concerned with limitations that are “baked into” the very enterprise. The main blind spot, I will argue, is consciousness—but not for the reasons typically given. Current work in philosophy of mind can sometimes seem arcane, so my goal today will be to answer the question: why (...) bother? I have spent a whole book and several articles trying to make sense of the qualitative dimension of consciousness, but there is no point in trying to sell a solution unless we have first established (independently of any academic literature) that there is a real problem to solve. So, if I can’t convey my position in under an hour, I can at least convey the issue that motivates it. (shrink)

Work in philosophy of mind often engages in descriptive phenomenology, i.e., in attempts to characterize the phenomenal character of our experience. Nagel’s famous discussion of what it’s like to be a bat demonstrates the difficulty of this enterprise (1974). But while Nagel located the difficulty in our absence of an objective vocabulary for describing experience, I argue that the problem runs deeper than that: we also lack an adequate subjective vocabulary for describing phenomenology. We struggle to describe our own (...) phenomenal states in terms we ourselves find adequately expressive. This paper aims to flesh out why our phenomenological vocabulary is so impoverished – what I call the impoverishment problem. As I suggest, this problem has both practical and philosophical import. After fleshing out the problem in more detail, I draw some suggestive morals from the discussion in an effort to point the way forward towards a solution. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I (...) argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

According to Max Deutsch, conceptual engineers face a dilemma: either they target semantic meanings, in which case they are engaged in an infeasible activity (the “implementation challenge”), or they target speaker meanings, in which case they are engaged in a trivial enterprise (the “trivialization challenge”). Focusing on the first horn of the dilemma, I argue that the “transubstantiation version” of the implementation challenge only holds for representationalist approaches to conceptual engineering, according to which concepts are individuated by their representational (...) relation to phenomena or sets of objects in the world. From a non-representationalist (e.g. pragmatist) perspective, the dilemma is therefore dissolved. Nevertheless, Deutsch’s article still deserves a response from the pragmatist, since it raises a more general challenge to conceptual engineering that I dub the “practical implementation challenge”. I offer a pragmatist solution to that challenge, drawing on John Dewey’s theory of inquiry and Robert Brandom’s inferentialism. This solution includes recommendations distributed across all stages of the engineering process, in privileging projects that are (1) problem-driven, (2) bottom-up; ensuring that the proposal is (3) clearly justified, (4) maintains continuity with the existing inferential role; and finally, in (5) enforcing the novel concept in practices of deontic scorekeeping, (6) backed up by institutional or intellectual authority. (shrink)

Propositional attitude sentences, such as (1) Pierre believes that snow is white, have proved to be formidably difficult to account for in a semantic theory. It is generally agreed that the that-clause ‘that snow is white’ purports to refer to the proposition that snow is white, but no agreement has been reached on what this proposition is. Sententialism is a semantic theory which tries to undermine the very enterprise of understanding what proposition is referred to in (1): according to (...) sententialists, in (1) reference is made to the sentence “Snow is white”. Sententialism is generally considered doomed. The two main reasons why are the famous translation argument, firstly suggested by Alonzo Church, and a problem raised by Stephen Schiffer. The purpose of this paper is to provide a unified solution to both criticisms. What I take to be the key ingredient sententialists may exploit is an observation that concerns the nature of languages and quotations: since quotation marks display the quoted material, if you are a speaker of the language the quoted material belongs to, you usually cannot but understand what is quoted. Moreover, I show that sententialists may appeal to that very observation also in order to answer another problem, pointed out by Kent Bach. I conclude that there are good reasons for resisting the temptation of introducing propositions in order to account for propositional attitude sentences. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine learning (...) techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. -/- . (shrink)