Integrated Information Processing Research Articles

MixFormer: End-to-End Tracking With Iterative Mixed Attention.

Visual object tracking often employs a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, in this paper, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and we propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows us to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer trackers simply by stacking multiple MAMs and placing a localization head on top. Specifically, we instantiate two types of MixFormer trackers, a hierarchical tracker MixCvT, and a non-hierarchical simple tracker MixViT. For these two trackers, we investigate a series of pre-training methods and uncover the different behaviors between supervised pre-training and self-supervised pre-training in our MixFormer trackers. We also extend the masked autoencoder pre-training to our MixFormer trackers and design the new competitive TrackMAE pre-training technique. Finally, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer trackers set a new state-of-the-art performance on seven tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10 k, OTB100, TOTB and UAV123. In particular, our MixViT-L achieves AUC scores of 73.3% on LaSOT, 86.1% on TrackingNet and 82.8% on TOTB.

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in industry to support IIIE. Traceability management is considered the foundation of information management in MBSE. However, a lack of integration between stakeholders, development processes, and models can decrease the effectiveness and efficiency of the system development. A modified MBSE toolchain prototype has been developed to implement traceability management; however, a lack of formal and structured specifications makes it difficult to describe the complex topology in traceability management scenarios using this MBSE toolchain, such as creating traceability between heterogeneous models, which leads to poor reusability of this MBSE toolchain in other traceability management scenarios. To formalize traceability management scenarios using the MBSE toolchain, a cognitive thread (CT) ontology is developed in this study. The CT ontology is a specification expressing the information of stakeholders, models, and development processes for traceability management, providing the cognition capability to analyze the interrelationships between them. Based on the implementation of the modified MBSE toolchain, the concepts and interrelationships in the CT ontology are identified. The CT ontology is designed to develop the MBSE toolchain prototype for building, managing, and analyzing traceability in various traceability management scenarios. A case study of an adaptive cruise control system design is used to evaluate the completeness of the CT ontology through qualitative and quantitative analyses. The results demonstrate that the proposed CT ontology formalizes the information related to traceability management while using the proposed MBSE toolchain and can also be used in common traceability management scenarios to design other complex engineered systems.

Multimodal consistency-specificity fusion based on information bottleneck for sentiment analysis

Sentiment analysis, a subtask of affective computing, endows machines with the ability to sense and comprehend emotions. Recently, research attention has shifted from traditional isolated modality to ubiquitous multi-modalities, requiring to model the complex relationships between modalities and extract the task-relevant information. However, current methods only focus on modality consistency neglecting the complementary relationship and specificity information within each modality. Furthermore, most studies regard multimodal fusion as a mere process of information integration without considering the control of information flow. We propose a variational model that explicitly decomposes unimodal representations into two types, capturing consistency and specificity information, respectively. Following the information bottleneck principle, the implementation optimizes the fusion representation by maximizing its mutual information with consistency and specificity representations while minimizing mutual information with raw inputs. With information integrity constraints and task label supervision, the fusion representation preserves task-relevant information and discards irrelevant noise. Finally, quantitative and qualitative experiments on two benchmark datasets show that our method achieves competitive performance compared to recent baselines, for multimodal sentiment analysis.

Effects of a Passive Back-Support Exosuit on Postural Control and Cognitive Performance During a Fatigue-Inducing Posture Maintenance Task.

To evaluate the effectiveness of passive back-support exosuit on postural control and cognitive performance during a fatigue-inducing posture maintenance task. Wearable support systems (exoskeletons/exosuits) reduce physical demands but may also influence postural control and cognitive performance by reducing muscular fatigue. Eighteen participants visited on two different days to test an exosuit system and performed dual-task cognitive assessments based on human information processing (information acquisition, information integration, and action implementation) while maintaining a 35° trunk flexion posture for 16minutes. Center-of-pressure (CoP), cognitive performance, and perceived workload were recorded, while erector spinae muscle activity was captured to quantify muscle fatigue. The exosuit was effective in reducing erector spinae muscle fatigue during the static posture maintenance task (61% less in Δmedian frequency: -9.5Hz (EXO-Off) versus -3.7Hz (EXO-On)). The fatigue-inducing task increased CoP velocity as a function of time (29% greater: 9.3mm/sec (pre) versus 12.0mm/sec (post)), and exosuit use decreased CoP velocity (23% less: 12.1mm/sec (EXO-Off) versus 9.4mm/sec (EXO-On)). The exosuit was also effective at mitigating cognitive degradation, as evidenced by a higher hit-to-signal ratio (8% greater: 81.3 (EXO-Off) versus 87.9 (EXO-On)) in the information integration task and reducing perceived workload in all stages of human information processing. Exosuit provided benefits of postural control and information integration processing during a 16-min static posture maintenance task. Torso exoskeletons/suits can have positive implications for occupations with concurrent physical and cognitive demands.

Sparse mixed attention aggregation network for multimodal images fusion tracking

Recent years have witnessed the exciting performance of trackers based on Transformer. However, they usually separate the process of information extraction and integration, weakening the information interaction between the target and search region. In addition, they depend on traditional Transformer to model the long range dependency, which leads to a lack of focus on the primary information needed by high-accuracy trackers. In this paper, a sparse mixed attention aggregation model is proposed for robust tracking based on visible and thermal infrared images. To be specific, a backbone network composed of sparse mixed attention is designed to achieve information extraction and integration. This is helpful to obtain specific discriminative feature information and enhance their communication. To give full play to the complementary visible and thermal information, a confidence aware aggregation network is designed, which can learn the reliable confidence of visible and thermal branches. Finally, a corner-based localization head is introduced to estimate the target state. Extensive experiments on three large-scale multimodal tracking benchmarks demonstrate the superior tracking ability of the proposed tracker over other advanced trackers.

IS PSYCHOLOGY ALREADY CONCEPTUALLY COMPLETED, OR ABOUT INFORMATIONAL ACTIVITY OF THE BRAIN WHICH IS “OBSESSIVELY” ACCOMPANIED BY MENTAL ACTIVITY

The article considers a “paradoxical” situation of general scientific significance, which can be formulated in the question “Why does the human brain, being a physical entity, nevertheless process information biologically or socially expediently?”, although no physical law supports this. A way out of this situation is offered. It consists in indicating that the limbic region of the brain realizes the ability of a person to subjectively evaluate the biological (social) value of external objects and phenomena. Such an explanatory concept, which is, in fact, a “product” of both humanitarian and natural knowledge, allows, in our opinion, to “cross” the line of conceptual and categorical misunderstanding between these “irreconcilable” branches of knowledge. And this intersection is carried out through understanding the essence of the information activity of the brain, which occurs with the help of mental phenomena.
 It is argued that in the neural networks of the brain, along with the usual physical causality, there is an informational causality that orchestrates this physical causality. But it acts due to the presence in the brain, again, of mental phenomena. And above all, the ability of a person to subjectively evaluate (either positively or negatively) the surrounding world, and therefore, to be motivated to eliminate states of uncertainty through the process of information integration (unification, summation, accumulation), which in the language of psychology is mainly a thinking process.
 The leading role of psychology in solving this general scientific problem is considered – primarily because of the scale of its research object, which is the psyche. Psyche, which, it is believed, is an evolutionarily mature “tool” of direct human existence and adaptation to constantly changing world – to uncertainty, to information deficit.

Differential cortical network engagement during states of un/consciousness in humans

What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.

Understanding health misinformation sharing among the middle-aged or above in China: roles of social media health information seeking, misperceptions and information processing predispositions

PurposeThe ballooning health misinformation on social media raises grave concerns. Drawing upon the S-O-R (Stimulus-Organism-Response) model and the information processing literature, this study aims to explore (1) how social media health information seeking (S) affects health misinformation sharing intention (R) through the channel of health misperceptions (O) and (2) whether the mediation process would be contingent upon different information processing predispositions.Design/methodology/approachData were collected from a survey comprising 388 respondents from the Chinese middle-aged or above group, one of China's most susceptible populations to health misinformation. Standard multiple linear regression models and the PROCESS Macro were adopted to examine the direct effect and the moderated mediation model.FindingsResults bolstered the S-O-R-based mechanism, in which health misperceptions mediated social media health information seeking's effect on health misinformation sharing intention. As an indicator of analytical information processing, need for cognition (NFC) failed to moderate the mediation process. Contrarily, faith in intuition (FI), an indicator reflecting intuitive information processing, served as a significant moderator. The positive association between social media health information seeking and misperceptions was stronger among respondents with low FI.Originality/valueThis study sheds light on health misinformation sharing research by bridging health information seeking, information internalization and information sharing. Moreover, the authors extended the S-O-R model by integrating information processing predispositions, which differs this study from previous literature and advances the extant understanding of how information processing styles work in the face of online health misinformation. The particular age group and the Chinese context further inform context-specific implications regarding online health misinformation regulation.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0157.

Mental Modeling of Entrepreneurial Opportunity Based on the Principle of Information Visualization

What arouses entrepreneurial action? The role of entrepreneurial opportunities in stimulating entrepreneurial action under uncertainty is a subject of intense scholarly debate. This article engages in a crucial theoretical exploration to extend the boundaries of opportunity by reframing it as an artifact. Reframing the opportunity as an artifact (crystallized information) shifts the dialogue and perspectives. Through information visualization and mental modeling approaches, information is aggregated, integrated, and configured to generate an experience that triggers actions. The interaction, transformation, and representation of information are intended to reduce uncertainty and guide entrepreneurial enactment. This article presents an integrated information processing model showing how information excites, instigates, and motivates enactment. This contextualization aims to develop a descriptively accurate and prescriptively useful concept of opportunity as an artifact. Theoretically, analytically, and conceptually, the focus on information as the fundamental unit of analysis shifts the discussion by featuring inward to move the scholarly debate on opportunity forward. This re-pivoting can avoid the calls for abandonment and debunking of the opportunity construct.

Nonlinear mathematical model of an economic soliton

In modern conditions of economic transformation, the functioning and development of production systems is characterized by instability, nonlinearity and dynamism of the main indicators and parameters. The financial and production system is affected by a large number of market, inflationary, social and other disturbances, which leads to the losses in financial stability, a decrease in production volumes, a decrease in demand for manufactured products, etc. Therefore, it is necessary to have an effective management system and operational decision-making. The relevant direction for solving this problem is the development of a flexible intelligent control system that makes it possible to diagnose quickly the unfavorable state of the financial and production system and activate promptly the mechanisms that return the system to equilibrium. In this case, information becomes the main resource of management, which performs an integrating role and is a necessary component when using all other resources. All this predetermines the use of information technology management. With a high dynamism and instability of the external environment, it becomes necessary to apply the methods and approaches of the new science of management, concentrating its attention on the theory of complex systems and nonlinear dynamics, with the help of which complex control systems can cope effectively with uncertainty and rapid changes. The development and use of modern information technologies for enterprise management are based on the introduction of integrated information processing technology and the creation of mathematical methods and computer modeling tools. In the work of information technologies, management is implemented by creating an integrated intelligent computerized system based on the use of economic and mathematical methods, computer technology and communication means, i.e. implements a fundamentally new management platform, which is based on the integration of management information through a mechanism for generalizing the information database and knowledge.

An integrative model of information processing and contextual factors on exploring information systems outsourcing success

Information Systems (IS) outsourcing contracts often fail to deliver the planned business benefits. One major reason is that organizations have poor understanding of outsourcing. This study examined IS outsourcing success by integrating Information Processing Theory with four contextual factors (three organizational readiness factors: IT skills readiness, financial readiness and technology readiness, and one moderator factor: environmental dynamism). We collected 216 questionnaires to validate the proposed hypotheses. The statistical analysis revealed three findings. First, the fit between task uncertainty and organizational structural mechanism was significantly related to IS outsourcing success. Second, the negative moderating power of environmental dynamism on the relationship of IS outsourcing success with the fit between task uncertainty and organizational structural mechanism was significant. Finally, the three organizational readiness factors were significantly related to IS outsourcing success. This study provided new insights on the future development of Information Processing Theory and the improvement of IS outsourcing project performance.

Proposal for the Integration of Health and Safety into the Design of Road Projects with BIM

The high number of accidents in the construction sector makes the concept of prevention through design (PtD), which starts with the integration of an occupational risk assessment in this phase, increasingly important. To this end, BIM (building information modelling) is a methodology that provides benefits related to the management of health and safety in the design phase. Its application in linear work projects is less developed than its application in building, even more so with regard to health and safety in BIM. This research proposes a methodology for integrating risk assessment into the design phase of BIM road projects, structuring the information, establishing the information integration processes, its analysis and risk management, and automating its integration into the model through zones. As a result, the research enables risk assessment in the BIM model through zones, differentiating risk values and allowing for the analysis of interferences between certain activities and the study of other specific activities in the design. As a result of the structure and organisation of the data, it is possible to export the data to IFC for coordination with other stakeholders. Thus, the final contribution of the research is the introduction of health and safety into road projects conducted with BIM, in compliance with legal requirements.

Advanced Manufacturing Configuration by Sample-Efficient Batch Bayesian Optimization

We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization procedure. \cmtb{The novel acquisition function is demonstrated, analyzed and compared on state-of-the-art benchmarking problems. We apply the optimization approach to atmospheric plasma spraying and fused deposition modeling.} Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost.

Development of methods and technologies for creating intelligent scientific and educational internet resources

The purpose of this work is to develop methods, technologies and tools for creating and maintaining intelligent scientific and educational internet resources (ISEIR) based on a service-oriented approach and Semantic Web technologies. The main purpose of ISEIR is to provide meaningful access to scientific and educational information resources of a given field of knowledge and integrated information processing services. According to the preliminary concept, an intelligent scientific and educational internet resource will be an information system accessible via the internet which provides ontology-based systematization and integration of scientific knowledge, data and information resources into a single information space together with a meaningful effective access to them as well as supporting their use in solving various scientific and educational tasks. ISEIR is equipped with an ergonomic web-based user interface and special editors designed to manage the knowledge integrated into it. The proposed approach to the construction of intelligent scientific and educational internet resources is the basis for the developed technology in creating and maintaining information environments for distributed learning.

A Transient and Biocompatible Biomemristor Integrated with NonVolatile Memory and Artificial Synapse

AbstractTransient electronics have been intensively studied due to their potential in bio‐diagnostics and therapeutic areas. However, most devices only serve as the terminal of data collection and storage, and the clinical decision must rely on doctor analysis, which cannot provide timely warning and treatment when facing an emergency. Thus, it is critically important to build a device integrated with data storage and neuromorphic computing that can operate in physiological environments. In this work, a multifunctional memristor with an Ag/combustion zinc oxide/W structure is proposed. The memristor exhibits reliable digital resistive switching performance, including a uniform switching voltage, stable retention time, and robust on/off ratio. Simultaneously, the conductance‐modulation capabilities along with biological synaptic behaviors such as short/long‐term plasticity are fulfilled in this memristor. Moreover, a three‐layer memristor‐based neuromorphic network is constructed to perform pattern recognition and achieves a high accuracy of more than 92.2 percent. Dissolution and cell toxicity tests demonstrate the degradable and biocompatible characteristics of the memristor. This work may provide a feasible strategy to integrate information processing and data storage into one biomemristor and pave the way for innovating artificial synapses in bioelectronics applications.

How does negative evidence impact argument strength during semantic category-based induction? An event-related potential study.

Negative evidence may influence category-based inductive reasoning, but the underlying time course of cognitive brain processing is unclear. This study adopted event-related potentials (ERPs) to investigate this effect by manipulating the category type (related vs. unrelated) and argument type (positive vs. negative) in a semantic category-based induction task. The behavioral results showed that, under unrelated conditions, a negative argument produced greater argument strength and shorter reaction time than a positive argument did. Meanwhile, the ERP results found a negative argument elicited larger P300, N400, and slow negative component amplitudes than a positive argument, which could possibly embody the expectation-related processing produced by memory updating, semantic integration, and the more complex inference-driven information integration and interpretation processes and cognitive load in hypothesis generation. This study provides novel evidence on the temporal course of how negative evidence influences argument strength during semantic category-based induction.

FORMALIZATION OF PROCESSES AND PHENOMENA IN THE SYSTEM "REASONABLE INSTITUTION OF HIGHER EDUCATION" ON THE EXAMPLE OF SPECIALTY 193 "GEODESY AND EARTHLES"

The article analyzes the state of informatization of processes and phenomena in modern institutions of higher education. The main conceptual provisions of modeling of processes and phenomena of higher education institutions are offered in the work. Models of correspondence of options of interaction of units of providing higher education institutions and procedures of formalization technology, models of educational and methodical providing, models of digital platforms of knowledge of institutions of higher education and models of procedures of interaction of elements of management of institutions of higher education by methods of category theory are developed. Methods of systems analysis, mathematical logic, category theory, heuristic methods were used to build models. The results were obtained. The problem is formulated, which is to unite the positive aspects of existing and existing information technologies, as well as the elimination of negative aspects that hinder the processes of information integration and the development of intellectual rules for interaction. Developed an example of formalization, in our opinion, the most complex and most important for the university educational and methodological support, abstracting from most types of support. Schematic solutions, as well as experience in organizing student learning based on the support system of educational processes were shown using models of disciplines and professional knowledge of students, as well as by combining them in one specialty, called digital platform of professional knowledge. A metamodel of a higher level of generalization has been developed, which allows its use in modeling other types of software. It is characterized by the complexity of relations between participants in the learning process, on the one hand, standardization and strict requirements for learning processes on the other hand, which led to the construction of learning processes in the language of theory of categories and functions. Formalization of learning processes in a more abstract language allowed to develop this metamodel. The development of the basis for the knowledge base of the intelligent system "Smart Institution of Higher Education" will solve a wide range of tasks at different levels of the hierarchy of management, control, monitoring, scheduling processes and phenomena in higher education institutions.

A weighted constraint satisfaction approach to human goal-directed decision making.

When we plan for long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must also be considered. But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants' choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks.

Robustness and Flexibility of Neural Function through Dynamical Criticality.

In theoretical biology, robustness refers to the ability of a biological system to function properly even under perturbation of basic parameters (e.g., temperature or pH), which in mathematical models is reflected in not needing to fine-tune basic parameter constants; flexibility refers to the ability of a system to switch functions or behaviors easily and effortlessly. While there are extensive explorations of the concept of robustness and what it requires mathematically, understanding flexibility has proven more elusive, as well as also elucidating the apparent opposition between what is required mathematically for models to implement either. In this paper we address a number of arguments in theoretical neuroscience showing that both robustness and flexibility can be attained by systems that poise themselves at the onset of a large number of dynamical bifurcations, or dynamical criticality, and how such poising can have a profound influence on integration of information processing and function. Finally, we examine critical map lattices, which are coupled map lattices where the coupling is dynamically critical in the sense of having purely imaginary eigenvalues. We show that these map lattices provide an explicit connection between dynamical criticality in the sense we have used and “edge of chaos” criticality.

Graph analysis of nonlinear fMRI connectivity dynamics reveals distinct brain network configurations for integrative and segregated information processing

The human brain is organized into functional networks, whose spatial layout can be described with functional magnetic resonance imaging (fMRI). Interactions among these networks are highly dynamic and nonlinear, and evidence suggests that distinct functional network configurations interact on different levels of complexity. To gain new insights into topological properties of constellations interacting on different levels of complexity, we analyze a resting state fMRI dataset from the human connectome project. We first measure the complexity of correlational time series among resting state networks, obtained from sliding window analysis, by calculating their sample entropy. We then use graph analysis to create two functional representations of the network: A ‘high complexity network’ (HCN), whose inter-node interactions display irregular fast changes, and a ‘low complexity network’ (LCN), whose interactions are more self-similar and change more slowly in time. Graph analysis shows that the HCNs structure is significantly more globally efficient, compared to the LCNs, indicative of an architecture that allows for more integrative information processing. The LCNs layout displays significantly higher modularity than the HCNs, indicative of an architecture lending itself to segregated information processing. In the HCN, subcortical thalamic and basal ganglia networks display global hub properties, whereas cortical networks act as connector hubs in the LCN. These results can be replicated in a split sample dataset. Our findings show that investigating nonlinear properties of resting state dynamics offers new insights regarding the relative importance of specific brain regions to the two fundamental requirements for healthy brain functioning, that is, integration and segregation.