Different Types Of Information Research Articles

Cross-Task Mental Workload Recognition Based on EEG Tensor Representation and Transfer Learning.

The accurate evaluation of mental workload of operators in human machine systems is of great significance in ensuring the safety of operators and the correct execution of tasks. However, the effectiveness of EEG based cross-task mental workload evaluation are still unsatisfactory because of the different EEG response patterns in different tasks, which hindered its generalization in real scenario severely. To solve this problem, this paper proposed a feature construction method based on EEG tensor representation and transfer learning, which was verified in various task conditions. Specifically, four working memory load tasks with different types of information were designed firstly. The EEG signals of participants were collected synchronously during task execution. Then, the wavelet transform method was used to perform time-frequency analysis of multi-channel EEG signals, and three-way EEG tensor (time-frequency-channel) features were constructed. EEG tensor features from different tasks were transferred based on the criteria of feature distribution alignment and class-wise discrimination criteria. Finally, the support vector machine was used to construct a 3-class mental workload recognition model. Results showed that compared with the classical feature extraction methods, the proposed method can achieve higher accuracy in both within-task and cross-task mental workload evaluation (91.1% for within-task and 81.3% for cross-task). These results demonstrated that the EEG tensor representation and transfer learning method is feasible and effective for cross-task mental workload evaluation, which can provide theoretical basis and application reference for future researches.

Development of two data bases with comments in Bulgarian language and application of supervised learning approaches on them for comparative sentiment analysis. А brief overview

The purpose of the current paper is to make an overview of the work done so far by the authors and make a summary of the results and reflections on the performed sentiment analysis on user comments in two different fields in Bulgarian language. As a starting point for the authors’ work is the development of two databases with users’ reviews and their preprocessing to become usable source of information for different types of analysis projects. As a result of the preprocessing is a revised Bulgarian language-driven algorithm for data preprocessing for Bulgarian language. The second part of the project is implemented into two steps: sentiment analysis using the supervised learning approaches developed for the two databases and a comparative sentiment analysis of the two databases, following their additional examination.

Consumers’ Experience and Information Selection

When asked to give up an item, people often demand a much higher price than what it is actually worth to them. This is the Endowment Effect Although numerous marketing literature on the endowment effect have investigated into how ownership is associated with information processing, very few have considered the effects of prior-product experiences on the cognitive process. In this research, we study how positive and negative product experiences influence consumers’ information processing and interpretation. Study 1 provides preliminary evidence that both ownership and experience influences the recall of different types of information. Study two not only replicates the finding of study one but also extends the implication of study one by showing that both ownership and product experience affects interpretation of ambiguous information. The findings will contribute the consumer market by providing different patterns of information protocol between sellers and buyers and the influences of prior experiences.

Effects of the information gap in the stock market: A case of Dar Es salaam Stock Exchange

The study intended to examine the effects of the information gap in the Dare es salaam stock exchange stock market. To meet this goal, three objectives were involved; the gap between the information offered by DSE and those needed by investors, the effects of the information gap on the trading in securities in DSE, and measures to apply to tackle the information gap in DSE. The study adopted a mixed research design. This method provides different types of information, such as detailed views of participants qualitatively and scores on instruments quantitatively, and when combined, they should yield the same results. The research was carried out in Dar es Salaam, the city of big investors in Tanzania, the head office of the Capital markets and Security Authority (CMSA), and the Dar es salaam stock exchange authority (DSE). Eighteen brokers and dealers were interviewed, and a questionnaire was distributed to 169 market dealers, brokers, custodians, nominated advisors, DSE employees, registered trustees, financial analysts, and customers to collect quantitative data. The data collection tools used were a questionnaire for quantitative data and an interview guide for qualitative data. Descriptive statistics, content analysis, and coding approaches were used to analyse and interpret data.

Fan cells in lateral entorhinal cortex directly influence medial entorhinal cortex through synaptic connections in layer 1.

Standard models for spatial and episodic memory suggest that the lateral entorhinal cortex (LEC) and medial entorhinal cortex (MEC) send parallel independent inputs to the hippocampus, each carrying different types of information. Here, we evaluate the possibility that information is integrated between divisions of the entorhinal cortex prior to reaching the hippocampus. We demonstrate that, in mice, fan cells in layer 2 (L2) of LEC that receive neocortical inputs, and that project to the hippocampal dentate gyrus, also send axon collaterals to layer 1 (L1) of the MEC. Activation of inputs from fan cells evokes monosynaptic glutamatergic excitation of stellate and pyramidal cells in L2 of the MEC, typically followed by inhibition that contains fast and slow components mediated by GABAA and GABAB receptors, respectively. Inputs from fan cells also directly activate interneurons in L1 and L2 of MEC, with synaptic connections from L1 interneurons accounting for slow feedforward inhibition of L2 principal cell populations. The relative strength of excitation and inhibition following fan cell activation differs substantially between neurons and is largely independent of anatomical location. Our results demonstrate that the LEC, in addition to directly influencing the hippocampus, can activate or inhibit major hippocampal inputs arising from the MEC. Thus, local circuits in the superficial MEC may combine spatial information with sensory and higher order signals from the LEC, providing a substrate for integration of 'what' and 'where' components of episodic memories.

Rational speech comprehension: Interaction between predictability, acoustic signal, and noise.

During speech comprehension, multiple sources of information are available to listeners, which are combined to guide the recognition process. Models of speech comprehension posit that when the acoustic speech signal is obscured, listeners rely more on information from other sources. However, these models take into account only word frequency information and local contexts (surrounding syllables), but not sentence-level information. To date, empirical studies investigating predictability effects in noise did not carefully control the tested speech sounds, while the literature investigating the effect of background noise on the recognition of speech sounds does not manipulate sentence predictability. Additionally, studies on the effect of background noise show conflicting results regarding which noise type affects speech comprehension most. We address this in the present experiment. We investigate how listeners combine information from different sources when listening to sentences embedded in background noise. We manipulate top-down predictability, type of noise, and characteristics of the acoustic signal, thus creating conditions which differ in the extent to which a specific speech sound is masked in a way that is grounded in prior work on the confusability of speech sounds in noise. Participants complete an online word recognition experiment. The results show that participants rely more on the provided sentence context when the acoustic signal is harder to process. This is the case even when interactions of the background noise and speech sounds lead to small differences in intelligibility. Listeners probabilistically combine top-down predictions based on context with noisy bottom-up information from the acoustic signal, leading to a trade-off between the different types of information that is dependent on the combination of a specific type of background noise and speech sound.

Modeling of the Process of Formative Evaluation of Metasubject Results in the System of Basic General Education

This article examines the application of the principles of a convergent approach to teaching subjects of natural science and socio-humanitarian cycles within the framework of the subject-content block as a separate structural link of the formative assessment model that forms the meta-subject results of schoolchildren. Communicative-normative universal educational activity of formative assessment, including teamwork, information literacy skills, willingness to work with different types of information, etc. It is described as a convergent educational technology that acts as a key development tool. It is based on the integration of learning strategies, didactic methods and evaluation procedures in order to form a wide range of competencies among schoolchildren, primarily meta-disciplines. The procedural block of the proposed pedagogical model is a formative assessment of the results of meta-subject education as a step-by-step dynamic process. It includes a number of successive stages: primary diagnostics of students' educational achievements through formative assessment; formation of students' metasubject results based on the convergence of evaluative and didactic technologies aimed at the development of universal learning activities, metacognitive skills, information skills, mastery of interdisciplinary concepts; systematic monitoring of students' achievements at different stages of the educational process. Step-by-step analysis of the achieved results ensures effective further adjustment and forecasting of individual progress, taking into account the needs and interests of the individual. The main concept in the model of formative assessment of metasubject results of schoolchildren developed by us is the concept of «convergence» and in the model of formative assessment of metasubject results of primary school students, the subject-content block is allocated as a separate structural link, within which the application of the principles of a convergent approach to teaching subject disciplines of the natural science cycle is provided.

Die Integration prosodischen und syntaktischen Wissens bei der Ermittlung der Textkohärenz im schriftlichen Textverstehen

Reading is a highly complex cognitive process. From a neurobiological point of view, it involves at least six linguistic sub-competences including orthographic, semantic, syntactic, phonetic and prosodic competence. Each of these skills is required of the reader to extract different types of information from the text, ranging from the perceptual and the syntactic to the lexical and pragmasemantic one. Based on the main results of cognitive-oriented research on text and reading, this paper illustrates how prosodic competence, combined with formal coherence patterns, can be considered and employed as a reading strategy, enabling the reader to progressively grasp the meanings of a text. In the first part particular attention will be paid to the description of the syntax-prosody interface as well as of its function as a means of encoding of information structure in German. With regard to the teaching of German as a foreign language, the second part of the paper will present a method to train intonational reading. This approach aims to raise foreign language readers’ awareness of prosodic features of written texts and to introduce them to melodic patterns which can be encountered while reading. Such a practical training may prove to be advantageous, as the most typical and apparently “uncorrectable” errors committed by foreign language learners when speaking and reading German texts are almost always related to the domains of phonology and prosody.

New or existing, does it matter?

Both when designing new and when evaluating existing structures, methods of analysis must be based on established engineering theory and practice. Also, the general principles of structural reliability regarding the treatment of uncertainties when verifying the established requirements apply to both, design and assessment. Taking further into account that new or existing civil engineering works are usually unique, either because they are prototypes or because they are exposed to specific conditions, from an engineering point of view, structural analysis and verification must be carried out, both in the design and in the assessment, under case-specific conditions according to the same principles. However, important differences exist between assessment and design, for example regarding the state of information and its updating through different types of information, the structural condition, reliability requirements, verification methods or decision options. Such differences, which are briefly summarized in the contribution, can often lead to a high level of conservatism when using design-oriented methods for assessment purposes. There is therefore a need to develop a generally recognized, coherent and harmonized set of rules for the assessment of existing structures. The CEN Technical Specification for assessment and retrofitting partially closes this gap, but only establishes principles on assessmentspecific reliability aspects. The consistent application of these generic assessment rules in practice requires further assumptions, or even case-specific developments. Practical applications are therefore of paramount importance in identifying relevant issues to consider in the future development of assessment methods and codes.

Automatic translation of sign language with multi-stream 3D CNN and generation of artificial depth maps

Sign languages play an essential role in the cognitive and social development of the deaf, consisting of a natural form of communication and being a symbol of identity and culture. However, hearing loss has a severe social impact due to an existing communication barrier, preventing access to essential services such as education and health. A bi-directional sign language translation may be the solution to bridging the communication gap between the deaf and the listener, completing a two-way communication cycle. Virtual personal assistants can benefit from this technology by extending how users interact with the intelligent system. With this idea, in this work we develop a multi-stream deep learning model to recognize signs of Brazilian (BSL), Indian (ISL), and Korean (KSL) Sign Languages. We combine different types of information for the classification task, using single-stream and multi-stream 3D Convolutional Neural Networks. In addition, considering the largest source of sign data globally – the internet – we propose a depth sensor-free classification method, with depth maps artificially generated through Generative Adversarial Networks. In order to consider the main parameters that encode sign languages, the final architecture is composed of a multi-stream network that receives the segmented hands, the faces, the distances and speeds of the points of articulation, and the RGB frames associated with artificial depth maps. Finally, we provide a visual explanation to understand which regions were important for model decision-making. The best models were obtained using the multi-stream network, presenting an accuracy of 0.91 ± 0.07, and f1-score of 0.90 ± 0.08 on publicly available BSL data set. The results suggest that the multi-stream network with artificially generated depth maps is suitable for the task of sign recognition in different languages.

보편적 행동지원 실행충실도 평가도구 고찰

The purpose of this study is to review the fidelity measures in school-wide positive behavioral interventions and supports; SWPBIS) with an emphasis on their instrumental characteristics and contents. A total of eight instruments, both domestically and internationally developed, were chosen and reviewed in terms of the purpose, the methods of assessment, the criterion scores on adequacy, the composition, and the accessibility. Also, through the in-depth investigation of the item contents (from 258 items), core and sub-elements were identified, and all relevant items within a specific core element were reviewed according to three dimensions including exposure of the intervention, quality of the intervention providers, and responsiveness of the intervention recipients. As a result, the fidelity measures in the universal behavioral supports are used in a school or research and provide multiple functions (e.g. school teams’ use for the regular implementation monitoring, survey of school faculty and staff’s perception) with varied assessment methods. To enhance the user’s understanding, most measures serve different types of information (e.g., measurement, scoring, utilization methods) and materials (e.g., data collection form, action plan form) in addition to the rating scales and are open to the public at no cost through relevant websites or research publications. Also, the review of the item contents found 10 core elements(e.g., defining and teaching the expected behaviors) and 60 sub-elements (e.g., defining the expectations and rules in a clear and positive manner), and those elements were measured evenly in all three dimensions. In the end, the study provided several implications regarding the further development and utilization of fidelity measures in universal behavioral supports.

Differential item functioning of the CSI’D in the MexCog

AbstractDifferential item functioning of the CSI’D in the MexCogBackgroundPrevious research shows that Spouse and Child informants in the MexCog report higher levels of cognitive impairment than other family and non‐family informants, even when controlling for direct cognitive assessments. The question remains, are the higher levels of cognitive impairment reported by spouses and children because they are informants for the most impaired older adults or because they respond to the questions differently? Additionally, direct cognitive assessments of low education populations may downwardly bias results. Individuals who are illiterate or who have had no formal schooling may score worse on cognitive assessments due inability to answer questions that require reading and writing or unfamiliarity with testing in general. The extent to which informant assessments of cognition can be used to better capture cognitive impairment for low educated population has not been studied.MethodsUsing item response theory (IRT) we examine the extent to which there is differential item functioning (DIF) between different types of informants and other associated with lower direct assessment for the Community Screening Interview for Dementia (CSI’D).ResultsThe anticipated results of this study will allow us to compare the observed CSI’D Score to an IRT weighted CSI’D score, controlling for DIF. The difference in observed and IRT weighted CSI’D score will show to what extent the differences in CSI’D score are due to differences in measurement.ConclusionThe item level bias, or lack of, will shed light on the importance of informants in measurement of cognitive impairment. Knowing how different informants answer informant questionnaires could have important implications for population‐based studies of cognitive decline.

Preferential looking to eyes versus mouth in early infancy: heritability and link to concurrent and later development.

From birth, infants orient preferentially to faces, and when looking at the face, they attend primarily to eyes and mouth. These areas convey different types of information, and earlier research suggests that genetic factors influence the preference for one or the other in young children. In a sample of 535 5-month-old infant twins, we assessed eye (relative to mouth) preference in early infancy, i.e., before neural systems for social communication and language are fully developed. We investigated the contribution of genetic and environmental factors to the preference for looking at eyes, and the association with concurrent traits and follow-up measures. Eye preference was independent from all other concurrent traits measured, and had a moderate-to-high contribution from genetic influences (A=0.57; 95% CI: 0.45, 0.66). Preference for eyes at 5months was associated with higher parent ratings of receptive vocabulary at 14 months. No statistically significant association with later autistic traits was found. Preference for eyes was strikingly stable across different stimulus types (e.g., dynamic vs. still), suggesting that infants' preference at this age does not reflect sensitivity to low-level visual cues. These results suggest that individual differences in infants' preferential looking to eyes versus mouth to a substantial degree reflect genetic variation. The findings provide new leads on both the perceptual basis and the developmental consequences of these attentional biases.

Unsupervised end-to-end infrared and visible image fusion network using learnable fusion strategy.

Infrared and visible image fusion aims to reconstruct fused images with comprehensive visual information by merging the complementary features of source images captured by different imaging sensors. This technology has been widely used in civil and military fields, such as urban security monitoring, remote sensing measurement, and battlefield reconnaissance. However, the existing methods still suffer from the preset fusion strategies that cannot be adjustable to different fusion demands and the loss of information during the feature propagation process, thereby leading to the poor generalization ability and limited fusion performance. Therefore, we propose an unsupervised end-to-end network with learnable fusion strategy for infrared and visible image fusion in this paper. The presented network mainly consists of three parts, including the feature extraction module, the fusion strategy module, and the image reconstruction module. First, in order to preserve more information during the process of feature propagation, dense connections and residual connections are applied to the feature extraction module and the image reconstruction module, respectively. Second, a new convolutional neural network is designed to adaptively learn the fusion strategy, which is able to enhance the generalization ability of our algorithm. Third, due to the lack of ground truth in fusion tasks, a loss function that consists of saliency loss and detail loss is exploited to guide the training direction and balance the retention of different types of information. Finally, the experimental results verify that the proposed algorithm delivers competitive performance when compared with several state-of-the-art algorithms in terms of both subjective and objective evaluations. Our codes are available at https://github.com/MinjieWan/Unsupervised-end-to-end-infrared-and-visible-image-fusion-network-using-learnable-fusion-strategy.

How Different Types of Information and Its Presentation Sequence Affect Reading Speed and Cognitive Processing Efficiency

How Different Types of Information and Its Presentation Sequence Affect Reading Speed and Cognitive Processing Efficiency

Bioinformatics in Plant Breeding and Research on Disease Resistance.

In the context of plant breeding, bioinformatics can empower genetic and genomic selection to determine the optimal combination of genotypes that will produce a desired phenotype and help expedite the isolation of these new varieties. Bioinformatics is also instrumental in collecting and processing plant phenotypes, which facilitates plant breeding. Robots that use automated and digital technologies to collect and analyze different types of information to monitor the environment in which plants grow, analyze the environmental stresses they face, and promptly optimize suboptimal and adverse growth conditions accordingly, have helped plant research and saved human resources. In this paper, we describe the use of various bioinformatics databases and algorithms and explore their potential applications in plant breeding and for research on plant disease resistance.

Detecting Prosthetic Restorations Using Artificial Intelligence on Panoramic Radiographs

Aim. This study applied a CNN (convolutional neural network) algorithm to detect prosthetic restorations on panoramic radiographs and to automatically detect these restorations using deep learning systems. Materials and Methods. This study collected a total of 5126 panoramic radiographs of adult patients. During model training, .bmp, .jpeg, and .png files for images and .txt files containing five different types of information are required for the labels. Herein, 10% of panoramic radiographs were used as a test dataset. Owing to labeling, 2988 crowns and 2969 bridges were formed in the dataset. Results. The mAP and mAR values were obtained when the confidence threshold was set at 0.1. TP, FP, FN, precision, recall, and F1 score values were obtained when the confidence threshold was 0.25. The YOLOv4 model demonstrated that accurate results could be obtained quickly. Bridge results were found to be more successful than crown results. Conclusion. The detection of prosthetic restorations with artificial intelligence on panoramic radiography, which is widely preferred in clinical applications, provides convenience to physicians in terms of diagnosis and time management.

When more is less: Adding action effects to reduce crosstalk between concurrently performed tasks

Dual-task costs are thought to reflect the architecture of the cognitive processes that guide voluntary action. Thus, manipulations that affect dual-task costs can provide insight into how we represent and select behavior as well as allow us to better design machines and controls for safer, more efficient performance. This line of research has revealed that the sensory events that follow the responses (i.e., action effects) can affect dual-task performance even though the sensory events occur after the actions are produced. The present study assessed three hypotheses regarding how action effects impact dual-task performance: a monitoring bottleneck, central stage shortening, and crosstalk. Across two experiments, we manipulated the content of two concurrently-performed tasks: a visual task that used either spatial or nonspatial stimuli (Experiment 1) and an auditory task that used responses with or without experimentally-induced auditory action effects (Experiments 1 and 2). In Experiment 1, dual-task costs were reduced when experimentally-induced auditory action effects were present, independent of the content of the visual task. In Experiment 2, the dual-task costs depended on the content of the experimentally-induced action effects, such that costs were larger when action effects emphasized ordinal (number) information, which overlapped with the unmanipulated action effects from the visual spatial task. Strikingly, dual-task costs were reduced when added, post-response events supported greater separation between task representations relative to when no post-response events were added. These results support the crosstalk hypothesis, as action effects appear to alter task representations so that they emphasize different types of information, reducing the degree of crosstalk.

Information sharing behaviors in text-based communication during emergency response.

Emergency response teams consist of highly experienced professionals who must effectively communicate to respond to crisis situations characterized by high levels of uncertainty and complexity. Although the existing literatures on team collaboration and information processing are well established, research examining team information sharing through text-based communication technology facing emergency situations is limited. With the increasing complex nature of emergency response situations, emergency response organizations are likely to introduce new technologies to address such events. In this paper, we examine text-based information sharing behaviors related to team situation awareness in a simulated fire-rescue operation of 62 teams. We elaborate on different types of information sharing behaviors and identify a typology and linguistic features as well as differences between high- and low-performing teams in the occurrence of team situational information sharing behaviors. We conclude by discussing implications for emergency response teams and implications for future research avenues.

2.E. Workshop: The city of proximity: Accessible, Inclusive, Sustainable, Healthy and Salutogenic

Abstract According to the “Urban Health Rome Declaration” at European meeting “G7 Health” that defines the strategic aspects and actions to improve Urban, Environmental and Public Mental Health into the cities, and referring to the Agenda 2030 in which the 11th SDG argue about “Sustainable Cities and Communities. Make cities and human settlements inclusive, safe, resilient and sustainable”, one of the most expressive syntheses of the challenging relationship between urban planning and Public Health is stated by WHO (2016): “Health is the precondition of urban sustainable development and the first priority for urban planners”. Referring to the Healthy Cities & Urban Health definitions, we can consider Public Health not merely an aspect of individual health protection and promotion, but a collective condition, strongly influenced by the environmental context and by the strategies implemented by local Governments. The “Health in All Policies” strategy, clearly underlines how health depend by the quality of outdoor and indoor living environments. In this scenario, healthy living and the requirements for healthy places, infrastructure for the public good and Public Health, cycling, walking, disintegrating the role of polluting traffic from the urban environments, social vulnerability and equality are just a few aspects in complex puzzle when designing the urban spaces for healthy, active, walkable cities. The lockdown due to the pandemic has prevented travels, forcing many people to work at home and reducing the possibility of accessing services in the territory. This condition has further highlighted the importance of urban living areas capable of satisfying basic needs within a reasonably easy range of accessibility. The concept of the “15 minutes city” is a useful vision to represent the city of proximity, where it is possible to meet the needs for sustainable, fair, quality, and healthy living. This dimension of proximity can be central to formulating strategies to improve the quality of urban life. A place of proximity, therefore not only defined based on the physical characteristics and people's uses, but also based on the data collected from a public health perspective in which it is also possible to try to test different types of information and build the conditions to suggest suitable policies and projects. Aim of the Workshop - organized by the two EUPHA Section URB+ENV - it would like to be to build the capacity and knowledge between participants about the main topics and urban features capable to have relevant Urban Public and Environmental Health outcomes. Additional scope is to collected case studies and research experiences considered virtuous at the international level, analyzed in detail to highlight the main urban and architectural features of those healthy experiences and the related health outcomes, such as sedentary lifestyle reduction, increase of the attractiveness of places, reduction of air and noise pollution. Key messages • Cities for people, promoting Urban Public Health, Environmental Health and active mobility, require optimization of public spaces for citizens and their activities. • Case studies and research experiences to highlight the main urban and architectural features of those healthy experiences and the related health outcomes, such as sedentary lifestyle reduction.