Prototypical Task Research Articles

When a notification at the right time is not enough: the reminding process for socially assistive robots in institutional care.

Reminding is often identified as a central function of socially assistive robots in the healthcare sector. The robotic reminders are supposed to help people with memory impairments to remember to take their medicine, to drink and eat, or to attend appointments. Such standalone reminding technologies can, however, be too demanding for people with memory injuries. In a co-creation process, we developed an individual reminder robot together with a person with traumatic brain injury and her care personnel. During this process, we learned that while current research describe reminding as a prototypical task for socially assistive robots, there is no clear definition of what constitutes a reminder nor that it is based on complex sequences of interactions that evolve over time and space, across different actions, actors and technologies. Based on our data from the co-creation process and the first deployment, we argue for a shift towards a sequential and socially distributed character of reminding. Understanding socially assistive robots as rehabilitative tools for people with memory impairment, they need to be reconsidered as interconnected elements in institutional care practices instead of isolated events for the remindee.

Design study of a disruptive self-powered power plant prototype

This research work discusses a preliminary disruptive prototype design of a self-powered thermal power plant doing work by strictly isothermal closed processes using forced convection heat transfer both for adding and extracting heat. It includes useful work by expansion and vacuum-based contraction due to adding heat to and extracting heat from a working fluid. A cycle analysis considering useful work due to vacuum-based contraction is the core of the problem-solving methodology. The findings will be applied to the prototyping implementation task, which includes the cascade and series of cascade coupling of several regenerative power units. The empirical results of the case studies provide the data needed to carry out a prototyping task considering the results of real cases subjected to realistic irreversibilities and heat recovery factors that make the self-feeding power plant an interesting design option. This extraordinary result confirms the technical viability of real machines that exhibit the ability to provide more energy than they use, that is, second-class perpetual motion machines

Enhancing SMT-based Weighted Model Integration by structure awareness

The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress has been made in dealing with purely discrete or purely continuous domains, adapting the developed solutions to tackle hybrid domains, characterized by discrete and continuous variables and their relationships, is highly non-trivial. Weighted Model Integration (WMI) recently emerged as a unifying formalism for probabilistic inference in hybrid domains. Despite a considerable amount of recent work, allowing WMI algorithms to scale with the complexity of the hybrid problem is still a challenge. In this paper we highlight some substantial limitations of existing state-of-the-art solutions, and develop an algorithm that combines SMT-based enumeration, an efficient technique in formal verification, with an effective encoding of the problem structure. This allows our algorithm to avoid generating redundant models, resulting in drastic computational savings. Additionally, we show how SMT-based approaches can seamlessly deal with different integration techniques, both exact and approximate, significantly expanding the set of problems that can be tackled by WMI technology. An extensive experimental evaluation on both synthetic and real-world datasets confirms the substantial advantage of the proposed solution over existing alternatives. The application potential of this technology is further showcased on a prototypical task aimed at verifying the fairness of probabilistic programs.

SurfShare

Shared Mixed Reality experiences allow two co-located users to collaborate on both physical and digital tasks with familiar social protocols. However, extending the same to remote collaboration is limited by cumbersome setups for aligning distinct physical environments and the lack of access to remote physical artifacts. We present SurfShare, a general-purpose symmetric remote collaboration system with mixed-reality head-mounted displays (HMDs). Our system shares a spatially consistent physical-virtual workspace between two remote users, anchored on a physical plane in each environment (e.g., a desk or wall). The video feed of each user's physical surface is overlaid virtually on the other side, creating a shared view of the physical space. We integrate the physical and virtual workspace through virtual replication. Users can transmute physical objects to the virtual space as virtual replicas. Our system is lightweight, implemented using only the capabilities of the headset, without requiring any modifications to the environment (e.g. cameras or motion tracking hardware). We discuss the design, implementation, and interaction capabilities of our prototype, and demonstrate the utility of SurfShare through four example applications. In a user experiment with a comprehensive prototyping task, we found that SurfShare provides a physical-virtual workspace that supports low-fi prototyping with flexible proxemics and fluid collaboration dynamics.

Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model.

To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semi-logarithmic graphing of concentration-time profiles and calculating area under the curve and area under the moment curve from time zero to infinity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.

Student experience and new media to leverage an Infocommunicational case study model

The use of a student’s own mobile digital device for learning purposes has been driven by extremely different perspectives. On one side some educational actors advocating the integration and strategically well-planned use of such technologies. On the other end of this spectrum some actors just completely continue to deny the use of such technologies, with students obliged to leave their mobile technologies switched off and kept away in a specific place controlled by the teacher in the classroom. This paper reports an empirical study that used student’s smartphone experience narratives, in their daily lives, to design and validate an infocommunicational services model for a learning ecosystem. This work was developed in a school cluster at the north of Portugal in a case study research setup with 141 students, 49 teachers and 46 parents. A smartphone application prototype was designed as a research instrument to simulate the model’s characteristics and used to inquire the participants and validate the proposed model. The 8 different prototype task scenarios are explained and the final list of infocommunicational services that comprise the model proposed, designed and tested within this educational community, are a main outcome of this paper. The characteristics of the model are directly related to the case study community’s wishes and needs and due to this with generalizable constraints. On the other hand, the research process reveals potential to be adapted and applied to any other educational community, ecosystem, or school cluster.

Symbolic Learning in Children with Autism: Predictive Value of a Prototype Task

RESUMO Testes preditivos de aprendizagem podem ser úteis para planejar intervenções mais efetivas para crianças com autismo. O objetivo desse estudo foi avaliar se um protótipo de tarefa, desenvolvido para estender o Teste ABLA-R (Avaliação de Habilidades Básicas de Aprendizagem), seria preditivo de aprendizagem simbólica (formação de classes de equivalência). Três crianças com autismo foram avaliadas pelo ABLA-R e pela tarefa protótipo (denominada Nível 7) e, em seguida, foram expostas a um protocolo de ensino e teste de relações de equivalência. As predições do Nível 7 se confirmaram para dois participantes e parcialmente para o terceiro. Embora sejam necessários mais estudos, os resultados sugerem que a tarefa do Nível 7 pode ser uma potencial adição ao ABLA-R.

Dynamic concept-aware network for few-shot learning

Few-shot learning (FSL) aims to recognize novel classes with extremely limited samples via adapting the prior knowledge learned from base classes. Most of existing methods for feature alignment in FSL consider the correspondence of semantic concepts between support and query images. However, encoding task-specific information for query features has not been sufficiently explored. Therefore, in this paper, we propose a dynamic concept-aware network (DCAN), which efficiently encodes task-specific structural concepts and adaptively dynamic alignment. Concretely, this is achieved by dynamic prototype task awareness (DPTA) and cross-correlation dynamic alignment (CoDA). The DPTA module assigns concept weights to each pixel position feature of support images so that dynamically generate prototypes to encode task-specific information with attention mechanism. The CoDA module first calculates the co-attention maps between image representations, and adaptively learns channel-dependent and spatial-dependent dynamic meta-filters based on inputs. Combining two dynamic modules to obtain final embeddings that generalize well to novel categories. Extensive experiments demonstrate that DCAN outperforms the state-of-the-art methods on four FSL classification benchmarks. Our code is available at https://github.com/zjgans/dcan.

Task-specific network interactions across key cognitive domains.

Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.

Shift of contents in prototypical tasks used in education reforms

The paper discusses the shift of contents in prototypical tasks provoked by the current educational reform in Austria. The paper starts with the educational backboard of the process of changes in particular with the out tting of the students' abilities in different taxonomies and its implementation in the competence models of Mathematics. A methodological didactical point of view on the process is given additionally. Examples out of a specific collection of math problems which arise from the educational reform are integrated and analysed in the context of educational principles and methods. The discussion ends with a short evaluation of the role of traditional approaches to tasks in the ongoing reform. A bundle of tasks as proof that they are still alive is presented finally.
 Subject Classification: 97B50, 97D40, 97D50

Explicit and implicit theory of mind and social competence: A social information processing framework

Theory of mind (ToM) refers to children's understanding that mental states, such as beliefs desires, or intentions, rather than reality, drive actions implicitly or explicitly. The research community considers proficiency with prototypical tasks as marking the transition from earlier appearing implicit ToM, observed from the first year of life, to a more advanced explicit ToM, consolidated between the ages of four and six. Despite unsettled questions concerning continuity or discontinuity of underlying cognitive mechanisms, researchers rely on explicit measures of ToM beyond early childhood, neglecting implicit measures. Breaking new ground, we align explicit (standard) and implicit (picture-elicited storytelling) ToM tasks with their respective requirements for social information processing and revisit relations between ToM and social competence with kindergarteners using both types of tasks. Findings challenge the prevailing emphasis on explicit ToM and encourage consideration of the information processing requirements of measures to discern mental states.

NOVICE DESIGNERS' USE OF PROTOTYPES AS COMMUNICATION TOOLS

AbstractPrototypes are critical design artifacts, and recent studies have established the ability of prototypes to facilitate communication. However, prior work suggests that novice designers often fail to perceive prototypes as effective communication tools, and struggle to rationalize design decisions made during prototyping tasks. To understand the interactions between communication and prototypes, design pitches from 40 undergraduate engineering design teams were collected and qualitatively analysed. Our findings suggest that students used prototypes to explain and persuade, aligning with prior studies of design practitioners. The results also suggest that students tend to use prototypes to justify design decisions and adverse outcomes. Future work will seek to understand novice designers’ use of prototypes as communication tools in further depth. Ultimately, this work will inform the creation of pedagogical strategies to provide students with the skills needed to effectively communicate design solutions and intent.

Declarative data serving

Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in "the right place at the right time" can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing.

Lost in Translation: Examining the Complex Relationship Between Prototyping and Communication

Abstract Prototyping plays a pivotal role in the engineering design process. Prototypes represent physical or digital manifestations of design ideas, and as such act as effective communication tools for designers. While the benefits of prototyping are well-documented in research, the fundamental ways in which the construction of a prototype affects designers' reflection on and evaluation of their design outcomes and processes are not well understood. The relationships between prototypes, designers' communication strategies, and recollection of design processes is of particular interest in this work, as preliminary research suggests that novice designers tend to struggle to clearly articulate the decisions made during the design process. This work serves to extend prior work and build foundational knowledge by exploring the evaluation of design outcomes and decisions, and communication strategies used by novice designers during prototyping tasks. A controlled in situ study was conducted with 45 undergraduate engineering students. Results from qualitative analyses suggest that a number of rhetorical patterns emerged in students' communications, suggesting that a complicated relationship exists between prototyping and communication.

Evaluation of Visuospatial Short-term and Working Memory from the First to Second Year of Life: A Novel Task

ABSTRACT The prototypical tasks for assessing visuospatial memory in infancy are based on the search for a hidden object in two locations. Fewer studies include more locations, delayed responses nor changes in the object’s position. Our aim was to assess the visuospatial short-term and working memory in 12, 15, 18 and 22-month-old children (N = 65). Assessment included our experimental task, a working memory task and a cognitive developmental scale. Short-term and working memory abilities increased markedly at 22 months compared to younger ages and the performance of the children in our experimental task is related to other tasks previously used.

HOSVD prototype based on modular SW libraries running on a high-performance CPU+GPU platform

Efficient prototyping is an invaluable resource for modern enterprises and research centers. An efficient prototyping tool exhibits high throughput while maintaining flexibility, and reduces design and validation efforts, resulting in low time-to-market and high competitiveness. This paper presents a modular implementation of high-performance software (SW) libraries running on a Heterogeneous Computing Platform (HCP) based on CPU+GPU. The proposed SW libraries enable a fast and easy comparison of a prototype under different implementation criteria and maintain a high throughput and reusability due to their modular definition. These features accelerate the prototyping task by removing the overhead of designing and validating ad-hoc implementations. The novelty and benefits of this proposal are presented by prototyping and analysis of the multilinear SVD or Higher-Order SVD (HOSVD), an important, widely-used, and computationally demanding tensor decomposition. The mean square error (MSE), processing time, and speedup of this case study show its high performance, while modularity maintains flexibility. The HOSVD prototype reaches a maximum speedup of 17× that of one of the most important implementations in the state of the art.

Sensing form - finger gaiting as key totactile object exploration - a data glove analysis of a prototypical dailytask

BackgroundMotor hand skill and associated dexterity is important for meetingthe challenges of daily activity and an important resource post-stroke. In thiscontext, the present study investigated the finger movements of right-handedsubjects during tactile manipulation of a cuboid, a prototypical task underlyingtactile exploration. During one motor act, the thumb and fingers of one handsurround the cuboid in a continuous and regular manner. While the object ismoved by the guiding thumb, the opposed supporting fingers are replaced oncethey reach their joint limits by free fingers, a mechanism termed fingergaiting.MethodsFor both hands of 22 subjects, we acquired the time series ofconsecutive manipulations of a cuboid at a frequency of 1 Hz, using a digitaldata glove consisting of 29 sensors. Using principle component analysis, wedecomposed the short action into motor patterns related to successivemanipulations of the cuboid. The components purport to represent changing graspconfigurations involving the stabilizing fingers and guiding thumb. The temporalfeatures of the components permits testing whether the distinct configurationsoccur at the frequency of 1 Hz, i.e. within the time window of 1 s, and, thus,taxonomic classification of the manipulation as finger gaiting.ResultsThe fraction of variance described by the principal componentsindicated that three components described the salient features of the singlemotor acts for each hand. Striking in the finger patterns was the prominent andvarying roles of the MCP and PIP joints of the fingers, and the CMC joint of thethumb. An important aspect of the three components was their representation ofdistinct finger configurations within the same motor act. Principal componentand graph theory analysis confirmed modular, functionally synchronous action ofthe involved joints. The computation of finger trajectories in one subjectillustrated the workspace of the task, which differed for the right and lefthands.ConclusionIn this task one complex motor act of 1 s duration could bedescribed by three elementary and hierarchically ordered grasp configurationsoccurring at the prescribed frequency of 1 Hz. Therefore, these configurationsrepresent finger gaiting, described until now only in artificial systems, as theprincipal mechanism underlying this prototypical task.Trial registrationclinicaltrials.gov, NCT02865642, registered 12 August 2016.

Opening the black box: Developing metrics to assess the cognitive processes of prototyping

While past work has demonstrated the ability of prototypes to boost design project performance, influence stakeholder buy-in, and improve design team communication, prototyping remains a ‘black-box’ process. The specific mechanisms through which prototypes affect designer cognition remains unknown. To date, there has been little work to operationalize theory and enable the fine-grain measurement of cognitive processes during prototyping tasks. In short, there is a lack of appropriate metrics to establish the intangible value of prototyping tasks via the measurement of the knowledge generated. The purpose of this work is to propose metrics that more holistically assess the value of prototypes. The proposed metrics are implemented in a design project to validate their utility in studying prototyping tendencies.

UDSMProt: universal deep sequence models for protein classification.

MotivationInferring the properties of a protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification are tailored to single classification tasks and rely on handcrafted features, such as position-specific-scoring matrices from expensive database searches. We argue that this level of performance can be reached or even be surpassed by learning a task-agnostic representation once, using self-supervised language modeling, and transferring it to specific tasks by a simple fine-tuning step.ResultsWe put forward a universal deep sequence model that is pre-trained on unlabeled protein sequences from Swiss-Prot and fine-tuned on protein classification tasks. We apply it to three prototypical tasks, namely enzyme class prediction, gene ontology prediction and remote homology and fold detection. The proposed method performs on par with state-of-the-art algorithms that were tailored to these specific tasks or, for two out of three tasks, even outperforms them. These results stress the possibility of inferring protein properties from the sequence alone and, on more general grounds, the prospects of modern natural language processing methods in omics. Moreover, we illustrate the prospects for explainable machine learning methods in this field by selected case studies.Availability and implementationSource code is available under https://github.com/nstrodt/UDSMProt.Supplementary information Supplementary data are available at Bioinformatics online.

The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving

Abstract This paper presents results from an experiment using electroencephalography to measure neurophysiological activations of mechanical engineers and industrial designers when designing and problem-solving. In this study, we adopted and then extended the tasks described in a previous functional magnetic resonance imaging study reported in the literature. The block experiment consists of a sequence of three tasks: problem-solving, basic design and open design using a physical interface. The block is preceded by a familiarizing pre-task and then extended to a fourth open design task using free-hand sketching. This paper presents the neurophysiological results from 36 experimental sessions of mechanical engineers and industrial designers. Results indicate significant differences in activations between the problem-solving and the open design tasks. The paper focuses on the two prototypical tasks of problem-solving layout and open design sketching and presents results for both aggregate and temporal activations across participants within each domain and across domains.