Information Symbols Research Articles

A New Distance-Type Fuzzy Inference Method Based on Characteristic Parameters

Reasoning is a cognitive activity that leverages knowledge to generate solutions to problems. Knowledge representations in the brain require both symbolic and graphical information since visual information is figurative and conveys a large amount of information. Consequently, graphical knowledge representation is often employed in reasoning. Distance-type fuzzy inference utilizes the distance information between the antecedent and the set of facts as the basis for inference. Compared to Mamdani inference, the distance-type fuzzy inference method not only satisfies the convexity and asymptotic properties of the inference results but also adheres to the separation rule (modus ponens), a fundamental principle in inference. This paper discusses extensions of distance-type fuzzy inference methods to handle spatial figures. In this paper, we first explain the distance-type fuzzy inference method. Then, we discuss the concept representation in the feature space and independent parameters that can completely express the characteristics of a figure in space, which are defined as “characteristic parameters”. Furthermore, we describe the correspondence between figures and vectors in the feature space, propose a new distance-type fuzzy inference method based on characteristic parameters and describe its characteristics. Finally, an example is used to demonstrate the inference results of this new distance-type fuzzy inference method.

Robust Symbol Detection Based on Quaternion Neural Networks in Wireless Polarization-Shift-Keying Communications.

Quaternion neural networks (QNNs) form a class of neural networks constructed with quaternion numbers. They are suitable for processing 3-D features with fewer trainable free parameters than real-valued neural networks (RVNNs). This article proposes symbol detection in wireless polarization-shift-keying (PolSK) communications by employing QNNs. We demonstrate that quaternion plays a crucial role in the symbol detection of PolSK signals. Existing artificial-intelligence communication studies mainly focus on RVNN-based symbol detection in digital modulations having constellations in complex plane. However, in PolSK, information symbols are represented as the state of polarization, which can be mapped on the Poincare sphere and thus its symbols have a 3-D data structure. Quaternion algebra offers a unified representation to process 3-D data with rotational invariance and, therefore, it keeps the internal relationship among three components of a PolSK symbol. Hence, we can expect that QNNs learn the distribution of received symbols on the Poincare sphere with higher consistency to detect the transmitted symbols more efficiently than RVNNs. We compare PolSK symbol detection accuracy of two types of QNNs, RVNN, existing methods such as least-square and minimum-mean-square-error channel estimations, as well as detection knowing perfect channel state information (CSI). Simulation results including symbol error rate show that the proposed QNNs outperform the existing estimation methods and that they reach better results with two to three times fewer free parameters than the RVNN. We find that QNN processing will bring practical use of PolSK communications.

Applying Knowledge Mapping Techniques to Enhance Contextual Understanding and Accurate Correspondence in Translating Metaphorical Information in Japanese Literature

Abstract Metaphorical information is prevalent in literary texts, and understanding the cultural and historical contexts of Japanese literature can significantly enhance the translation of its metaphors. This paper introduces a knowledge graph specifically designed for translating metaphorical content in Japanese literary works. This graph facilitates the categorization, extraction, and storage of symbolic information. To refine the knowledge graph further, the RAEA model is employed, coupled with a distinct attention mechanism assigned to each node of symbolic information. This approach significantly improves the entity alignment in translations, thereby enhancing translational accuracy. In constructing a dataset that includes background symbolic information, the knowledge graph demonstrated a precision of 0.9256 and a recall of 0.9136. Additionally, the graph achieved an average Mean Reciprocal Rank (MRR) index of 0.810, markedly surpassing traditional translation models. This paper’s knowledge graph thus offers an effective methodology for the precise translation and interpretation of symbolic information in Japanese literature, aiding in the deeper comprehension and accurate representation of such content.

Information system for monitoring number of fruits and their condition using computer vision technology

The main objectives of process automation are to improve the quality and productivity of process execution. An automated process has more stable characteristics than a manually executed process. One of the tools of automation is artificial intelligence in general and computer vision in particular. Computer vision (also called machine vision) is a scientific trend in the field of artificial intelligence and related technologies for acquiring images of real-world objects, processing them and using the obtained data to solve various kinds of applied tasks without (full or partial) human participation. From an engineering perspective, computer vision seeks to automate tasks that can be performed by the human visual system. Computer vision tasks include ways of acquiring, processing, analysing, and understanding digital images and extracting data from the real world to produce numerical or symbolic information. In the context of crop farming, automation is primarily capable of increasing yields and improving the quality of the final product, by better controlling environmental conditions and timely detection of any abnormalities in the development and growth of plants and fruits, by increasing productivity, by replacing a variety of more accurate and responsive automated systems (e.g. irrigation or temperature control) with human involvement, and thereby simplifying the role of humans in cultivation, and by improving the quality of the final product. In the paper, the main objective of the designed information system is the recognition in a digital image of plant fruits with subsequent classification based on known colour ranges in advance. In order to achieve this goal, the following tasks should be solved: minimisation of possible negative effects of distortions of the original image; analysing the image to look for tomato fruits on it.

Визуальная репрезентация исторического города в цифровых медиа

The article analyses the visual representation of the historical city in digital media as one of the leading ways of urban identity forming. The author shows that the connection of the image with the city, which is a territorial carrier of visual and symbolic information, and with city digital representation is established and coordinated with the help of an intermediary – digital media, including social networks. On the example of Veliky Novgorod, it is investigated how the image of the city in the media environment shapes the idea of the real urban space for most Internet users. The article identifies the main elements of urban space that emphasize Veliky Novgorod’s status as a historical city: culturalhistorical and natural-landscape. The visual representation of the historic city emphasizes the uniqueness of a place and expresses local identity, which allows to distinguish a certain urban space in the endless flow of visual data. At the same time, the author notes that digital media do not display the entire urban space, but only the part that is recorded and then actively disseminated by Internet users. Such incompleteness of the visual representation of the historical city provokes the emergence of socio-cultural distortions: the reproduction of basic visual patterns works to form a stereotypical image that does not reflect the heterogeneity of the real urban environment.

Semiotic Interpretation of Literary Metaphors in Henry James’ Psychological Fiction under the Semantic Association Network Modeling

Abstract Henry James enriched the representation of realism with the aesthetic practice of psychological realism theory, and his literary metaphors revealed the colorfulness of the human consciousness field. In this paper, under the background of semiotics, based on two algorithms, Semantic Association Network, Associative Component Analysis, and Dependent Syntax, we constructed a tensor representation model of literary text, extracted the symbolic information of sentences in the scholarly text, and combined the information of textual words through the association coefficients for the tensor representation of sentence metaphors. Quantitatively calculating the metaphoric semantics in the extracted literary sentences is followed by labeling and recognition of the literary sentences based on the metaphoric information. Take Henry James’, the Aristocats as an example and interpret the symbolic information in it using the model constructed in this paper. The symbolic scene of the “garden” in the novel represents the meaning of innocence and simplicity, which has the highest conceptual similarity enhancement of 3.415. Most of the emotions behind the readers’ evaluations of Henry James’s novels have positive metaphors, and the favorable rating range is mainly concentrated in the range of 0-100. In Henry James’s novels, the emotional metaphor of “repression” is more characteristic than other metaphors, with a maximum of 1.896. This paper investigates the interpretation of metaphors in Henry James’s novels, which can assist in the comprehension of the books.

A physical layer secret key consistency enhancement scheme using constellation decision information

Secret key generation based on a wireless channel (WC-SKG) is a promising solution to address the security issues in wireless communication. However, the consistency of channel estimation between two legal communication nodes in WC-SKG is often poor due to the receiver noise, signal power, etc., leading to a low secret key generation rate (SKGR). Although there are several denoising algorithms such as orthogonal transformation to address this issue, existing schemes overlook the fact that data symbols are also affected by the channel. This results in existing schemes only using the pilot symbols for channel estimation and not fully utilizing the received signal power of the WC-SKG. To address this issue, we propose a consistency enhancement algorithm based on constellation decision information (CEA-CDI), which utilizes both pilot symbols and soft decision information of data symbols to improve SKGR. Monte Carlo simulation and numerical results demonstrate that our proposed scheme can improve performance by approximately 16 dB compared to initial channel estimation.

The automated discovery of kinetic rate models – methodological frameworks

Two automated knowledge discovery methodologies (ADoK-S & ADoK-W) are created whereby symbolic regression, parameter estimation, information criteria and model-based design of experiments synergize for the optimized discovery of kinetic rate models.

Asynchronous Bidirectional Communication in Cell-Free Networks

We consider a bidirectional communication between two single-antenna transceivers using multiple multi-antenna access points (APs) in a cell-free network architecture. In such a network, because of different propagation delays associated with different APs, the end-to-end link is a multi-path channel that results in inter-symbol-interference (ISI) in the signals received at the transceivers. To tackle ISI, we resort to cyclic prefix (CP) assisted block transmission of the information symbols and employ joint pre- and post-channel equalizers at both the transceivers to mitigate the impact of intra-block interference. Considering the amplify-and-forward technique at the APs, we cast the joint design of equalizers, beamforming matrices, and transceivers’ transmit powers as a power minimization problem while guaranteeing predefined data rates at the transceivers. Assuming symmetric beamforming matrices at the APs, we devise a semi-closed-form solution for this problem. We prove rigorously that at the optimum only a synchronous subset of the APs should participate in the information exchange between the two transceivers. This is achieved by proving that at the optimum, the pre-equalizer matrices should be unitary and the post-equalizer matrices should be invertible.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.

Pelvic ring disruptions result from blunt injury mechanisms and are potentially lethal mainly due to associated injuries and massive pelvic hemorrhage. The severity of pelvic fractures in trauma victims is frequently assessed by grading the fracture according to the Tile AO/OTA classification in whole-body Computed Tomography (CT) scans. Due to the high volume of whole-body CT scans generated in trauma centers, the overall information content of a single whole-body CT scan and low manual CT reading speed, an automatic approach to Tile classification would provide substantial value, e. g., to prioritize the reading sequence of the trauma radiologists or enable them to focus on other major injuries in multi-trauma patients. In such a high-stakes scenario, an automated method for Tile grading should ideally be transparent such that the symbolic information provided by the method follows the same logic a radiologist or orthopedic surgeon would use to determine the fracture grade. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grading. To achieve interpretability despite processing high-dimensional whole-body CT images, we design a neurosymbolic algorithm that operates similarly to human interpretation of CT scans. The algorithm first detects relevant pelvic fractures on CTs with high specificity using Faster-RCNN. To generate robust fracture detections and associated detection (un)certainties, we perform test-time augmentation of the CT scans to apply fracture detection several times in a self-ensembling approach. The fracture detections are interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. We apply a Bayesian causal model to recover likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides fracture location and types, as well as information on important counterfactuals that would invalidate the system's recommendation. Our approach achieves an AUC of 0.89/0.74 for translational and rotational instability,which is comparable to radiologist performance. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box methods.

Translation of Metaphorical Information in Japanese Literature Combined with Text Mining Techniques

Abstract Japanese literature is renowned for its conciseness and the profound, multi-layered meanings conveyed through its economical use of language and rich symbolic content. This paper explores the creative impact of these linguistic features in Japanese literature by analyzing the deep meanings and varied classifications of literary metaphors. The construction of a Japanese literature corpus involved the collection, cleaning, and organization of Japanese literary texts, followed by the selection of specific linguistic features. The corpus was then aligned using a dynamic programming approximation algorithm. A semantic representation model of the Japanese literature text corpus was developed by integrating the Attention Word Embedding Model with the Extended Topic Information Model, and metaphoric corpus features were fused using the static fusion strategy of Extended Topic Information and Attention Word Embedding. This study also examines the translation of symbolic information in Japanese literature, focusing on linguistic and stylistic features. The findings reveal that translations into Chinese tend to employ augmentation translation, resulting in 1,706 additional sentences compared to the original Japanese texts. Furthermore, the lexical density in the Chinese translation reference corpus is 2.45 percentage points higher than in the Japanese literature corpus. Analysis of verb modifications shows that among the 90 altered verbs in the Chinese translation corpus, only 24 have corresponding instances in the original Japanese texts. Text mining techniques were employed to conduct a deep analysis of symbolic content, enhancing the understanding of the nuanced connotations inherent in Japanese literature.

Peningkatkan QoS Layanan Video Call dengan Metode DASTFBC Sistem MIMO2x2 pada Jaringan LTE

ABSTRACT
 The DASTFBC (Diagonal Algebraic Space Time Frequency Block Code) method is a coding scheme that utilizes different diversity sources, namely space, time and frequency. This method is one method that can be used to improve video call services. The DASTFBC method uses a rotation matrix in the DAST code which is formed using algebraic number field theory. The code matrix structure consists of rotated information symbols, expanded using a square diagonal code matrix. This method is used to overcome signal power fluctuations at the receiver which cause the service quality to decrease (not comply with) BER video call standards. The DASTFBC method is applied to the LTE network MIMO system with 64 QAM modulation. This method will be simulated in the Matlab program to obtain results to improve video call services. The DASTFBC method simulation of BER was carried out for 4 user speed scenarios, namely 5km/hour, 40km/hour, 100km/hour, 120km/hour. Significant results of the DASTFBC method can already be seen when the user moves at 40km/ hour. With this method, a smaller BER value is obtained, namely 2x10-4, this shows that with the DASTFBC method the video call BER standard (10-3) can be met. The value of BER obtained when using DASTFBC with a speed of 120km/h is 8.5x10-3 this value almost exceeds the standard BER video call. So that the maximum speed of the user when using the DASTFBC method is 120km/h above that speed the standard value of the BER video call cannot be fulfilled.
 Keywords: video call, LTE network, MIMO, DASTFBC

An Improved and Optimized Gated Recurrent Unit and Long Short-Term Memory Model for Fake News Detection

This study presents a novel approach for detecting counterfeit news, employing an advanced hybrid model that integrates Enhanced Gated Recurrent Unit and Long Short-Term Memory networks, termed as IGRU-LSTM. Initially, the database is assembled from the Information Security and Object Technology (ISOT) database and Wikipedia databases. From the database, the real and fake news is detected by considering news reviews. The dataset may contain unwanted information line URLs and symbols, which should be corrected to achieve efficient fake news detection. So, the pre-processing technique should be considered such as special symbol removal, URLS removal, upper to lower case conversion and replace contractions. After that, the pre-processed data is sent to the embedding procedure for word embedding. Finally, the IGRU-LSTM classifier is utilized for classifying real and fake news detection. In the combined GRU-LSTM framework, we incorporate the Enhanced Wild Horse Optimisation (EWHO) algorithm to optimize the selection of optimal weighting parameters. We utilize MATLAB for implementing this method. To evaluate the effectiveness of our approach, we analyze key performance metrics like precision, recall, and accuracy, and compare them with established methods including CNN-PSO, CNN-FO, and standard CNN.

Longitudinal resting‐state EEG along the Alzheimer’s disease continuum: Sample size considerations for clinical trials.

AbstractBackgroundTo enable successful inclusion of resting‐state electroencephalography (EEG) outcome measures in Alzheimer’s disease (AD) clinical trials, we (retrospectively) mapped the progression of EEG markers over time in amyloid‐positive subjects.MethodsResting‐state 21‐channel EEG was recorded in 173 subjects positive for amyloid deposition (subjective cognitive decline, n = 16; mild cognitive impairment, n = 94; dementia due to AD, n = 63). Two or more EEG recordings were available for all subjects. We computed whole‐brain and regional relative power (i.e., theta (4‐8Hz), alpha1 (8‐10Hz), alpha2 (10‐13Hz), beta (13‐30Hz)), peak frequency, signal variability (i.e., theta Permutation Entropy) and functional connectivity values (i.e., alpha and beta corrected Amplitude Envelope Correlation, theta Phase Lag Index, weighted Symbolic Mutual Information, inverted Joint Permutation Entropy). Whole‐group linear mixed effects models were used to model the development of EEG markers over time. Group‐wise analysis was performed to investigate potential differences in change trajectories between diagnostic groups. The longitudinal relation between EEG and cognition was evaluated. Finally, we estimated the minimum sample size required to detect a stabilizing treatment effect using EEG markers, in hypothetical clinical trials of 1‐ or 2‐year duration.ResultsWhole‐group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased alpha2 and beta power), with strongest effects for temporal and parieto‐occipital regions. Signal variability showed stronger decline on a global scale. We report no significant longitudinal changes in functional connectivity strength. Disease severity at baseline influenced EEG markers’ rates of change, with fastest deterioration in MCI subjects. A decrease of the parieto‐occipital peak frequency, temporal relative alpha2 and beta power, as well as an increase in temporal relative theta power, were significantly associated with a decline in Mini Mental State Examination score. We estimate that 1‐year trials, focusing on amyloid‐positive MCI subjects, require 148 or 438 subjects per arm to detect a stabilizing treatment effect on temporal relative theta‐ or beta power, respectively.ConclusionResting‐state EEG markers can facilitate early detection of target engagement in an objective, non‐invasive, low‐cost manner. Spectral measures, particularly recorded from temporal regions, present robust secondary endpoints for AD interventions.

Self-Organisation of Prediction Models.

Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a selective advantage from the beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and the resulting favourable decisions on actions. This information processing chain originated from mere physical interaction processes prior to life, here denoted as structural information exchange. From there, the self-organised transition to symbolic information processing marks the beginning of life, evolving through the novel purposivity of trial-and-error feedback and the accumulation of symbolic information. The emergence of symbols and prediction models can be described as a ritualisation transition, a symmetry-breaking kinetic phase transition of the second kind previously known from behavioural biology. The related new symmetry is the neutrally stable arbitrariness, conventionality, or code invariance of symbols with respect to their meaning. The meaning of such symbols is given by the structural effect they ultimately unleash, directly or indirectly, by deciding on which actions to take. The early genetic code represents the first symbols. The genetically inherited symbolic information is the first prediction model for activities sufficient for survival under the condition of environmental continuity, sometimes understood as the "final causality" property of the model.

A new dynamic spatial information design framework for AR-HUD to evoke drivers’ instinctive responses and improve accident prevention

Driver’s instinctive responses and skill-based behaviors enable them to react faster and better control their vehicle in dangerous situations. This study incorporated dynamic spatial information design (DSID) in an augmented reality head-up display (AR-HUD) under manual driving conditions. By integrating the skill, rule, and knowledge (SRK) taxonomy and situation awareness (SA) theory, our AR-HUD successfully evoked drivers’ instinctive responses and improved driving safety. First, we converted symbol and sign information processed at the knowledge-based and rule-based levels, respectively, into signal information processed at the skill-based level. Then we developed four AR-HUD interfaces with different dynamic designs for use in a hazardous scenario at an intersection. Finally, we investigated each design’s impact on drivers’ SA and driving performance. Experimental results demonstrated that our DSID enhanced drivers’ SA and accident-avoidance capabilities while reducing their cognitive workload. Among the four AR-HUD interfaces, the one that incorporated all three information elements under study (i.e., lateral warning, dynamic driving space, and speedometer) performed the best. This indicates that our proposed framework has potential applications in other similar dangerous driving scenarios, thus contributing to the development of safer and more efficient driving environments.

Fault detection and isolation in uncertain dynamic systems using composite optimization and inferential sensing

Inferential sensing is a cost-effective and reliable approach to replace expensive and impractical hardware sensing, while yielding robust fault detection and isolation (FDI) during system maintenance or operation. Yet, their high computational footprint necessitates the employment of advanced and efficient estimation techniques, along with symbolic mathematics and automatic differentiation. Herein, we attempt to combine model-driven sensor selection methods with inferential sensing to accurately identify faults in the presence of system noise and uncertainty. During sensor selection, we employ criteria from information theory to maximize the estimability of system faults from available system measurements. The most informative sensor set stems from a comparison of all possible sensor suites via information theoretic criteria expressed as functions of the Fisher Information Matrix. Optimal inferential sensors are calculated through a novel fusion of symbolic regression and information theory. For built-in test deployment, we utilize k-Nearest Neighbors classification to assess the capability of each sensor suite for all plausible instantiations of uncertainty and system noise, and evaluate FDI performance benefits from the inclusion of inferential sensor(s). The proposed computational framework is deployed with steady-state and dynamic models of a cross-flow plate-fin heat exchanger, at varying levels of measurement noise and uncertainty. As shown, the augmented system of composite sensors (i.e., inferential and hardware) provides accurate information on system fault(s) and reduces the evidence of uncertainty. Compared to traditional sensor approaches, the proposed techniques are shown to be more precise while simultaneously granting increased insight into details otherwise hidden by noise. Thus, they form a robust and reliable tool when conducting fault diagnosis and possibly toward system safety.

Action-rule-based cognitive control enables efficient execution of stimulus-response conflict tasks: a model validation of Simon task performance.

The human brain can flexibly modify behavioral rules to optimize task performance (speed and accuracy) by minimizing cognitive load. To show this flexibility, we propose an action-rule-based cognitive control (ARC) model. The ARC model was based on a stochastic framework consistent with an active inference of the free energy principle, combined with schematic brain network systems regulated by the dorsal anterior cingulate cortex (dACC), to develop several hypotheses for demonstrating the validity of the ARC model. A step-motion Simon task was developed involving congruence or incongruence between important symbolic information (illustration of a foot labeled "L" or "R," where "L" requests left and "R" requests right foot movement) and irrelevant spatial information (whether the illustration is actually of a left or right foot). We made predictions for behavioral and brain responses to testify to the theoretical predictions. Task responses combined with event-related deep-brain activity (ER-DBA) measures demonstrated a key contribution of the dACC in this process and provided evidence for the main prediction that the dACC could reduce the Shannon surprise term in the free energy formula by internally reversing the irrelevant rapid anticipatory postural adaptation. We also found sequential effects with modulated dip depths of ER-DBA waveforms that support the prediction that repeated stimuli with the same congruency can promote remodeling of the internal model through the information gain term while counterbalancing the surprise term. Overall, our results were consistent with experimental predictions, which may support the validity of the ARC model. The sequential effect accompanied by dip modulation of ER-DBA waveforms suggests that cognitive cost is saved while maintaining cognitive performance in accordance with the framework of the ARC based on 1-bit congruency-dependent selective control.

Joint mitigation of frequency offset and phase noise for coherent free-space optical communication in atmospheric turbulence

In this paper, taking the atmospheric turbulence into account, two low-complexity algorithms which can jointly compensate the frequency offset (FO) and phase noise (PN) caused by laser line-width and atmospheric turbulence were proposed in coherent free-space optical (FSO) communications with polarization division multiplexing QPSK (PDM-QPSK) scheme. By introducing phase-conjugated counterparts for training symbols or information symbols on one of two orthogonal polarization states, the FO and PN were sensed simultaneously for a transmitted block or for each symbol. Simulations and experiments were performed to evaluate the BER performance. The results both manifested significant bit-error-rate (BER) performance improvement.

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.