Hasil untuk "Cybernetics"

F. V. Taylor

Florentin Koch

This article introduces Recursivism as a conceptual framework for analyzing contemporary artistic practices in the age of artificial intelligence. While recursion is precisely defined in mathematics and computer science, it has not previously been formalized as an aesthetic paradigm. Recursivism designates practices in which not only outputs vary over time, but in which the generative process itself becomes capable of reflexive modification through its own effects. The paper develops a five-level analytical scale distinguishing simple iteration, cumulative iteration, parametric recursion, reflexive recursion, and meta-recursion. This scale clarifies the threshold at which a system shifts from variation within a fixed rule to genuine self-modification of the rule itself. From this perspective, art history is reinterpreted as a recursive dynamic alternating between internal recursion within movements and meta-recursive transformations of their generative principles. Artificial intelligence renders this logic technically explicit through learning loops, parameter updates, and code-level self-modification. To distinguish Recursivism from related notions such as generative art, cybernetics, process art, and evolutionary art, the article proposes three operational criteria: state memory, rule evolvability, and reflexive visibility. These concepts are examined through case studies including Refik Anadol, Sougwen Chung, Karl Sims, and the Darwin-Godel Machine. The article concludes by examining the aesthetic, curatorial, and ethical implications of self-modifying artistic systems.

Chenhao Bai, Zhuo Chen, Yunsheng Li et al.

Efficient liquid manipulation is crucial in chemical engineering, biological research, clinical applications, and materials science. Bubbles, such as boiling, rising, and cavitating bubbles, have been widely employed to enhance mixing and mass transfer through their unique hydrodynamic behaviors. Yet, conventional bubble-based approaches often face limited scalability and poor performance in high-viscosity environments. Here, we introduce a strategy that employs low-energy acoustic excitation of rising microbubbles to achieve scalable and efficient mass transfer across macroscale and microscale domains. By coupling buoyancy-driven convection with localized acoustic microstreaming, acoustic rising microbubbles simultaneously extend the operational workspace and intensify local mass transfer. Particle image velocimetry and computational fluid dynamics analyses characterize the distinct contributions of buoyancy-induced flows, acoustically induced microstreaming, and their superimposed effects. Various chemical and biomedical applications, including efficient high-viscosity mixing, accelerated chemical material synthesis, altered cell membrane permeability, promoted cell lysis, and thrombus clearance, demonstrate the great potential of the proposed acoustic rising bubbles for efficient mass transfer in laboratory and industrial liquid manipulations.

Collins M. Mudogo, Angeline Mulwa, Dorothy Kyalo

Background: Although the field of digital health is rapidly growing, there is scanty information on the impact of these interventions on the overall performance of health projects. Objectives: We assessed the influence of utilisation of four types of digital health interventions (DHIs) and application of monitoring and evaluation (ME) practices on performance of human immunodeficiency virus (HIV) projects. Method: This was a cross-sectional survey across eight public health facilities providing care to HIV patients and where all the four types of DHIs were being implemented in Kisumu County, Kenya. A total of 191 service providers who were at their stations of work on the day of data collection were recruited into the study. Aspects of utilisation of the DHIs, application of ME practices and performance of the HIV projects were measured using standardised 12 statements on a 5-point Likert scale. Results: Using a multi-linear regression model, we established that the four DHIs could potentially explain 22% (R2 = 0.22; p-value 0.001 at 95% confidence interval) of variation in performance of HIV projects. Application of best ME practices could further explain the variation of the relationship between utilisation of DHIs and performance of HIV or AIDS projects up to 33.2% (R2 = 0.332; p-value 0.001 at 95% confidence interval). Conclusion: Optimal utilisation of DHIs improves performance of HIV projects. Contribution: This study provides evidence on the importance of utilising digital health in managing health projects. Further, it augments the central role of monitoring and evaluation in project performance.

M. Burgess, R. I. M. Dunbar

The human capacity for working together and with tools builds on cognitive abilities that, while not unique to humans, are most developed in humans both in scale and plasticity. Our capacity to engage with collaborators and with technology requires a continuous expenditure of attentive work that we show may be understood in terms of what is heuristically argued as`trust' in socio-economic fields. By adopting a `social physics' of information approach, we are able to bring dimensional analysis to bear on an anthropological-economic issue. The cognitive-economic trade-off between group size and rate of attention to detail is the connection between these. This allows humans to scale cooperative effort across groups, from teams to communities, with a trade-off between group size and attention. We show here that an accurate concept of trust follows a bipartite `economy of work' model, and that this leads to correct predictions about the statistical distribution of group sizes in society. Trust is essentially a cognitive-economic issue that depends on the memory cost of past behaviour and on the frequency of attentive policing of intent. All this leads to the characteristic `fractal' structure for human communities. The balance between attraction to some alpha attractor and dispersion due to conflict fully explains data from all relevant sources. The implications of our method suggest a broad applicability beyond purely social groupings to general resource constrained interactions, e.g. in work, technology, cybernetics, and generalized socio-economic systems of all kinds.

Kyrylo S. Malakhov, Sergii V. Kotlyk, Mykola G. Petrenko

This article explores the theoretical aspects of transdisciplinary research, with a specific focus on its application to telerehabilitation. The integration of multiple disciplines – ranging from medicine, digital health, and informatics to engineering and the social sciences – is increasingly necessary to address the complex challenges of delivering effective remote rehabilitation services. The article begins by outlining the conceptual framework, distinguishing between disciplinary, interdisciplinary, multidisciplinary, and transdisciplinary approaches, and highlighting the importance of transcending traditional disciplinary boundaries. The theoretical foundations discussed provide a basis for understanding how the convergence of diverse fields can lead to innovative solutions in telerehabilitation. The integration of disciplines is examined in detail, illustrating how collaborative efforts across medicine, technology, and behavioral sciences can enhance patient outcomes, improve accessibility, and foster the development of personalized rehabilitation plans. The article also covers the practical implications for clinical practice, emphasizing the need for a more collaborative model of care delivery and the potential for cost-effective, scalable solutions. Looking toward the future, the article  identifies key areas for research, including the development of advanced technologies, exploration of new therapeutic modalities, and consideration of ethical and social impacts. The need for standardization and interoperability in telerehabilitation systems is also underscored, as these will be critical to ensuring the seamless integration of various technologies and platforms.  

Julia Krämer

The General Data Protection Regulation (GDPR) obliges data controllers to inform users about data processing practices. Long criticised for inefficiency, privacy policies face a substantive shift with the recent introduction of privacy labels by the Apple App Store and the Google Play Store. This paper illustrates how privacy disclosures of apps are governed by both the GDPR and the contractual obligations of app stores and is complemented by empirical insights into the privacy disclosures of 845,375 apps from the Apple App Store and 1,657,353 apps from the Google Play Store. While the GDPR allows for the use of privacy labels as a complementary tool next to privacy policies, the design of the privacy labels does not satisfy the standards set in Art. 5(1)(a) GDPR and Art. 12-14 GDPR. The app stores may consequently distort the compliance of apps with data protection laws. The empirical data highlight further problems with the privacy labels. The design of the labels favours disclosures of developers that offer a variety of apps that can process data across different services and contradictory disclosures do not get flagged nor verified by app stores. The paper contributes to the overall discussion of how app stores in their role as intermediaries govern privacy standards and the impact of private sector-led initiatives.

Tao Yan, Zhe Xu, Simon X. Yang et al.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in three-dimensional space is a challenging but practical problem. To address this problem, this paper develops a novel consensus based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

Matteo Priorelli, Federico Maggiore, Antonella Maselli et al.

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations. Active Inference offers a modern formulation of these ideas, in terms of inferential mechanisms and prediction-error-based control, which can be linked to neural mechanisms of living organisms. This article provides a technical illustration of Active Inference models in continuous time and a brief survey of Active Inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control. Crucially, in Active Inference, all these different facets of motor control emerge from the same optimization process - namely, the minimization of Free Energy - and do not require designing separate cost functions. Therefore, Active Inference provides a unitary perspective on various aspects of motor control that can inform both the study of biological control mechanisms and the design of artificial and robotic systems.

Pedram Rabiee, S. Alireza Seyyed Mousavi, Amelia J. S. Sheffler et al.

This article presents results from an experiment in which 44 human subjects interact with a dynamic system to perform 40 trials of a command-following task. The reference command is unpredictable and different on each trial, but all subjects have the same sequence of reference commands for the 40 trials. The subjects are divided into 4 groups of 11 subjects. One group performs the command-following task without preview of the reference command, and the other 3 groups are given preview of the reference command for different time lengths into the future (0.5 s, 1 s, 1.5 s). A subsystem identification algorithm is used to obtain best-fit models of each subject's control behavior on each trial. The time- and frequency-domain performance, as well as the identified models of the control behavior for the 4 groups are examined to investigate the effects of reference-command preview. The results suggest that preview tends to improve performance by allowing the subjects to compensate for sensory time delay and approximate the inverse dynamics in feedforward. However, too much preview may decrease performance by degrading the ability to use the correct phase lead in feedforward.

Wenqi Cao, Gianluigi Pillonetto

Many problems arising in control require the determination of a mathematical model of the application. This has often to be performed starting from input-output data, leading to a task known as system identification in the engineering literature. One emerging topic in this field is estimation of networks consisting of several interconnected dynamic systems. We consider the linear setting assuming that system outputs are the result of many correlated inputs, hence making system identification severely ill-conditioned. This is a scenario often encountered when modeling complex cybernetics systems composed by many sub-units with feedback and algebraic loops. We develop a strategy cast in a Bayesian regularization framework where any impulse response is seen as realization of a zero-mean Gaussian process. Any covariance is defined by the so called stable spline kernel which includes information on smooth exponential decay. We design a novel Markov chain Monte Carlo scheme able to reconstruct the impulse responses posterior by efficiently dealing with collinearity. Our scheme relies on a variation of the Gibbs sampling technique: beyond considering blocks forming a partition of the parameter space, some other (overlapping) blocks are also updated on the basis of the level of collinearity of the system inputs. Theoretical properties of the algorithm are studied obtaining its convergence rate. Numerical experiments are included using systems containing hundreds of impulse responses and highly correlated inputs.

Alin-Gabriel Vaduva, Simona-Vasilica Oprea, Dragos-Catalin Barbu

The web offers large volumes of data that is unstructured and fails to be further processed if not extracted and organized into local variables or into databases. In this paper, we aim to extract data from the Internet using web scraping and analyse it with Natural Language Processing (NLP). Our purpose is to understand customers’ opinions by extracting reviews and investigating them in Python. The positive or negative insight of the reviews, along with the word cloud offer additional tools to understand the customers, predict their behaviour and underpin problems signalled in the reviews. TextBlob and BERTweet are applied to analyse the reviews. To enhance the comprehension of the outcomes, a comparison is drawn between the classifications generated by the BERTweet model and those provided by the TextBlob API, a widely used Python library for performing various NLP tasks. Furthermore, the reviews are pre-processed to clean them from line breaks, punctuation characters etc. and a n-grams analysis is performed to better understand the positive and negative reviews. The frequency of the reviews displays the concrete problems faced by customers visiting the hotel in various seasons. It helps decision makers to take measures and improve the quality of the hotel services.

Hadi Givi, Mohammad Dehghani, Stepan Hubalovsky

This paper presents a new bio-inspired metaheuristic algorithm called Red Panda Optimization (RPO) that imitates the natural behaviors of red pandas in nature. The main design idea of RPO is derived from two characteristic natural behaviors of red pandas: (i) foraging strategy, and (ii) climbing trees to rest. The proposed RPO approach is mathematically modeled in two phases of exploration based on the simulation of red pandas’ foraging strategy and exploitation based on the simulation of red pandas’ movement in climbing trees. The main advantage of the proposed approach is that there is no control parameter in its mathematical modeling, and for this reason, it does not need a parameter adjustment process. The performance of RPO is evaluated on fifty-two standard benchmark functions including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types as well as CEC 2017 test suite. The optimization results obtained by the proposed RPO approach are compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that RPO, by maintaining the balance between exploration and exploitation, is effective in solving optimization problems and its performance is superior over competitor algorithms. Based on the analysis of the optimization results, RPO has provided more successful performance compared to the competitor algorithms in 100% of unimodal functions, 100% of high-dimensional multimodal functions, 100% of fixed-dimensional multimodal functions, and 86.2% of CEC 2017 test suite benchmark functions. Also, the statistical analysis of the Wilcoxon rank sum test shows that the superiority of RPO in the competition with the compared algorithms is significant from a statistical point of view. In addition, the results of implementing RPO on four engineering design problems confirms the ability of the proposed approach to handle real-world optimization applications.

Ruopeng An, Yuyi Yang, Fan Yang et al.

As the Chat Generative Pre-trained Transformer (ChatGPT) achieves increased proficiency in diverse language tasks, its potential implications for academic integrity and plagiarism risks have become concerning. Traditional plagiarism detection tools primarily analyze text passages, which may fall short when identifying machine-generated text. This study aims to introduce a method that uses both prompts and essays to differentiate between machine-generated and human-written text, with the goal of enhancing classification accuracy and addressing concerns of academic integrity. Leveraging a dataset of student-written essays responding to eight distinct prompts, we generated comparable essays with ChatGPT. Similarity scores within machine-generated essays (“within” scores) and between human-written and machine-generated essays (“between” scores) were computed. Subsequently, we used the percentile scores of the “between” scores within the “within” scores distribution to gauge the probability of an essay being machine-generated. Our proposed method achieved high classification accuracy, with an AUC score of 0.991, a false positive rate of 0.01, and a false negative rate of 0.037 in the test set. This validates its effectiveness in distinguishing between machine-generated and human-written essays and shows that it outperforms existing approaches based solely on text passages. This research presents a straightforward and effective method to detect machine-generated essays using prompts, providing a reliable solution to maintain academic integrity in the era of advanced language models like ChatGPT. Nevertheless, the method is not without its limitations, warranting further research to investigate its performance across diverse educational contexts, various prompts, and different model hyperparameters.

Tatsuya Chuman, Hitoshi Kiya

The security of learnable image encryption schemes for image classification using deep neural networks against several attacks has been discussed. On the other hand, block scrambling image encryption using the vision transformer has been proposed, which applies to lossless compression methods such as JPEG standard by dividing an image into permuted blocks. Although robustness of the block scrambling image encryption against jigsaw puzzle solver attacks that utilize a correlation among the blocks has been evaluated under the condition of a large number of encrypted blocks, the security of encrypted images with a small number of blocks has never been evaluated. In this paper, the security of the block scrambling image encryption against ciphertext-only attacks is evaluated by using jigsaw puzzle solver attacks.

Abicumaran Uthamacumaran, Hector Zenil

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems oncology. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.

N.Y. Ilyasova, D.V. Kirsh, N.S. Demin

In this work, we propose a decision-making support system for automatically mapping an effective photocoagulation pattern for the laser treatment of diabetic retinopathy. The purpose of research to create automated personalization of diabetic macular edema laser treatment. The results are based on analysis of large semi-structured data, methods and algorithms for fundus image processing. The technology improves the quality of retina laser coagulation in the treatment of diabetic macular edema, which is one of the main reasons for pronounced vision decrease. The proposed technology includes original solutions to establish an optimal localization of multitude burns by determining zones exposed to laser. It also includes the recognition of large amount of unstructured data on the anatomical and pathological locations' structures in the area of edema and data optical coherent tomography. As a result, a uniform laser application on the pigment epithelium of the affected retina is ensured. It will increase the treatment safety and its effectiveness, thus avoiding the use of more expensive treatment methods. Assessment of retinal lesions volume and quality will allow predicting the laser photocoagulation results and will contribute to the improvement of laser surgeon's skills. The architecture of a software complex comprises a number of modules, including image processing methods, algorithms for photocoagulation pattern mapping, and intelligent analysis methods.

E. Robinson

Xiao Wang, Jianing Li, Lin Zhu et al.

Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency. In practice, visible cameras can better perceive texture details and slow motion, while event cameras can be free from motion blurs and have a larger dynamic range which enables them to work well under fast motion and low illumination. Therefore, the two sensors can cooperate with each other to achieve more reliable object tracking. In this work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task. Our dataset consists of 820 video pairs captured under low illumination, high speed, and background clutter scenarios, and it is divided into a training and a testing subset, each of which contains 500 and 320 videos, respectively. Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods by extending current single-modality trackers into dual-modality versions. More importantly, we further build a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data. Extensive experiments on the proposed VisEvent dataset, FE108, COESOT, and two simulated datasets (i.e., OTB-DVS and VOT-DVS), validated the effectiveness of our model. The dataset and source code have been released on: \url{https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark}.

Yuyu Guo, Lei Bi, Dongming Wei et al.

Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In this study, we provide a novel motion estimation framework of Dense-Sparse-Dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation. For this purpose, we introduce an unsupervised 3D landmark detection network to extract spatially sparse but representative landmarks for the target organ motion estimation. In the second stage, we derive the sparse motion displacement from the extracted sparse landmarks of two images of different time points. Then, we present a motion reconstruction network to construct the motion field by projecting the sparse landmarks displacement back into the dense image domain. Furthermore, we employ the estimated motion field from our two-stage DSD framework as initialization and boost the motion estimation quality in light-weight yet effective iterative optimization. We evaluate our method on two dynamic medical imaging tasks to model cardiac motion and lung respiratory motion, respectively. Our method has produced superior motion estimation accuracy compared to existing comparative methods. Besides, the extensive experimental results demonstrate that our solution can extract well representative anatomical landmarks without any requirement of manual annotation. Our code is publicly available online.