Hasil untuk "Cybernetics"

Stephen Hughes

It is difficult to give a full assessment of this book (which was previewed at last year's Mental Health Congress) since it is the first part of a two volume work, which should no doubt be viewed as a whole. Nevertheless, the important place it will have in the field of psychiatric literature is already obvious. Whereas many psychoanalysts have tended to harden in their views with the passage of years?as do people in all walks of life?Bowlby has taken the opposite course. On the basis of his trained sensitivity to human behaviour, he has reached out to such new sciences as animal ethology and cybernetics, which were unknown in Freud's day. The result is provocative, enlightening and profoundly stimulating, though not everyone will be able to accept fully the mixture that emerges. The concept of instinctive behaviour in man still remains profoundly difficult, though the author's version is a more acceptable one than most which have been offered before. As far as the mother-child tie is concerned, this makes infinitely more sense in the light of human evolution?as interpreted by Bowlby?than on the basis of conventional psychoanalytical theory. Those who do not have the chore of reading this book in full will be able to get an impression of its most important parts from Bowlby's contribution to the 1968 Congress proceedings, which are to appear shortly. The present work is a worthy successor to that now classic monograph on maternal separation with which the author came to the world's attention in 1951.

J. Alexander, G. Morgan

N. Wiener

Magoroh Maruyama

Norbert Wiener

Kilosanidze Barbara, Kakauridze George, Chaganava Irakli et al.

We propose a precise method for measuring the rotation angle of the polarization plane using a polarization-holographic grating combined with a quarter-wave plate. This approach enables real-time measurements with high accuracy.

Xinzhuo Yu, Yunzhi Zhuge, Sitong Gong et al.

Understanding the inter-relations and interactions between tasks is crucial for multi-task dense prediction. Existing methods predominantly utilize convolutional layers and attention mechanisms to explore task-level interactions. In this work, we introduce a novel decoder-based framework, Parameter Aware Mamba Model (PAMM), specifically designed for dense prediction in multi-task learning setting. Distinct from approaches that employ Transformers to model holistic task relationships, PAMM leverages the rich, scalable parameters of state space models to enhance task interconnectivity. It features dual state space parameter experts that integrate and set task-specific parameter priors, capturing the intrinsic properties of each task. This approach not only facilitates precise multi-task interactions but also allows for the global integration of task priors through the structured state space sequence model (S4). Furthermore, we employ the Multi-Directional Hilbert Scanning method to construct multi-angle feature sequences, thereby enhancing the sequence model's perceptual capabilities for 2D data. Extensive experiments on the NYUD-v2 and PASCAL-Context benchmarks demonstrate the effectiveness of our proposed method. Our code is available at https://github.com/CQC-gogopro/PAMM.

S. Beer

Duanduan Liu

With the rapid development of machine learning, challenging question and answer datasets have also emerged, and the machine reading comprehension technology has emerged. Traditional machine reading comprehension methods mostly focus on the understanding word level semantics, with the weak ability to extract logical relationships from text, resulting in the lower ability of logical reasoning. In order to strengthen the ability of machine reading comprehension method to extract the logical relationship of text and the ability of logical reasoning, a neural symbol model based on logical reasoning was proposed, and the logical expressions captured by the neural symbol model were converted into text input and trained in a mixed reasoning reading comprehension model based on symbolic logic. The mixed reasoning reading comprehension model based on symbolic logic is different from the traditional machine reading comprehension model. It uses symbolic definition and logical capture to extract logical symbols and generate logical expressions. The research results show that the accuracy and F-measure values of the neural symbol model based on the logical reasoning are 70.08% and 70.05%, respectively, when the training set sample size is 4000. The accuracy of the mixed reasoning reading comprehension model based on symbolic logic in the logical reasoning data set of the standard postgraduate entrance examination is 88.31%, which is higher than the 58.74% of the language perception map network model. The accuracy rate in the four-choice and one-choice question-and-answer data set is 40.92%, which is 1.58% higher than that of the language awareness graph network model. In summary, the neural symbol model and hybrid inference reading comprehension model proposed in the study have superior performance, which can capture the logical relationship of text in data sets well, improve the model feature abstraction and reasoning ability, effectively shorten the training time and improve the model efficiency.

Iryna Voronenko, Alla Bohush, Oleksandr Voronenko et al.

In this work, the main trends in research and publication activity in digital transformation in the world and Ukraine are analyzed using meta- and bibliometric analysis. For this purpose, bibliometric data on scientific publications on the topic of digital transformations in the Google Scholar and Scopus databases were selected, which were additionally analyzed using the VOSviewer software package. Based on filtering the results obtained, an array was formed that included 366 scientific publications for 2019–2023 in Ukrainian in the Google Scholar database and 3,703 scientific publications in English for 2020–2023 in the Scopus database. Dynamic time analysis revealed a significant surge of scientific interest in the topic of digital transformations in recent years, while structural analysis revealed the multi-industry structure of existing research. The creation of bibliographic maps of keywords and publication maps allowed us to form an idea of the main thematic areas of research in the context of digital transformations and their opinion leaders. The data obtained became the basis for formulating recommendations for further areas of research in digital transformation, in particular, on the development of a unified roadmap for the digital transformation of education at different educational qualification levels and for various specialties. This will contribute to the formation of a single systemic approach to the digital transformation of Ukraine as a guarantee for the state’s sustainable development, well-being, strengthening of national security, speeding up the pace of European integration processes, as well as promoting national interests at the international level.

William Rupush, Oscar Grånäs

We present a new proposal for distinguishing heat from work based on a control-theoretic observability decomposition. We derive a Hermitian operator representing instantaneous dissipation of observable energy, and suggest a generalization of the von-Neumann entropy which can account for the model-uncertainty also present in pure states if the measured observables are informationally incomplete. In this view, the transition from a fundamental to a thermodynamic model consists in mapping the fundamental density matrix to an effective one, generally of lower dimension, encoding only what is observable given the constraints of our sensor and actuator capabilities. The generalized entropy captures the information loss incurred in this mapping. The theory is illustrated for the central spin model, where we show that the application of external controls can increase the size of thermal fluctuations and lower the entropy.

Kamran Farajzadeh, Mohammad Taghi Taghavifard, Abbas Toloie Ashlaghi et al.

Purpose: Collecting data from users using conventional traditional methods, in order to achieve a specific purpose, is often expensive, time-consuming, and accompanied by disadvantages affecting the research results, the aim of the current research is to provide a model based on gamification in order to obtain the way of thinking of people through a case study of internet taxi applications in Iran (Snapp, Tapsi, and Carpino). Method: Research is applied in terms of aim and descriptive in terms of nature. Data collection was done by a researcher-made questionnaire, whose validity and reliability were confirmed based on expert opinion and Cronbach's alpha coefficient. The statistical population was all the users of the mentioned applications and members of Telegram, which was 180 people as a statistical sample. Findings: Comparing the results to determine the popularity of brands in the two methods was almost the same, in addition, the cost and time of using the proposed method was significantly less and the percentage of audience participation was higher. Conclusion: By presenting the proposed framework, the limitations of common data collection methods were removed and more accurate and higher quality data were used to understand people's thinking, it is also possible to use this method for all researchers.

Mohammad Hassan zadeh, HamidReza Mahmoodi, Davoud Haseli et al.

Objectives:This research aims to prioritize the actions of public libraries in Iran to increase the overall satisfaction of users based on the Kano model and the asymmetric performance effect.Methods: The research method is descriptive in terms of data collection and practical in terms of purpose. The statistical population of this research is the number of 10,000 public library users in Iran, of which 400 people were selected as a sample according to Morgan's table by random cluster sampling. The research tool is a questionnaire that was created and validated by the researcher. Data were analyzed with SPSS software. 23 versions were analyzed.Results: The results showed that the three features of public library services, social and cultural programs, promotion, and introduction of the library are among the basic services with low performance, and the feature of library costs is among the basic services with high performance. The characteristic of the library's fame and popularity is among the functional services with low performance, and the characteristics of librarians, equipment, and collections are among the group of functional services with high performance. The characteristics of ancillary services and social participation are among motivational services with low performance, and the characteristics of activity time, space, place, the feeling of comfort and security, and library system and software are among motivational services with high performance.Conclusions: Basic and low-performance functional services cause dissatisfaction with public libraries, which should be the first priority to increase user satisfaction. In the second priority, low-performance motivational services should be upgraded to high-performance, and in the last priority, the level of basic, functional, and high-performance motivational services should be maintained at the current level. This type of prioritization of actions in public libraries has been done by considering the two principles of highlighting negative performance compared to positive performance and also the ability to remember positive events against negative events.

Fatemeh Heidarnezhad, Fatima Fahimnia

Objective: The main purpose of this study is to identify the relationship between the level of participation in knowledge management and the critical thinking tendency among librarians of central libraries of public universities in Tehran. Methodology: This research is descriptive-correlational in terms of research method and is a survey branch. In addition, according to the purpose, it falls into the category of applied research. The statistical population of this study consisted of librarians of the central libraries of public universities in Tehran, whose number was 210 people. Using the Morgan table, a sample number of 136 people was obtained. Data were collected using two standard questionnaires: Employee Participation in Knowledge Management (Kulkarni et al. (2007)) and the California Critical Thinking Tendency Questionnaire (1992). The supervisor validated both questionnaires. The reliability of the questionnaires was obtained using Cronbach's alpha coefficient for the questionnaire on employee participation in knowledge management was 0.946 and for the questionnaire of critical thinking tendency was 0.933. The data analysis was done through descriptive statistics and inferential statistics using SPSS software. Results: The results of data analysis showed that there is a significant positive relationship between the variable of critical thinking tendency and participation in knowledge management. Conclusion: Researchers define and examine critical thinking in two dimensions: critical thinking skills and tendencies, in previous researches the relationship between knowledge management and the skills dimension was investigated, but in this research, the relationship between participation in knowledge management and the dimension of tendency to critical thinking was investigated.

Yuki Kuroda, Yusuke Yamanoi, Shunta Togo et al.

The usability of a prosthetic hand differs significantly from that of a real hand. Moreover, the complexity of manipulation increases as the number of degrees of freedom to be controlled increases, making manipulation with biological signals extremely difficult. To overcome this problem, users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from falling. In previous studies, these have been left to the operating skills of the user, which is extremely difficult to achieve. In this study, we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user’s operation technique. The required grasping technique is achieved by determining the correlation between the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli, such as myoelectricity, sense of touch, and grasping objects. The agents of the 16-DOF robot hand were trained with the myoelectric signals of six participants, including one child with a congenital forearm deficiency. Consequently, each agent could open and close the hand in response to the myoelectric stimuli and could accomplish the object pickup task. For the tasks, the agents successfully identified grasping patterns suitable for practical and stable positioning of the objects. In addition, the agents were able to pick up the object in a similar posture regardless of the participant, suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

Yawen Cui, Zitong Yu, Wei Peng et al.

Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously. The current protocol of FSCIL is built by mimicking the general class-incremental learning setting, while it is not totally appropriate due to the different data configuration, i.e., novel classes are all in the limited data regime. In this paper, we rethink the configuration of FSCIL with the open-set hypothesis by reserving the possibility in the first session for incoming categories. To assign better performances on both close-set and open-set recognition to the model, Hyperbolic Reciprocal Point Learning module (Hyper-RPL) is built on Reciprocal Point Learning (RPL) with hyperbolic neural networks. Besides, for learning novel categories from limited labeled data, we incorporate a hyperbolic metric learning (Hyper-Metric) module into the distillation-based framework to alleviate the overfitting issue and better handle the trade-off issue between the preservation of old knowledge and the acquisition of new knowledge. The comprehensive assessments of the proposed configuration and modules on three benchmark datasets are executed to validate the effectiveness concerning three evaluation indicators.

Tianxi Wang, Feng Xue, Yu Zhou et al.

For the 2D laser-based tasks, e.g., people detection and people tracking, leg detection is usually the first step. Thus, it carries great weight in determining the performance of people detection and people tracking. However, many leg detectors ignore the inevitable noise and the multiscale characteristics of the laser scan, which makes them sensitive to the unreliable features of point cloud and further degrades the performance of the leg detector. In this paper, we propose a multiscale adaptive-switch Random Forest (MARF) to overcome these two challenges. Firstly, the adaptive-switch decision tree is designed to use noisesensitive features to conduct weighted classification and noiseinvariant features to conduct binary classification, which makes our detector perform more robust to noise. Secondly, considering the multiscale property that the sparsity of the 2D point cloud is proportional to the length of laser beams, we design a multiscale random forest structure to detect legs at different distances. Moreover, the proposed approach allows us to discover a sparser human leg from point clouds than others. Consequently, our method shows an improved performance compared to other state-of-the-art leg detectors on the challenging Moving Legs dataset and retains the whole pipeline at a speed of 60+ FPS on lowcomputational laptops. Moreover, we further apply the proposed MARF to the people detection and tracking system, achieving a considerable gain in all metrics.

Steve Dixon

Gregory Bateson observed that cybernetics is not essentially about "exchanging information across lines of discipline, but in discovering patterns common to many disciplines". This paper adopts his line of thought to join the dots between cybernetics and the philosophy of Existentialism, and then interconnect both with contemporary art. It demonstrates that while terminologies may differ, many of the three fields' primary concerns closely cohere. The world's most ground-breaking artists are found to apply and fuse cybernetic paradigms and Existentialist themes, from Robert Rauschenberg and Marina Abramović to Damien Hirst, Stelarc and Anish Kapoor. The research offers the first detailed comparison between cybernetics and Existentialism, and reveals surprising commonalities. Feedback loops, circular causality and negative entropy are not only central tenets of cybernetics, but also of Existentialism. Autonomy, autopoiesis and interactivity equally unite both fields, and each is visionary and forward looking in seeking radical change and transformations. Both explored artistic endeavours, with Existentialists Jean-Paul Sartre and Albert Camus equally renowned for their powerful novels and plays as their philosophical works, while cybernetic art became a major phenomenon in the 1960s following the landmark exhibition <em>Cybernetic Serendipity: the Computer in the Arts</em> (1968), and influenced artistic practices thereafter.

Weiming Xiang

Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stages of modeling, sensing, and control at both offline and online levels. This paper addresses the run-time safety monitoring problem of dynamical systems embedded with neural network components. A run-time safety state estimator in the form of an interval observer is developed to construct lower-bound and upper-bound of system state trajectories in run time. The developed run-time safety state estimator consists of two auxiliary neural networks derived from the neural network embedded in dynamical systems, and observer gains to ensure the positivity, namely the ability of estimator to bound the system state in run time, and the convergence of the corresponding error dynamics. The design procedure is formulated in terms of a family of linear programming feasibility problems. The developed method is illustrated by a numerical example and is validated with evaluations on an adaptive cruise control system.

Guisong Liu, Wenjie Deng, Xiurui Xie et al.

As the third-generation neural networks, Spiking Neural Networks (SNNs) have great potential on neuromorphic hardware because of their high energy-efficiency. However, Deep Spiking Reinforcement Learning (DSRL), i.e., the Reinforcement Learning (RL) based on SNNs, is still in its preliminary stage due to the binary output and the non-differentiable property of the spiking function. To address these issues, we propose a Deep Spiking Q-Network (DSQN) in this paper. Specifically, we propose a directly-trained deep spiking reinforcement learning architecture based on the Leaky Integrate-and-Fire (LIF) neurons and Deep Q-Network (DQN). Then, we adapt a direct spiking learning algorithm for the Deep Spiking Q-Network. We further demonstrate the advantages of using LIF neurons in DSQN theoretically. Comprehensive experiments have been conducted on 17 top-performing Atari games to compare our method with the state-of-the-art conversion method. The experimental results demonstrate the superiority of our method in terms of performance, stability, robustness and energy-efficiency. To the best of our knowledge, our work is the first one to achieve state-of-the-art performance on multiple Atari games with the directly-trained SNN.