Validity Of Task Research Articles

Auto-filtering validation in citizen science biodiversity monitoring: a case study

Abstract. Data quality is the primary concern for researchers working on citizen science projects. The collected data by citizen science participants are heterogeneous and therefore must be validated. There are several validation approaches depending on the theme and objective of the citizen science project, but the most common approach is the expert review. While expert validation is essential in citizen science projects, considering it as the only validation approach can be very difficult and complicated for the experts. In addition, volunteers can get demotivated to contribute if they do not receive any feedback regarding their submissions. This project aims at introducing an automatic filtering mechanism for a biodiversity citizen science project. The goals of this project are to first use an available historical database of the local species to filter out the unusual ones, and second to use machine learning and image recognition techniques to verify if the observation image corresponds with the right species type. The submissions that does not successfully pass the automatic filtering will be flagged as unusual and goes through expert review. The objective is on the one hand to simplify validation task by the experts, and on the other hand to increase participants’ motivation by giving them real-time feedback on their submissions. Finally, the flagged observations will be classified as valid, valid but uncommon, and invalid, and the observation outliers (rare species) can be identified for each specific region.

A Curated Image Parameter Data Set from the Solar Dynamics Observatory Mission

Abstract We provide a large image parameter data set extracted from the Solar Dynamics Observatory (SDO) mission’s Atmospheric Imaging Assembly (AIA) instrument, for the period of 2011 January through the current date, with the cadence of 6 minutes, for nine wavelength channels. The volume of the data set for each year is just short of 1 TiB. Toward achieving better results in the region classification of active regions and coronal holes, we improve on the performance of a set of 10 image parameters, through an in-depth evaluation of various assumptions that are necessary for calculation of these image parameters. Then, where possible, a method for finding an appropriate setting for the parameter calculations was devised, as well as a validation task to show our improved results. In addition, we include comparisons of JP2 and FITS image formats using supervised classification models, by tuning the parameters specific to the format of the images from which they are extracted and specific to each wavelength. The results of these comparisons show that utilizing JP2 images, which are significantly smaller files, is not detrimental to the region classification task that these parameters were originally intended for. Finally, we compute the tuned parameters on the AIA images and provide a public API (see http://dmlab.cs.gsu.edu/dmlabapi/) to access the data set. This data set can be used in a range of studies on AIA images, such as content-based image retrieval or tracking of solar events, where dimensionality reduction on the images is necessary for feasibility of the tasks.

Everyday Memory in Healthy Aging: Porous but Not Distorted

Most studies targeting age-association of memory functions report a decline in recognition hits and an increase in false alarms. The goal of the present study was to assess these findings in tasks with day-to-day relevance. We investigated healthy young (YA; age 26.90 ± 3.55 years) and old (OA; age 69.80 ± 5.85 years) adults. Participants were asked to watch six news and six commercials and complete a recognition task relating to the information presented in the videos. OA had a lower hit rate in both news and commercials compared to YA. However, the number of false alarms (FA) was the same in both age groups. Applying signal detection theory, we found age differences in discriminability for both news and commercials paradigm. The groups showed no differences in bias and both chose a liberal answering tendency. We interpret our finding as a result of complex recognition items in an ecologically valid task. Multi-feature items offer an advantage in correct rejection—it is enough to know that at least one feature of an item is false. This benefit does not extend to hits, where all features of an item need to be recognized. This indicates that recognition memory of naturalistic stimuli in OA is porous, but not distorted.

Changes in corticospinal excitability associated with post-error slowing

According to available evidence, after making an erroneous decision people tend to slow down on the next decision. This empirical regularity, known as “post error slowing” (PES), has been traditionally interpreted as the result of a conservative response criterion adopted to avoid future errors and it is supposed to be driven by changes in the excitability of the motor system. However, the consequences of errors have been almost exclusively investigated by means of button-press tasks, which have been criticized because of their limited ecological validity and it is still unclear to what extent errors bias the motor system activity during the planning and the on-line control of complex and realistic goal-directed actions. To overcome these potential limitations, in the present study, we investigated the effect of errors on the preparation and execution of the reach-to-grasp movement, one of the most significant daily life actions. In addition to reaction times (RTs), we measured motor-evoked potential (MEP) to explore the influence of errors on corticospinal (CS) excitability, and we applied kinematical analysis to examine the underlying reorganization of the movement following an error. The results of the present study showed that MEPs tend to be reduced after the failure to reach and grasp an object, supporting the traditional interpretation of PES. Furthermore, in addition to RTs, we found that error-reactivity strategically influences the grasping component of the action, whereas the reaching component appears to be impermeable to PES. These findings demonstrate that the error-reactivity is a strong empirical phenomenon, which spreads into the motor system at the level of both movement preparation and execution, even when more realistic and ecologically valid tasks are used.

Visual Information Processing in Young and Older Adults.

Decline in information processing with age is well-documented in the scientific literature. However, some discrepancy remains in relation to which cognitive domains are most susceptible to the aging process and which may remain intact. Furthermore, information processing has not been investigated nor considered as a function of affect, familiarity and complexity of tasks in a single experimental study. Thus, the current study investigated rate of visual information processing in 67 young university students (M age = 19.64 years) and 33 educated healthy older adults (M age = 70.33 years), while accounting for depression, anxiety and stress symptoms using the DASS. Rates of visual processing were measured as minimum time of stimulus exposure duration required for correct object recognition on a simple visual task [Inspection Time (IT)], and on a more complex visual cognitive task known as Change Detection (CD)] as well as words per minute on a text reading task (FastaReada). The results demonstrated significantly slower performance by older adults on the IT and CD, but comparable rates of text reading on a semantically more complex, but ecologically valid and familiar visual task that requires organized sequential shifts in attention via eye movements, continuous visual processing, access to working memory and semantic comprehension. The results also demonstrated that affective influences did not play a role in the older adults task performance, and that changes in cognitive domains may begin with older adults being slower to attend to and identify newly appearing familiar objects, as well as slower to encode and embed new information in memory during tasks that require a less practiced/familiar task strategy.

Intergenerational transmission of emotion dysregulation: Part I. Psychopathology, self-injury, and parasympathetic responsivity among pregnant women.

The World Health Organization recently reported that maternal mental health is a major public health concern. As many as one in four women suffer from psychiatric disorders at some point during pregnancy or the first postpartum year. Furthermore, self-injurious thoughts and behaviors (SITBs) represent one of the leading causes of death among women during this time. Thus, efforts to identify women at risk for serious forms of psychopathology and especially for SITBs are of utmost importance. Despite this urgency, current single-diagnostic approaches fail to recognize a significant subset of women who are vulnerable to perinatal stress and distress. The current study was among the first to investigate emotion dysregulation-a multilevel, transdiagnostic risk factor for psychopathology-and its associations with stress, distress, and SITBs in a sample of pregnant women (26-40 weeks gestation) recruited to reflect a range of emotion dysregulation. Both self-reported emotion dysregulation and respiratory sinus arrhythmia, a biomarker of emotion dysregulation, demonstrated expected associations with measures of mental health, including depression, anxiety, borderline personality pathology, and SITBs. In addition, self-reported emotion dysregulation was associated with blunted respiratory sinus arrhythmia responsivity to an ecologically valid infant cry task. Findings add to the literature considering transdiagnostic risk during pregnancy using a multiple-levels-of-analysis approach.

Odor lateralization and spatial localization: Null effects of blindness

People can navigate through an environment using different sensory information, including olfactory cues. Correct intranasal localization and external location of odors can be learned, and some people are able to lateralize olfactory stimuli above chance, which raises the question: What determines the spectrum of olfactory localization abilities. Here, we explored whether odor lateralization and localization abilities are increased in the course of sensory compensation. In a series of studies, we combined two different aspects of odor localization. Study 1 compared abilities of 69 blind people (Mage = 41 ± 1.6 years; 32 females) and 45 sighted controls (Mage = 38.3 ± 2.1 years; 25 females) to correctly lateralize eucalyptol, an odorant with a strong trigeminal component, presented to either nostril. Studies 2 and 3 involved a more ecologically valid task, namely spatial localization of olfactory stimuli. In Study 2, 13 blind individuals (Mage = 28.5 ± 3.5 years; seven females) and 16 sighted controls (Mage = 34.9 ± 3.2 years; ten females) tried to localize a single odorant, while in Study 3, 97 blind individuals (Mage = 43.1 ± .5 years; 48 females) and 47 sighted controls (Mage = 38.7 ± .7 years; 27 females) attempted to localize a single target odor in an experimental olfactory space comprising four different odorants. Blind and sighted subjects did not differ in their abilities to lateralize and to localize odors, and their performance across all tasks suggests that odor lateralization and localization are important for navigation in an environment regardless of visual status.

An innovative energy efficiency metric for data analytics and diagnostics in telecommunication applications

This paper introduces and indicates how to deploy a novel energy metric, to be adopted for advanced monitoring and diagnosis of telecommunication central offices and data centers. Such an activity is motivated by the worldwide increasing telecommunication players awareness of the need to substantially reduce their energy demand, both to increase their market competitiveness and meet the stringent greenhouse gas emission regulations. The proposed metric, named utilization factor, was thus defined according to the peculiar energy breakdown of central offices. The aim was to conceive an index that focuses more on telecommunication energy adsorption and, in turn, enables climatic independent efficiency evaluation of the central offices under investigation. Then, suitable data-processing techniques were applied to develop a reliable utilization factor predictive model, whose identification and validation tasks were carried-out over an extended central offices database. The availability of a large amount of data was suitably exploited through data analytics approaches, particularly enabling diagnosis-oriented model development. Upon successful testing of its accuracy, the model was finally proven to be a strategic tool to perform model-based fault detection and isolation of relevant faults that may arise during central office monitoring tasks, such as abnormal data acquisition and non-optimal energy management. The suitability of the proposed metric, to be deployed as an innovative and synthetic energy index, was evaluated over an extended fleet of central offices and data-centers. It was found that the majority (about 70%) of tested central offices exhibits either adequate energy and thermal management or sensor-related only faults.

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random and therefore attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.

Designing Jumping Task on Percent using PMRI and Collaborative Learning

Contrast to the shared task at the beginning of mathematics lesson, jumping task is the central part of the teaching that uses higher order thinking skills (HOTs) in Japan. In Indonesia, the curriculum 2013 revised stress on how to use HOTs in the mathematics lesson, the daily life context, and collaborative learning. The goal of this research to know the characteristics of the jumping task which valid, practical and useful supporting primary school students learning percents. This research uses design research as a method. In designing the content materials and their contexts, this research uses PMRI (Indonesian version of Realistic Mathematics Education instructional theory. Moreover, in the designing and implementing learning process, this research use lesson study for learning community (LSLC). Results of this research are valid and practical jumping tasks on mathematics topic percents. Students with low performance have difficulties in solving jumping tasks. However, with collaborative learning, students can answer the percent jumping tasks.

Developing ethnomathematical tasks in the context of yogyakarta to measure critical thinking ability

One of the students’ abilities in learning mathematics is the critical thinking ability, which can be developed by giving taks of mathematics problems. This research aimed to develop and produce valid and practical ethnomathematical tasks within the context of Yogyakarta that may potentially improve students’ critical thinking abilities. This type of research was design research using development studies. This research was conducted at a junior high school in Yogyakarta, Indonesia. The research participants were 24 eighth grade students. The data collection instruments were validation sheets, questionnaires, and ethnomathematical tasks within the context of Yogyakarta. The research data was analyzed descriptively. The results show that the ethnomathematical tasks within the context of Yogyakarta were valid, practical, and can potentially measure the critical thinking skills.

Development of an SEMG-Handgrip Force Model Based on Cross Model Selection

In this paper, a cross model selection (CMS) technology is proposed to accurately estimate the handgrip force from the surface electromyography (SEMG). The proposed model is based on a cross model and a sequence combination analysis (SCA). The cross model is built by sampling the data collected from constant force training tasks (isometric muscle contraction) and force-varying training tasks (gradual dynamic muscle contraction). Isometric contraction data are used to build SEMG-force relationships with regularization. Then, the cross-validation (CV) technique is employed by using gradual dynamic contraction data to obtain the optimal regularization parameter. According to the zero-approaching characteristics of the parameter selected by CV, this paper proposes an SCA method to select a suitable combination of coefficient terms for the CMS model. Through the constant force and force-varying validation tasks, the experimental results showed that the handgrip force estimated from SEMG using the proposed model has higher accuracy than the former models.

Please be logical, I am in a bad mood: An electrophysiological study of mood effects on reasoning

Several behavioral studies have reported a detrimental effect of emotion on reasoning tasks, either when the content of the reasoning and/or the mood state of the individual are emotionally loaded. However, the neural mechanisms involved in this phenomena remain largely unexplored. In an event-related potentials (ERPs) study, we examined the consequences of an induced mood over the electrophysiological signals obtained while processing logical and illogical categorical conclusions. Prior to performing a syllogism reading task, we aimed to induce, by using short film clips, high arousal negative and positive moods and neutral affective states to participants in three separate recording sessions. Our mood induction procedure was only successful at inducing a highly arousing negative state. Behaviorally, participants committed more errors overall while judging the invalidity versus the validity of illogical and logical conclusions, respectively, but no influences from mood state emerged at this logical validity task. Electrophysiologically and overall a negative going N400 deflection was larger for illogical relative to logical conclusions in a parietal region between 300 and 420 ms. However, further analysis revealed that the logical conclusions were only more expected (smaller N400 amplitudes) in the negative relative to the neutral and the positive sessions, providing support to theoretical views that posit that a more analytic reasoning style might be implemented under a negative mood state. These results provide further electrophysiological evidence of the influence of mood on other cognitive processes, particularly on the anticipation and processing of logical conclusions during online reasoning tasks.

Provable Data Integrity in the Pharmaceutical Industry Based on Version Control Systems and the Blockchain.

In the pharmaceutical industry, process validation tasks are based on the raw data and its derived analytical results generated from the process. Process validation failure affects both patient safety and the economic success of the manufacturing company. Hence, data integrity is highly critical in this area. Regulatory agencies, such as the Food and Drug Administration (FDA), reacted to past data integrity breaches by publishing new guidelines on data integrity for the correct handling of data in the pharmaceutical context.In this contribution, we want to show how data integrity can be improved on a technological level, removing the need for trusted third parties and centralized systems for this task. Therefore, we implemented an approach that uses existing tools, currently mostly used by software developers, and combined them with a new smart contract built on top of the Ethereum blockchain. In a case study, we test how data manipulation or backdating of results can be easily detected and how regulatory agencies can audit the complete data flow from the regulatory report back to the original raw data.The results of this contribution outline a possible road map for the development of production-ready tools, such as versioned database systems that natively interoperate with distributed ledgers. This will improve the trustworthiness of pharmaceutical manufacturing data by both protecting the intellectual property of the industrial company and improving the safety of the patients.LAY ABSTRACT: In the pharmaceutical industry, economically driven manufacturing companies are regulated and controlled by regulatory agencies. The pharmaceutical manufacturing companies need to produce large amounts of process and analytical data to show that their products are safe for patients. As the decisions of the regulatory agencies rely on this data, manufacturing companies need to prove how their generated data can be protected from technical breaches or data manipulation.As of today, the available technical solutions to provide data integrity are not working well enough. Regulatory agencies have published multiple documents highlighting the current data integrity issues.In this contribution, we show how blockchain, a technology that multiple cryptocurrencies like Bitcoin rely on, can help to improve the integrity of manufacturing data and data science analysis procedures. Therefore, we combined a smart contract on the Ethereum blockchain with tools currently mostly used by software developers. The presented workflow shows how data integrity can be guaranteed on a technological level without the need for trusted third parties.

On mathematical modelling task using health context for grade 5

This research aims to generate valid mathematical modelling task using health context for grade 5 in Auladi Integrated Islamic Elementary School, Palembang. To construct mathematical modeling task, ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model was used. The subjects of the research are students of CLASS V at Auladi Integrated Islamic Elementary School, Palembang. The data collection technique was performed by walkthrough, observation, and interview. The result of this research is valid and practical mathematical modeling task using health context.

Reliable Biomolecular Structural Modelling with Small-Angle Scattering

The small-angle scattering (SAS) of X-rays or neutrons from biomolecules in solution can provide (1) unique structural insights, (2) information on the range of potential conformations present, (3) and powerful restraints for integrative/hybrid structural modelling (IHM). The rotational and ensemble averaging of the signal presents multiple challenges to the reliable utilisation of SAS data: (1) demonstrating SAS profile represents the species of interest, (2) reliably evaluating model fits, and (3) over-interpretation and over-fitting of data. A community driven project has been underway for more than a decade now to define the information that should be made available regarding sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular SAS experiments. These guidelines (Trewhella et al. (2017) Acta D73, 710) provide a reporting framework so that “readers can independently assess the quality of the data and the basis for any interpretations presented” concerning the results of bimolecular SAS experiments, including the generation or testing of 3D models. The validation task forces for SAS (Trewhella et al. (2013) Structure 21, 875) and for IHM (Sali et al. (2015) Structure 23, 1156) of the world-wide Protein Data Bank are additionally addressing the complex issues concerning the archiving and validation of IHM models that depend upon computational methods and disparate data from multiple techniques (Burley et al. (2017) Structure 25, 1317). Finally, the question of how many parameters are justified by the model evidence inherent in the SAS profile is an important question to answer when modelling conformational ensembles (Potrzebowski, Trewhella and Andr⌈ (2018) in review PLOS Computational Biology). This presentation will address the recommended reporting framework and its rationale, the reliable incorporation of SAS data in IHM, and the strengths and limits of SAS in ensemble modelling.

Esports: The Chess of the 21st Century.

For many decades, researchers have explored the true potential of human achievement. The expertise field has come a long way since the early works of de Groot (1965) and Chase and Simon (1973). Since then, this inquiry has expanded into the areas of music, science, technology, sport, academia, and art. Despite the vast amount of research to date, the capability of study methodologies to truly capture the nature of expertise remains questionable. Some considerations include (i) the individual bias in the retrospective recall of developmental activities, (ii) the ability to develop ecologically valid tasks, and (iii) difficulties capturing the influence of confounding factors on expertise. This article proposes that expertise research in electronic sports (esports) presents an opportunity to overcome some of these considerations. Esports involves individuals or teams of players that compete in video game competitions via human-computer interaction. Advantages of applying the expert performance approach in esports include (i) developmental activities are objectively tracked and automatically logged online, (ii) the constraints of representative tasks correspond with the real-world environment of esports performance, and (iii) expertise has emerged without the influence of guided systematic training environments. Therefore, this article argues that esports research provides an ideal opportunity to further advance research on the development and assessment of human expertise.

Method of Investigation for Numerical Simulation on Aero-Optical Effect Based on WCNS-E-5

Profound importance has been attached to aero-optical effects on distortion of optical signals and image aberration for imaging guidance aircraft to eliminate such detrimental impact with respect to the disciplinary intersection of fluid dynamics and optical engineering. Even though numerical simulation has become an irreplaceable technique in this field, few attempts focusing on method validation are reported, despite the widely existing simulative studies. Because of the urgent demand of such a validation task, the authors conduct a rigorous discussion on the effectiveness of a combinational WCNS-E-5 and ray-tracing method in aero-optical simulation. In section order, oblique shock wave, three-dimensional supersonic mixing layer, and subsonic commingled flow are employed for comparison with theoretical results, grid sensitivity analysis, and experimental data validation, respectively. Results suggest that grid independence of aero-optical parameters still holds, and reasonably prescribed inflow conditions may realize little difference between simulated optical path difference rms and measured data from the wind-tunnel test.

Relevant Parameters for the Classification of Reading Books Depending on the Degree of Textual Readability in Primary and Compulsory Secondary Education (CSE) Students

This paper tries to establish the most important parameters of readability when it comes to choosing reading books for students in the second and third stages of primary and compulsory secondary education (CSE). A computational system is postulated, through data-mining techniques, capable of automatically classify reading texts by virtue of syntactic, lexical, semantics, and topological parameters regarding their overall content. The validity of this process is ensured through a careful selection by editorial experts used to accomplish the same validation task among different texts. In this paper, the results in the automatic classification of the degree of readability reached are promising; they allow to continue with a deeper investigation in this field of analysis with the purpose of progressing in the age rank studied, as well as in the text themes defining the corpus.

Automated Nonintrusive Analysis of Electronic System Level Designs

Due to the ever increasing complexity of hardware systems, designers strive for higher levels of abstractions in the early stages of the design process. Modeling hardware at the electronic system level (ESL) is one way to address this demand, with the C++-based system modeling framework SystemC and its abstract communication library transaction level modeling (TLM) having become de-facto standards for ESL system design. While the C++ compiler is sufficient to compile and simulate a given ESL design, for tasks of design understanding, debugging, or validation (where access to the details of design’s structure and behavior is necessarily required), design needs to be processed by an appropriate tool. This problem is often solved by adding instrumentation code to either the design or the library, usually resulting in incomplete logs, work overhead and/or incompatibilities. This paper introduces an approach that automatically extracts information about both, structure and behavior of SystemC designs and TLM transactions, nonintrusively. The information is retrieved from a given design by running it in debug mode while being connected to a preprogrammed debugger, thus leaving the existing sources and workflows untouched while collecting a vast amount of data without user intervention. Illustrating use cases, value change dump files of the SystemC models’ behavior and unified modeling language activity diagrams of transaction protocols are created automatically from simulation runs.