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The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Federated cloud systems increase the reliability and reduce the cost of the computational support. The resulting combination of secure private clouds and less secure public clouds, together with the fact that resources need to be located within different clouds, strongly affects the information flow security of the entire system. In this paper, the clouds as well as entities of a federated cloud system are assigned security levels, and a probabilistic flow sensitive security model for a federated cloud system is proposed. Then the notion of opacity --- a notion capturing the security of information flow --- of a cloud computing systems is introduced, and different variants of quantitative analysis of opacity are presented. As a result, one can track the information flow in a cloud system, and analyze the impact of different resource allocation strategies by quantifying the corresponding opacity characteristics.

The aim of this research is to test the volatility spillover between the stock values of the major liner shipping companies in the world and the freight indices measuring the freight revenues in the container market. The dataset in the study consists of 9 stock values of the top 10 biggest liner companies in 2018 and 3 container freight indices on weekly basis covering the period between 05.11.2010 and 06.07.2018. Volatility spillovers have been determined to some companies' stock values from some container freight indices, and the most influential freight index has been determined as New ConTex. Moreover, some volatility spillovers from some companies' stock values to freight indices have been also determined. These results suggest that some stock investors position their portfolios in the market according to the flow of information from freight indices while some investors position by following macro variables.

Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

Review of the monograph of Doctor of Philology, Professor of Voronezh State University Ludmila I. Grishaeva “Variation of Text in Communication“ (L.I. Grishaeva. Voronezh: Nauka-Unipress, 2020. — 291 p. ISBN 978-5- 4292-0207-5). The monograph accumulates the results of a long-term research performed by the author and her scientific school that reflect rapid developments of text linguistics of the early 21st century. As an independent scientific discipline, this field represents an important theoretical and methodological basis, important also for dissertation research the results of which are included in the book. The monograph under review consists of an introduction, five chapters and a conclusion. L. I. Grishaeva skillfully substantiates the concept of text varying, proceeding from the textual-grammatical mechanisms of fixation, activation and translation of the information about the world. The book gives the detailed coverage to the issues of varying of semantic and syntactic text structure at micro and macro levels. Taking into consideration numerous factors that determine the "essence" and "limits" of text variation a wide range of topics significant for the text, communicative stylistics and media linguistics are considered in the monograph. They all lay at the intersection of disciplines and provide a deep explanation of how the principles of dividing the information flow, fit to various communicative situations, mean primly the cultural decoding and encoding of messages. Achievement of pragmatic and intentional effects by the speech sender in the communication process is illustrated by a number of relevant examples of the most representative types of German-language media texts. It is the media text with its maximum communicative and functional potential that reflects the fine tuning by the solution of communicative and cognitive tasks; it distinctively manifests the influence and role of language cultures, which are optimal for confirming or refuting the theory postulated in the book.

Information exchange in the human brain is crucial for vital tasks and to drive diseases. Neuroimaging techniques allow for the indirect measurement of information flows among brain areas and, consequently, for reconstructing connectomes analyzed through the lens of network science. However, standard analyses usually focus on a small set of network indicators and their joint probability distribution. Here, we propose an information-theoretic approach for the analysis of synthetic brain networks (based on generative models) and empirical brain networks, and to assess connectome’s information capacity at different stages of dementia. Remarkably, our framework accounts for the whole network state, overcoming limitations due to limited sets of descriptors, and is used to probe human connectomes at different scales. We find that the spectral entropy of empirical data lies between two generative models, indicating an interpolation between modular and geometry-driven structural features. In fact, we show that the mesoscale is suitable for characterizing the differences between brain networks and their generative models. Finally, from the analysis of connectomes obtained from healthy and unhealthy subjects, we demonstrate that significant differences between healthy individuals and the ones affected by Alzheimer’s disease arise at the microscale (max. posterior probability smaller than 1%) and at the mesoscale (max. posterior probability smaller than 10%)., Author Summary Capitalizing on network information theory we propose a new framework for the analysis and comparison of empirical and synthetic human connectomes, in health and disease. We quantitatively assess the ability of our approach in identifying differences and similarities in the structural connectivity of the human brain, ranging between microscopic and macroscopic scales. Relying on two different diffusion processes, namely classic and maximal entropy random walks, we show that the mesoscale is suitable for capturing differences between empirical connectomes and their synthetic models, while differences between healthy subjects and patients affected by Alzheimer’s disease are mostly significant at the microscopic scale.

The priority of the maritime transport operation is a high level of security, which must be constantly monitored by shipowners with a security management system. The origin of the International Code for the Management of Safe Ship Operations and Pollution Prevention and the revised guidelines for the administration of the ISM Code by administrations, the first formal mandatory standard for safety management and pollution prevention goes back to the early 1980s. Arrangement of safe ship management and operation and consequent minimization of errors human being by far the most common cause of marine disasters is its main. The shipowner's and ship's certification procedure for the Safety Management System (SMS) is in line with Regulation (EC) No 336/2006 [1] of the European Parliament and the Council of Europe of 15 February 2006 on the implementation of the ISM Code in the Community, together with the Commission's amendment European Union of June 16, 2008. The documentation contains a developed Safety Management System adapted to the conditions of a given shipowner. All elements of the system must be documented, and, above all, the duties must be described in the organizational structures. Documentation must indicate how management supervises activities at various levels, employee participation and individual management representatives in individual processes, and how the safety management system is constantly improved. The shipowner maintains a security management system in line with the requirements of the Code and complies with the requirements of the certification system. The amount of information and the speed of changes taking place in the environment makes it necessary to introduce a system of information management and channels of their flow not only within the organization, but also in communication with third parties. Proper management of information resources increases the efficiency and effectiveness of the system. In the publication, the possibility of a formalized description of information flow in a well-functioning shipowner's safety management system.

Introduction. The analysis of development and application of BIM technologies in the Russian construction industry has proven the absence of the system approach and uniform concepts of industry-specific information modelling technologies. Against this background, relevant issues include the strategic objective, or the digital transformation of the industry with the help of a uniform federal information platform, as well as development of long-term models designated for the organization and management of the whole life cycle of capital construction facilities. Materials and methods. The article covers features and opportunities provided by digital technologies, including cloud computing and big data, the industrial Internet of things and the blockсhain, in terms of practical prospects and information characteristics of the tasks that include organization of subsystems, participants and construction project stages. Results. The structure of an information model designated for the organization and management of a capital construction facility’s life cycle is presented as a set of interrelated information flows for consecutive stages, subsystems and project participants. It’s been proven that traditional principles of organization and management restrain the accumulated powerful potential of digital transformation; therefore, the development of the architecture of a unified BIM platform requires new production concepts for the organization and management of project information flows. The co-authors propose an implementation pattern for the management concept and model: it represents a virtual design company, which organizes interaction between participants of a capital construction project and manages its life cycle according to new information and communication principles. Conclusions. A virtual project company integrates the functions of digital integrated production concepts, including the BIM platform, the platform-based production chain and the virtual production corporation, which correspond to the features of transformations of successive stages and the composition of participants contributing to the facility’s life cycle. Any further development and introduction of the concept of a virtual design company can serve as the organizational, technological and structural management base for the whole life cycle of a project and a new pattern of the work process organization and interaction between project developers.

Background: It is a commonly held view that information flow between widely separated regions of the cerebral cortex is a necessary component in the generation of wakefulness (also termed “connected” consciousness). This study therefore hypothesized that loss of wakefulness caused by propofol anesthesia should be associated with loss of information flow, as estimated by the effective connectivity in the scalp electroencephalogram (EEG) signal. Methods: Effective connectivity during anesthesia was quantified by applying bivariate Granger to multichannel EEG data recorded from 16 adult subjects undergoing a slow induction of, and emergence from, anesthesia with intravenous propofol. During wakefulness they were conducting various auditory and motor tasks. Functional connectivity using EEG coherence was also estimated. Results: There was an abrupt, substantial, and global decrease in effective connectivity around the point of loss of responsiveness. Recovery of behavioral responsiveness was associated with a comparable recovery in information flow pattern (expressed as normalized values). The median (interquartile range) change was greatest in the delta frequency band: decreasing from 0.15 (0.21) 2 min before loss of behavioral response, to 0.06 (0.04) 2 min after loss of behavioral response (P < 0.001). Regional decreases in information flow were maximal in a posteromedial direction from lateral frontal and prefrontal regions (0.82 [0.24] 2 min before loss of responsiveness, decreasing to 0.17 [0.05] 2 min after), and least for information flow from posterior channels. The widespread decrease in bivariate Granger causality reflects loss of cortical coordination. The relationship between functional connectivity (coherence) and effective connectivity (Granger causality) was inconsistent. Conclusions: Propofol-induced unresponsiveness is marked by a global decrease in information flow, greatest from the lateral frontal and prefrontal brain regions in a posterior and medial direction. Loss of information flow may be a useful measure of connected consciousness., In healthy adult volunteers, propofol anesthesia–induced loss of consciousness was associated with an abrupt, substantial, and global decrease in connectivity. These changes are comparably reversed at regain of consciousness. These observations suggest that information flow is an important indicator of wakefulness. Supplemental Digital Content is available in the text.

Twitter is an emerging form of news media with a wide spectrum of participants involving in news dissemination. Owing to their open and interactive nature, individuals, non-media, and non-commercial participants may play a greater role on this platform; thus, it is deemed to disrupt conventional media structures and introduce new ways of information flow. While this may be true in certain aspects in news dissemination such as allowing a broader range of participants, the authors' analysis of the involvement and influence of the different participant types, based on a large tweets dataset collected during the Ukraine's conflict event (2013-2014), portrays a different picture. Specifically, the results unveil that while non-commercial participants were the most “involved” in generating tweets about the news event, the retweets they attracted, a common measure of influence, were among the lowest. In contrast, mass media and sources related to journalists, professional associations and commercial organizations garnered the highest retweets.

Multivoxel pattern analysis (MVPA) has become a standard tool for decoding mental states from brain activity patterns. Recent studies have demonstrated that MVPA can be applied to decode activity patterns of a certain region from those of the other regions. By applying a similar region-to-region decoding technique, we examined whether the information represented in the visual areas can be explained by those represented in the other visual areas. We first predicted the brain activity patterns of an area on the visual pathway from the others, then subtracted the predicted patterns from their originals. Subsequently, the visual features were derived from these residuals. During the visual perception task, the elimination of the top-down signals enhanced the simple visual features represented in the early visual cortices. By contrast, the elimination of the bottom-up signals enhanced the complex visual features represented in the higher visual cortices. The directions of such modulation effects varied across visual perception/imagery tasks, indicating that the information flow across the visual cortices is dynamically altered, reflecting the contents of visual processing. These results demonstrated that the distillation approach is a useful tool to estimate the hidden content of information conveyed across brain regions.

Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI.Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the Alzheimer's Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by using non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA). Edge- and node-wise Student's t-test was conducted for between-group comparison. Support vector classification was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state.Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein, and phosphorylated tau protein with the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures.Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state.

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

Social interaction is of vital importance for human beings. While the hyperscanning approach has been extensively used to study interpersonal neural synchronization (INS) during social interactions, functional near-infrared spectroscopy (fNIRS) is one of the most popular techniques for hyperscanning naturalistic social interactions because of its relatively high spatial resolution, sound anatomical localization, and exceptionally high tolerance of motion artifacts. Previous fNIRS-based hyperscanning studies usually calculate a time-lagged INS using wavelet transform coherence (WTC) to describe the direction and temporal pattern of information flow between individuals. However, the results of this method might be confounded by the autocorrelation effect of the fNIRS signal of each individual. For addressing this issue, a method termed partial wavelet transform coherence (pWTC) was introduced, which aimed to remove the autocorrelation effect and maintain the high temporal-spectrum resolution of the fNIRS signal. In this study, a simulation experiment was performed first to show the effectiveness of the pWTC in removing the impact of autocorrelation on INS. Then, step-by-step guidance was offered on the operation of the pWTC based on the fNIRS dataset from a social interaction experiment. Additionally, a comparison between the pWTC method and the traditional WTC method and that between the pWTC method and the Granger causality (GC) method was drawn. The results showed that pWTC could be used to determine the INS difference between different experimental conditions and INS's directional and temporal pattern between individuals during naturalistic social interactions. Moreover, it provides better temporal and frequency resolution than the traditional WTC and better flexibility than the GC method. Thus, pWTC is a strong candidate for inferring the direction and temporal pattern of information flow between individuals during naturalistic social interactions.

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data—such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing.

In recent years, some studies brought the complex networks theory into neuroscience. Connectivity patterns are widely studied and vary from the data acquisition method: fMRI, EEG, and so on. Those connectivity data can be classified into three main types: anatomical, functional, and effective. The first one relies only on anatomical connections, the second relies on the correlations of signals recorded from different parts of the brain, and the latter on the direction of the information flow. In this work, it is proposed to study the variability of the parameters of complex networks, for two methods of effective connectivity (namely: Transfer Entropy and Phase Transfer Entropy) and EEG data, of 10 patients with absence epilepsy. It is known that the topology of the complex network of the epilepsy crisis varies substantially between patients. However, for the same patient, effective connectivity topologies may vary, but they should not. The variation of network parameters, for the same complex network (the same patient), can lead to misinterpretations of the analyzed phenomena. In this work, preliminary results indicate that such variability indeed occurs, for the same patient, even in strictly similar measures, such as Transfer Entropy and Phase Transfer Entropy.

The total energy the brain consumed and the intensities of information flows across different brain regions in an intellectual activity may help to explain an individual’s intelligence level. To verify this assumption, 43 students aged 18–25 were recruited as the research subjects. Their intelligence quotients (IQ) were scored by using Wechsler Adult Intelligence Scale (WAIS), while their electroencephalogram (EEG) signals were recorded simultaneously by using Neuroscan system. The total energy and distribution patterns of EEG signals were acquired in Curry 8.0. The intensities of information flow across different brain regions were measured by Phase Slope Index (PSI). 20 channels and 190 channel combinations were selected for data analysis. The results show that the IQ score negatively correlates to the EEG energy and positively correlates to the intensities of information flows at specific frequency bands in specific channel pairs, especially in some long distance (18–24[Formula: see text]cm) channel pairs.

In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved with deeper and more advanced networks, although the performance varies between language pairs and translation directions. The accuracy of manual evaluation in EN-DE, DE-EN, EN-ZH, and ZH-EN is 95.7%, 94.8%, 93.4%, and 91.7%, respectively. In addition, we show that under-translation is the most significant error type in NMT, which contrasts with the more diverse error profile previously observed for statistical machine translation. To better understand the root of the under-translation of negation, we study the model's information flow and training data. While our information flow analysis does not reveal any deficiencies that could be used to detect or fix the under-translation of negation, we find that negation is often rephrased during training, which could make it more difficult for the model to learn a reliable link between source and target negation. We finally conduct intrinsic analysis and extrinsic probing tasks on negation, showing that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation in hidden states but nevertheless leave room for improvement., To appear at TACL and to be presented at ACL 2021. Authors' final version

After the military communications network is attacked by the enemy, in order to ensure that the information flow in the command and control organization can continue to be transmitted smoothly, the damaged network topology needs to be repaired in a timely and effective manner. The problem of repairing military communication network under the circumstance of nodes are hit in the network is described. This paper propose a method to repair damaged network by adding edges to the damaged network topology, which aims at maximizing network invulnerability. Network topological connection cost is based on average connection degree. The repair model is established with the consideration of connect cost limit and network connectivity. The model solving method based on discrete artificial bee colony algorithm is designed. Finally, proposed repair method is tested and compared with other three edge addition repair methods with a specific case of network. The experimental results show that the proposed method could enhance damaged network's invulnerability and outperforms the compared methods.

The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

The article deals with the use of digital forms in the manufacturing environment to reduce the amount of printed paper and streamline the flow of information of production and logistic processes. The aim of this article is to point out the possibility of using simple and commonly available solutions to collect the necessary data. The problem is the need for higher computer skills of ordinary users. It can be used in both logistics and production environments. In a medium-sized company, the amount of paper printed using a printer may be approximately 19000 pcs per year, which consider checklists, forms, diaries of processes or maintenance. This quantity amounts to approximately 1140 EUR. The article focuses on the tools, which are commonly available software to smaller and larger companies in the manufacturing industry, and of course in other industries.

Significance The rapid comprehension of speech is a remarkable but poorly understood human capacity. Central to this process is the integration of the meaning of each word, as it is heard, into the listener’s interpretation of the utterance. Here we focus on the real-time flow of neural activity that underpins this combinatorial process, using multivariate pattern analysis and computational semantic models to discover the contextual constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of future words in the utterance. This combination of methods reveals a continuous information flow across the left-hemisphere language system, strongly constraining the immediate activation of word meanings and providing a neural substrate for seamless real-time speech comprehension., Human speech comprehension is remarkable for its immediacy and rapidity. The listener interprets an incrementally delivered auditory input, millisecond by millisecond as it is heard, in terms of complex multilevel representations of relevant linguistic and nonlinguistic knowledge. Central to this process are the neural computations involved in semantic combination, whereby the meanings of words are combined into more complex representations, as in the combination of a verb and its following direct object (DO) noun (e.g., “eat the apple”). These combinatorial processes form the backbone for incremental interpretation, enabling listeners to integrate the meaning of each word as it is heard into their dynamic interpretation of the current utterance. Focusing on the verb-DO noun relationship in simple spoken sentences, we applied multivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoencephalographic data to map out the specific representational constraints that are constructed as each word is heard, and to determine how these constraints guide the interpretation of subsequent words in the utterance. Comparing context-independent semantic models of the DO noun with contextually constrained noun models reflecting the semantic properties of the preceding verb, we found that only the contextually constrained model showed a significant fit to the brain data. Pattern-based measures of directed connectivity across the left hemisphere language network revealed a continuous information flow among temporal, inferior frontal, and inferior parietal regions, underpinning the verb’s modification of the DO noun’s activated semantics. These results provide a plausible neural substrate for seamless real-time incremental interpretation on the observed millisecond time scales.

Purpose Although prior studies have noted the importance of trust for project performance, research remains scant on describing the role of trust when using lean design management (LDM) in projects. The purpose of this paper is to explore the connection between LDM and interpersonal trust in solving construction projects’ design management problems. Design/methodology/approach A qualitative study was conducted that included 29 trust- and LDM-themed semi-structured interviews in the USA (California), Brazil and Finland; 11 focus group discussions were also organized to validate the interview findings. Findings The study reveals how LDM contributes to solving design management problems through two distinct but interconnected mechanisms: improved information flow; and improved trust among project team members. A conceptual framework was crafted to illustrate the mechanisms in building trust by means of the social domain of LDM concepts. Research limitations/implications The conceptual framework requires testing through an international survey or through multiple case studies. Practical implications The results indicate that design management would benefit from trustful environments and that trust may be the catalyst for actors’ engagement with LDM. Managers in charge of design within projects can use the conceptual framework when selecting the appropriate LDM tools, which should include both the social and technical domains. Originality/value The study emphasizes the importance of the social domain of LDM concepts. Previous studies have focussed on information flow aspects of LDM but have overlooked the value of interpersonal trust in solving design management problems.

Construction projects are characterised by complexity in the technical, organisational and environmental sphere. The organisational complexity of such projects makes it necessary to manage relationships between actors who fulfil various functions. Formal organisational structures that have been developed for this purpose do not always reflect the actual relationships between construction project participants. In literature, scholars more and more often point to the need to identify and monitor such informal relationships and attempt to manage them in order to effectively carry out projects. Structural analysis of so-called self-organising networks of relationships between project participants is carried out on the basis of established structural measures by performing Social Network Analysis (SNA). In a situation when inappropriate communication between project participants relative to management staff expectations is detected, interventions meant to improve communication in such networks are possible. The goal of the article is proposing an optimisation-oriented approach to planning such interventions while taking various constraints, such as communication costs, into consideration. As a part of this optimisation, the authors proposed a method from the heuristic methods group. This solution will support decision-making in terms of intervening within an informal relationship structure. The method was presented on the example of an actual construction project involving the construction of a complex of housing buildings. the self-organising network structure was defined on the basis of a survey carried out among the project’s participants and concerned communication between them over a four-week period. As a result of the structural network analysis, abnormalities in communication between project participants were detected. The optimisation method developed by the authors pointed to possibilities of improving communication effectiveness within this network. The effects of the analysis confirmed the application potential of the method that was presented.

Separation is the primary consideration in cloud computing security. A series of security and safety problems would arise if a separation mechanism is not deployed appropriately, thus affecting the confidence of cloud end-users. In this paper, together with characteristics of cloud computing, the separation issue in cloud computing has been analyzed from the perspective of information flow. The process of information flow in cloud computing systems is formalized to propose corresponding separation rules. These rules have been verified in this paper and it is shown that the rules conform to non-interference security, thus ensuring the security and practicability of the proposed rules.

Background/Introduction: Widespread network disruption has been hypothesized to be an important predictor of outcomes in patients with refractory temporal lobe epilepsy (TLE). Most studies e...

Background Public awareness is crucial for successful deployment of tidal energy, a renewable energy source that can provide clean electricity to remote islands. However, considering public attitudes on tidal energy are not well known, especially in developing countries, a barrier exists in implementing public engagement strategies. This study aims to contribute by identifying strategies for information provision—the initial step in public engagement—and estimate how these can be engaged to enhance support for tidal energy among the local public in a remote area of a developing country, in this case, Flores Timur Regency, Indonesia, considering their socio-cultural background. Methods In this paper, we employ statistical analyses using multinomial probit modelling to identify the key variables that shape information flow. The aptness of the variables is then verified using post-estimation techniques for their use as input parameters for the simulation of the information flow in the field study area. Agent-based simulation (ABS) is employed to replicate the actual conditions in Flores Timur Regency, Indonesia, and simulate the flow of information through the local community. Results According to the multinomial probit estimations, the people belonging to the top hierarchical group show a higher probability to support tidal energy compared to the members belonging to the lower groups. Understandably, around twice as many information flow cycles are needed to disseminate information to the members of the lowest hierarchical group, compared to the members of the top hierarchical group. The results also show that increasing the amount of available information has a positive impact on information dissemination. Conclusions This study demonstrated that information provision is highly effective with propagation of information that specifically highlights the individual benefits, rather than the community benefits of tidal energy. Additionally, savings in terms of costs, time, and efforts can be realized if the most influential members of the local community are targeted initially before including all other stakeholders. The study also indicated that locals absorb more information and increase their support for tidal energy when additional data is made available. Finally, as long-term strategy, information provision becomes most effective when the local population gains higher educational capabilities.

Since its introduction, Building Information Modelling has evolved into a major technology in the construction industry, where information flows play a major role. However, the very presence of waste within these flows prevents the technology from reaching its full potential. This paper aims to develop a taxonomy focused on the sources of waste within information flows in BIM projects, as existing taxonomies focus primarily on work and material flows. Using a collaborative design science research approach, the study was divided into two phases: first, semi-structured interviews were used to collect data on BIM practices. The data was then used to identify similarities and contradictions in the information flows using process maps. Second, the ShareLab approach was used to validate the findings through a common agreement. The paper’s main contribution is the taxonomy of sources of waste in BIM project information flows, as it closes knowledge gaps in one of the main flows of construction projects. Another contribution is its use of a new approach to validate an artefact in a Design Science Research methodology named the ShareLab approach.

As the energy infrastructure of smart cities, smart grid upgrades traditional power grid systems with state-of-the-art information and communication technologies. In particular, as the full deployment of the Internet of Things in the power grid (a.k.a. power Internet of Things or PIoT), the newly introduced information flow together with inherent energy flow makes it more efficient for power generation, transmission, distribution, and consumption. To further exploit the precious energy and the latest 5G technologies, this article boosts to add a value flow in the smart grid, mainly including the value created by innovative services and market mechanisms and the value added by the information flow. Specifically, by integrating PIoT with cyber-physical systems, this article sketches a conceptual framework of the cyber-physical power system (CPPS). The CPPS carries out holistic perception and ubiquitous connection of distributed energy sources and electrical facilities and builds up a smarter power grid with global information interaction, intelligent decision-making, and real-time agile control. Finally, for illustration purposes, we conduct a case study regarding an intelligent home management system.

The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC., Accepted to ACL-IJCNLP 2021 main conference

Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task. Effective connectivity among all scalp channels was calculated using directed phase transfer entropy (dPTE) for delta, theta, alpha, beta, and lower-gamma frequency bands. Group differences were evaluated using permutation tests in connectivity between regions. Significant posterior to anterior patterns of information flow in theta frequency bands were found in healthy subjects (p-value

Due to individual differences, different subjects have different response to different emotions. Only using the electroencephalogram (EEG) data that can best describe emotions, can the constructed brain networks better reflect the brain connection patterns. This paper proposes a window selection method of time-varying sequence to select critical EEG data that are most relevant to emotions. The generalized orthogonalized partial directed coherence (gOPDC) method is used to construct effective brain networks based on critical EEG data, which can explore the statistical relationship and the information flow between nodes. At the same time, the false connections caused by volume conduction in EEG channels can also be overcome. We extract network features for brain effective connectivity analysis and emotion recognition. The results of the experiments indicate that emotion is mainly related to the frontal region of the brain, and the parietal lobe also plays an important role in low valence emotions. The connection of brain network is the densest in negative emotions and the least in calm and relaxed emotions. The average classification accuracy reaches 78.78% by using KNN classifier on critical EEG data, and the classification results are better than using all data and the intermediate data.

The mammalian frontal and auditory cortices are important for vocal behaviour. Here, using local field potential recordings, we demonstrate for the first time that the timing and spatial pattern of oscillations in the fronto-auditory cortical network of vocalizing bats (Carollia perspicillata) predict the purpose of vocalization: echolocation or communication. Transfer entropy analyses revealed predominantly top-down (frontal-to-auditory cortex) information flow during spontaneous activity and pre-vocal periods. The dynamics of information flow depended on the behavioural role of the vocalization and on the timing relative to vocal onset. Remarkably, we observed the emergence of predominantly bottom-up (auditory-to-frontal cortex) information transfer patterns specific echolocation production, leading to self-directed acoustic feedback. Electrical stimulation of frontal areas selectively enhanced responses to echolocation sounds in auditory cortex. These results reveal unique changes in information flow across sensory and frontal cortices, potentially driven by the purpose of the vocalization in a highly vocal mammalian model.

Studying brain function is a challenging task. In the past, we could only study brain anatomical structures post-mortem, or infer brain functions from clinical data of patients with a brain injury. Nowadays technology, such as functional magnetic resonance imaging (fMRI), enable non-invasive brain activity observation. Several approaches have been proposed to interpret brain activity data. The brain connectivity model is a graphical tool that represents the interaction between brain regions, during certain states. It depicts how a brain region cause changes to other parts of the brain, which can be implied as information flow. This model can be used to help interpret how the brain works. There are several mathematical frameworks that can be used to infer the connectivity model from brain activity signals. Granger causality is one such approach and is one of the first that has been applied to brain activity data. However, due to the concept of the framework, such as the use of pairwise correlation, combined with the limitation of brain activity data such as low temporal resolution in case of fMRI signal, makes the interpretation of the connectivity difficult. We therefore propose the application of the Tigramite causal discovery framework on fMRI data. The Tigramite framework uses measures such as causal effect to analyze causal relations in the system. This enables the framework to identify both direct and indirect pathways or connectivities. In this paper, we applied the framework to the Human Connectome Project motor task-fMRI dataset. We then present the results and discuss how the framework improves interpretability of the connectivity model. We hope that this framework will help us understand more complex brain functions such as memory, consciousness, or the resting-state of the brain, in the future.

We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods., 14 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1904.04745

In this study, we report the investigations conducted on the mimetic behavior of a new humanoid robot called Alter3. Alter3 autonomously imitates the motions of a person in front of it and stores the motion sequences in its memory. Alter3 also uses a self-simulator to simulate its own motions before executing them and generates a self-image. If the visual perception (of a person's motion being imitated) and the imitating self-image differ significantly, Alter3 retrieves a motion sequence closer to the target motion from its memory and executes it. We investigate how this mimetic behavior develops interacting with human, by analyzing memory dynamics and information flow between Alter3 and a interacting person. One important observation from this study is that when Alter3 fails to imitate a person's motion, the person tend to imitate Alter3 instead. This tendency is quantified by the alternation of the direction of information flow. This spontaneous role-switching behavior between a human and Alter3 is a way to initiate personality formation (i.e., personogenesis) in Alter3.

Author(s): Weiser, Sydney Claire | Advisor(s): Ackman, James | Abstract: The neocortex contains a constellation of sensory-motor regions whose functional interactions provide the basis of cognition and behavior. Simultaneously recording neuronal group activity across the cortical hemispheres is essential for understanding the nature of information flow across cerebral networks. Moreover, unbiased and robust methods for measuring functional interactions across the cortex within individual living subjects is critical for substantive tests of genetic and environmental factors that influence brain development and function. To this end, we image pan-neuronally expressed genetically encoded calcium indicators transcranially across the neocortex of unanesthetized, behaving mice throughout development. Recording from behaving mice produces a unique set of challenges, including optical and blood artifacts associated with movement. In addition, areal patterning of the cortex can vary among individuals, ages, and genotypes thus an unbiased, flexible workflow for video acquisition and analysis is necessary for producing high quality segmentations of functional structure across neocortex. To address these challenges, we have developed an eigendecomposition-based workflow that isolates hemodynamic and optical artifacts to recover underlying calcium activity patterns, and segments independent regions of the brain to create maps of functional units across the developing cortex. These unique, data-driven maps provide a reference for understanding developmental, genetic, as well as individual variation between functional units of the brain, and provide a method for extracting optimized time courses from the cortical surface without the need for stimulation-based mapping or anatomical post-processing and alignment. In addition, we quantify the quality of separation of independent sources and use the resulting metrics as feedback to optimize our video acquisition parameters. The open source methods developed here are flexible enough to be utilized with various subject ages and genotypes, as well as numerous experimental configurations and computer architectures. Here we additionally present applications of these methods to anesthesia and development datasets. Overcoming these challenges opens the possibility of using these techniques to help address a number of key objectives in neuroscience, including the quantification of robust inter-areal dynamics between functional motifs across cerebral networks.

ive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our model can outperform various methods. We also conduct zero-shot setting experiments on the Argumentative Dialogue Summary Corpus, the results show that our model can better generalized to the new domain.

Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores the dynamic information flow across dialogue utterances. In this work, we propose the DialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue generation task. Besides, we propose the Flow score, an effective automatic metric for evaluating interactive human-bot conversation quality based on the pre-trained DialoFlow, which presents high chatbot-level correlation ($r=0.9$) with human ratings among 11 chatbots. Code and pre-trained models will be public. \footnote{\url{https://github.com/ictnlp/DialoFlow}}, Comment: ACL2021 main conference (long paper)

Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information to quantify bidirectional information flow in this study. We found that there has information flow between the scene-selective areas, e.g., OPA, PPA, RSC, and object-selective areas, e.g., LOC. Specifically, strong information flow exists between RSC and LOC. It explained that functionally coupled between RSC and LOC plays a vital role in visual scenes/object categories or recognition in our daily lives. Meanwhile, we also found weak reverse-directed information flow in the visual scenes and objects neural networks.

As information flow in the electronic online environment is increasing, the need for automated processing is increasing. Every minute, 456,000 tweets were written on Twitter. The data in the Mongolian language is already joined in that stream and created their own space since a long time ago. However, in the Mongolian language, there is a lack of research, language resources, and an implemented system by now. In this work, we tried to classify the document level sentiment polarity of Mongolian tweets based on the RNN method of deep learning. The model was tested at the 62,917 SemEval English data, the highest F1 score of 64.7.