Abstract and reduced-context representations in fault-finding training

The ability to transfer problem-solving expertise to new situations is regarded as an important practical skill but it is known to be sensitive to contextual differences. Employing abstract and reduced-context training tasks has been proposed as a method of fostering transferable skill but the evidence supporting this approach is limited. The research reported here aimed at assessing the effectiveness of training with diagnostic tasks of varying degrees of contextual detail. The criterion test used to measure training effectiveness was a context-rich diagnostic task that involved locating faults in a representation of a water supply and drainage system (WSDS). In the first study reported (baseline study), subjects were trained and tested on the WSDS task. Various training interventions were employed and a set of diagnostic heuristics proved to be the most effective. Two pilot studies were then carried out to determine whether training with abstract diagnostic tasks could produce transfer to the criterion test, the WSDS. Little evidence of transfer was found; therefore, it was decided to incorporate some context into training but not so much as to obscure important, conceptual task features. This was achieved by constructing reduced-context representations (RCO) of the WSDS at more than one level of abstraction (main study). Training with those representations took less time than training with the WSDS but transfer to the criterion test (the WSDS) was equally efficient. During training with the RCO representations, a technique of refusing costly test information was employed which is thought to have helped subjects discover efficient search strategies and consistently transfer them to the criterion test. This was contrasted with the verbal diagnostic heuristics of the baseline study, which had proved to be effective, but which were not always transferred consistently to the criterion test. This raises the question as to whether diagnostic strategies are necessarily best conveyed solely in verbal form, if the intention is to train transferable skill.     

An informational perspective on skill transfer in human-machine systems

Differentiation of perceptual invariants is proposed as a theoretical approach to explain skill transfer for control at the human-machine interface. I propose that sensitivity to perceptual invariants is enhanced during learning and that this sensitivity forms the basis for transfer of skill from one task to another. The hypothesis implies that detection and discrimination of critical features, patterns, and dimension of difference are important for learning and for transfer. This account goes beyond other similarity conceptions of transfer. To the extent that those conceptions are specific, they cannot account for effects in which performance is better following training on tasks that are less rather than more similar to the criterion task. In essence, this is a theory about the central role of low-dimensional informational patterns for control of behavior within a high-dimensional environment, and about the adjustment of an actor's sensitivity to changes in those low-dimensional patterns.     

Learning from abstract and contextualized representations: The effect of verbal guidance

An experiment examined the effects of providing explicit verbal guidance to learners in integrating information with abstract or contextualized representations during computer-based learning of engineering. Verbal guidance supported learners in identifying correspondences and making mental connections among multiple textual and diagrammatic representations. Results from a 2 (abstract (A) or contextualized (C) representation) × 2 (no guidance or guidance) design showed that without guidance, abstract representations led to better transfer than contextualized representations. Moreover, learners in the contextualized representation group benefitted from the guidance, while the abstract representation group did not benefit from guidance. These findings suggest that abstract representations promote the development of deep, transferrable knowledge and that verbal guidance denoting correspondences among representations can facilitate learning when less effective representational formats are utilized.     

The effect of training systemic information on the retention of fault‐finding skills in manufacturing industries

This study examines how the process of skill retention (including both recall and reconstruction) can benefit from systemic training information that covers several structural, functional, physical (e.g., causal links), and supervisory features (e.g., system control and instrumentation). An experiment was carried out in which a group of participants who received systemic information was compared to a group trained in plant theory and to another three groups trained in heuristics that ranged in terms of high‐ and low‐level diagnostic information. Learning curves revealed that all groups achieved high accuracy scores in diagnosing faults in a distillation plant. Six weeks later, a retention test showed that the systemic group (S) had a better recall score than the groups trained in theory (T) and high‐level heuristics (HH). In addition, the S group had a better reconstruction score than the groups receiving the high‐ and low‐level heuristics (H+) and the low‐level diagnostic information (HL). An interesting finding was that high‐level diagnostic information seemed to support the reconstruction phase of the retention process, whereas low‐level information better supported the recall phase. Overall, it appeared that systemic information enabled trainees to acquire task cues to help recall and reconstruct diagnostic heuristics. © 2004 Wiley Periodicals, Inc. Hum Factors Man 14: 197–217, 2004.     

Iterative-Improvement-Based Heuristics for Adaptive Scheduling of Tasks Sharing Files on Heterogeneous Master-Slave Environments

The scheduling of independent but file-sharing tasks on heterogeneous master-slave platforms has recently found important applications in Grid environments. The scheduling heuristics recently proposed for this problem are all constructive in nature and based on a common greedy criterion which depends on the momentary completion time values of the tasks. We show that this greedy decision criterion has shortcomings in exploiting the file-sharing interaction among tasks since completion time values are inadequate to extract the global view of this interaction. We propose a three-phase scheduling approach which involves initial task assignment, refinement, and execution ordering phases. For the refinement phase, we model the target application as a hypergraph and, with an elegant hypergraph-partitioning-like formulation, we propose using iterative-improvement-based heuristics for refining the task assignments according to two novel objective functions. Unlike the turnaround time, which is the actual schedule cost, the smoothness of proposed objective functions enables the use of iterative-improvement-based heuristics successfully since their effectiveness and efficiency depend on the smoothness of the objective function. Experimental results on a wide range of synthetically generated heterogeneous master-slave frameworks show that the proposed three-phase scheduling approach performs much better than the greedy constructive approach.     

The cognitive benefits of dynamic representations in the acquisition of spatial navigation skills

A representational theory of the mind suggests that human experiences and activities are underpinned by mental representations. This abstract task representation paradigm may explain a cognitive benefit of dynamic instructional visualisations over static alternative in the acquisition of novel procedural motor skills. In this sequel work, we explore and extend this view through empirical investigations of novel skill acquisitions in a separate but related domain of spatial navigation. We compare the post-learning virtual maze navigational performance of sixty novel learners across two groups. After controlling for spatial orientation ability and prior video gaming experience, participants that learned the task using dynamic instructional visualisations recorded significantly better performance measures than those in the static group. Additionally, within-group comparisons also show that the beneficial advantage of dynamic instructional visualisations over statics remained consistent across different task complexities. These findings provide further evidence to support the view that dynamic instructional visualisations afford more efficient transfer of novel procedural skills through computer based training than static visualisations. This has implications for instructional design especially when rapid novel situational awareness is desired such as in briefings for emergency firefighting or tactical military operations.     

Cough once for danger: icons versus abstract warnings as informative alerts in civil aviation

OBJECTIVE: An experiment investigated the efficacy of auditory icons as warning signals in an aviation context. BACKGROUND: Iconic signals, such as a cough to signal dangerous levels of carbon monoxide, convey information about the nature of an incident and alert the operator that there is a problem, whereas signals that are arbitrarily associated with a critical incident provide relatively less information. Warning recognition speed and accuracy are likely to be influenced by modality of presentation (visual, auditory, auditory + visual) and task demand (low, high). METHODS: The 172 participants completed a computer-based training session and test task that involved responding to abstract or iconic auditory (1 s), visual, or auditory + visual warnings associated with seven critical incidents while performing low- and high-demand concurrent tasks. RESULTS: Significantly fewer training trials were required to learn iconic warnings than abstract warnings. An advantage for iconic warnings persisted into the test phase, evident most consistently as greater warning recognition accuracy. The effect was observed in both high- and low-demand conditions. Auditory abstract warnings, in particular, elicited slow reaction times and poor accuracy. CONCLUSION: Associations between a small number of meaningful environmental sounds and critical incidents can be learned with ease relative to more abstract associations, although training is required and response times are relatively slow. APPLICATION: Sets of distinctive auditory iconic warnings can be designed to alert and inform pilots about non-time-pressured events. Potential applications of language-neutral icons as informative warnings include civil, commercial, and defense aircraft.     

Knowledge and strategies in diagnostic skill

A theoretical framework for diagnosis in technical environments is presented, consisting of three layers. At the first layer, the task structure, top-level goals of the diagnostic tasks are identified that have to be fulfilled during task execution. This task structure may also be viewed as a global strategy to carry out the diagnostic task. The second layer of knowledge consists of the relevant local strategies by means of which values are obtained for goals in the task structure. The third layer consists of underlying domain knowledge. This theoretical framework is used to interpret the results as presented in the literature on diagnosis in technical environments. Finally, based on this framework, recommendations are made with respect to the training of diagnostic skill.     

Sense representations for Portuguese: experiments with sense embeddings and deep neural language models

Sense representations have gone beyond word representations like Word2Vec, GloVe and FastText and achieved innovative performance on a wide range of natural language processing tasks. Although very useful in many applications, the traditional approaches for generating word embeddings have a strict drawback: they produce a single vector representation for a given word ignoring the fact that ambiguous words can assume different meanings. In this paper, we explore unsupervised sense representations which, different from traditional word embeddings, are able to induce different senses of a word by analyzing its contextual semantics in a text. The unsupervised sense representations investigated in this paper are: sense embeddings and deep neural language models. We present the first experiments carried out for generating sense embeddings for Portuguese. Our experiments show that the sense embedding model (Sense2vec) outperformed traditional word embeddings in syntactic and semantic analogies task, proving that the language resource generated here can improve the performance of NLP tasks in Portuguese. We also evaluated the performance of pre-trained deep neural language models (ELMo and BERT) in two transfer learning approaches: feature based and fine-tuning, in the semantic textual similarity task. Our experiments indicate that the fine tuned Multilingual and Portuguese BERT language models were able to achieve better accuracy than the ELMo model and baselines.

     

Computerized working memory training: Can it lead to gains in cognitive skills in students?

Given that working memory is an important cognitive skill that is linked to academic success, there is increasing attention given to exploring ways to support working memory problems in struggling students. One promising approach is computerized training, and the aim of the present study focused on whether computerized working memory training could result in both near and far transfer training effects; and whether such effects would be maintained over time. Students were allocated into one of three groups: Nonactive Control, Active Control, where they trained once a week (WMT-Low frequency); Training group, where they trained four times a week (WMT-High frequency). All three groups were tested on measures of working memory, verbal and nonverbal ability, and academic attainment before training; and re-tested on the same measures after training, as well as 8 months later. The data indicate gains in both verbal and visuo-spatial working memory tasks for the high-frequency Training group. Improvements were also evidenced in tests of verbal and nonverbal ability tests, as well as spelling, in the high-frequency Training group. There were some maintenance effects when students were tested 8 months later. Possible reasons for why the computerized working memory training led to some far transfer effects in the high-frequency Training group are included in the discussion.     

Practice and carryover effects when using small interaction devices

The use of interaction devices in modern work often challenges the human motor system, especially when these devices introduce unfamiliar transformations to the user. In this paper we evaluated expert performance and skill differences between experts and novices when using small motion- and force-controlled interaction devices (touchpad and mini-joystick) in an applied text-editing task. Firstly, experts performed better with their familiar input device than with an unfamiliar one. Particularly touchpad experts operating the unfamiliar mini-joystick showed highly asymmetric carryover costs. Results showed that the efficient performance of experts depended on domain-specific skills, which were not transferable. Secondly, with considerable practice (more than observed for simple and short tasks) novices were brought up to higher levels of performance. The motion-transformation between hand and cursor action was easier in understanding and application than the force-transformation. Thus, the touchpad was used more efficiently than the mini-joystick. In conclusion, practice effects found so far are considerably underestimated when it comes to an applied task. The results give reason to develop and implement skill-sensitive training procedures, since the acquisition of domain-specific skills is critical for expert performance. As a consequence, training procedures might be essential for complex applications and/or unfamiliar device transformations.     

Communication-aware heuristics for run-time task mapping on NoC-based MPSoC platforms

Efficient run-time mapping of tasks onto Multiprocessor System-on-Chip (MPSoC) is very challenging especially when new tasks of other applications are also required to be supported at run-time. In this paper, we present a number of communication-aware run-time mapping heuristics for the efficient mapping of multiple applications onto an MPSoC platform in which more than one task can be supported by each processing element (PE). The proposed mapping heuristics examine the available resources prior to recommending the adjacent communicating tasks on to the same PE. In addition, the proposed heuristics give priority to the tasks of an application in close proximity so as to further minimize the communication overhead. Our investigations show that the proposed heuristics are capable of alleviating Network-on-Chip (NoC) congestion bottlenecks when compared to existing alternatives. We map tasks of applications onto an 8 × 8 NoC-based MPSoC to show that our mapping heuristics consistently leads to reduction in the total execution time, energy consumption, average channel load and latency. In particular, we show that energy savings can be up to 44% and average channel load is improved by 10% for some cases.     

A comparison of human and near-optimal task management behavior

OBJECTIVE: The primary contribution of this work is the development of an abstract framework to which a variety of multitasking scenarios can be mapped. The metaphor of a juggler spinning plates was introduced to represent an operator performing multiple concurrent tasks. BACKGROUND: This allowed seeking a quantitative model for management of multiple continuous tasks instead of a model for completing multiple discrete tasks, which was considered in previous studies. METHODS: The multitasking performance of 10 participants in five scenarios was measured in a low-fidelity simulator (named Tardast), which was developed based on the concept of the juggler metaphor. This performance was then compared with a normative model, which was a near-optimal solution to a mathematical programming problem found by tabu search heuristics. RESULTS: Tabu outperformed the participants overall, although the best individual performance nearly equaled that of tabu. It was also observed that participants initially tended to manage numerous tasks poorly but that they gradually learned to handle fewer tasks and excel in them. CONCLUSION: This suggests that they initially overreacted to the penalization associated with poor performance in the software. Participants' strategic task management (e.g., what tasks to handle) was more significant in obtaining a good score than their tactical task management (e.g., how often to switch between two tasks). APPLICATION: Potential applications include better design of equipment, procedures, and training of operators of complex systems.     

Benchmark-problem instances for static scheduling of task graphs with communication delays on homogeneous multiprocessor systems

Scheduling program tasks on processors is at the core of the efficient use of multiprocessor systems. Most task-scheduling problems are known to be NP-Hard and, thus, heuristics are the method of choice in all but the simplest cases. The utilization of acknowledged sets of benchmark-problem instances is essential for the correct comparison and analysis of heuristics. Yet, such sets are not available for several important classes of scheduling problems, including multiprocessor scheduling problem with communication delays (MSPCD) where one is interested in scheduling dependent tasks onto homogeneous multiprocessor systems, with processors connected in an arbitrary way, while explicitly accounting for the time required to transfer data between tasks allocated to different processors. We propose test-problem instances for the MSPCD that are representative in terms of number of processors, type of multiprocessor architecture, number of tasks to be scheduled, and task graph characteristics (task execution times, communication costs, and density of dependencies between tasks). Moreover, we define our task-graph generators in a way appropriate to ensure that the corresponding problem instances obey the theoretical principles recently proposed in the literature.     

Contextual Resource Negotiation-Based Task Allocation and Load Balancing in Complex Software Systems

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents.     

Making instructions “visible” on the interface: an approach to learning fault diagnosis skills through guided discovery

Operator training in fault diagnosis of complex systems has taken several forms including heuristics, decision flow charts and qualitative plant modelling. Having to comply to a specific learning strategy, however, may increase workload in remembering instructions, constrain people in accommodating their own styles and deny opportunities for exploiting other strategies. A means for increasing learning flexibility and adaptability would be to manipulate the design of the interface in ways that prompt operators to recall past instructions or develop their own strategies. In this study, instructions are madevisible on the interface by presenting trainees with a set of tell-tale signs derived from diagnostic heuristics.A group of subjects T (new) was trained in using this interface while verbal instructions (e.g. plant theory) were provided to guide discovery of diagnostic rules; a second group T (old) received the same plant theory but practised on a conventional interface. Two other groups used the conventional interface and were trained to apply a set of heuristics with or without the support of a plant theory (H+T and H groups, respectively). Making instructions visible helped the T (new) group to achieve higher accuracy scores than the T (old) group on a subset of fault scenarios. On a near-transfer task, both the T (new) and T (old) groups were superior to the heuristics (H) group. On transfer to another plant, the T (new) group maintained superiority to the heuristics (H) group and exceeded the T (old) group only in a subset of fault scenarios; the differences between theT (new) and the H+T groups were not significant. The results may indicate that making instructionsvisible could enhance acquisition and also the transfer of complex skills while allowing for flexibility and adaptability in the learning environment.     

Training by exploration: facilitating the transfer of procedural knowledge through analogical reasoning

This study compared exploration-based training and instruction-based training as methods of acquiring and transferring procedural device knowledge, and examined whether any differences in learning outcomes could be explained by the trainees' use of analogical reasoning from either abstract or concrete representations of devices in memory. The exploration trainees experimented with three analogous simulated devices in order to discover the procedures governing their operations, whereas the instructed trainees followed procedural examples contained in manuals. After a 2-day post-training delay, trainees were exposed to a novel transfer device, which was either analogous or disanalogous to the three training devices. Performance on the novel device, subjects' perceptions of the similarity among devices' functions and subjects' recall (written and behavioural) of the three training devices' operations, all provided data indicating that exploration-based training promoted the use of analogical reasoning in knowledge transfer and facilitated the induction of abstract device representations (schemas). No such claim could be made for instruction-based training. Implications for the future of exploration as a training method and suggestions for future research are discussed.     

Utility-based resource management in an oversubscribed energy-constrained heterogeneous environment executing parallel applications

The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of a computing system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 h). We have designed, analyzed, and compared the performance of a set of heuristic techniques to maximize system performance when scheduling dynamically arriving parallel tasks onto a high performance computing (HPC) system that is oversubscribed and energy constrained. We consider six utility-aware heuristics and four existing heuristics for comparison. A new concept of temporary place-holders is compared with scheduling using permanent reservations. We also present a novel energy filtering technique that constrains the maximum energy-per-resource used by each task. We conducted a simulation study to evaluate the performance of these heuristics and techniques in multiple energy-constrained oversubscribed HPC environments. We conduct an experiment with a subset of the heuristics on a physical testbed system for one scheduling scenario. We demonstrate that our proposed utility-aware resource management heuristics are able to significantly outperform existing techniques.