Hasil untuk "Cybernetics"

Christer Carlsson

As the contributions to this section of HSM are not very numerous I have found it necessary to generate some news of my own. Besides informing you about coming events, I will also put forward some concerns regarding present attitudes towards the theory of fuzzy sets and its applicability III management-oriented fields of research. Let us take as 'management-oriented fields of research' those labeled as behavioral science, cybernetics, decision theory, human systems management, management science, MCDM, operational research, probability theory, general systems research, utility theory what a variety of scientific endeavours aimed at functions any normally gifted and/or successful manager describes as mostly based on intuition, personality, experience and common sense. Into this jungle entered the theory of fuzzy sets in the early 70s with the fairly modest claim to be a way for handling imprecision, because it also claimed probability theory is no good for that purpose (see Bellman-Zadeh [3, B-141]):

A. Rasheed, O. San, T. Kvamsdal

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

C. Waddington

J. Morley, L. Floridi, Libby Kinsey et al.

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741–742, 1960. https://doi.org/10.1126/science.132.3429.741; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the ‘what’ of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)—rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

C. DeYoung

G. Simondon

W. Brock, C. Hommes, J. Rosser

S. Papert

C. J. Nuesse

WenXi Wang, JunQi Zhang

Rapid transit of emergency vehicles is critical for saving lives and reducing property loss but often relies on surrounding ordinary vehicles to cooperatively adjust their driving behaviors. It is important to ensure rapid transit of emergency vehicles while minimizing the impact on ordinary vehicles. Centralized mathematical solver and reinforcement learning are the state-of-the-art methods. The former obtains optimal solutions but is only practical for small-scale scenarios. The latter implicitly learns through extensive centralized training but the trained model exhibits limited scalability to different traffic conditions. Hence, existing methods suffer from two fundamental limitations: high computational cost and lack of scalability. To overcome above limitations, this work proposes a scalable distributed vehicle control method, where vehicles adjust their driving behaviors in a distributed manner online using only local instead of global information. We proved that the proposed distributed method using only local information is approximately equivalent to the one using global information, which enables vehicles to evaluate their candidate states and make approximately optimal decisions in real time without pre-training and with natural adaptability to varying traffic conditions. Then, a distributed conflict resolution mechanism is further proposed to guarantee vehicles' safety by avoiding their decision conflicts, which eliminates the single-point-of-failure risk of centralized methods and provides deterministic safety guarantees that learned methods cannot offer. Compared with existing methods, simulation experiments based on real-world traffic datasets demonstrate that the proposed method achieves faster decision-making, less impact on ordinary vehicles, and maintains much stronger scalability across different traffic densities and road configurations.

Phumzile D. Mogoale, Agnieta Pretorius, Refilwe C. Mogase et al.

Background: Artificial intelligence (AI) technology is transforming education through personalised learning and creates dynamic, adaptive learning environments that cater to each student’s unique strengths and challenges. Developed countries have largely integrated AI technologies into their learning institutions, while the discipline is in its infancy in developing countries such as South Africa (SA). Objectives: This study aims to contextualise and recommend the strategy that institutions of higher learning in SA can adopt to integrate AI into their institutions. Method: A systematic literature review (SLR) method was followed. Publications published between 2018 and 2024 in the Multidisciplinary Digital Publishing Institute (MDPI) and Taylor Francis Online databases using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Following an initial search, 114 documents were retrieved, and, using inclusive criteria, 29 papers were chosen for analysis. The databases were selected because of their unique benefits in terms of accessibility, material breadth, and researcher-specific functions, unlike other sources. Results: Results show that to integrate AI, the following should be considered: planning, collaborations, training, and ethical standards to guarantee responsible use and productivity. This will enhance teaching and learning, well preparing students for a future whereby AI is widely used in the workplace. Conclusion: To integrate AI into learning institutions, a tailored approach needs to ensure that the AI technology improves teaching, enhances administrative procedures, and adheres to the institution’s rules and regulations. Contribution: This article forms a theoretical and methodological contribution to advancing knowledge that may inform policy and practice makers.

E.G. Abramochkin, V.V. Kotlyar

We theoretically and numerically investigate helical Ince-Gaussian modes, hIGp,q(x, y, ε). Explicit analytical expressions are derived that describe dependence of the orbital angular momentum of the helical Ince-Gaussian modes at p=2, 3, 4, 5 on the ellipticity parameter ε. For this purpose, the earlier obtained expansions of Ince-Gaussian modes in terms of Hermite-Gaussian modes are used. We demonstrate that in general the orbital angular momentum is an even function of ε, which changes non-monotonically when ε varies from zero to infinity. At zero ε, the orbital angular momentum is equal to the index q of the Ince-Gaussian mode, whereas at ε=∞, the orbital angular momentum is [q(p–q+1)]1/2. Topological charge of the helical Ince-Gaussian mode depends on ε and is equal to the index q at ε=0 and to the index p at ε=∞.

Vitaly Vanchurin

We present a unified geometric framework for modeling learning dynamics in physical, biological, and machine learning systems. The theory reveals three fundamental regimes, each emerging from the power-law relationship $g \propto κ^α$ between the metric tensor $g$ in the space of trainable variables and the noise covariance matrix $κ$. The quantum regime corresponds to $α= 1$ and describes Schrödinger-like dynamics that emerges from a discrete shift symmetry. The efficient learning regime corresponds to $α= \tfrac{1}{2}$ and describes very fast machine learning algorithms. The equilibration regime corresponds to $α= 0$ and describes classical models of biological evolution. We argue that the emergence of the intermediate regime $α= \tfrac{1}{2}$ is a key mechanism underlying the emergence of biological complexity.

A. Shuparskyy, Yuriy Furgala

From the viewpoint of the evolution of computing devices and corresponding use cases, the article structures an information systems timeline progressing through early electromechanical devices, electronic vacuum tubes, solid-state and integrated circuit electronics, the advent of microprocessors, personal and remote computing, and distributed systems of numerous autonomous devices, while exploring different approaches to a digital representation of computational entities. By establishing the research scope and organizing scholarly sources chronologically, the article reveals and selects connections between individual events, provides an overview and critical analysis, and highlights expected future expansions and shifts in approaches to digital representation. Thus, the article examines a shift from operations and operands, their further complication into code and data structures, and the transition from procedural to structured and object-oriented programming (OOP). Client-server applications implemented with static OOP and relational data management are examined as the pinnacle of monolithic architecture. The subsequent domain-driven design (DDD) and microservices architecture are examined as contemporary methods for remote cloud computing environments. The article then discusses the rise of the Internet of Things (IoT), the emergence of smart things and digital twins, describes advanced and novel use cases of global digitalization, such as Industry 5.0 ideas, and reveals the limitations of extant methods for corresponding digital representation. Ultimately, the article introduces a novel method for digital representation employing the post-non-classical paradigm in computer science, which eschews predefined structures in favor of dynamic, interaction-based representations, enabling flexible and adaptive design of distributed systems. Future research directions include the formal specification of this approach and the development of tools for its implementation in complex distributed systems.

Olha Khomiak

Introduction. Ellipsoids that approximate the half-ball in Rn (n≥2) and their volume is smaller than the volume of the ball can be used to construct algorithms for solving the problem of minimization of a convex (smooth or non-smooth) function, the problem of minimization of a convex function on a sphere, a general problem convex programming, saddle point problems of convex-concave functions and others. The speed of convergence of such algorithms will be determined by the ratio of the volume of the approximating ellipsoid to the volume of the ball. The purpose of the work is to describe properties of three ellipsoids for approximation of a n-dimensional half-ball, the volume reduction factor of which depends only on n - dimension of space. The first is the well-known ellipsoid of minimum volume, which is used in the classical Yudin-Nemirovsky-Shor ellipsoid method. It provides a minimum volume reduction factor and can be used if n≥2. If n=1, then the classical ellipsoid method replaces the dichotomy method. The other two ellipsoids are close to the minimum volume ellipsoid and at large n guarantee volume reduction coefficients close to the minimum. The results. It is shown that when n=1 the first approximate ellipsoid provides a coefficient of reduction of the segment length equal to 2–√2 ≈ 0.5858. The second approximate ellipsoid is new and the volume reduction factor for it is slightly larger than the factor for the first approximate ellipsoid. If n=1, then it provides a coefficient of reduction of the segment length equal to (√5–1)/2 ≈ 0.6180. For three ellipsoids, comparative results are given in terms of volume reduction coefficients (n=1/20) and the number of iterations for solving problems with relative accuracy 1e-10 (n=1/30). Conclusions. A new ellipsoid has been constructed to approximate the n-dimensional half-ball, which is close in volume to the minimal ellipsoid. The volume reduction factor of the proposed ellipsoid is approximated by an asymptotic formula 1–1/(2n)+1/n², which differs little from the formula 1–1/(2n) for an ellipsoid of minimum volume.

Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou et al.

Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on unlabeled target subject since the difference in individual motor characteristics. The unsupervised domain adaptation (UDA) method has become an effective way to this problem. However, the labeled data are collected from multiple source subjects that might be different not only from the target subject but also from each other. The current UDA methods for HMI recognition ignore the difference between each source subject, which reduces the classification accuracy. Therefore, this paper considers the differences between source subjects and develops a novel theory and algorithm for UDA to recognize HMI, where the margin disparity discrepancy (MDD) is extended to multi-source UDA theory and a novel weight-aware-based multi-source UDA algorithm (WMDD) is proposed. The source domain weight, which can be adjusted adaptively by the MDD between each source subject and target subject, is incorporated into UDA to measure the differences between source subjects. The developed multi-source UDA theory is theoretical and the generalization error on target subject is guaranteed. The theory can be transformed into an optimization problem for UDA, successfully bridging the gap between theory and algorithm. Moreover, a lightweight network is employed to guarantee the real-time of classification and the adversarial learning between feature generator and ensemble classifiers is utilized to further improve the generalization ability. The extensive experiments verify theoretical analysis and show that WMDD outperforms previous UDA methods on HMI recognition tasks.

Mingqian Li, Qiao Han, Ruifeng Li et al.

In multi-task learning, labels are often missing irregularly across samples, which can be fully labeled, partially labeled or unlabeled. The irregular label presence often appears in scientific studies due to experimental limitations. It triggers a demand for a new training and inference mechanism that could accommodate irregularly present labels and maximize their utility. This work focuses on the two-label learning task and proposes a novel training and inference framework, Dual-Label Learning (DLL). The DLL framework formulates the problem into a dual-function system, in which the two functions should simultaneously satisfy standard supervision, structural duality and probabilistic duality. DLL features a dual-tower model architecture that allows for explicit information exchange between labels, aimed at maximizing the utility of partially available labels. During training, missing labels are imputed as part of the forward propagation process, while during inference, labels are predicted jointly as unknowns of a bivariate system of equations. Our theoretical analysis guarantees the feasibility of DLL, and extensive experiments are conducted to verify that by explicitly modeling label correlation and maximizing label utility, our method makes consistently better prediction than baseline approaches by up to 9.6% gain in F1-score or 10.2% reduction in MAPE. Remarkably, DLL maintains robust performance at a label missing rate of up to 60%, achieving even better results than baseline approaches at lower missing rates down to only 10%.

Rachel Griffin

Scholarship has long identified the business imperative to create an advertiser-friendly environment as a key influence on social media content moderation. However, "brand safety" – the industry term for advertisers’ measures to avoid content perceived as reflecting negatively on their brands – remains understudied. Drawing on policy statements from industry actors, as well as extant academic literature, this article makes four contributions. First, it proposes four distinct mechanisms through which branding imperatives influence platforms’ content governance. Second, it highlights two current trends: growing efforts by major advertisers to directly influence platforms’ content policies, and a shift in industry terminology from brand safety (avoiding content widely considered objectionable) to "suitability" (evaluating appropriate content for a particular brand) – which promises advertisers greater customisation, but in fact promotes the standardisation of content governance across major platforms. Third, it explores the policy implications of these developments, in particular for equal participation and freedom of public debate on social media. Finally, it briefly explores the relevance to these concerns of the EU’s 2022 Digital Services Act, suggesting that it fails to adequately address a marketised logic in which the production and distribution of online media content is increasingly shaped by what is deemed suitable for branding objectives.

Alexander Sukhinov, Alexander Chistyakov, Elena Timofeeva et al.

This paper covers the construction and research of a scheme to solve the problem with nonlinear dispersion wave equations, described by the model Korteweg–de Vries equation. The article proposes approximating the equation based on improved “Upwind Leapfrog” schemes. Its difference operator is a linear combination of operators of the “Standard Leapfrog” and “Upwind Leapfrog” difference schemes, while the modified scheme is obtained from schemes with optimal weight coefficients. Combining certain values of the weight coefficients mutually compensates for approximation errors. In addition, the modified scheme acquires better properties compared with the original schemes. The results of test calculations of solutions of the nonlinear Korteweg–de Vries equation are presented, illustrating the advantages of the modified scheme.

Andres Soler, Luis Alfredo Moctezuma, Eduardo Giraldo et al.

Abstract High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.