In order to achieve a novel disaster early warning mode of “autonomous modeling + integrated early warning + root cause tracing”, and improve the knowledge engineering infrastructure for integrated intelligent disaster warning, this study developed an ontology model for integrated knowledge graphs of disaster based on the “human-machine-environment” system engineering theory. The model was constructed through a top-down approach across four dimensions: temporal, spatial, managerial, and process mechanisms. It encompasses eight core concepts in this field of personnel, equipment, environment, region, process, document, index, and disaster, along with three categories of data attributes (basic information, spatial information, and temporal information). Three types of relationships were established: spatial positioning, numerical correlations, and process mechanism linkages. A hybrid data storage architecture integrating relational, spatial, temporal, and graph databases was built. Data extraction for entities, attributes, and relationships was achieved through a combination of rule-driven workflow engines and manual data supplementation, forming mine-specific disaster-integrated knowledge graphs. By adopting graph database relationship reasoning methods, coupled with anomaly identification criteria for graph objects, root cause analysis of mine disasters was realized. The results show that a “top-down” knowledge graph construction scheme that involves expert modeling and regularized data extraction is suitable in the early stage in the field of integrated analysis of mine disasters; the framework of “eight core concepts, three data attributes, and three relationship types” significantly enriches disaster knowledge systems; the hybrid methodology of rule-driven engines and manual supplementation effectively addresses the needs of mines at varying intelligentization stages; knowledge graph relationship reasoning integrated with object attribute anomaly detection provides a robust technical solution for accident causation analysis.
Olga V. Sergeeva, Marina R. Zheltukhina, Elena B. Ponomarenko
In the 21st century, the interdisciplinary research interest is increasingly aroused by the linguistic problem of realizing educational discourse, especially in the media space. Information ecology and cyber security make it possible to form the skill of making adequate educational, and managerial decisions in the field of education under the conditions of saturation with information, information noise in the digital environment. The purpose of the study is to identify the genre features of the educational media discourse in the context of information ecology and cyber security. A functional-genre analysis of the educational media discourse in the digital media space is carried out in the study. By applying a set of methods (descriptive method, content analysis, discursive analysis, linguosemiotic analysis, linguopragmatic analysis, functional-genre analysis, interpretive analysis), the genre media reflection of the regulation of society’s activities to achieve hygienic safety goals related to information is studied, which constitutes the scientific novelty of the study. The main genres of the educational media discourse that are significant for the development of genre theory have been identified, those are: analytical, popularizing, explanatory, didactic, regulating, recommendation, discussion, multimedia, case study genres. Their analyses allow conclude that an ecosystem comfortable for training students is formed due to compliance with the requirements of information ecology, information and Internet hygiene, ethics, cyber security, which are among the preventive trends and protective measures in the digital environment. The analysis of factual material emphasizes the importance of ensuring the safety of students as one of the key tasks of the modern educational process, considering the active influence of the media environment. It is established that media articles inform an addressee about the activities of preventive medicine and state sanitary and epidemiological services that are developing norms that reflect the safe organization of the work and educational process using information tools in the digital media space. Documents presented in various media genres determine the norms of lighting at different times of the day, the norms of noise parameters and work with electronic teaching aids and other acceptable conditions to ensure high-quality work without harm to health. The identified genre features of the educational media discourse in the context of information ecology and cyber security clearly demonstrate that the informational ecology is a promising direction for the study and development of the media discourse, incl. educational media discourse, based on the material of various linguistic cultures.
Language. Linguistic theory. Comparative grammar, Semantics
In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.
This paper introduces a method for calculating the quantum relative entropy of channels, an essential quantity in quantum channel discrimination and resource theories of quantum channels. By building on recent developments in the optimization of relative entropy for quantum states [Koßmann and Schwonnek, arXiv:2404.17016], we introduce a discretized linearization of the integral representation for the relative entropy of states, enabling us to handle maximization tasks for the relative entropy of channels. Our approach here extends previous work on minimizing relative entropy to the more complicated domain of maximization. It also provides efficiently computable upper and lower bounds that sandwich the true value with any desired precision, leading to a practical method for computing the relative entropy of channels.
Damiano Azzolini, Elena Bellodi, Rafael Kiesel
et al.
Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, possibly accounting for the uncertainty of the environment. In this paper, we introduce the possibility to encode decision theory problems with Probabilistic Answer Set Programming under the credal semantics via decision atoms and utility attributes. To solve the task we propose an algorithm based on three layers of Algebraic Model Counting, that we test on several synthetic datasets against an algorithm that adopts answer set enumeration. Empirical results show that our algorithm can manage non trivial instances of programs in a reasonable amount of time. Under consideration in Theory and Practice of Logic Programming (TPLP).
Standard information theory says nothing about how much meaning is conveyed by a message. We fill this gap with a rigorously justifiable, quantitative definition of ``pragmatic information'', the amount of meaning in a message relevant to a particular decision. We posit that such a message updates a random variable, $ω$, that informs the decision. The pragmatic information of a single message is then defined as the Kulbach-Leibler divergence between the prior and posterior probabilities of $ω$; the pragmatic information of a message ensemble is the expected value of the pragmatic information of the ensemble's component messages. We justify these definitions by proving that the pragmatic information of a single message is the expected difference between the shortest binary encoding of $ω$ under the a priori and a posteriori distributions, and that the average of the pragmatic values of individual messages, when sampled a large number of times from the ensemble, approaches its expected value. Pragmatic information is non-negative and additive for independent decisions and ``pragmatically independent'' messages. Also, pragmatic information is the information analogue of free energy: just as free energy quantifies the part of a system's total energy available to do useful work, so pragmatic information quantifies the information actually used in making a decision. We sketch 3 applications: the single play of a slot machine, a.k.a. a ``one armed bandit'', with an unknown payout probability; a characterization of the rate of biological evolution in the so-called ``quasi-species'' model; and a reformulation of the efficient market hypothesis of finance. We note the importance of the computational capacity of the receiver in each case.
Abstract The area of one-bit compressed sensing (1-bit CS) focuses on the recovery of sparse signals from binary measurements. Over the past decade, this field has witnessed the emergence of well-developed theories. However, most of the existing literature is confined to fully random measurement matrices, like random Gaussian and random sub-Gaussian measurements. This limitation often results in high generation and storage costs. This paper aims to apply semi-tensor product-based measurements to 1-bit CS. By utilizing the semi-tensor product, this proposed method can compress high-dimensional signals using lower-dimensional measurement matrices, thereby reducing the cost of generating and storing fully random measurement matrices. We propose a regularized model for this problem that has a closed-form solution. Theoretically, we demonstrate that the solution provides an approximate estimate of the underlying signal with upper bounds on recovery error. Empirically, we conduct a series of experiments on both synthetic and real-world data to demonstrate the proposed method’s ability to utilize a lower-dimensional measurement matrix for signal compression and reconstruction with enhanced flexibility, resulting in improved recovery accuracy.
Abstract The recognition of cutting state of coal-rock is the key technology to realize “unmanned” mining in coal face. In order to realized real-time perception and accurate judgment of coal-rock cutting state information, this paper combined the field test sampling, construction technology of complex coal seam, virtual prototype technology, bidirectional coupling technology, data processing theory, image fusion method, and deep learning theory to carry out multi domain deep fusion experimental research on multi-source heterogeneous data of coal and rock cutting state. The typical complex coal seam containing gangue, inclusion, and minor fault in Yangcun mine of Yanzhou mining area was taken as the engineering object. The high-precision three-dimensional simulation model of the complex coal seam that can update and replace particles was constructed. Based on the simulation results of Discrete Element Method-Multi Flexible Body Dynamics (DEM-MFBD), the one-dimensional original vibration acceleration signals of the key components of the shearer cutting part were determined, including spiral drum, rocker arm shell, and square head. After transforming one-dimensional original signal data into two-dimensional time–frequency images by Short-time Fourier Transform, morphological wavelet image fusion technology was used to realize the effective fusion of characteristic information of spiral drum, rocker arm shell, and square head under different working conditions. Based on the deep learning theory, the DCGAN-RFCNN (Deep Convolutional Generative Adversarial Networks-Random Forest Convolutional Neural Networks) coal and rock cutting state recognition network model was constructed. Combining convolution neural network with random forest recognition classifier, RFCNN coal and rock cutting state recognition classification model was constructed, and the recognition network model was trained to obtain the model recognition results. Through the comparative experimental analysis of the RFCNN network model with different recognition network models and different synthetic sample numbers in the recognition network, the effectiveness of the recognition network model was verified. The results show that: When synthetic samples are not included in each working condition in the RFCNN model, the average recognition rate is 90.641%. With the increase of the number of synthetic samples, the recognition rate of coal and rock cutting state increases. When the number of synthetic samples added to each working condition reaches 5000, the recognition effect is the best, and the average recognition rate reaches 98.344%, which verifies the superiority of enriching the data set by using the improved DCGAN network. Also, the RFCNN outperformed the other variants: it obtained higher recognition accuracy by 25.085, 21.925 and 19.337%, respectively, over SVW, CNN, and AlexNet. Also, the experimental platform of shearer cutting coal and rock was built, where the coal and rock cutting state recognition network was trained and tested based on the migration learning theory. Through the statistical test results, the accuracy of coal and rock cutting state recognition is 98.64%, which realizes the accurate recognition of coal and rock cutting state.
Objective: The study aims to explore the style of leadership of small business women by applying the theory of power competition to analyze competence influencing leadership in power SMEs competitiveness. Study will focus on the abilities for growth in a period longer than a profitability period short for SMEs. Ability growth is considered a fundamental factor for SMEs, so follow up a study about the influence of women in competence to SMEs performance growth. Research Design & Methods: A quantitative research (positivism). The research location is SMEs in Batu City with 98 respondents. Data analysis methods use validity, reliability, classic assumption, and hypothesis tests. Findings: The female leadership competencies with five dimensions (strategic visions, operation management, professional knowledge, hands-on experience, relationship building) have a positive and significant effect on performance from the perspective of consumers. Implications & Recommendations: The study is that it can provide an understanding of female leadership, that the spirit of competence within oneself needs to be developed so that the business that is built can progress and develop rapidly. Contribution & Value Added: The study expected to be able to provide information as a basis for consideration, support, and contribution of ideas to decision makers in an effort to be able to increase income and carry out business development.
Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these systems presents a long-standing and complex scientific challenge. This challenge is rooted in the difficulty of assessing a system's overall effectiveness in assisting users to complete tasks through interactive support, and further exacerbated by the substantial variation in user behaviour and preferences. To address this challenge, user simulation emerges as a promising solution. This book focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We begin with a background of information access system evaluation and explore the diverse applications of user simulation. Subsequently, we systematically review the major research progress in user simulation, covering both general frameworks for designing user simulators, utilizing user simulation for evaluation, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. Realizing that user simulation is an interdisciplinary research topic, whenever possible, we attempt to establish connections with related fields, including machine learning, dialogue systems, user modeling, and economics. We end the book with a detailed discussion of important future research directions, many of which extend beyond the evaluation of information access systems and are expected to have broader impact on how to evaluate interactive intelligent systems in general.
In this paper, we present electromagnetic signal and information theory (ESIT). ESIT is an interdisciplinary scientific discipline, which amalgamates electromagnetic theory, signal processing theory, and information theory. ESIT is aimed at studying and designing physically consistent communication schemes for the transmission and processing of information in communication networks. In simple terms, ESIT can be defined as physics-aware information theory and signal processing for communications. We consider three relevant problems in contemporary communication theory, and we show how they can be tackled under the lenses of ESIT. Specifically, we focus on (i) the theoretical and practical motivations behind antenna designs based on subwavelength radiating elements and interdistances; (ii) the modeling and role played by the electromagnetic mutual coupling, and the appropriateness of multiport network theory for modeling it; and (iii) the analytical tools for unveiling the performance limits and realizing spatial multiplexing in near field, line-of-sight, channels. To exemplify the role played by ESIT and the need for electromagnetic consistency, we consider case studies related to reconfigurable intelligent surfaces and holographic surfaces, and we highlight the inconsistencies of widely utilized communication models, as opposed to communication models that originate from first electromagnetic principles.
Ewelina Kania, Grzegorz Śladowski, Elżbieta Radziszewska-Zielina
et al.
Communication and information flowduring construction project execution is often discussed in the literature. Numerous scholars note the presence of problems with communication and information flow and highlight that these problems also affect construction project completion time and cost. The vast majority of studies on the impact of communication on construction project completion time and cost takes on a qualitative character and there is a lack of quantitative analyses of this subject. To address these deficiencies, the authors of this paper propose a quantitative approach to assessing communication between construction project participants in the aspect of its impact on said project’s completion time and cost. The authors used meta-network theory to model and analyse the problem, as it can fully depict the problem’s complexity. The method proposed allows for dynamic identification of key information flow paths between project participants, which determine its performance in an essential way. The proposed approach can support decision-makers in effective management of communication between a construction project’s participants, which has a positive carryover to achieving planned project goals. The method was tested on a real-world development project that featured the construction of a housing complex in Katowice, Poland.
With the rapid development of digital information technology, life has become more convenient for people; however, the digital divide for the elderly was even more serious, so they became a forgotten group in the internet age over time. Residents' demand for healthcare is rising, but the wisdom healthcare service supported by digital information technology is less acceptable to the elderly due to the digital divide. Based on the knowledge gap theory and combining the value perception and satisfaction model, this study explores the influence of the digital divide for the elderly on wisdom healthcare satisfaction and takes the perceived value of wisdom healthcare as a mediator, and artificial intelligence and big data as moderators into the research framework. Based on the data of 1,052 elderly people in China, the results show that the digital divide for the elderly has a negative influence on wisdom healthcare satisfaction and perceived value. Moreover, it is found that wisdom healthcare perception value mediated the relationship between the digital divide for the elderly and the wisdom healthcare satisfaction, which enhances the negative effect of the digital divide for the elderly on wisdom healthcare satisfaction. Furthermore, the moderating effect of artificial intelligence and big data on the relationship between the digital divide for the elderly and the perceived value of wisdom healthcare is opposite to that between the perceived value of wisdom healthcare and wisdom healthcare satisfaction. Therefore, this study has a reference value for the development and optimization of smart medical industry.
Hardik Rajpal, Pedro A.M. Mediano, Fernando E. Rosas
et al.
Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.