Various Levels Of Information Research Articles

Asymmetric Adaption in Social Learning: Understanding the Dilemma of Competition and Cooperation.

The constant challenge in social interactions involves making informed decisions in the face of competitive and cooperative dilemmas. The decision-making process can be influenced by various factors present in the social context. According to the behavior-pattern-categorization framework of information acquisition, potential biases may develop at all stages of decision-making as information about social context is progressively entered and integrated. In this study, employing the Chicken Game, we investigated the influence of varying information levels within the behavior-pattern-categorization framework (i.e., competitiveness of behavior choice, uncertainty of behavior pattern, and sociality of category) on decision-making in the dilemma of competition and cooperation. Combined with reinforcement learning models, our findings from three experiments showed that participants exhibited basic complementary behavior, becoming less competitive against highly competitive opponents and vice versa. Notably, individuals exhibited varying adaptation rates to different levels of opponent competitiveness and fluctuations. Specifically, participants adapted slower to highly competitive opponents and faster to cooperative opponents. This asymmetric adaptation in social learning is related to the rate at which various levels of information are updated. The current study disentangles the different levels of information acquisition and highlights the asymmetric processing that can occur during the updating of information within each level.

Evaluating the effective factors of BIM for enhancing the construction interface management

Maintaining the proper interface management among the construction stakeholders is becoming more crucial due to the interconnected nature of construction work. Adopting and implementing building information modelling (BIM) technology can aid in the proper execution of construction projects. Therefore, the main goal of this study is to evaluate the effective factors of BIM using DeLone and McLean’s (DM) model. The DM model's factors evaluate the system or technology in terms of system quality, information quality, service quality, and most importantly, the system's net benefits and user satisfaction. The questionnaire was developed with the expert's opinion and the survey was conducted. As megaprojects have more complicated and various levels of information sharing, this study targeted collecting responses from industrial project practitioners in India. From the survey, a total of 166 responses were obtained. Using these data, the frequency test, reliability test, and the relative importance index (RII) were performed. These effective factors of BIM contribute significantly to project performance improvement through proper interface management and communication among stakeholders. The findings of the study emphasized the importance of user satisfaction and the net benefits of BIM. Thus, this study provides further insights to increase the adoption and implementation of BIM technology.

A novel reinforcement learning based Heap-based optimizer

The Heap-Based Optimization (HBO), a novel meta-heuristic algorithm, constructs a hierarchical heap resembling a company structure to facilitate interaction among search agents. This enables the search agent to learn various levels of information from the population to obtain improved solutions, but it has drawbacks such as limited search capability and slow convergence speed when tackling complex optimization problems. To address these challenges, we propose an enhanced heap optimizer based on reinforcement learning(RLHBO), which demonstrates robust stability, rapid convergence, and high solution accuracy. Firstly, fixed operators are replaced with a reinforcement learning dynamic control search strategy in RLHBO to regulate the search range of agents. This prevents agents from being restricted in their search when encountering local or global optima, thus enhancing their search capability. Additionally, RLHBO introduces a dimensional self-learning strategy and a convergence strategy based on search direction similarity to address issues such as invalid and discrete solutions, accelerating convergence. Furthermore, an initialization strategy based on quasi-oppositional learning is employed to increase the diversity of the algorithm-generated population, surpassing the limitations of random initialization and further boosting convergence speed. Extensive testing on various complex functions from CEC2017 and CEC2022 demonstrates that RLHBO, as presented in this paper, outperforms HBO and numerous other advanced MAs in terms of search capability and convergence speed.

The role of accounting information systems characteristics in improving the quality of insurance services An applied study for the National Insurance Company

Our modern world is experiencing rapid development at various levels of information and social systems, and it is natural for this development to be reflected in people and organizations. This development was in the information system and the field of its application. The characteristics of accounting information systems are a fundamental goal in the success or failure of the organization, because this information is used to support and coordinate administrative operations. In addition to using it as a tool for coordination and communication within the organization and its external environment on the other hand. It is considered one of the most important information that the organization needs, and the accounting information system used within the insurance company is specific to it and is compatible with the nature of its work. This is because it is used to process the collection of insurance premiums in addition to paying compensation. The accounting information system collects, classifies and communicates information to the company's management. Therefore, the use of accounting information shows us the financial reality of the insurance company because it depends on the collection of premiums and compensation.

The Effects of Varying Degrees of Information on Teamwork a Study on Fighter Pilots

A team of fighter pilots in a distributed environment with limited access to information rely on technology to pursue teamwork. In order to design systems that support distributed teamwork, it is, therefore, necessary to understand how access to information affects the team members. Certain factors, such as mutual performance monitoring, shared mental models, adaptability, and backup behavior are considered essential for effective teamwork. We investigate these factors in this work, focusing on how visually communicated information affects fighter pilots’ perception of these factors. For that, a questionnaire including the teamwork factors in relation to certain defined scenarios that contain various levels of information was distributed to fighter pilots. We show that the studied factors are affected by the level of information available to the pilots. Especially, mutual performance monitoring increases with the degree of available information.

Is Historical Cartography a Useful Tool for Landscape Analysis? A Perspective from Inland Spain (Zorita de los Canes, Guadalajara) from the Middle Ages to the Present

Historical cartography continues to be an essential resource in developing strategies for the analysis of anthropised landscapes. In recent years, the digitisation and conversion of a large number of pre-20th-century maps to modern geographic coordinate systems and data hierarchisation in GIS-integrated databases have opened up huge possibilities. In this paper, we highlight some of the advantages and issues that we observed in using historical cartography in the Iberian Peninsula heartlands by comparing archaeological data, textual sources and maps and various levels of information obtained for our area of study: the southeast of the present-day province of Guadalajara, Spain. Using the longue durée approach conceptualised by Fernand Braudel (1976), historical cartography enabled us to delve deeper into two essential aspects, land use and the road network, while providing elements of diachrony that suggest changes in the landscape in specific and disruptive periods.

Hierarchical few-shot learning with feature fusion driven by data and knowledge

Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance.

Deep Reinforcement Learning Reward Function Design for Autonomous Driving in Lane-Free Traffic

Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as there is no leader vehicle or lane-changing operation. Therefore, the observations of the vehicles need to properly accommodate the lane-free environment without carrying over bias from lane-based approaches. The recent successes of deep reinforcement learning (DRL) for lane-based approaches, along with emerging work for lane-free traffic environments, render DRL for lane-free traffic an interesting endeavor to investigate. In this paper, we provide an extensive look at the DRL formulation, focusing on the reward function of a lane-free autonomous driving agent. Our main interest is designing an effective reward function, as the reward model is crucial in determining the overall efficiency of the resulting policy. Specifically, we construct different components of reward functions tied to the environment at various levels of information. Then, we combine and collate the aforementioned components, and focus on attaining a reward function that results in a policy that manages to both reduce the collisions among vehicles and address their requirement of maintaining a desired speed. Additionally, we employ two popular DRL algorithms—namely, deep Q-networks (enhanced with some commonly used extensions), and deep deterministic policy gradient (DDPG), which results in better policies. Our experiments provide a thorough investigative study on the effectiveness of different combinations among the various reward components we propose, and confirm that our DRL-employing autonomous vehicle is able to gradually learn effective policies in environments with varying levels of difficulty, especially when all of the proposed rewards components are properly combined.

Semi-supervised Medical Image Segmentation through Dual-task Consistency

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data. However, most of the existing SSL algorithms in literature tend to regularize the model training by perturbing networks and/or data. Observing that multi/dual-task learning attends to various levels of information which have inherent prediction perturbation, we ask the question in this work: can we explicitly build task-level regularization rather than implicitly constructing networks- and/or data-level perturbation and then regularization for SSL? To answer this question, we propose a novel dual-task-consistency semi-supervised framework for the first time. Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target. The level set representation is converted to an approximated segmentation map through a differentiable task transform layer. Simultaneously, we introduce a dual-task consistency regularization between the level set-derived segmentation maps and directly predicted segmentation maps for both labeled and unlabeled data. Extensive experiments on two public datasets show that our method can largely improve the performance by incorporating the unlabeled data. Meanwhile, our framework outperforms the state-of-the-art semi-supervised learning methods.

Сервис определения таксационных показателей лесов на основе автоматизированной обработки разноспектральных данных дистанционного зондирования земли

The article considers a service for providing consumers with various levels of information about forest taxing indicators based on the processing of multispectral data recorded by modern domestic remote sensing spacecraft in the optical spectrum range.

Multi-level supervised hashing with deep features for efficient image retrieval

Image hashing based on deep convolutional neural networks (CNN), deep hashing, has acquired breakthrough in image retrieval. Although deep features from various CNN layers have various levels of information, most of the existing deep hashing methods extract the feature vector only from the output of the penultimate fully-connected layer, focusing primarily on semantic information whilst ignoring detailed structure information. This calls for research on multi-level hashing, utilizing multi-level features to exploit different levels of CNN characteristics. To fill this gap, a novel image hashing method, Multi-Level Supervised Hashing with deep feature (MLSH), is proposed in this paper to further exploit multiple levels of deep image features. It uses a multiple-hash-table mechanism to integrate multi-level features extracted from an individual deep convolutional neural network. It takes advantage of the complementarity among multi-level features from various layers of a single deep network. High-level features reveal the semantic content of the image, while low-level features provide the structural information that is missing in high-level features. Instead of simple concatenation, several hash tables are trained individually using different levels of features from different layers, which are then integrated for efficient image retrieval. The method has been systematically evaluated through experiments on three image databases, including CIFAR-10, MNIST and NUSWIDE, and has thus been demonstrated to set a new state of the art in image hashing, outperforming several state-of-the-art hashing methods. Furthermore, the recall and precision can be balanced and improved simultaneously.

Computational analysis of cortical EEG biosignals and neural dynamics underlying an integrated mind-body relaxation technique

Abstract Recent advances in brain computer interfaces (BCI) have brought paradigm shifts in pre-clinical neuroscience research relating various levels of information abstraction in brain. Recent years have seen several neuro electrophysiology studies on traditional and cultural stress reduction methods especially, related to short duration practices that can alleviate workplace stress. With stress reduction using sustainable methods in context, this paper reports on the analysis of biosignals for understanding the neural dynamics in human subjects practicing traditional relaxation techniques using electroencephalography (EEG) technique. The study was conducted among 20 healthy volunteering undergraduate students. Students were randomized as experimental group (N=10), provided yoga and meditation training for 8 weeks, and control group (N=10) with no experience on yoga or meditation. As a reference for analyzing the effect of relaxation techniques in daily life activities, three cognitive tasks, namely, word memory task, psychomotor vigilance task, and steady hand-game task were performed by participants as a pre-EEG recording task. EEG recordings were taken for 160 seconds in eyes-opened relaxed state in a dimly-lit sound-proof environment. In experimental group, after practicing integrated yoga-meditation technique for 8 weeks, a post EEG recording was taken. Relating several functional areas of the brain, cortical activation of the α, β, γ and θ rhythms in the frontal, temporal and parietal lobes of the brain with significant power spectral density variations in experiment group indicating diversity in brain patterns among week-1 to week-8 practitioners was observed. This short-term study indicates the plausibility of integrated yoga-meditation as sustainable relaxation techniques that can influence in improving the coordination in daily life tasks.

A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model.

Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response function (DRF), informed by various levels of information including apical health response, can represent a template for convergent top-down, bottom-up analysis. In this paper, a general DRF for chronic chemical and other health stressors and mixtures is derived based on a general first-order model previously derived and demonstrated for illness progression. The derivation accounts for essential autocorrelation among initiating event magnitudes along a toxicological mode of action, typical of complex processes in general, and reveals the inverse relationship between the minimum illness-inducing dose, and the illness severity per unit dose (both variable across a population). The resulting emergent DRF is theoretically scale-inclusive and amenable to low-dose extrapolation. The two-parameter single-toxicant version can be monotonic or sigmoidal, and is demonstrated preferable to traditional models (multistage, lognormal, generalized linear) for the published cancer and non-cancer datasets analyzed: chloroform (induced liver necrosis in female mice); bromate (induced dysplastic focia in male inbred rats); and 2-acetylaminofluorene (induced liver neoplasms and bladder carcinomas in 20,328 female mice). Common- and dissimilar-mode mixture models are demonstrated versus orthogonal data on toluene/benzene mixtures (mortality in Japanese medaka, Oryzias latipes, following embryonic exposure). Findings support previous empirical demonstration, and also reveal how a chemical with a typical monotonically-increasing DRF can display a J-shaped DRF when a second, antagonistic common-mode chemical is present. Overall, the general DRF derived here based on an autocorrelated first-order model appears to provide both a strong theoretical/biological basis for, as well as an accurate statistical description of, a diverse, albeit small, sample of observed dose-response data. The further generalizability of this conclusion can be tested in future analyses comparing with traditional modeling approaches across a broader range of datasets.

In Vitro Dynamic Phenotyping for Testing Novel Mobilizing Agents.

A new method to quantify the influence of mobilization agents on the dynamics of human hematopoietic stem and progenitor cells (HSPC) is introduced. Different from the microscopy-based high-content screening relying on multiple staining, machine learning, and molecular-level perturbation, the proposed method sheds light on the "dynamics" of HSPC in the presence of extrinsic factors, including SDF1α and mobilization agents. A well-defined model of the bone marrow niche is fabricated by the deposition of planar lipid membranes on glass slides (called supported membranes) displaying ligand molecules at precisely controlled surface densities. The dynamics of human HSPC, CD34+ cells from umbilical cord blood or peripheral blood, are monitored by time-lapse, live cell imaging with a standard phase-contrast microscopy or a specially designed microinterferometry in the absence or presence of mobilization agents. After extracting the contour of each cell, one can analyze the dynamics of cell "shapes" step-by-step, yielding various levels of information ranging from the principal mode of deformation, the persistence of deformation patterns, and the energy consumption by HSPC in the absence and presence of mobilization agents. Moreover, by tracking the migration trajectories of HSPC, one can gain insight how mobilization agents influence the "motion" of HSPC. As these readouts can be connected to a theoretical model, this strategy enables one to classify the influence of not only mobilization agents but also target-specific inhibitors or other treatments in quantitative indices.

Directional fatigue accumulation in wind turbine steel towers

Wind turbines are subject to fatigue loads during their entire lifetime of 20-25 years. A main source of the fatigue loads is the turbulence, which varies with direction due to the surrounding terrain and wake effects inside wind farms. A common approach to assess wind turbine fatigue loads is to simulate the structural response based on a site-specific wind climate, described in the IEC 61400-1 standard. To reduce the amount of needed simulations the standard introduces an “effective turbulence” approximation that integrates directional variation of turbulence, resulting in an omnidirectional value. This method implicitly assumes that all wind turbine components face the wind directly, which is a conservative simplification for components below the yaw bearing.Using wind measurements from almost one hundred international sites, we show how this simplification leads to over-predictions of tower fatigue loads of up to 23% compared to directional fatigue accumulation. Three simplified models are developed to approximate the directional fatigue damage using various levels of information ranging from only the wind rose to full sector wise simulations. The first two recommended models may be used as proxies to decide if sector wise simulations are feasible, and the last model accurately predicts the full directional fatigue damage. The simplified models can contribute to a reduced material consumption of wind turbine towers, thereby reducing the cost of wind energy.

A hierarchical model of children’s research participation rights based on information, understanding, voice, and influence

ABSTRACTThis paper introduces a hierarchical model of children’s (3- to 8-year olds) research participation rights that builds on work by Roger Hart and integrates key participation rights drawn from UNCRC Article 12: information, understanding, voice, and influence. It provides insight into the various levels of information that can be provided to children, the understanding that results, the scope given to them to express their views, and the degree to which their voices ultimately exert influence in research contexts. Each of these four elements plays a unique role in upholding and enhancing children’s rights in research and emphasises the need for rights to be woven into the fabric of a research project from its conception. Considering children’s rights of research participation in a more integrated manner, as suggested by the hierarchy, has the potential to improve participation experiences for children and to value them as citizens with meaningful rights.

Geotecnologias Aplicadas à Análise Ambiental da Bacia Hidrográfica do Rio Olho d´Água, Município de Jardim, Mato Grosso do Sul - Brasil

The preservation of natural resources is essential to the balance of Environmental systems. Water is an indispensable natural resource for life. In order to implement the environmental management of this resource, the National Water Resources Policy was created based on some principles, such as the adoption of the watershed as a planning unit. The use of geotechnologies constitutes an important tool for this work, since it allows evaluating the area to be studied, to visualize the situation of the vegetation cover and the soil, besides attaching them to georeferenced databases. The objective of this work is the environmental analysis of the Olho D’Agua River Watershed, located in the Municipality of Jardim, State of Mato Grosso do Sul. For the delimitation of this watershed, SRTM data were used and with the objective of evaluating the quality of the coverage of this area, the NDVI was calculated based on the scenes of Landstat TM sensor. Subsequently, the classes of Permanent Preservation Area were analyzed. It was concluded that the conservation status of Permanent Preservation Area is in compliance with environmental legislation, fulfilling its role in maintaining biodiversity and the quality of water resources. The use of geotechnologies allowed the integration of various levels of information, generating maps, characterizing the environment and producing information that helps in the planning and management of Cabeceira do Prata Farm.

An integrated information lifecycle management framework for exploiting social network data to identify dynamic large crowd concentration events in smart cities applications

With the current availability of an extreme diversity of data sources and services, emerging from the Internet of Things and Cloud domains, the challenge is shifted towards identifying intelligent, abstracted and adaptive ways of correlating and combining the various levels of information. The purpose of this work is to demonstrate such a combination, on one hand at the service level, through integrating smart cities platforms for user level data, and on the other hand at Complex Event Processing, Storage and Analytics capabilities together with Twitter data. The final goal is to identify events of interest to the user such as Large Crowd Concentration (LCC) in a given area, in order to enrich application level information with related event identification that can enable more sophisticated actions on behalf of that user. The identification is based on observation of Twitter activity peaks compared to historical data on a dynamic time and location of interest. The approach is validated through a two-month experiment in the city of Madrid, identifying LCCs in sporting events around two sports venues and analyzing various approaches with relation to the needed thresholds definition.

The value of pathogen information in treating clinical mastitis.

The objective of this study was to determine the economic value of obtaining timely and more accurate clinical mastitis (CM) test results for optimal treatment of cows. Typically CM is first identified when the farmer observes recognisable outward signs. Further information of whether the pathogen causing CM is Gram-positive, Gram-negative or other (including no growth) can be determined by using on-farm culture methods. The most detailed level of information for mastitis diagnostics is obtainable by sending milk samples for culture to an external laboratory. Knowing the exact pathogen permits the treatment method to be specifically targeted to the causation pathogen, resulting in less discarded milk. The disadvantages are the additional waiting time to receive test results, which delays treating cows, and the cost of the culture test. Net returns per year (NR) for various levels of information were estimated using a dynamic programming model. The Value of Information (VOI) was then calculated as the difference in NR using a specific level of information as compared to more detailed information on the CM causative agent. The highest VOI was observed where the farmer assumed the pathogen causing CM was the one with the highest incidence in the herd and no pathogen specific CM information was obtained. The VOI of pathogen specific information, compared with non-optimal treatment of Staphylococcus aureus where recurrence and spread occurred due to lack of treatment efficacy, was $20.43 when the same incorrect treatment was applied to recurrent cases, and $30.52 when recurrent cases were assumed to be the next highest incidence pathogen and treated accordingly. This indicates that negative consequences associated with choosing the wrong CM treatment can make additional information cost-effective if pathogen identification is assessed at the generic information level and if the pathogen can spread to other cows if not treated appropriately.

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text.