Dynamic Streaming Research Articles

C-Score: An Alternative Banking Methodology

In this day and age, we talk the dialect of assets, stock market investigation and transaction arranged business, or to put a more extensive perspective, money. The current framework focuses on credit score as a default standard for advance application and profiting other banking facilities. The real aspect which is by all accounts missing is adaptability and a connect with the customer. This indirectly prompts a disarray of customer satisfaction and acquisition. The objective of this paper is to build up a superior association with the customers and to create a framework with more pliant aspects, thinking about a more extensive scope of factors for deciding the advance status of a potential applicant. Keeping in mind the end goal to help our speculation, we have contrived a mathematical equation that enables us to perform calculations in light of bigger scope of factors which help decide the applicant's status. This status will appear as a value, which we call the C-Score. This value is utilized to set the level of advantages which can be profited by a customer, accordingly featuring the efficiency of a customer. A calculation is constructed utilizing random forest regression to monitor defaulters and understanding the stream of transactions with respect to advance installments, which is additionally a part of the C-Score. Machine Learning is utilized to play out the calculations at a dynamic stream, the variance being for each customer individually.

Distance to large rivers affects fish diversity patterns in highly dynamic streams of Central Amazonia.

Longitudinal-zonation hypotheses generally predict gradual changes in fish composition from upstream to downstream due to changes in habitat conditions, but largely disregard downstream effects on upstream segments. Floodplains of large rivers represent areas of high connectivity during flood periods and can act as stable refuges in dry seasons, which may attenuate deterministic constraints imposed by local conditions on fish assemblages in surrounding habitats. In this study, we investigated the effects of proximity to large rivers on taxonomic- and functional-diversity patterns of stream-fish assemblages in an extensive region of Central Amazonia. We sampled 31 headwater-stream reaches in nine catchments in the Purus and Madeira Rivers interfluve between December 2014 and March 2015. Ninety seven fish species from seven orders and 19 families were captured. The results indicate that distance to large rivers is more important than distance among sites and local conditions in explaining functional and taxonomic diversity of stream-fish assemblages at large spatial scales. We also found a decrease in taxonomic and functional richness towards headwaters, mainly related to the loss of benthic and sedentary species along the distance gradient. These species may be favored by the proximity to refuge areas and high resource availability near the floodplain. In contrast, upstream assemblages were mainly occupied by small-sized, nektonic species with higher dispersal capacity, highly dependent of allochthonous resources. Downstream effects could be detected for many kilometers upstream in hydrographic catchments and this reinforces the crucial role of connectivity between fluvial habitats in maintenance of stream-fish diversity patterns in the region.

Spatial frequency tuning of single-glance familiar face recognition in a dynamic visual stream

Spatial frequency tuning of single-glance familiar face recognition in a dynamic visual stream

A Distributed Approach to LARS Stream Reasoning (System paper)

AbstractStream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing.

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study.

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.

Dynamic stream network intermittence explains emergent dissolved organic carbon chemostasis in headwaters

AbstractDissolved organic carbon (DOC) concentrations vary among headwaters, with variation typically decreasing with watershed area. We hypothesized that streamflow intermittence could be an important source of variation in DOC concentrations across a small watershed, through (a) temporal legacies of drying on organic matter accumulation and biotic communities and (b) spatial patterns of connectivity with DOC sources. To test these hypotheses, we conducted three synoptic water chemistry sampling campaigns across a 25.5‐km2 watershed in south‐eastern Idaho during early spring, late summer, and late fall. Using changepoint analysis, we found that DOC variability collapsed at a consistent location (watershed areas ~1.3 to ~1.8 km2) across seasons, which coincided with the watershed area where variability in streamflow intermittence collapsed (~1.5 km2). To test hypothesized mechanisms through which intermittence may affect DOC, we developed temporal, spatial, and spatio‐temporal metrics of streamflow intermittence and related these to DOC concentrations. Streamflow intermittence was a strong predictor of DOC across seasons, but different metrics predicted DOC depending on season. Seasonal changes in the effects of intermittence on DOC reflected seasonal changes from instream to flowpath controls. A metric that captured spatial connectivity to sources significantly predicted DOC during high flows, when DOC is typically controlled by transport. In contrast, a reach‐scale temporal metric of intermittence predicted DOC during the late growing season, when DOC is typically controlled by instream processes and when legacy effects of drying (e.g., diminished biological communities) would likely affect DOC. The effects of intermittence on DOC extend beyond temporal legacies at a point. Our results suggest that legacy effects of intermittence do not propagate downstream in this system. Instead, snapshots of spatial patterns of intermittence upstream of a reach are critical for understanding spatial patterns of DOC through connectivity to DOC sources, and these processes drive patterns of DOC even in perennial reaches.

Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields

As one of the various data mining areas, erasable pattern mining is a method for solving financial problems that can be caused in various industrial fields. Since the concept of erasable pattern mining was proposed, various relevant techniques have been developed. Dealing with dynamic stream data is an important issue in different data mining areas including erasable pattern mining. In order to deal with partial data with the latest information, a sliding window-based approach was proposed. However, the method still has performance limits in many cases because of its inefficient data structures and mining techniques. Motivated by this problem, we propose a new efficient erasable pattern mining algorithm based on the sliding window that can guarantee robustness and reliability and deal with the latest stream data more effectively. In addition, the newly proposed list data structure and mining techniques allow the proposed method to extract erasable patterns in a more efficient way. Results of empirical experiments using well-known benchmark datasets guarantee that the proposed algorithm outperforms previous state-of-the-art approaches in various aspects.

Quantifying spatiotemporal variation in headwater stream length using flow intermittency sensors.

Scientists and policymakers increasingly recognize that headwater regions contain numerous temporary streams that expand and contract in length, but accurately mapping and modeling dynamic stream networks remain a challenge. Flow intermittency sensors offer a relatively new approach to characterize wet stream length dynamics at high spatial and temporal resolutions. We installed 51 flow intermittency sensors at an average spacing of 40m along the stream network of a high-relief, headwater catchment (33ha) in the Valley and Ridge of southwest Virginia. The sensors recorded the presence or absence of water every 15min for 10months. Calculations of the wet network proportion from sensor data aligned with those from field measurements, confirming the efficacy of flow intermittency sensors. The fine temporal scale of the sensor data showed hysteresis in wet stream length: the wet network proportion was up to 50% greater on the rising limb of storm events than on the falling limb for dry antecedent conditions, at times with a delay of several hours between the maximum wet proportion and peak runoff at the catchment outlet. Less stream length hysteresis was evident for larger storms with higher event and antecedent precipitation that resulted in peak runoff > 15mm/day. To assess spatial controls on stream wetting and drying, we performed a correlation analysis between flow duration at the sensor locations and common topographic metrics used in stream network modeling. Topography did not fully explain spatial variation in flow duration along the stream network. However, entrenched valleys had longer periods of flow on the rising limbs of events than unconfined reaches. In addition, large upslope contributing areas corresponded to higher flow duration on falling limbs. Future applications that explore the magnitude and drivers of stream length variability may provide further insights into solute and runoff generation processes in headwater regions.

Improved features and dynamic stream weight adaption for robust Audio-Visual Speech Recognition framework

This paper investigates the enhancement of a speech recognition system that uses both audio and visual speech information in noisy environments by presenting contributions in two main system stages: front-end and back-end. The double use of Gabor filters is proposed as a feature extractor in the front-end stage of both modules to capture robust spectro-temporal features. The performance obtained from the resulted Gabor Audio Features (GAFs) and Gabor Visual Features (GVFs) is compared to the performance of other conventional features such as MFCC, PLP, RASTA-PLP audio features and DCT2 visual features. The experimental results show that a system utilizing GAFs and GVFs has a better performance, especially in a low-SNR scenario. To improve the back-end stage, a complete framework of synchronous Multi-Stream Hidden Markov Model (MSHMM) is used to solve the dynamic stream weight estimation problem for Audio-Visual Speech Recognition (AVSR). To demonstrate the usefulness of the dynamic weighting in the overall performance of AVSR system, we empirically show the preference of Late Integration (LI) compared to Early Integration (EI) especially when one of the modalities is corrupted. Results confirm the superior recognition accuracy for all SNR levels the superiority of the AVSR system with the Late Integration.

Saving species, time and money: Application of unmanned aerial vehicles (UAVs) for monitoring of an endangered alpine river specialist in a small nature reserve

The increasing importance of conservation and restoration of our natural capital is associated with a growing demand for reliable and cost-effective scientific data to support decision making and monitoring of implemented measures. Such data include information about habitats, abiotic conditions and disturbances. The small extent or small-scale structure of most restoration sites requires a high spatial resolution exceeding that provided by standard satellite imagery. When the site is still unstable during the initial phase or when frequent disturbance is expected, additionally a high temporal resolution is required. In this case study, we demonstrate the usefulness of a UAV (unmanned aerial vehicle) for monitoring river and floodplain restoration based on a conservation project aimed to preserve Germany's last remaining population of the highly endangered alpine river plant C. chondrilloides. This population is confined to a small, 5 ha area within a highly dynamic alluvial fan in the Bavarian Alps. We used the data acquired by UAV to monitor stream channel dynamics, to quantify bedload transport, erosion and deadwood structures and to characterize vegetation cover and height with the aim of identifying types and loss of habitats and consequently estimating extinction risk. The results show a highly dynamic stream channel and considerable bedload transport and erosion between flood events. The majority of the remaining C. chondrilloides population is found on terraces which are prone to erosion and escape habitats are missing. Between 2016 and 2017 we documented a loss of almost 25% of the species' potential habitats due to erosion events. Three percent of the existing population was lost to those events. Substantial spread of the species was only at the edge terrace which is subject to a high risk of extinction. Our study further demonstrates that many important parameters for the implementation of evidence-based conservation can be easily and cost-effectively derived from standard RGB images taken by UAVs and GIS software. As a consequence, we encourage the increased use of UAVs for restoration and monitoring practice.

Controls on fluvial carbon efflux from eroding peatland catchments

AbstractGlobal peatlands store an unparalleled proportion of total global organic carbon but it is vulnerable to erosion into fluvial systems. Fluvial networks are being recognized as areas of carbon transformation, with eroded particulate organic carbon processed to dissolved organic carbon and CO2. Existing studies indicate biodegradation and photodegradation as key processes controlling the transformation of organic carbon in fluvial systems, with initial concentrations of dissolved organic carbon (DOC) identified as a control on the rate of carbon mineralization. This study manipulates temperature and incident light intensity to investigate carbon mineralization rates in laboratory simulations of peatland sediment transport into fluvial systems. By directly measuring gaseous CO2 emissions from sampled stream water, the relationship of temperature and light intensity with carbon efflux is identified. In simulations where sediment (as particulate organic matter, POM) is absent, temperature is consistently the dominant factor influencing carbon efflux rates. This influence is independent of the initial DOC concentration of the water sample. In simulations where POM was added, representing a peatland river receiving eroded terrestrial sediment, initial DOC concentration predicts 79% of the variation in total gaseous carbon efflux whereas temperature and light intensity predict 12% and 3%, respectively. When sampled stream water's mineralization rates in the presence of added POM are analysed independently, removing DOC as a model variable, the dominant variable affecting CO2 efflux is opposite for each sample. This study presents novel data suggesting peatland erosion introduces further complexity to dynamic stream systems where rates of carbon transformation processes and the influence of specific environmental variables are interdependent. Anthropogenic climate change is identified as a leading risk factor perpetuating peatland erosion; therefore, understanding the fate of terrestrial sediment in rivers and further quantifying the benefits of protecting peatland soils will be of increasing importance to carbon budgeting and ecosystem function studies.

Change detection in a dynamic stream of attributed networks

While anomaly detection in static networks has been extensively studied, only recently have researchers focused on dynamic networks. This trend is mainly due to the capacity of dynamic networks to represent complex physical, biological, cyber, and social systems. This article proposes a new methodology for modeling and monitoring dynamic attributed networks for quick detection of temporal changes in network structures. In this methodology, the generalized linear model (GLM) is used to model static attributed networks. This model is then combined with a state transition equation to capture the dynamic behavior of the system. Extended Kalman filter (EKF) is used as an online, recursive inference procedure to predict and update network parameters over time. In order to detect changes in the underlying mechanism of edge formation, prediction residuals are monitored through an exponentially weighted moving average (EWMA) control chart. The proposed modeling and monitoring procedure is examined through simulations for attributed binary and weighted networks. Email communication data from the Enron corporation is used as a case study to show how the method can be applied in real-world problems.

Dynamic Graph Stream Algorithms in o(n) Space

In this paper we study graph problems in the dynamic streaming model, where the input is defined by a sequence of edge insertions and deletions. As many natural problems require varOmega (n) space, where n is the number of vertices, existing works mainly focused on designing {O}(ncdot mathrm {poly}log n) space algorithms. Although sublinear in the number of edges for dense graphs, it could still be too large for many applications (e.g., n is huge or the graph is sparse). In this work, we give single-pass algorithms beating this space barrier for two classes of problems. We present o(n) space algorithms for estimating the number of connected components with additive error varepsilon n of a general graph and (1+varepsilon )-approximating the weight of the minimum spanning tree of a connected graph with bounded edge weights, for any small constant varepsilon >0. The latter improves upon the previous O(ncdot mathrm {poly}log n) space algorithm given by Ahn et al. (SODA 2012) for the same class of graphs. We initiate the study of approximate graph property testing in the dynamic streaming model, where we want to distinguish graphs satisfying the property from graphs that are varepsilon -far from having the property. We consider the problem of testing k-edge connectivity, k-vertex connectivity, cycle-freeness and bipartiteness (of planar graphs), for which, we provide algorithms using roughly {O}(n^{1-varepsilon }cdot mathrm {poly}log n) space, which is o(n) for any constant varepsilon . To complement our algorithms, we present varOmega (n^{1-O(varepsilon )}) space lower bounds for these problems, which show that such a dependence on varepsilon is necessary.

Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain

Experimental and modeling work of neural activity has described recurrent and attractor dynamic patterns in cerebral microcircuits. However, it is still poorly understood whether similar dynamic principles exist or can be generalizable to the large-scale level. Here, we applied dynamic graph theory-based analyses to evaluate the dynamic streams of whole-brain functional connectivity over time across cognitive states. Dynamic connectivity in local networks is located in attentional areas during tasks and primary sensory areas during rest states, and dynamic connectivity in distributed networks converges in the default mode network (DMN) in both task and rest states. Importantly, we find that distinctive dynamic connectivity patterns are spatially associated with Allen Human Brain Atlas genetic transcription levels of synaptic long-term potentiation and long-term depression-related genes. Our findings support the neurobiological basis of large-scale attractor-like dynamics in the heteromodal cortex within the DMN, irrespective of cognitive state.

Hybrid image sharing scheme using non-recursive hash key based stream cipher

This paper proposes an efficient (k, n)-Hybrid image sharing scheme (HISS) using non-recursive hash key based stream cipher. The sequence generated using non-linear based boolean function is used to construct the Toeplitz matrix to generate the non-recursive hash key. Further, the algorithm is proposed to generate the non-recursive hash key based stream cipher i.e., determined by the Toeplitz matrix and the public-key. Hence, the generated dynamic key stream and basic matrices corresponding to white and black pixels, which are used to construct a Hybrid image sharing scheme without any distortion in the reconstructed image. The generated dynamic key stream is validated for correlation analysis, avalanche effect in terms of the Number of Bit Change Rate (NBCR) and by randomness test using NIST statistical test suit. The proposed Hybrid image sharing scheme is analyzed through correlation coefficients in horizontal, vertical and diagonal direction with entropy analysis, and pixel similarity analysis in terms of Number of Pixels Change Rate (NPCR).

Diel Stream Temperature Effects on Nitrogen Cycling in Hyporheic Zones

AbstractStream temperature often varies diurnally and seasonally, and these variations propagate into hyporheic zones (HZs), forming dynamic and heterogeneous thermal patterns. The complex thermal distribution creates potential biogeochemical hotspots and hot moments. Yet, how diel temperature variations affect HZ nitrogen cycling is unknown. We thus conducted a series of multiphysics numerical simulations of nonisothermal fluid flow and multicomponent reactive solute transport to investigate this problem. We imposed a sinusoidally varying stream temperature representing diel warming and cooling and studied the effects of different temperature means and amplitudes on HZ nitrate removal efficiency inside a streambed with a dune. The results showed that the time‐variable nitrification, denitrification, and nitrate removal efficiency responded differently to the diel stream temperature signal. The temporal variation of spatially averaged nitrification rate tracks the stream temperature signal, whereas the spatially averaged denitrification variation pattern has a more complex connection to temperature. We observed a persistent hotspot where significant denitrification rates are present over the 24‐hr period. We further evaluated and estimated the bulk nitrate removal efficiency calculated by time integration of spatially averaged reaction rates over a day. For denitrification‐dominant systems, the bulk nitrate removal efficiency for cases with dynamic stream temperature was effectively the same as those with an equivalent constant mean temperature. Therefore, the bulk removal efficiency of a thermally dynamic diel system may be represented by an equivalent isothermal system given stable flow conditions. However, since large instantaneous variations in various rates were observed, the results imply that randomly timed field measurements are unlikely to be representative. This has to be considered for both past and future synoptic observational studies.

Tracing Sapiential Traditions in Ancient Judaism. Edited by Hindy Najman, Jean-Sébastien Rey, and Eibert J. C. Tigchelaar

This volume covers traditions and articulations of Jewish wisdom from the Hebrew Bible through to late antiquity and the Middle Ages. The authors reflect on wisdom as a valid though imposed and sometimes limiting category. Concentration is on the Hebrew Bible, Second Temple, and finally rabbinic systems of wisdom and knowledge. The volume seems to focus on terminological and literary challenges (What is wisdom?), on the grouping or sub-grouping of texts within the sphere of wisdom, or wisdom-like (Is this or that text a wisdom text?), and focuses particularly on the transmission, continuity, and development of different aspects of form, context, and content (How is wisdom expressed over the centuries?). The first essay, ‘Is “Wisdom Literature” a Useful Category?’ by Stuart Weeks, explores the history of modern scholarship on ‘wisdom’ in the Hebrew Bible and how its residual echoes remain as methodological problems today. Weeks shows how eighteenth- and nineteenth-century scholars framed early debates and definitions of wisdom through searches for coherence and shared features in content and form, rather than context and purpose, as well as limited/limiting notions of education in ancient Israel. James Kugel, ‘The Theme of Long-Range Planning in the Joseph Narrative and Some Second Temple Period Writings’, compares elements of wisdom, specifically divine long-range planning, in the Joseph narrative (Genesis 37; 39–48, e.g. Gen 50:20) with similar glimpses of divine chronological certitude in Daniel, 1 Enoch, 4 Ezra, the Slavonic ‘Ladder of Jacob’, Ages of Creation (4Q180), and Jubilees. Next, Stéphanie Anthonioz (‘A Reflection on the Nature of Wisdom: From Psalm 1 to Mesopotamian Traditions’) considers the sapiential motif in Psalm 1 of tree metaphor (Ps. 1:3), observing that the wisdom motifs and didactic language in Psalm 1 witness to dynamic streams of tradition in ancient Israel that are also characteristic of ancient Near Eastern iconography and literature—namely, Gilgamesh.

Robust and Efficient Dynamic Stream Cipher Cryptosystem

In this paper a new technique based on dynamic stream cipher algorithm is introduced. The mathematical model of dynamic stream cipher algorithm is based on the idea of changing the structure of the combined Linear Feedback Shift Registers (LFSR's) with each change in basic and message keys to get more complicated encryption algorithm, and this is done by use a bank of LFSR's stored in protected file and we select a collection of LFSR's randomly that are used in algorithm to generate the encryption (decryption) key. We implement Basic Efficient Criteria on the suggested Key Generator (KG) to test the output key results. The results of applying BEC prove the robustness and efficiency of the proposed stream cipher cryptosystem.

Flow simulation and experimental study of a dynamic asymmetric impinging stream

Classical impinging stream technology can effectively enhance heat and mass transfer and is widely used in various chemical fields. The energy of a particle is rapidly reduced after penetrating the reverse jet due to the fluid resistance in a steady symmetrical impinging stream reactor; thus, the effective particle motion range is restricted. In this paper, we propose a new dynamic asymmetrical impinging stream reactor with dynamic sinusoidal variation in the inlet velocity. We investigated the flow field structure and characteristics of the dynamic asymmetric impinging stream reactor based on numerical simulation and experimental verification. The impingement surface was instantaneously moving in the axial region of the impinging stream reactor, thereby increasing the range of motion of the main active area. In this paper, we discuss the axial turbulent kinetic energy distribution and single particle motion behavior in the context of a dynamic asymmetrical impinging stream with an instantaneously moving impinging surface. In the simulation, the maximum turbulence kinetic energy was still located in the impingement region in the dynamic asymmetric impinging stream reactor (in agreement with that of the steady symmetric impinging stream technology), and the position variation in the maximum turbulent kinetic energy was consistent with that of the impinging surface. In addition, the notable improvements in the particle residence time and motion distance in the main active area have obvious practical advantages in engineering compared with using steady symmetrical impinging stream technology.

Wireless Video Caching and Dynamic Streaming Under Differentiated Quality Requirements

This paper considers one-hop device-to-device (D2D)-assisted wireless caching networks that cache video files of varying quality levels, with the assumption that the base station can control the video quality but cache-enabled devices cannot. Two problems arise in such a caching network: file placement problem and node association problem. This paper suggests a method to cache videos of different qualities, and thus of varying file sizes, by maximizing the sum of video quality measures that users can enjoy. There exists an interesting trade-off between video quality and video diversity, i.e., the ability to provision diverse video files. By caching high-quality files, the cache-enabled devices can provide high-quality video, but cannot cache a variety of files. Conversely, when the device caches various files, it cannot provide a good quality for file-requesting users. In addition, when multiple devices cache the same file but their qualities are different, advanced node association is required for file delivery. This paper proposes a node association algorithm that maximizes time-averaged video quality for multiple users under a playback delay constraint. In this algorithm, we also consider request collision, the situation where several users request files from the same device at the same time, and we propose two ways to cope with the collision: scheduling of one user and non-orthogonal multiple access. Simulation results verify that the proposed caching method and the node association algorithm work reliably.