Gathering Tasks Research Articles

The Hunger Games: Homeostatic State-Dependent Fluctuations in Disinhibition Measured with a Novel Gamified Test Battery.

Food homeostatic states (hunger and satiety) influence the cognitive systems regulating impulsive responses, but the direction and specific mechanisms involved in this effect remain elusive. We examined how fasting, and satiety, affect cognitive mechanisms underpinning disinhibition using a novel framework and a gamified test-battery. Thirty-four participants completed the test-battery measuring three cognitive facets of disinhibition: attentional control, information gathering and monitoring of feedback, across two experimental sessions: one after overnight fasting and another after a standardised meal. Homeostatic state was assessed using subjective self-reports and biological markers (i.e., blood-derived liver-expressed antimicrobial protein 2 (LEAP-2), insulin and leptin). We found that participants who experienced greater subjective hunger during the satiety session were more impulsive in the information gathering task; results were not confounded by changes in mood or anxiety. Homeostatic state did not significantly influence disinhibition mechanisms linked to attentional control or feedback monitoring. However, we found a significant interaction between homeostatic state and LEAP-2 on attentional control, with higher LEAP-2 associated with faster reaction times in the fasted condition only. Our findings indicate lingering hunger after eating increases impulsive behaviour via reduced information gathering. These findings identify a novel mechanism that may underpin the tendency to overeat and/or engage in broader impulsive behaviours.

Correction to: Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Recent advancement in wireless charging technologies has enabled us to design and development of Wireless Rechargeable Sensor Networks (WRSNs) for sensing and data gathering tasks for a very long duration. The fundamental research challenge in WRSN is to design efficient path scheduling for Mobile Wireless Charging Vehicles (MWCVs) such that it maximizes utility of energy resource of MWCVs and minimizes average delay in charging process of the network. Most of the existing solutions for path scheduling of MWCVs suffer from high charging latency,poor energy usage efficiency, and low scalability issues. In order to overcome these issues, this research paper proposed a novel algorithm for scheduling of multiple mobile rechargers using Hybrid meta-heuristic technique. In the proposed Hybrid meta-heuristic-based algorithm, best features of Cuckoo Search and Genetic Algorithm are combined to optimize the path scheduling problem. This work derives a novel fitness function for optimizing the performance of the scheduling. To show the effectiveness of the proposed scheme, an extensive simulation experiments are performed under different network scenarios and results are compared with the latest state-of-art schemes. Result analysis confirms advantages of the proposed scheme in terms of charging latency, total travel distance and energy usage efficiency.

Journalists on COVID-19 Journalism: Communication Ecology of Pandemic Reporting

In the midst of the COVID-19 pandemic, journalists have the challenging task of gathering and distributing accurate information. Journalists exist as a part of an ecology in which their work influences and is influenced by the environment that surrounds it. Using the framework of disaster communication ecology, this study explores the discursive construction of journalism during the COVID-19 crisis. To understand this process in the field of journalism, we unpacked discourses concerning the coronavirus pandemic collected from interviews with journalists during the pandemic and from the U.S. journalism trade press using the Discourses of Journalism Database. Through discourse analysis, we discovered that during COVID-19 journalists discursively placed themselves in a responsible but vulnerable position within the communication ecology—not solely as a result of the pandemic but also from environmental conditions that long preceded it. Journalists found their reporting difficult during the pandemic and sought to mitigate the forces challenging their work as they sought to reverse the flow of misinformation.

DAC6 and the Challenges Arising from Its Disclosure Obligation

Directive 2018/822 (‘DAC6’) aims at ensuring the exchange of information between Member States of potentially tax-aggressive arrangements. To do so, the European legislator has entrusted intermediaries across the European Union with the task of gathering and reporting a number of potentially aggressive tax arrangements to improve tax transparency and stifle tax avoidance. Unfortunately, a closer analysis of the text has revealed multiple issues that may threaten the well-functioning of the system and could impede the action of the intermediaries. From missing definitions and a lack of guidance to new concepts in dire need of proper interpretation, intermediaries are left with a daunting task to tackle. Risks of delays and over-disclosure, arising from the inconsistencies and uncertainties left by the text of the instrument, may not only undermine the effectiveness of the reporting obligation, but also that of the rest of the system altogether. DAC6, Directive 2018/822, Disclosure, Reporting, Intermediaries, Arrangement, Tax-aggressive, Main Benefit Test, Hallmarks, Oversharing

Role of the Hippocampus in Decision Making Under Uncertainty

The role of the hippocampus in decision making is poorly understood. Because it represents future state values, we hypothesized that it might be required specifically when decisions involve evaluation of uncertain rewards. Here, we investigated how a rare group of nineteen individuals with focal hippocampal damage valued reward against uncertainty compared to evaluation of reward against another key attribute -- physical effort. Hippocampal damage was associated with reduced sensitivity to reward when making uncertain decisions, but crucially not when decisions required evaluating reward against the effort required to obtain it. Further, reduced reward sensitivity against uncertainty correlated with severity of hippocampal damage. A similar effect was also observed in an active information gathering task, where participants acquired information to reduce uncertainty, but had to pay a cost to do so. Together, these findings provide evidence for a context-sensitive role of the hippocampus in value-based decision making, apparent under conditions of uncertainty but not for allocation of effort.

Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for localizing potential gas sources are preferable. This work investigates a Reinforcement Learning framework that allows a robotic agent to learn how to localize gas sources. We propose a solution that assists Reinforcement Learning with existing domain knowledge based on a model of the gas dispersion process. In particular, we incorporate a priori domain knowledge by designing appropriate rewards and observation inputs for the Reinforcement Learning algorithm. We show that a robot trained with our proposed method outperforms state-of-the-art gas source localization strategies, as well as robots that are trained without additional domain knowledge. Furthermore, the framework developed in this work can also be generalized to a large variety of information gathering tasks.

Geomatics—An Open Access Journal

Geomatics is an information technology discipline which integrates the tasks of gathering, storing, processing, modeling, analyzing, and delivering spatially referenced or location information [...]

Gathering robots in graphs: The central role of synchronicity

The Gathering task for k robots disposed on the n vertices of a graph G requires robots to move toward a common vertex from where they do not move anymore.When dealing with very weak robots in terms of capabilities, considering synchronous or asynchronous settings may heavily affect the feasibility of the problem. In fact, even though dealing with asynchronous robots in general requires more sophisticated strategies with respect to the synchronous counterpart, sometimes it comes out that asynchronous robots simply cannot solve the problem whereas synchronous robots can.We study general properties of graphs that can be exploited in order to accomplish the gathering task in the synchronous setting, obtaining an interesting and innovative sufficient condition for the feasibility of the gathering task in graphs, regardless the topology.Furthermore, we consider dense and symmetric graphs like complete and complete bipartite graphs where in general the topology does not allow to distinguish vertices where to finalize the gathering. In such topologies, we fully characterize the solvability of the gathering task in the synchronous setting by suitably combining some strategies arising from the general approach with specific techniques dictated by the considered topologies.From the lower bound point of view in terms of number of synchronous time units required to accomplish the gathering task, in general nothing better than Ω(DG), with DG being the diameter of the input graph G, can be provided. For both complete and complete bipartite graphs, we prove a lower bound of Ω(logϕ⁡k), with ϕ being the well-known golden ratio. Combined with the provided algorithms, this reveals to be asymptotically tight for complete graphs while for complete bipartite graphs an additive factor of δ(k) is achieved, with δ(k) being the function that returns the number of divisors of the integer k.

Rasterisation-based progressive photon mapping

Ray tracing on the GPU has been synergistically operating alongside rasterisation in interactive rendering engines for some time now, in order to accurately capture certain illumination effects. In the same spirit, in this paper, we propose an implementation of progressive photon mapping entirely on the rasterisation pipeline, which is agnostic to the specific GPU architecture, in order to synthesise images at interactive rates. While any GPU ray tracing architecture can be used for photon mapping, performing ray traversal in image space minimises acceleration data structure construction time and supports arbitrarily complex and fully dynamic geometry. Furthermore, this strategy maximises data structure reuse by encompassing rasterisation, ray tracing and photon gathering tasks in a single data structure. Both eye and light paths of arbitrary depth are traced on multi-view deep G-buffers, and photon flux is gathered by a properly adapted multi-view photon splatting. In contrast to previous methods exploiting rasterisation to some extent, due to our novel indirect photon splatting approach, any event combination present in photon mapping is captured. We evaluate our method using typical test scenes and scenarios for photon mapping methods and show how our approach outperforms typical GPU-based progressive photon mapping.

RETRACTED: Multipath routing mechanism using embedded architecture of interfaced circuit

Abstract Mobile Adhoc Network (MANET) is a group of remote compact hubs confining a short term framework without any guidance of any settled communication establishment. In view of confined resources, visit organizes packets and surprising topological changes, proactive gathering designs get high overheads in this condition. To keep the security of compact uniquely selected frameworks various wellbeing endeavors are arranged, for instance, encryption estimations, firewalls et cetera. However there is some degree of threatening exercises. In this work, a Modified IDEA-HMAC based secure disseminated grouping calculation for MAODV routing protocol is proposed for secure message transmission, routing through Cluster based Intrusion Detection System in MANET. This algorithm for Intrusion recognition frameworks are proposed to distinguish any intruder in the system and its vindictive exercises. It is additionally utilized for cluster arrangement, upkeep and purging activities and grouping data for speedy course disclosure, supports and cluster conveyance. The Modified IDEA is utilized for encryption, it is one of the safe and most generally utilized square figures and the cryptographic quality of Modified IDEA depends on a blend of three inconsistent gathering tasks – XOR, expansion and particular duplication and it is secure against powerless key assaults.

Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement

Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest when constant communication cannot be assumed. This is common in tasks involving information gathering with multiple independently operating sensor devices that may operate over large physical distances, such as unmanned aerial vehicles, or in communication limited environments such as in the case of autonomous underwater vehicles. In this paper, we frame the information gathering task as a general decentralized partially observable Markov decision process (Dec-POMDP). The Dec-POMDP is a principled model for co-operative decentralized multi-agent decision-making. An optimal solution of a Dec-POMDP is a set of local policies, one for each agent, which maximizes the expected sum of rewards over time. In contrast to most prior work on Dec-POMDPs, we set the reward as a non-linear function of the agents’ state information, for example the negative Shannon entropy. We argue that such reward functions are well-suited for decentralized information gathering problems. We prove that if the reward function is convex, then the finite-horizon value function of the Dec-POMDP is also convex. We propose the first heuristic anytime algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving discrete problems an order of magnitude larger than previous state-of-the-art. We also propose an extension to continuous-state problems with finite action and observation spaces by employing particle filtering. The effectiveness of the proposed algorithms is verified in domains such as decentralized target tracking, scientific survey planning, and signal source localization.

DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

AbstractWe present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co‐correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain‐ability in the field of visual analytics.

UK parliamentary select committees: crowdsourcing for evidence-based policy or grandstanding?

ABSTRACT In the United Kingdom, the influence of parliamentary select committees on policy depends substantially on the ‘seriousness’ with which they approach the task of gathering and evaluating a wide range of evidence and producing reports and recommendations based on it. However, select committees are often charged with being concerned with ‘political theatre’ and ‘grandstanding’ rather than producing evidence-based policy recommendations. This study, based on a survey of 919 ‘discretionary’ witnesses, including those submitting written and oral evidence, examines the case for arguing that there is political bias and grandstanding in the way select committees go about selecting witnesses, interrogating them and using their evidence to put reports together. While the research finds some evidence of such ‘grandstanding’ it does not appear to be strong enough to suggest that the role of select committees is compromised as a crowdsourcer of evidence.

An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications

Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.

Tximeta: Reference sequence checksums for provenance identification in RNA-seq.

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

New technologies - BlockChain as a Service (BCaaS) for healthcare

Background: The healthcare domain is undergoing a major shift towards electronic health records (EHR) and data management as illustrated by the global trend in Figure 1 with a shift over recent times from passive technology adopters towards more investigators and explorers. There is a vital need for a universal patient record in a distributed ledger and new approaches to ensure collaborative health when we can manage our own wellness outside of the system to improve outcomes and contain healthcare costs1,2. There are a few immediate needs to address which are patient-centric management regarding high incidence chronic diseases and identification and optimization of at-risk parameters. Methods: This is an ideation for exploring “Blockchain” implementation to match the correct individual to their health data which is critical to their medical care. It is recognized that a significant proportion of EHR are not accurately matched within the same healthcare system3 and this hinders critical success factors such as effective patient management, patient satisfaction, and continuity of quality care (Figure 2). Results: The following outcomes and advantages are expected to be delivered for patient care records management offering transparent and verifiable records which are encrypted to provide streamlined clinical data flow benefitting patients. Core benefits also include enhancing transparency to patients, reducing threats of hacking, increasing patient records safety, eliminating workarounds, and minimizing IT expenditures and downtimes. Conclusion: As a promising breakthrough, this idea should result in patients no longer needing to perform the tedious task of gathering records from various providers. Instead patients would only need to provide access to their health blockchain, hence retaining control of their data and not spending time and energy keeping their data up to date1.

Minimal Path-Based Reliability Model for Wireless Sensor Networks With Multistate Nodes

Wireless sensor networks (WSNs) find application in various fields like environmental monitoring, health-care, land security, and many more. To ease our day-to-day activity, WSNs have become an integral tool for complex data gathering tasks. Monitoring a phenomenon by a WSN depends on the collective data provided by the sensor nodes. To ensure reliable operation of WSNs, it is important to quantify the performance of such networks in terms of network reliability measures. This article studies the reliability of WSNs with multistate nodes and proposes an approach to evaluate the flow-oriented network reliability of WSNs consisting of multistate sensor nodes. The proposed method takes into account the dynamic state of the network due to multistate sensor nodes. The proposed approach includes enumeration of shortest minimal paths from application-specific flow satisfying sensor nodes (source nodes) to the sink node. It then proposes a modified sum-of-disjoint products approach to evaluate WSN reliability in the presence of multistate nodes from the enumerated shortest minimal paths. Simulations are performed on WSNs of various sizes to show the applicability of the proposed approach on arbitrary WSNs.

Generosity, selfishness and exploitation as optimal greedy strategies for resource sharing

Resource sharing outside the kinship bonds is rare. Besides humans, it occurs in chimpanzee, wild dogs and hyenas, as well as in vampire bats. Resource sharing is an instance of animal cooperation, where an animal gives away part of the resources that it owns for the benefit of a recipient. Taking inspiration from blood-sharing in vampire bats, here we show the emergence of generosity in a Markov game, which couples the resource sharing between two players with the gathering task of that resource. At variance with the classical evolutionary models for cooperation, the optimal strategies of this game can be potentially learned by animals during their life-time. The players act greedily, that is, they try to individually maximize only their personal income. Nonetheless, the analytical solution of the model shows that three non trivial optimal behaviours emerge depending on conditions. Besides the obvious case when players are selfish in their choice of resource division, there are conditions under which both players are generous. Moreover, we also found a range of situations in which one selfish player exploits another generous individual, for the satisfaction of both players. Our results show that resource sharing is favoured by three factors: a long time horizon over which the players try to optimize their own game, the similarity among players in their ability of performing the resource-gathering task, as well as by the availability of resources in the environment. These concurrent requirements lead to identify necessary conditions for the emergence of generosity.

Financial Performance at Heritage Foods Limited

This paper depicts a Financial Performance venture helpful in both preparerbased and client based starting courses in budgetary bookkeeping. The venture expects understudies to examine slants in corporate execution, to assess corporate budgetary choices, to talk about non-fiscal summary issues that would be critical to potential financial specialists, to analyze two organizations inside an industry, and to make speculation suggestions. [1],[ 3],[5] The task's prerequisites are completelydetailed on a solitary page, and the undertaking is appropriate to organizations in any industry. The venture requires just restricted presentation in the study hall, is effectively versatile to reflect educator inclinations, and perhaps utilized as either an individual or a gathering task. This paper incorporates the total task necessity sheet, a reviewing rubric, a group assessment structure, instances of things that understudies may be relied upon to address in responding to the inquiries presented, remarks on the aftereffects of utilizing the venture, and extra proposals for execution. [2 ],[ 4],[6]

‘When you are a data collector you must expect anything’. Barriers, boundaries and breakthroughs: insights from the South African data-collection experience

The impact of the research process on the researcher is an emerging topic of interest. Data collection in most low- and middle-income countries (LMICs) is often the responsibility of community members who are identified and trained specifically for data collection. When research involves data on mental health and social well-being, data collectors may have specific competency needs and the task of data gathering may impact data collectors. This study aims to explore the experiences and needs of data collectors within South Africa using qualitative methods to examine the impact of data collection on data collectors. Nineteen data collectors, involved in face-to-face data collection, completed semi-structured interviews exploring their insights, attitudes and experiences. Thematic analysis revealed barriers and challenges associated with research, complexities regarding boundaries within the participant-data collector relationship and the benefits of being involved with research for the individual and the community. Numerous challenges and opportunities are outlined. Findings expose the beneficial and often overlooked contribution of data collectors and warrants key considerations in the planning and implementation of future research to ensure adequate support and standardization of practice.