Information Streams Research Articles

Harmful Microalgae Detection: Biosensors versus Some Conventional Methods.

In the last decade, there has been a steady stream of information on the methods and techniques available for detecting harmful algae species. The conventional approaches to identify harmful algal bloom (HAB), such as microscopy and molecular biological methods are mainly laboratory-based and require long assay times, skilled manpower, and pre-enrichment of samples involving various pre-experimental preparations. As an alternative, biosensors with a simple and rapid detection strategy could be an improvement over conventional methods for the detection of toxic algae species. Moreover, recent biosensors that involve the use of nanomaterials to detect HAB are showing further enhanced detection limits with a broader linear range. The improvement is attributed to nanomaterials’ high surface area to volume ratio, excellent biological compatibility with biomolecules, and being capable of amplifying the electrochemical signal. Hence, this review presents the potential usage of biosensors over conventional methods to detect HABs. The methods reported for the detection of harmful algae species, ranging from conventional detection methods to current biosensor approaches will be discussed, along with their respective advantages and drawbacks to indicate the future prospects of biosensor technology for HAB event management.

Secure Communication Scheme Based on a New 3-D Jerk Chaotic System

Chaos theory plays a significant role in the engineering fields and especially in the security of data transmission in communication systems. In this paper, a new 3-D jerk chaotic system has been suggested. The fundamental dynamical properties, including the fixed points, chaotic attractors, bifurcation diagrams, and Lyapunov exponents are investigated. In communication systems, data security and the implementation of designed transceivers of secure communication scheme can be considered the main challenge for applying such systems. In this work, a 3-D new jerk chaotic scheme has been proposed to be used in data encryption of communication systems. The proposed jerk chaotic system is used for constructing identical transceivers of communication scheme that are presented by a master (transmitter) and a slave (receiver). In the master end, a useful information beam is encrypted through a chaotic signal. On the other hand, in the slave side, the original information beam is retrieved from the mutual transmitted beam by a chaotic synchronization mechanism. The communication scheme transceivers are synchronized by an adaptive observer synchronization mechanism. Finally, the system is tested and evaluated on a stream of binary information that is encrypted in the transmitter and recovered in the receiver. The results show that our proposal cryptosystem has good performance in term of securing transmitted data.

Online measurement of learning temporal statistical structure in categorization tasks

The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive behavioral design to characterize the VSL trajectory during ongoing task performance. In sequential categorization tasks, we assessed two previously identified VSL markers: priming of the second predictable image in a pair manifested by a reduced reaction time (RT) and greater accuracy, and the anticipatory effect on the first image revealed by a longer RT. First, in Experiment 1A, we used an adapted paradigm and replicated these VSL markers; however, they appeared to be confounded by motor learning. Next, in Experiment 1B, we confirmed the confounding influence of motor learning. To assess VSL without motor learning, in Experiment 2 we (1) simplified the categorization task, (2) raised the number of subjects and image repetitions, and (3) increased the number of single unpaired images. Using linear mixed-effect modeling and estimated marginal means of linear trends, we found that the RT curves differed significantly between predictable paired and control single images. Further, the VSL curve fitted a logarithmic model, suggesting a rapid learning process. These results suggest that our paradigm in Experiment 2 seems to be a viable online tool to monitor the behavioral correlates of unsupervised implicit VSL.

Information technology & media sociology in a (still) pandemic world

ABSTRACT The current period of disruptive social change is inextricably bound up with new means and modes of communication, information, and media streams. The Communication, Information Technologies & Media Sociology section of the American Sociological Association (CITAMS) locates these factors at the center of our collective interests, investigating them through a plethora of methods, theories, and empirical cases. Each year, CITAMS runs a special issue in ICS showcasing select works presented at the previous year’s American Sociological Association conference and the affiliated Media Sociology preconference. Papers in the 2022 CITAMS Special Issue reflect a social context defined by a prolonged global pandemic and wrought by democratic uncertainty. Across these social circumstances, technology and media loom large. Simultaneously, everyday life continues and classic CITAMS scholarship sustains relevance for the ways people interact, construct identity, consume, and mobilize. All of this and more are contained in the pages of this year’s Special Issue, from which readers can get a sense of what CITAMS has to offer and consider how their own work may fit within the broad CITAMS umbrella.

Intelligence Gathering, Relazioni, and the Ars Apodemica

ABSTRACT This analysis considers some of the methods and purposes of gathering intelligence in sixteenth-century diplomacy. Regular despatches from ambassadors provided a steady stream of information, but of particular importance was the development of the formal relation, the relazione, pioneered in Venice and subsequently imitated elsewhere. Two imitations by English authors – Robert Beale and Daniel Rogers – are evaluated, one with a critical relationship to the evolution of an emerging genre of advice literature on travel, the ars apodemica. By the close of the century, the aspects to which the private traveller was to direct their attention mirrored the information to be contained in relazioni for the purposes of the state.

The importance of an exaggerated attention bottleneck for understanding psychopathy.

The psychopath has long captured the imagination. A name such as Ted Bundy evokes a morbid curiosity. The crimes committed by Bundy are so cruel that it is hard to imagine how someone could do such things. In this review we discuss evidence that exaggeration in an attention bottleneck is one mechanism that makes it possible for psychopathic individuals to be adept at focusing on a single stimulus feature or goal but struggle to process multiple streams of information simultaneously. This exaggeration may partly explain the behavioral, affective, and social deficits that are apparent among psychopathic individuals. Further research on this attentional mechanism may promote a science that adequately captures the complexity of psychopathic behavior and offers new avenues for intervention.

Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor.

The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic vision sensor has outstanding properties compared to sensors of traditional cameras, with very high dynamic range, high temporal resolution, low power consumption, and does not suffer from motion blur. Hence, dynamic vision sensors have considerable potential for computer vision in scenarios that are challenging for traditional cameras. However, the spatiotemporal event stream has low visualization and is incompatible with existing image processing algorithms. In order to solve this problem, this paper proposes a new adaptive slicing method for the spatiotemporal event stream. The resulting slices of the spatiotemporal event stream contain complete object information, with no motion blur. The slices can be processed either with event-based algorithms or by constructing slices into virtual frames and processing them with traditional image processing algorithms. We tested our slicing method using public as well as our own data sets. The difference between the object information entropy of the slice and the ideal object information entropy is less than 1%.

Is Class-Incremental Enough for Continual Learning?

The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of the learning environment. Here, we challenge the current trend in the continual learning literature to experiment mainly on class-incremental scenarios, where classes present in one experience are never revisited. We posit that an excessive focus on this setting may be limiting for future research on continual learning, since class-incremental scenarios artificially exacerbate catastrophic forgetting, at the expense of other important objectives like forward transfer and computational efficiency. In many real-world environments, in fact, repetition of previously encountered concepts occurs naturally and contributes to softening the disruption of previous knowledge. We advocate for a more in-depth study of alternative continual learning scenarios, in which repetition is integrated by design in the stream of incoming information. Starting from already existing proposals, we describe the advantages such class-incremental with repetition scenarios could offer for a more comprehensive assessment of continual learning models.

Evaluating mental workload during multitasking in simulated flight.

BackgroundPilots must process multiple streams of information simultaneously. Mental workload is one of the main issues in man–machine interactive mode when dealing with multiple tasks. This study aimed to combine functional near‐infrared spectroscopy (fNIRS) and electrocardiogram (ECG) to detect changes in mental workload during multitasking in a simulated flight.MethodsTwenty‐six participants performed three multitasking tasks at different mental workload levels. These mental workload levels were set by varying the number of subtasks. fNIRS and ECG signals were recorded during tasks. Participants filled in the national aeronautics and space administration task load index (NASA‐TLX) scale after each task. The effects of mental workload on scores of NASA‐TLX, performance of tasks, heart rate (HR), heart rate variability (HRV), and the prefrontal cortex (PFC) activation were analyzed.ResultsCompared to multitasking in lower mental workload conditions, participants exhibited higher scores of NASA‐TLX, HR, and PFC activation when multitasking in high mental workload conditions. Their performance was worse during the high mental workload multitasking condition, as evidenced by the higher average tracking distance, smaller number of response times, and longer response time of the meter. The standard deviation of the RR intervals (SDNN) was negatively correlated with subjective mental workload in the low task load condition and PFC activation was positively correlated with HR and subjective mental workload in the medium task load condition.ConclusionHR and PFC activation can be used to detect changes in mental workload during simulated flight multitasking tasks.

Parallel functional subnetworks embedded in the macaque face patch system.

During normal vision, our eyes provide the brain with a continuous stream of useful information about the world. How visually specialized areas of the cortex, such as face-selective patches, operate under natural modes of behavior is poorly understood. Here we report that, during the free viewing of movies, cohorts of face-selective neurons in the macaque cortex fractionate into distributed and parallel subnetworks that carry distinct information. We classified neurons into functional groups on the basis of their movie-driven coupling with functional magnetic resonance imaging time courses across the brain. Neurons from each group were distributed across multiple face patches but intermixed locally with other groups at each recording site. These findings challenge prevailing views about functional segregation in the cortex and underscore the importance of naturalistic paradigms for cognitive neuroscience.

A Scalable Real-Time Agent-Based Information Retrieval Engine

In distributed information retrieval systems, information in web should be ranked based on a combination of multiple features. Linear combination of ranks has been the dominant approach due to its simplicity and efficiency. Such a combination scheme in distributed infrastructure requires that ranks in resources or agents are comparable to each other. The main challenge is how to transform the raw rank values of different criteria appropriately to make them comparable before any combination. In this manuscript, we will demonstrate how to rank Web documents based on its resource-provided information stream and how to combine and incorporate several raking schemas in one time. The system was tested on the queries provided by a Text Retrieval Conference (TREC), and our experimental results showed that it is robust and efficient compared with similar platforms that used offline data resources.

Online nonparametric monitoring of heterogeneous data streams with partial observations based on Thompson sampling

With the rapid advancement of sensor technology driven by Internet-of-Things-enabled applications, tremendous amounts of measurements of heterogeneous data streams are frequently acquired for online process monitoring. Such massive data, involving a large number of data streams with high sampling frequency, incur high costs on data collection, transmission, and analysis in practice. As a result, the resource constraint often restricts the data observability to only a subset of data streams at each data acquisition time, posing significant challenges in many online monitoring applications. Unfortunately, existing methods do not provide a general framework for monitoring heterogeneous data streams with partial observations. In this article, we propose a nonparametric monitoring and sampling algorithm to quickly detect abnormalities occurring to heterogeneous data streams. In particular, an approximation framework is incorporated with an antirank-based CUSUM procedure to collectively estimate the underlying status of all data streams based on partially observed data. Furthermore, an intelligent sampling strategy based on Thompson sampling is proposed to dynamically observe the informative data streams and balance between exploration and exploitation to facilitate quick anomaly detection. Theoretical justification of the proposed algorithm is also investigated. Both simulations and case studies are conducted to demonstrate the superiority of the proposed method.

Cuckoo Search Augmented MapReduce for Predictive Scheduling With Big Stream Data

Handling an information stream is a basic report for streaming application. There were numerous strategies which help during Bigdata streaming, however it can't deal with the tremendous information. To advance the productivity with least time intricacy, a Cuckoo Search Augmented Map Reduce for Predictive Scheduling (CSA-MRPS) system is presented. This technique incorporates cycles in preprocessing and prescient booking for stream information examination. In preprocessing, nonstop information streams are discretized utilizing Khiops and it begins from the constant time spans, consolidates the closest time as indicated by the Chi-square worth with lesser time intricacy. MapReduce work is applied to discretized information for prescient investigation utilizing Multi-Objective Ranked Cuckoo Search Optimization (MRCSA). It characterize the target capacities for the handling units, for example, CPU time, memory utilization, transfer speed use and energy utilization. Thus, CSA-MRPS Mechanism predicts the asset upgraded preparing unit with high position through the planning system.

Experienced entropy drives choice behavior in a boring decision-making task

Boredom has been defined as an aversive mental state that is induced by the disability to engage in satisfying activity, most often experienced in monotonous environments. However, current understanding of the situational factors inducing boredom and driving subsequent behavior remains incomplete. Here, we introduce a two-alternative forced-choice task coupled with sensory stimulation of different degrees of monotony. We find that human subjects develop a bias in decision-making, avoiding the more monotonous alternative that is correlated with self-reported state boredom. This finding was replicated in independent laboratory and online experiments and proved to be specific for the induction of boredom rather than curiosity. Furthermore, using theoretical modeling we show that the entropy in the sequence of individually experienced stimuli, a measure of information gain, serves as a major determinant to predict choice behavior in the task. With this, we underline the relevance of boredom for driving behavioral responses that ensure a lasting stream of information to the brain.

Combination and competition between path integration and landmark navigation in the estimation of heading direction

Successful navigation requires the ability to compute one's location and heading from incoming multisensory information. Previous work has shown that this multisensory input comes in two forms: body-based idiothetic cues, from one's own rotations and translations, and visual allothetic cues, from the environment (usually visual landmarks). However, exactly how these two streams of information are integrated is unclear, with some models suggesting the body-based idiothetic and visual allothetic cues are combined, while others suggest they compete. In this paper we investigated the integration of body-based idiothetic and visual allothetic cues in the computation of heading using virtual reality. In our experiment, participants performed a series of body turns of up to 360 degrees in the dark with only a brief flash (300ms) of visual feedback en route. Because the environment was virtual, we had full control over the visual feedback and were able to vary the offset between this feedback and the true heading angle. By measuring the effect of the feedback offset on the angle participants turned, we were able to determine the extent to which they incorporated visual feedback as a function of the offset error. By further modeling this behavior we were able to quantify the computations people used. While there were considerable individual differences in performance on our task, with some participants mostly ignoring the visual feedback and others relying on it almost entirely, our modeling results suggest that almost all participants used the same strategy in which idiothetic and allothetic cues are combined when the mismatch between them is small, but compete when the mismatch is large. These findings suggest that participants update their estimate of heading using a hybrid strategy that mixes the combination and competition of cues.

Financial University Social Media Informational Stream Architecture: Brand Promotion

The article presents the results of applied research, the purpose of which was to study the informational streams of the Financial University under the Government of the Russian Federation aimed at the formation and promotion of the brand in social media. The cognitive mapping and automated social-media analysis implemented during the study made it possible to identify the features of internal and external branding of the Financial University. The social media brand of the Financial University is mainly aimed at communication and the formation of images of internal audiences, primarily students. To build an external brand, the Financial University uses traditional media and attracts influential actors. The transformation of this approach is due to the entry of the Financial University to the new digital platforms Telegram and TikTok, which allow the use of social media, including the formation of an external social media brand.

Reader Comments Agentive Power in COVID-19 Digital News Articles: Challenging Parascientific Information?

The recent COVID-19 pandemic has triggered an enormous stream of information. Parascientific digital communication has pursued different avenues, from mainstream media news to social networking, at times combined. Likewise, citizens have developed new discourse practices, with readers as active participants who claim authority. Based on a corpus of 500 reader comments from The Guardian, we analyse how readers build their authorial voice on COVID-19 news as well as their agentive power and its implications. Methodologically, we draw upon stance markers, depersonalisation strategies, and heteroglossic markers, from the perspective of discursive interpersonality. Our findings unearth that stance markers are central for readers to build authority and produce content. Depersonalised and heteroglossic markers are also resorted, reinforcing readers’ authority with external information that mirrors expert scientific communication. Conclusions suggest a strong citizen agentive power that can either support news articles, spreading parascientific information, or challenge them, therefore, contributing to produce pseudoscientific messages.

VERBUM – virtual enhanced reality for building modelling (virtual technical tour in digital twins for building conservation)

The digital transformation of the construction sector is also involving cultural and architectural heritage conservation management to solve criticalities of information exchange in refurbishment/restoration, from the preliminary steps until the execution and monitoring of interventions. Nevertheless, time and resources required to complete digital models (point clouds, 3D meshes and HBIM model) are extensive and this can cause interruption of knowledge communication among professionals. The VERBuM project (Virtual Enhanced Reality For Building Modelling) aims at investigating how a central Virtual Technical Tour (VTT), would guarantee a continuous stream of information when other disruptive technologies are integrated in the process and their related products are linked to the VTT. The use of a VTT, based on 360° photos, may fill time and resources gaps as it is a rapid up-to-date and high-fidelityto-reality tool. The fostering of the paradigmatic change in refurbishment/restoration process requires the development of all-in-one digital environments for digital twinning of cultural and architectural heritage and its assessment, aware of potentialities and criticalities to be overcame. The research moves from stakeholders’ information requirements to implement the VERBuM process supported by the central VTT, editable via cloud-based platform (VERBuM product) to exchange digital contents, uploaded in different file format, but consulted in VR by all the involved actors via web services, without any software product installation. The tool has been evaluated via SWOT analysis supported by Task-Technology Fit (TTF) model and users’ perceptions. The results provide mitigation measures of threats related to distrust in use of VTT within working groups and fruition of point clouds, meshes and BIM models, possible via WebGL-based libraries.

The Architecture, Methodology and Implementation of STEP-NC Compliant Closed-Loop Robot Machining System

Industrial robots are gradually being employed in machining processes, particularly the cutting process, owing to their flexibility, mobility, and economic efficiency. However, it is difficult to make the manufacturing process intelligent owing to the complexity of robot machining process information handling and programming. In this paper, the architecture of a STEP-NC compliant closed-loop robot machining system was designed, including its function model and information stream. A methodology based on STEP-NC was established to enable the analysis of high-level information directly and automatically generating robot program according to the actual machining conditions. The STEP-NC Application Activity Model (AAM) and Application Reference Model (ARM) of closed-loop robot machining system is built to integrate the machining process data, monitoring and inspection data, mechanical equipment data, machining status data and inspection result data within a unified data flow, making it possible to realize intelligent manufacturing and adaptively adjusting the robot machining process. The proposed closed-loop robot machining system was implemented based on an open STEP-NC interpreter that interprets the high level information in STEP-NC directly to reduce machining robot programming time. An industrial camera was integrated with the robot for rawpiece positioning, then the STEP-NC interpreter can generate robot path rapidly according to the parameters of manufacturing features and position of rawpiece. The STEP-NC interpreter can generate a robot control program or communicate with a software controller using an application program interface, so it can be integrated with both existing industrial robot controllers and future open robot controllers. Finally, case studies are conducted for the functional verification of the proposed STEP-NC compliant closed-loop robot machining system.

Acoustic Modelling From Raw Source and Filter Components for Dysarthric Speech Recognition

Acoustic modelling for automatic dysarthric speech recognition (ADSR) is a challenging task. Data deficiency is a major problem and substantial differences between typical and dysarthric speech complicate the transfer learning. In this paper, we aim at building acoustic models using the raw magnitude spectra of the source and filter components for ADSR. The proposed multi-stream models consist of convolutional, recurrent and fully-connected layers allowing for pre-processing various information streams and fusing them at an optimal level of abstraction. We demonstrate that such a multi-stream processing leverages information encoded in the vocal tract and excitation components and leads to normalising nuisance factors such as speaker attributes and speaking style. This leads to a better handling of dysarthric speech that exhibits large inter- and intra-speaker variabilities and results in a notable performance gain. Furthermore, we analyse the learned convolutional filters and visualise the outputs of different layers after dimensionality reduction to demonstrate how the speaker-related attributes are normalised along the pipeline. We also compare the proposed multi-stream model with various systems based on MFCC, FBank, raw waveform and i-vector, and, study the training dynamics as well as usefulness of the feature normalisation and data augmentation via speed perturbation. On the widely used TORGO and UASpeech dysarthric speech corpora, the proposed approach leads to a competitive performance of up to 35.3% and 30.3% WERs for dysarthric speech, respectively.