Low-level Tasks Research Articles

Understanding the Idea of Chemical Elements and Their Periodic Classification in Spanish Students Aged 16–18 Years

The work reported here involved a comparative study regarding the understanding that high school students (16–18 years) have of the concept of chemical elements and their periodic classification. More specifically, the level of knowledge on this topic was compared before and after the completion of baccalaureate studies in a sample of Spanish students. In order to achieve this goal, a questionnaire was developed that included 14 items in an open format, through which various aspects of the students’ understanding of the idea of chemical element and their periodic classification were assessed. In addition, the application of this knowledge to interpret and predict the behaviour and properties of elements and to carry out calculations on the atomic composition of the elements was evaluated. Aspects concerning the acquisition of scientific knowledge, the application of knowledge to different contexts and situations, and the use of scientific evidence to draw conclusions and knowledge about the nature and history of science were evaluated. The questionnaire was previously validated with a large group of students. The results of this study show that improvements occur primarily in addressing higher level cognitive questions (analysis, synthesis and evaluation) in comparison to the lower level tasks (capacity for retention, understanding or direct application of learning). In addition, students who start high school have a very limited understanding of the ideas behind the Periodic Table of the chemical elements and that their lack of understanding, to a large extent, remains upon completion of the baccalaureate. These results suggest that there are real difficulties in understanding this topic and show the limited influence of the studies completed in high school.

Usability first. Model-based approach for the use-oriented risk analysis of medical devices

Due to increasing automation, the number and complexity of technical components have increased in the medical context (e.g., in the clinic or in the home care sector) in recent years. Besides new effective and efficient therapeutic and diagnostic options, these devices entail a wide range of functions and very complex (often computer-based) user interfaces that may lead to human-induced risk potential. A systematic and early human risk analysis and a usability evaluation allow medical device manufacturers to identify and control risks within the human-machine interaction very efficiently. At the Department of Medical Engineering in the Helmholtz Institute for Biomedical Engineering at the RWTH Aachen University, a formal-analytical methodology and a corresponding software tool for prospective human-risk analysis and model-based usability evaluation has been developed. Based on a twofold approach, user interactive process sequences and their potential impacts on the overall process are identified and the resulting use-related risks are assessed. For this, the tasks are categorized (in system and user tasks) and modeled and temporally related within the framework of a high-level task analysis. Within a subsequent cognitive low-level task analysis, potentially critical parallel process sequences are then tested in order to detect a potential resource overload of the user. The subsequent corresponding human-risk analysis is developed according to a knowledge base (checklist) of taxonomies related to human error. The HiFEM (human-function effect modeling) methodology is universally applicable and can be used for the evaluation of human-computer interfaces as well as for the analysis of purely mechanical control interfaces and simple hand-held instruments (such as a scalpel and implant). In a comparative study, the HiFEM method clearly outperforms the classic FMEA (failure modes and effects analysis) process with regard to effectiveness, efficiency, learnability, and user satisfaction. Especially small and medium-sized enterprises that constitute the medical device industry can be supported by the new methodology in the context of risk management according to ISO 14971 as well as usability engineering in accordance with IEC 62366 and IEC 60601-1-6 as well as EN ISO 9241.

A search-set model of path tracing in graphs

We present a predictive model of human behaviour when tracing paths through a node-link graph, a low-level abstract task that feeds into many other visual data analysis tasks that require understanding topological structure. We introduce the idea of a search set, namely, the set of paths that users are most likely to search, as a useful intermediate level for analysis that lies between the global level of the full graph and the local level of the shortest path between two nodes. We present potential practical applications of a predicted search set in the design of visual encoding and interaction techniques for graphs. Our predictive model is based on extensive qualitative analysis from an observational study, resulting in a detailed characterization of common path-tracing behaviours. These include the conditions under which people stop following paths, the likely directions for the first hop people follow, the tendency to revisit previously followed paths and the tendency to mistakenly follow apparent paths in addition to true topological paths. The algorithmic implementation of our predictive model is robust to a broad range of parameter settings. We provide a preliminary validation of the model through a hierarchical multiple regression analysis comparing graph readability factors computed on the predicted search set to factors computed at the global level and the local shortest path solution. The tested factors included edge–edge crossings, node–edge crossings, path continuity and path length. Our approach provides modest improvements for predictions of RT and error using search-set factors.

The relationship between low-level visual tasks and steering control

The relationship between low-level visual tasks and steering control

A novel video dataset for change detection benchmarking.

Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website and will be updated with feedback from academia and industry in the future.

Robust carotid artery recognition in longitudinal B-mode ultrasound images.

Automatic segmentation of the arterial lumen from ultrasound images is an important task in clinical diagnosis. Carotid artery recognition, the first task in lumen segmentation, should be performed in a fully automated, fast, and reliable way to further facilitate the low-level task of arterial delineation. In this paper, a user-independent, real-time algorithm is introduced for carotid artery localization in longitudinal B-mode ultrasound images. The proposed technique acts directly on the raw image, and exploits basic statistics along with anatomical knowledge. The method's evaluation and parameter value optimization were performed on a threefold cross validation basis. In addition, the introduced algorithm was systematically compared with another algorithm for common carotid artery recognition in B-mode scans, separately for multi-frame and single-frame data. The data sets used included 2,149 images from 100 subjects taken from three different institutions and covering a wide range of possible lumen and surrounding tissue representations. Using the optimized values, the carotid artery was recognized in all the processed images in both multi-frame and single-frame data. Thus, the introduced technique will further reinforce automatic segmentation in longitudinal B-mode ultrasound images.

Intensive Interventions in Reading for Students with Reading Disabilities: Meaningful Impacts.

We use three data sources to build a rationale for why intensive interventions are necessary for students with pervasive reading disabilities: current data on the performance of students with disabilities on reading achievement measures over time, observation studies on students with reading disabilities in general and special education classrooms, and findings from intensive intervention studies for students with reading disabilities. Results of these data sources indicate that students with disabilities are not making progress in reading at the same rate as students without disabilities, reading instruction for students with reading disabilities is comprised of excessive amounts of low level tasks, and findings from intensive intervention studies suggest positive impacts for students with reading disabilities. We argue that students with reading disabilities require ongoing intensive interventions that are likely to require schools to change the contexts and practices for these students.

Development of a prototype for uncertainty-aware geovisual analytics of land cover change

Analyses of spatiotemporal data are affected by different kinds of uncertainty, and various studies have shown that their impact can be severe. This is especially true for land cover change analysis based on remotely sensed data – it has been shown that ignoring uncertainty can lead to unreliable results. Approaches are needed that incorporate information on uncertainty into the analysis. However, usable models and methods are still rare and the supposed positive effect of uncertainty information has not been extensively studied. In this contribution, we describe the development of ICchange, an interactive visual prototype for the exploratory analysis of land cover change following a geovisual analytics approach. Apart from serving as proof of concept, the prototype will be used to evaluate the role of uncertainty information during change analyses. We conducted a qualitative evaluation of the prototype using low-level tasks to test basic usability. The input from the study is used to improve the design. A special focus is placed on the visual representation of uncertainty in one of the views that did not perform satisfyingly. The prototype presented here will be used in future studies to evaluate the role of uncertainty in real-world change analysis.

Advert saliency distracts children's visual attention during task-oriented internet use

The general research question of the present study was to assess the impact of visually salient online adverts on children's task-oriented internet use. In order to answer this question, an experimental study was constructed in which 9- and 12-year-old Swedish children were asked to solve a number of tasks while interacting with a mockup website. In each trial, web adverts in several saliency conditions were presented. By both measuring children's task accuracy, as well as the visual processing involved in solving these tasks, this study allows us to infer how two types of visual saliency affect children's attentional behavior, and whether such behavioral effects also impacts their task performance. Analyses show that low-level visual features and task relevance in online adverts have different effects on performance measures and process measures respectively. Whereas task performance is stable with regard to several advert saliency conditions, a marked effect is seen on children's gaze behavior. On the other hand, task performance is shown to be more sensitive to individual differences such as age, gender and level of gaze control. The results provide evidence about cognitive and behavioral distraction effects in children's task-oriented internet use caused by visual saliency in online adverts. The experiment suggests that children to some extent are able to compensate for behavioral effects caused by distracting visual stimuli when solving prospective memory tasks. Suggestions are given for further research into the interdiciplinary area between media research and cognitive science.

A Multi-Level Typology of Abstract Visualization Tasks

The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.

MiC 교과서의 수학적 과제의 인지적 요구 정도 분석 -함수 내용을 중심으로-

본 연구에서는 2006년에 새롭게 출판된 MiC 교과서를 대상으로, Stein 외(2009)가 제안한 바 있는 인지적 요구 정도(cognitive demand level)에 따라 구체적이고 체계적인 분석 기준과 분석틀을 이용하여 MiC 교과서에서 다루고 있는 수학적 과제들의 유형을 분석하고자 하였다. MiC 교과서에서 다뤄지는 내용은 크게 수, 대수, 기하와 측정, 자료 분석과 통계인데, 본 연구에서는 모든 영역의 내용을 다루기에는 너무나 양이 방대하여, 본 연구에서는 학교 안팎의 실생활 소재나 문제 상황이 보다 풍부한 함수 영역을 선정하여 이에 한정하여 다루었다. 다만, MiC 교과서는 level 1, 2, 3의 세 권으로 구분되어 있는데, 함수 내용은 Level 3에만 제시되어 있으므로 본 연구에서는 level 3만을 대상으로 한다. 이 연구를 통하여 궁극적으로 MiC 교과서의 수학적 과제가 얼마만큼 융통성 있게 풍부하게 다뤄지는가를 분석을 통하여 파악해 봄과 동시에, 이로부터 도출된 양질의 결과를 토대로 우리나라 교과서 개발 및 구현을 위한 시사점을 도출하고자 한다. Instructional materials including problem situations or problems or tasks on real-life situations are considered as an important and significant factor to lead a successful math instruction. MiC Textbook is a representative one showing good examples and tasks including fluent realistic situations on the basis of the background of the Freudenthal's theory. This study explores concretely and in detail the type of level of mathematical tasks, by the subject of MiC Textbook. To accomplish this, this study reconstructs and establishes an elaborated analysis framework using 'the cognitive demand level' suggested by Stein, et, al. The cognitive demand level is comprized of four elements such as Memorization Tasks, Procedures Without Connections Tasks, Procedures With Connections Tasks, and Doing Mathematics Tasks. Memorization Tasks and Procedures Without Connections Tasks are considered as low level tasks, and Procedures With Connections Tasks and Doing Mathematics Tasks are as high level tasks. MiC Textbook is comprized of the four areas of 'number', 'algebra', 'geometry and measurement', and 'data analysis and statistics'. This study deals with the tasks relevant to Function content dealt with in MiC 3 level Textbook, and explore the level of cognitive demands on each task.

Link Mining: A Computer Vision and Pattern Mining Approach

This work addresses the important problem of discovery and analysis of social networks and link between frequent people observed from surveillance video footage where large amount of video data is collected routinely. A computer vision approach has been applied for detecting and extracting people within a group using HAAR classifier. This technique allows us to recognizing people by doing similarity matching between training faces and unknown detected face image. Therefore it is required to obtain high resolution face images of people in order to extract intrinsic feature information of facial images used in detection of person’s faces. We present a novel frequent pattern mining based approach in the domain of frequent person detection i.e. apriori to solve frequent association problem between social networks obtained from low level task of face recognition. Our approach is illustrated with promising results from a fully integrated camera system. General Terms Data Mining, Frequent Pattern Mining, Social Network Analysis, Dynamic Social Network Analysis

Subjective visibility depends on level of processing

Is visual awareness graded or binary? Experimental work has provided support for both possibilities, leading to two coexisting but contradictory theoretical accounts. Here we propose a promising candidate factor through which to integrate both accounts: the depth of stimulus processing required by the task. We compared color identification (a low-level task) with numerical judgements (a high-level task) performed on the very same colored number stimuli. Psychophysical curves were analyzed for both objective discrimination performance and subjective visibility ratings on a trial-by trial basis. We observed a graded relationship between stimulus duration and visibility in the low-level task, but a more non-linear relationship in the high-level task. Both patterns of results have previously been consistently associated with the graded and the dichotomous account, respectively. Follow-up experiments that manipulate the level of processing can further unify previously inconsistent results, thus integrating two major theories of visual awareness.

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.

The minimal preprocessing pipelines for the Human Connectome Project

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.

Involvement of the mentalizing network in social and non-social high construal

The dorsomedial prefrontal cortex (dmPFC) is consistently involved in tasks requiring the processing of mental states, and much rarer so by tasks that do not involve mental state inferences. We hypothesized that the dmPFC might be more generally involved in high construal of stimuli, defined as the formation of concepts or ideas by omitting non-essential features of stimuli, irrespective of their social or non-social nature. In an fMRI study, we presented pictures of a person engaged in everyday activities (social stimuli) or of objects (non-social stimuli) and induced a higher level of construal by instructing participants to generate personality traits of the person or categories to which the objects belonged. This was contrasted against a lower level task where participants had to describe these same pictures visually. As predicted, we found strong involvement of the dmPFC in high construal, with substantial overlap across social and non-social stimuli, including shared activation in the vmPFC/OFC, parahippocampal, fusiform and angular gyrus, precuneus, posterior cingulate and right cerebellum.

A Domestic Case Studies Probability to Overcome Software Failures

Computers are the pervasive technology of our time. As computer become critically tied to human life, it also becomes more important that interactions with them are under control. They are no longer a novelty, but are integrated into the fabric of our world, performing both high and low-level tasks. That is, computers may be used to eliminate heavy, redundant work and more. Sophisticated machines have been deployed to perform remote surgery or detect subterranean landmines in repopulated civilian areas. The increasing importance of computers in our lives means that it is essential that the design of computer systems incorporates techniques that can ensure reliability, safety and security. This paper will examine technological mishaps involving the use of computers. This review will include notorious software bugs that have affected finance, communication, transit, defense, health and medicine and others systems or industries. The sequence and etiology of these accidents will be discusses as well as how catastrophes may be avoided in the future through lessons and practices based on research.

Lower-Level Decision Task Solution While Optimising a Construction Project Schedule

A bi-level approach for optimisation of a construction project is discussed in the paper. Considered optimisation problem deals with identification of optimal project and a corresponding optimal schedule. Project structure is defined by applied order of technological operations. Application of decomposition-coordination principle facilitates problem solution. We therefore obtain tasks which belong to 2 distinct optimisation levels. The lower optimisation task level is devoted to optimal allocation of execution modes to operations while assuming considered project structures. The global optimisation task level pertains to choice of the best structure for a construction project. Solution of lower level tasks are applied in this regard. The main difficulty in global project schedule optimisation results from multiplicity of feasible construction project structures and a need for solution of lower level tasks. We generally consider application of Monte Carlo simulation (MC) for generating feasible project structures. Mixed linear programming (MILP) and MC is the applied to solve lower level tasks. We also apply metaheurstics combined with MILP to solve lower level tasks while generating feasible project structures. Effects of application of presented approaches for solving lower level task solution approaches are finally compared.

Two-level Task Scheduling for Parallel Game Tree Search Based on Necessity

It is difficult to fully utilize the parallelism of large-scale computing environments in alpha-beta search. The naive parallel execution of subtrees would result in much less task pruning than may have been possible in sequential execution. This may even degrade total performance. To overcome this difficulty, we propose a two-level task scheduling policy in which all tasks are classified into two priority levels based on the necessity for their results. Low priority level tasks are only executed after all high priority level tasks currently executable have started. When new high priority level tasks are generated, the execution of low priority level tasks is suspended so that high level tasks can be executed. We suggest tasks be classified into the two levels based on the Young Brothers Wait Concept, which is widely used in parallel alpha-beta search. The experimental results revealed that the scheduling policy suppresses the degradation in performance caused by executing tasks whose results are eventually found to be unnecessary. We found the new policy improved performance when task granularity was sufficiently large.

Assessing community-based environmental management: coordination, motivation, and performance

Despite increasing implementation of community-based management (CBM) approach, a comprehensive organisational analysis of this approach is still lacking. Therefore, this paper aims to propose a common platform for analysing CBM, to identify how CBM is actually organised, and to discover the key success factors of CBM. Aligning to an organisational perspective, this paper analyses the coordination, motivation, and performance of CBM by means of a content analysis using 30 selected cases. Employing crosstabs function (SPSS), the authors found out that generally the communities were involved in the projects (mostly in low-level tasks) and 67% of the projects can be categorised as successful. Community participation level (CPL) and some motivational aspects (i.e., expected benefits, knowledge, and interest of conservation) are correlated to the performance of CBM. It is recommended that in the future more consideration is given toward CPL, designing appropriate incentive systems, and planning proper empowerment.