Gathering Tasks Research Articles

Strategies for designing machine learning models in renewable energy with insufficient data

In the area of renewable energy including wind, solar, and electricity, Machine Learning has established a far-reaching conceptual and algorithmic design framework. However, the design of Machine Learning architectures typically relies on substantial amounts of data. The data acquisition process often involves complex tasks of gathering, cleaning, and labeling data. This challenge is exacerbated in the energy sector due to concerns related to data security and the uniqueness of development initiatives, leading to a potential scarcity of available data. Quite commonly, researchers encounter a vast array of innovative projects that have not been studied in the past and therefore only limited data become available. Therefore, there is an urgent and highly motivated need to develop machine learning strategies that can construct reliable models in the presence of limited data. In this study, three machine learning models tailored for small-scale data scenarios are developed and analyzed. The first model, a fuzzy cluster ensemble approach, focuses on utilizing small training samples treated as the core of clustering. By applying clustering algorithms, one derives membership functions reflecting the structure of data that facilitate accurate predictions. The second model, a kernel integration framework, combines kernel functions with limited training data to calculate activation levels, enabling accurate prediction outputs. For both models, the final predictions are generated by applying weighted aggregations, where the weights are derived from the respective membership grades and activation levels of the data. Additionally, a neural network contrastive learning model is introduced, which enhances the training process by employing pairwise comparisons among data points. This method effectively increases the amount of useable training data, improving the model's ability to make accurate predictions. Together, these approaches provide a robust solution for making reliable predictions even in data-scarce scenarios within renewable energy research. Moreover, the predictive performance of these models is quantified using the Root Mean Square Error criterion. This study also assesses the performance of the developed models by forming a granular characterization of results delivered by the designed models. A comprehensive suite of experimental studies is included, accompanied by an exhaustive comparative analysis. Compared to conventional machine learning models, especially neural networks, the designed models offer reliable results even with limited training data, showing an improvement of around 20 %, thus laying the groundwork for digitization processes in the realm of sustainable energy.

Decision cost hypersensitivity underlies Huntington's disease apathy.

The neuropsychiatric syndrome of apathy is now recognized to be a common and disabling condition in Huntington's disease (HD). However, the mechanisms underlying it are poorly understood. One way to investigate apathy is to utilise a theoretical framework of normal motivated behaviour, to determine where breakdown has occurred in people with this behavioural disruption. A fundamental computation underlying motivated, goal-directed behaviour across species is weighing up the costs and rewards associated with actions. Here, we asked whether people with apathy are more sensitive to costs of actions (physical effort and time delay), less sensitive to rewarding outcomes, or both. Based on the unique anatomical substrates associated with HD pathology, we hypothesised that a general hypersensitivity to costs would underpin HD apathy. Genetically confirmed carriers of the expanded Huntingtin gene (premanifest to mild motor manifest disease (n=53) were compared to healthy controls (n = 38). Participants performed a physical effort-based decision-making task (Apple Gathering Task) and a delay discounting task (Money Choice Questionnaire). Choice data was analysed using linear regression and drift diffusion models that also accounted for the time taken to make decisions. Apathetic people with HD accepted fewer offers overall on the Apple Gathering Task, specifically driven by increased sensitivity to physical effort costs, and not explained by motor severity, mood, cognition, or medication. Drift diffusion modelling provided further evidence of effort hypersensitivity, with apathy associated with a faster drift rate towards rejecting offers as a function of varying effort. Increased delay sensitivity was also associated with apathy, both when analysing raw choice and also drift rate, where there was moderate evidence of HD apathy drifting faster towards the immediately available (low cost) option. Furthermore, the effort and delay sensitivity parameters from these tasks were positively correlated. The results demonstrate a clear mechanism for apathy in HD, cost hypersensitivity, which manifests in both the effort and time costs associated with actions towards rewarding goals. This suggests that HD pathology may cause a domain-general disruption of cost processing, which is distinct to apathy occurrence in other brain disorders, and may require different therapeutic approaches.

Information gathering: Dissociable effects of autistic and alexithymic traits in youths aged 6-25 years.

Autistic youths tend to react negatively to uncertain events. Little is known about the cognitive processes associated with this intolerance of uncertainty, most notably the tendency to actively gather information to minimize uncertainty. Past research has relied on self-report measures that may not allow investigation of the multifaceted processes associated with intolerance of uncertainty, including information gathering. Alexithymia (difficulties in identifying and describing one's own emotions) commonly co-occurs with autistic traits, but its role in information gathering has rarely been considered. Accordingly, 97 typically developing youths (aged 6-25 years) performed an information gathering task in which they were asked to gather information to infer socioemotional (emotional state) and nonsocial (clothing preference) information about another person when information gathering was costly versus not costly. Dimensional autistic traits were associated with more information gathering regardless of costs and information type. Computational modeling suggested this may be because of the delayed emergence of subjective costs of information gathering in high autistic trait individuals, resulting in later guesses. Alexithymia was uniquely associated with inconsistent emotional responses to rewards and losses and to reduced information gathering about emotional states when assessed using parent-report measures. Future validation in youths diagnosed with autism is warranted to test the generalizability of the findings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Web Information Gathering, Which Subtask to Investigate? A User Study

his paper is an attempt to re-identify thesubtasks of the task of information gathering on the webwhich require further research. The study confirms thetypes of subtasks that need further consideration yet with different ordering of significance from what users indicatedin a previous study. The different ordering was concludedbased on the frequency of activities performed within eachsubtask. The study in Alhenshiri , et al. [1] recommendedthat web users performing information gathering tasks should be able to: re-find information from previoussessions for the same task more effectively, should alsohandle multiple sessions more effectively, and they shouldbe able to manage and organize information for the entire task not dispersed parts of it. The research discussed in thismanuscript revisits those recommendations in a user studycomparing a tool designed specifically for informationgathering tasks on the web and the plain browser while usedwith other assisting applications.

A computational approach to understanding effort-based decision-making in depression.

Motivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to collect rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. Effort-based decision-making was assessed using the Apple Gathering Task, in which participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: an overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was driven by lower effort acceptance bias, but not altered effort or reward sensitivity. This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

"What brought you in today?": Modeling patient-provider clinic visits to characterize rural providers' antibiotic treatment decisions.

Designing clinical decision support (CDS) tools is challenging because clinical decision-making must account for an invisible task load: incorporating non-linear objective and subjective factors to make an assessment and treatment plan. This calls for a cognitive task analysis approach. The objectives of this study were to 1.) understand healthcare providers' decision making during a typical clinic visit, and 2.) explore how antibiotic treatment decisions are made when they arise. Two cognitive task analysis methods were applied - Hierarchical Task Analysis (HTA) and Operations Sequence Diagramming (OSD) - to 39h of observational data collected at family medicine, urgent care, and emergency medicine clinical sites. The resulting HTA models included a coding taxonomy detailing ten cognitive goals and associated sub-goals and demonstrated how the goals occur as interactions between the provider and electronic health record, the patient, and the physical clinic environment. Although the HTA detailed resources for antibiotic treatment decisions, antibiotics were a minority of drug classes ordered. The OSD shows the sequence of events and when decisions are made solely at the provider level and when shared decision making occurs with the patient. Qualitative data from the observations informed a constructed vignette case example portraying select tasks from the HTA. These findings emphasize that the scope of disease states presenting to a generalist clinical setting is broad and could include acute exacerbations of rare diseases within a time-pressured environment. CDS must be accessible, time efficient, and fit within the resource gathering task before treatment decisions are made.

Informed expansion for informative path planning via online distribution learning

Mobile robots are essential tools for gathering knowledge of the environment and monitoring areas of interest as well as industrial assets. Informative Path Planning methodologies have been successfully applied making robots able to autonomously acquire information and explore unknown surroundings. Rapidly-exploring Information Gathering approaches have been validated in real-world applications, proving they are the way to go when aiming for Information Gathering tasks. In fact, RIG can plan paths for robots with several degrees of freedom and rapidly explore complex workspaces by using the state-of-the-art Voronoi-biased expansion. Nevertheless, it is an efficient solution when most of the area is unknown but its effectiveness decreases as the exploration/gathering evolves. This paper introduces an innovative informed expansion for IG tasks that combines the Kernel Density Estimation technique and a rejection sampling algorithm. By learning online the distribution of the acquired information (i.e., the discovered map), the proposed methodology generates samples in the unexplored regions of the workspace, and thus steers the tree toward the most promising areas. Realistic simulations and an experimental campaign, conducted in the underwater robotics domain, provide a proof-of-concept validation for the developed informed expansion methodology and demonstrate that it enhances the performance of the RIG algorithm.

Want to Gather? No Need to Chatter!

A team of mobile agents with different labels, starting from different nodes of an unknown anonymous network, must meet at the same node and declare that they all met. This task of gathering was traditionally considered assuming that agents at the same node can exchange information. We ask if this ability of talking is needed. The answer turns out to be no. We design two deterministic algorithms that accomplish gathering in a much weaker model. We only assume that each agent knows how many agents are at the node that it currently occupies. Our first algorithm assumes that agents know some upper bound N on the size of the network, and works in time polynomial in N and in the length of the smallest label. Our second algorithm does not assume any knowledge about the network, but its complexity is at least exponential in the size of the network and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product we solve, in the same weak model, the fundamental problem of leader election among agents. As an application we solve the gossiping problem in this model: if each agent has a message, all agents can learn all messages. This is perhaps our most surprising finding: agents without any transmitting devices can solve the most general information exchange problem if they can count the number of agents present at visited nodes.

Graph learning-based generation of abstractions for reinforcement learning

AbstractThe application of reinforcement learning (RL) algorithms is often hindered by the combinatorial explosion of the state space. Previous works have leveraged abstractions which condense large state spaces to find tractable solutions. However, they assumed that the abstractions are provided by a domain expert. In this work, we propose a new approach to automatically construct abstract Markov decision processes (AMDPs) for potential-based reward shaping to improve the sample efficiency of RL algorithms. Our approach to constructing abstract states is inspired by graph representation learning methods, it effectively encodes the topological and reward structure of the ground-level MDP. We perform large-scale quantitative experiments on a range of navigation and gathering tasks under both stationary and stochastic settings. Our approach shows improvements of up to 8.5 times in sample efficiency and up to 3 times in run time over the baseline approach. Besides, with our qualitative analyses of the generated AMDPs, we are able to visually demonstrate the capability of our approach to preserve the topological and reward structure of the ground-level MDP.

A human-supported robot swarm information gathering task planning method

A human-supported robot swarm information gathering task planning method

Gathering the Dust: Narrating National Hauntologies and Futures in Yvonne Adhiambo Owour’s Dust

ABSTRACT This paper offers a reading of Yvonne Owour’s Dust as a text that engages with national histories in a bid to trouble official Kenyan public memories and re-imagine Kenyan visions of the future. In this article, I argue that the text’s representation of histories of colonial and post-independence violence, injustices and exclusions is not only an act of memorialisation and mourning; it also invites the readers to imagine possibilities of dissent, healing, and inclusive and reconciliatory horizons. In this way, I posit that the text performs the task of re-membering and gathering. Taking on the novel’s repeated injunction to the reader to contemplate with the narrator ‘what endures?’, I argue that Owour prods us to look beyond the present, as we also think about the here and now in ways that are life-affirming and liberating. Ultimately, Owour’s novel gestures at the possibilities of crafting new beginnings and envisioning alternative, more restorative and transformative national socialities. Further, I argue that the text ties these Kenyan experiences to global black expressions of struggle, solidarity and futurity.

Neural correlates of risky decision making in Parkinson's disease patients with impulse control disorders.

Some patients with Parkinson's disease (PD) experience impulse control disorders (ICDs), characterized by deficient voluntary control over impulses, drives, or temptations regarding excessive hedonic behavior. The present study aimed to better understand the neural basis of impulsive, risky decision making in PD patients with ICDs by disentangling potential dysfunctions in decision and outcome mechanisms. We collected fMRI data from 20 patients with ICDs and 28 without ICDs performing an information gathering task. Patients viewed sequences of bead colors drawn from hidden urns and were instructed to infer the majority bead color in each urn. With each new bead, they could choose to either seek more evidence by drawing another bead (draw choice) or make an urn-inference (urn choice followed by feedback). We manipulated risk via the probability of bead color splits (80/20 vs. 60/40) and potential loss following an incorrect inference ($10 vs. $0). Patients also completed the Barratt Impulsiveness Scale (BIS) to assess impulsivity. Patients with ICDs showed greater urn choice-specific activation in the right middle frontal gyrus, overlapping the dorsal premotor cortex. Across all patients, fewer draw choices (i.e., more impulsivity) were associated with greater activation during both decision making and outcome processing in a variety of frontal and parietal areas, cerebellum, and bilateral striatum. Our findings demonstrate that ICDs in PD are associated with differences in neural processing of risk-related information and outcomes, implicating both reward and sensorimotor dopaminergic pathways.

Optimal Gathering Over Weber Meeting Nodes in Infinite Grid

The gathering over meeting nodes problem requires the robots to gather at one of the pre-defined meeting nodes. This paper investigates the problem with respect to the objective function that minimizes the total number of moves made by all the robots. In other words, the sum of the distances traveled by all the robots is minimized while accomplishing the gathering task. The robots are deployed on the nodes of an anonymous two-dimensional infinite grid which has a subset of nodes marked as meeting nodes. The robots do not agree on a global coordinate system and operate under an asynchronous scheduler. A deterministic distributed algorithm has been proposed to solve the problem for all those solvable configurations, and the initial configurations for which the problem is unsolvable have been characterized. The proposed gathering algorithm is optimal with respect to the total number of moves performed by all the robots in order to finalize the gathering.

Byzantine gathering in polynomial time

Gathering is a key task in distributed and mobile systems, which becomes significantly harder if some agents are subject to Byzantine faults, known as being the worst ones. We propose here to study the task of Byzantine gathering in an arbitrary graph: despite the presence of Byzantine agents, the goal is to ensure that all the other (good) agents, executing the same algorithm, eventually meet at the same node and stop. Initially, each agent gets as input a different label and some global knowledge that is common to all agents. The agents move in synchronous rounds and communicate with each other only when located at the same node. There are f Byzantine agents. These agents act in an unpredictable way, e.g., they may convey arbitrary informations or forge any label. In the literature, the gathering algorithms working in such a context all have an exponential time complexity in the number n of nodes and the labels of the good agents. In this paper, we design a deterministic algorithm to solve Byzantine gathering in time polynomial in n and the logarithm $$\ell $$ of the smallest label of a good agent, provided the agents are a strong team i.e., a team where the number of good agents is at least some quadratic polynomial in f. Our algorithm requires global knowledge that can be coded in $$O(\log \log \log n)$$ bits: we prove this size is of optimal order of magnitude to obtain a polynomial time complexity in n and $$\ell $$ with strong teams.

Smart Agent and it’s effect on Artificial Intelligence : A Review Study

Current networking technologies, as well as the ready availability of large quantities of data and knowledge on the Internet-based Infosphere, offer tremendous opportunities for providing more abundant and reliable information to decision makers and decision support systems. The use of the Internet has increased at a breakneck pace. Some prevailing features of the Infosphere, however, have hindered successful use of the Internet by humans or decision support machine systems. To begin with, the information available on the internet is disorganized, multi-modal, and spread around the globe on server pages. Second, every day, the number and variety of data sources and services grows dramatically. In addition, the availability, type, and dependability of information services are all changing all the time. Third, the same piece of knowledge can be obtained from a number of different sources. Fourth, due to the complex existence of information sources and possible information updating and maintenance issues, information is vague and probably incorrect. As a result, collecting, filtering, evaluating, and using information in problem solving is becoming increasingly difficult for a human or computer device. As a consequence, identifying information sources, accessing, filtering, and incorporating data in support of decision-making, as well as managing information retrieval and problem-solving efforts of information sources and decision-making processes, has become a critical challenge. To fix this issue, the idea of "Intelligent Software Agents" has been suggested. Although a precise definition of an intelligent agent is still a work in progress, the current working definition is that Intelligent Software Agents are programs that act on behalf of their human users to perform laborious information gathering tasks such as locating and accessing information from various on-line information sources, resolving inconsistencies in the retrieved information, filtering out irrelevant data.

Multi-resource scheduling for FPGA systems

In modern cloud data centers, reconfigurable devices (FPGAs) are used as an alternative to Graphics Processing Units to accelerate data-intensive computations (e.g., machine learning, image and signal processing). Currently, FPGAs are configured to execute fixed workloads, repeatedly over long periods of time. This conflicts with the needs, proper to cloud computing, to flexibly allocate different workloads and to offer the use of physical devices to multiple users. This raises the need for novel, efficient FPGA scheduling algorithms that can decide execution orders close to the optimum in a short time. In this context, we propose a novel scheduling heuristic where groups of tasks that execute together are interposed by hardware reconfigurations. Our contribution is based on gathering tasks around a high-latency task that hides the latency of tasks, within the same group, that run in parallel and have shorter latencies. We evaluated our solution on a benchmark of 37500 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 47.4% of the cases. It produces acceptable solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 90.1% of the cases.

Mothers gather: the fractured temporalities of Palestinian motherhood

This article is published as part of the Geografiska Annaler: Series B, Human Geography special issue ‘Palestinian Futures Anticipation, Imagination, Embodiments’, edited by Mikko Joronen, Helga Tawil-Souri, Merav Amir & Mark Griffiths. ABSTRACT The Arabic saying, ‘al-umm bitlim', literally ‘the mother gathers' speaks to the way mothers are imagined as a centripetal force that brings and holds the family together. For Palestinian refugees in Lebanon, now in their seventh decade of exile, the task of gathering and holding the family together becomes increasingly challenging. Decades of war and internecine fighting have made the destruction of home and its regeneration a way of life; rupture and loss are routine and prepared for; families are displaced yet again; and it is mothers, above all, who are tasked with managing survival and building futures for their children. As an analytic, motherhood affords access to the temporal and gendered imaginaries of family life and futurity. Ambivalently positioned between private and public – the domestic intimacies of child-rearing and larger social worlds – the roles ascribed to mothers reveal a shifting, intersecting set of societal expectations and historical forces. This paper explores what it means to sustain and care for children in and through and against conditions of protracted displacement and crisis, where the future appears foreclosed. It considers the disjunction between the imagined and lived selfhood of Suhaila, a mother from Shatila camp now seeking asylum in Belgium with her family.

Lessons learned from requirements gathering for virtual reality simulators

BackgroundThis paper shows how current collaborative virtual environments (VEs) such as Mozilla Hubs and AltspaceVR can aid in the task of requirements gathering in VR for simulation and training. MethodsWe performed a qualitative study on our use of these technologies in the requirements gathering of two projects. ResultsOur results show that requirements gathering in virtual reality has an impact on the process of requirements identification. We report advantages and shortcomings that will be of interest to future practitioners. For example, we found that VR sessions for requirements gathering in current VEs could benefit from better pointers and better sound quality. ConclusionCurrent VEs are useful for the requirements gathering task in the development of VR simulators and VR training environments.

Social Media and the COVID-19: South African and Zimbabwean Netizens’ Response to a Pandemic

Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa. The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social media

Metajournalistic Discourse as a Stabilizer within the Journalistic Field: Journalistic Practice in the Covid-19 Pandemic

ABSTRACT The COVID-19 Pandemic created a two-fold challenge for journalists: first, the task of gathering and distributing information vital to the responses of the public, and second, the challenge of mitigating the complexities of the journalism field. The purpose of this study is to connect the theoretical frameworks of metajournalistic discourse and field theory, using the touch point of journalistic practice. Prior research has postulated that metajournalistic discourse operates as a stabilizing force in the journalistic field. Using the timely test of the COVID-19 pandemic, this study seeks to explore the discursive construction of journalistic practice during a pandemic through the lens of both metajournalism and field through metadiscourse (n=141) gathered from the United States, United Kingdom, and Austria. This study will argue that metajournalistic discourse stabilizes the field by affirming the tools western journalists use in order to make sense of a crisis like COVID-19, and by providing a discursive avenue between the ideals of the journalistic field—in the journalistic doxa—and the habits of the field—as represented by journalistic habitus.