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Purpose - This paper aims to study individuals in international relations especially private individuals in global politics. Therefore the paper focuses on analyzing the case of Mark Zuckerberg the founder and chief executive of Facebook who affects the international arena. The paper illustrates Zuckerberg's strategies to assert wide influence and power within Facebook's network and through multiple networks. Design/methodology/approach - The paper follows new theories of studying the human agent in international relations, concentrating on private individuals as new actors in international relations (IR). Thus, depending on \"network making power theory\" and the \"three-dimensional power perspectives; (discursive, structural and instrumental)\", the paper illustrates the case of Mark Zuckerberg as a private entrepreneur and his authority in the era of social media dominance with a focus on: Zuckerberg's discursive/ideational power strategy. Zuckerberg's strategy to work as a switcher through multiple networks. The most obvious one is the Facebook network, through which he can assert global influence. Findings - Formal state officials are not the only type of individuals who can affect international relations. Technological evolution has empowered private individuals as influential actors in international relations (IR). Interdisciplinary approaches became essential tools in studying new actors affecting IR. There are new patterns of power linked to individuals without formal positions. Zuckerberg, CEO of Facebook and global philanthropist, is considered an influential actor in IR depending on programming and switching strategies to assert his power in a networked world. Originality/value - This paper is able to prove that there are new forms of power which belong to private individuals in a networked world.

This chapter provides a quick yet effective reference for body sensor network (BSN) programmers interested in developing their applications using the Signal Processing In‐Node Environment (SPINE) framework. It gives the necessary information for setting up the SPINE environment so to start programming as well as insights on how the framework itself can be customized and extended. The SPINE framework has two main components: sensor node side and server side. The core framework is now organized into three main parts, namely the communication, the sensing, and the processing parts. The SPINE framework provides, on the server side, simple Java APIs to develop applications on the coordinator. Therefore, the main strength of the SPINE framework allows users to be ready to develop applications in sensor networks without bothering with node‐side programming. The chapter further concentrates on the TinyOS port of SPINE2.

Programming is a vital skill in computer science and engineering-related disciplines. However, developing source code is an error-prone task. Logical errors in code are particularly hard to identify for both students and professionals, and a single error is unexpected to end-users. At present, conventional compilers have difficulty identifying many of the errors (especially logical errors) that can occur in code. To mitigate this problem, we propose a language model for evaluating source codes using a bidirectional long short-term memory (BiLSTM) neural network. We trained the BiLSTM model with a large number of source codes with tuning various hyperparameters. We then used the model to evaluate incorrect code and assessed the model’s performance in three principal areas: source code error detection, suggestions for incorrect code repair, and erroneous code classification. Experimental results showed that the proposed BiLSTM model achieved 50.88% correctness in identifying errors and providing suggestions. Moreover, the model achieved an F-score of approximately 97%, outperforming other state-of-the-art models (recurrent neural networks (RNNs) and long short-term memory (LSTM)).

Arduino is an open-source electronics platform based on cheap hardware and the easy-to-use software Integrated Development Environment (IDE). Nowadays, because of its open-source nature and its simple and accessible user experience, Arduino is ubiquitous and used among hobbyist and novice programmers for Do It Yourself (DIY) projects, especially in the Internet of Things (IoT) domain. Unfortunately, such diffusion comes with a price. Many developers start working on this platform without having a deep knowledge of the leading security concepts in Information and Communication Technologies (ICT). Their applications, often publicly available on GitHub (or other code-sharing platforms), can be taken as examples by other developers or downloaded and used by non-expert users, spreading these issues in other projects. For these reasons, this paper aims at understanding the current landscape by analyzing a set of open-source DIY IoT projects and looking for potential security issues. Furthermore, the paper classifies those issues according to the proper security category. This study’s results offer a deeper understanding of the security concerns in Arduino projects created by hobbyist programmers and the dangers that may be faced by those who use these projects.

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead.

The economics of information security has recently become a thriving and fast-moving discipline. As distributed systems are assembled from machines belonging to principals with divergent interests, we find that incentives are becoming as important as technical design in achieving dependability. The new field provides valuable insights not just into \"security\" topics (such as bugs, spam, phishing, and law enforcement strategy) but into more general areas such as the design of peer-to-peer systems, the optimal balance of effort by programmers and testers, why privacy gets eroded, and the politics of digital rights management.

Zero-day attacks pose a significant threat to software vendors, as they exploit previously unknown vulnerabilities, making them insidious and challenging to defend against. Predictive analysis offers a proactive approach to zero-day attack detection, enabling organizations to anticipate and mitigate threats before they manifest. By leveraging advanced techniques such as machine learning and game theory, predictive models can identify emerging attack patterns and adapt in real time to evolving threats. This paper proposes a zero-day attack optimization technique using supervised learning algorithms to identify system disruptions effectively. This paper presents innovative approaches to zero-day attack identification using advanced techniques such as Probabilistic Graph-based Back Propagation Neural Networks, Modified Bi-LSTM with Game Theory, ANN Auto Encoder, Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) and Residual Network (RESNET50). Multiple machine learning approaches were used to determine the most suited model for predicting zero-day assaults. A deep-convolutional n-zero-day network is introduced to distinguish zero-day malware from legitimate software, employing feature selection techniques and a diverse range of machine learning algorithms. This paper presents a novel methodology integrating Hybrid Game Theory (HGT) with Transfer Learning (TL), incorporating feature selection strategies and building upon earlier research. This paper contributes to the field by offering a comprehensive methodology for zero-day attack prediction and highlights areas for further research to address existing limitations and optimize outcomes in network security. Results demonstrate the efficacy of the proposed approach, achieving high detection rates and accuracy in identifying disruptions to network systems.

Voice mail. E-mail. Bar codes. Desktops. Laptops. Networks. The Web. In this exciting book, Gene Rochlin takes a closer look at how these familiar and pervasive productions of computerization have become embedded in all our lives, forcing us to narrow the scope of our choices, our modes of control, and our experiences with the real world. Drawing on fascinating narratives from fields that range from military command, air traffic control, and international fund transfers to library cataloging and supermarket checkouts, Rochlin shows that we are rapidly making irreversible and at times harmful changes in our business, social, and personal lives to comply with the formalities and restrictions of information systems. The threat is not the direct one once framed by the idea of insane robots or runaway mainframes usurping human functions for their own purposes, but the gradual loss of control over hardware, software, and function through networks of interconnection and dependence. What Rochlin calls the computer trap has four parts: the lure, the snare, the costs, and the long-term consequences. The lure is obvious: the promise of ever more powerful and adaptable tools with simpler and more human-centered interfaces. The snare is what usually ensues. Once heavily invested in the use of computers to perform central tasks, organizations and individuals alike are committed to new capacities and potentials, whether they eventually find them rewarding or not. The varied costs include a dependency on the manufacturers of hardware and software--and a seemingly pathological scramble to keep up with an incredible rate of sometimes unnecessary technological change. Finally, a lack of redundancy and an incredible speed of response make human intervention or control difficult at best when (and not if) something goes wrong. As Rochlin points out, this is particularly true for those systems whose interconnections and mechanisms are so deeply concealed in the computers that no human being fully understands them. The complete text ofTrapped in the Netis available online at http://pup.princeton.edu

Advances in generative algorithms have enhanced the quality and accessibility of artificial intelligence (AI) as a tool in building synthetic datasets. By generating photorealistic images and videos, these networks can pose a major technological disruption to a broad range of industries from medical imaging to virtual reality. However, as artwork developed by generative algorithms and cognitive robotics enters the arena, the notion of human-driven creativity has been thoroughly tested. When creativity is automated by the programmer, in a style determined by the trainer, using features from information available in public and private datasets, who is the proprietary owner of the rights in AI-generated artworks and designs? This Perspective seeks to provide an answer by systematically exploring the key issues in copyright law that arise at each phase of artificial creativity, from programming to deployment. Ultimately, four guiding actions are established for artists, programmers and end users that utilize AI as a tool such that they may be appropriately awarded the necessary proprietary rights. As artists are beginning to employ deep learning techniques to create new and interesting art, questions arise about how copyright and ownership apply to those works. This Perspective discusses how artists, programmers and users can ensure clarity about the ownership of their creations.

Open innovation (OI) is among the key strategic resources of enterprises, especially in high-tech sectors such as the ICT industry. The use of OI platforms and/or networks that facilitate access to and sharing of OI knowledge is gaining increasing interest. This study aimed to assess the factors motivating and hindering the use of OI platforms and/or networks in the ICT industry in Poland. The uniqueness of this approach lies in the use of a PROFIT analysis to develop proprietary models of the importance of the various motivating factors and barriers to the use of OI platforms and/or networks in the ICT industry in relation to the job position held. This study hypothesized that the knowledge of factors motivating and hindering the use of OI platforms and/or networks in the ICT industry varies across occupational groups. In order to verify the hypothesis and answer the formulated research questions, a diagnostic survey method with a survey technique was used. The results of this study confirm that the job position occupied by employees in the ICT industry is relevant to each of the factors that pose obstacles to their use of OI platforms and/or networks. Managers and management, as well as developers, are less likely to restrict the use of the aforementioned solutions due to organizational and/or administrative barriers, while more likely due to reluctance to share knowledge. For specialists and analysts, legal barriers and NIH syndrome are greater obstacles. For programmers, negative attitudes toward open innovation and lack of internal commitment to the company are less of an obstacle. Insufficient support from top management is a major barrier for administrative staff and programmers. The conclusions formulated can be useful in practice for managers in the ICT industry to make optimal use of access to OI.