Ambiguous Task Research Articles

Understanding climate health adaptation in practice: adaptation to the oak processionary moth in The Netherlands

Abstract Background Understanding of climate health adaptation and its organisation and implementation is limited. This presentation focuses on the adaptation to the oak processionary moth (OPM). The OPM spreads out northwards through Europe, and human contact leads to reactions in skin mucous membranes, conjunctivitis, pharyngitis and respiratory distress. Literature has focused largely on the ecology and epidemiology of the OPM. However, there is limited understanding of the organisation and implementation of climate health adaptation knowledge and coordinating an effective response. Methods In an explorative case study we examined the current OPM adaptation strategies in practice. Semi-structured interviews with 26 actors in The Netherlands were held to unravel the problem and the complexities involved in OPM adaptation. Results The results indicate that the context of OPM adaptation is multidimensional, given the involvement of many interdependent actors. At regional and local level early networks emerge aiming to improve the effectiveness of OPM adaptation. At the national level the Knowledge Platform OPM was set up, in which actors collaborate to collect and share knowledge and experience. Nevertheless, OPM strategies are based on ad hoc approaches with ambiguous tasks and expertise. In addition, actors have different perceptions and values concerning health, sustainability, risks and responsibilities influencing decision-making processes, collaboration and a coordinated approach. Conclusions The generation of knowledge and its translation into practical strategies calls for interdisciplinary cooperation in knowledge development. Climate health adaptation involves more than technical and organisational solutions alone. It also entails the development of a shared problem perception and solution space in which citizens are also engaged. Therefore, implementation of vertical and horizontal coordination climate health adaptation strategies are required.

Exploration of conflict resolution benefits among Nigerian construction professionals

Conflict resolution in the construction industry is the informal or formal procedure that two or more construction professionals use to find a peaceful solution to their misunderstanding. The resolution of conflict can be beneficial to construction professionals, if it is resolved or managed appropriately. Therefore, this study aimed to examine various benefits of conflict resolution among construction professionals in Nigeria. On the research methodology used in this research, a quantitative research method was used. A total number of 150 questionnaires were sent out and 135 were received back from the construction professionals in Nigeria. The construction professionals are quantity surveyors, architects, builders, construction managers, civil engineers, and project managers. The research data that was collected underwent cleaning and screening before the commencement of the analysis. Also, the data collected pertaining to this study was analysed using Statistical Package for Social Sciences (SPSS version 21). This research made use of descriptive and exploratory factor analysis. The study found that conflict resolution could assist in the generation of new insights and perception. This was ranked the highest followed by other benefits of conflict resolution like: reduction in tension, helps professionals to accomplish their ambitions, improves workplace conflicts, and reduces task ambiguity of professionals for construction professionals to enjoy the benefits of conflict resolution they must resolve conflicts among themselves as early as possible.

Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China

BackgroundWith the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval.MethodBased on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility.ResultApplying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975.ConclusionsWe introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing.

Philosophy as a Vocation and Personal Commitment: the Young Heidegger and the Question of Philosophy

Determining what philosophy is for the young Heidegger is a complex task. It is also an ambiguous task in that it is considered unresolved and intricate due to its subsidiary link to factical life. This paper will try to show that from the approach of worried concern and from a critique of the theoretical attitude and worldviews, Heidegger conceives that philosophizing is committing oneself to the possibility of carrying out a personal transformation lived as a commitment of a personal nature. From this situation, which will be critically scrutinized in the development of the present paper, I will determine philosophy to be the setting in motion of the self-enlightenment of the life of each existent.

Reflective learning? Understanding the student perspective in higher education

ABSTRACT Background With increasing recognition of the importance of lifelong learning, the skill of reflection has become ever more significant, especially in higher education. However, the quality of student reflection can vary and, when students are tasked with reflecting, an assumption is often made that they automatically understand how to reflect. As students continue to grapple with the act of reflecting, and the culture of being critically reflective, there is a need for greater understanding of students’ perceptions of reflection and the challenges encountered. Purpose The purpose of this small-scale, in-depth study was to understand the student perspective by inviting students to talk about reflection. There was a particular focus on exploring views and experiences from the standpoints of those directly undergoing and navigating the process. Method In this qualitative study, the participants were 11 undergraduate students from a university in Malaysia. The students completed a questionnaire and were then interviewed by the researcher. The data collected were analysed using a thematic approach. Findings Two core themes and six subthemes emerged from the data analysis: (1) views of reflection – initial experiences, understanding of reflection and transition; (2) challenges faced – the need for more process-focused feedback, vulnerability in reflection and inaction in reflection. The findings are discussed with relevant examples from the data. Conclusions The findings highlighted how reflection can be perceived as an ambiguous task, subject to a variety of interpretations. From the students’ viewpoint, reflection was often characterised as a product which was single-layered (non-iterative) and monologic, rather than iterative and dialogic. Insights offered by this analysis suggest the need for the student perspective to be more deeply understood and considered contextually, with the aim of designing teaching to better support and strengthen a reflective learning approach.

Advancing Mixture Models for Least Squares Optimization

Gaussian mixtures are a powerful and widely used tool to model non-Gaussian estimation problems. They are able to describe measurement errors that follow arbitrary distributions and can represent ambiguity in assignment tasks like point set registration or tracking. However, using them with common least squares solvers is still difficult. Existing approaches are either approximations of the true mixture or prone to convergence issues due to their strong nonlinearity. We propose a novel least squares representation of a Gaussian mixture, which is an exact and almost linear model of the corresponding log-likelihood. Our approach provides an efficient, accurate and flexible model for many probabilistic estimation problems and can be used as cost function for least squares solvers. We demonstrate its superior performance in various Monte Carlo experiments, including different kinds of point set registration. Our implementation is available as open source code for the state-of-the-art solvers Ceres and GTSAM.

Sleep-related monitoring on awakening mediates the relationship between insomnia-related interpretive bias and insomnia symptoms using the insomnia ambiguity paradigm.

A number of studies have examined and confirmed the presence of a sleep-related interpretive bias amongst poor sleepers and individuals with insomnia using an insomnia ambiguity task. This study explored possible mechanisms underlying the relationship between interpretive bias and insomnia using the insomnia ambiguity task. More importantly, the possible mediating role of sleep-associated monitoring, sleep preoccupation, sleep anticipatory anxiety and generalized anxiety was also examined. A total of N=176 participants were stratified into normal sleepers and those displaying insomnia symptoms. Participants completed an online version of the insomnia ambiguity task and questionnaire measures pertaining to sleep and anxiety. Data concerning task response time and time of testing were also collected. Individuals in the insomnia symptom group presented significantly higher sleep-related interpretive bias scores compared to normal sleepers. When sleepiness, sleep-associated monitoring, sleep preoccupation, sleep anticipatory anxiety and generalized anxiety were controlled for, only monitoring on awakening predicted sleep-related interpretive bias. Multiple mediation modelling demonstrated that sleep-associated monitoring on awakening mediated the relationship between interpretive bias and insomnia symptoms. The current outcomes are consistent with previous research, supporting the notion that insomnia is characterized by a disorder-consistent interpretive bias. Furthermore, monitoring for insomnia-consistent cues on awakening appears to mediate group differences in interpretive bias.

A Framework for Contractual Graphs.

This paper studies contractual graphs, where the formation of edges between nodes result in dyadic exchanges. Each dyadic exchange is analyzed as a contractual agreement that is implemented upon fulfilment of underlying conditions. As these dyadic exchanges proliferate, the resulting population of these exchanges creates a contractual graph. A contractual framework for graphs is especially useful in applications where AI-enabled software is employed to create or automate smart contracts between nodes. While some smart contracts may be easily created and executed, others may contain a higher level of ambiguity which may prevent their efficient implementation. Ambiguity in contractual elements is especially difficult to implement, since nodes have to efficiently sense the ambiguity and allocate appropriate amounts of computational resources to the ambiguous contractual task. This paper develops a two-node contractual model of graphs, with varying levels of ambiguity in the contracts and examines its consequences for a market where tasks of differing ambiguity are available to be completed by nodes. The central theme of this paper is that as ambiguity increases, it is difficult for nodes to efficiently commit to the contract since there is an uncertainty in the amount of resources that they have to allocate for completion of the tasks specified in the contract. Thus, while linguistic ambiguity or situational ambiguity might not be cognitively burdensome for humans, it might become expensive for nodes involved in the smart contract. The paper also shows that timing matters—the order in which nodes enter the contract is important as they proceed to sense the ambiguity in a task and then allocate appropriate resources. We propose a game-theoretic formulation to scrutinize how nodes that move first to complete a task are differently impacted than those that move second. We discuss the applications of such a contractual framework for graphs and obtain conditions under which two-node contracts can achieve a successful coalition.

Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow.

The Coarse-To-Fine (CTF) matching scheme has been widely applied to reduce computational complexity and matching ambiguity in stereo matching and optical flow tasks by converting image pairs into multi-scale representations and performing matching from coarse to fine levels. Despite its efficiency, it suffers from several weaknesses, such as tending to blur the edges and miss small structures like thin bars and holes. We find that the pixels of small structures and edges are often assigned with wrong disparity/flow in the upsampling process of the CTF framework, introducing errors to the fine levels and leading to such weaknesses. We observe that these wrong disparity/flow values can be avoided if we select the best-matched value among their neighborhood, which inspires us to propose a novel differentiable Neighbor-Search Upsampling (NSU) module. The NSU module first estimates the matching scores and then selects the best-matched disparity/flow for each pixel from its neighbors. It effectively preserves finer structure details by exploiting the information from the finer level while upsampling the disparity/flow. The proposed module can be a drop-in replacement of the naive upsampling in the CTF matching framework and allows the neural networks to be trained end-to-end. By integrating the proposed NSU module into a baseline CTF matching network, we design our Detail Preserving Coarse-To-Fine (DPCTF) matching network. Comprehensive experiments demonstrate that our DPCTF can boost performances for both stereo matching and optical flow tasks. Notably, our DPCTF achieves new state-of-the-art performances for both tasks - it outperforms the competitive baseline (Bi3D) by 28.8% (from 0.73 to 0.52) on EPE of the FlyingThings3D stereo dataset, and ranks first in KITTI flow 2012 benchmark. The code is available at https://github.com/Deng-Y/DPCTF.

The Generalization of Conscious Attentional Avoidance in Response to Threat Among Breast Cancer Women With Persistent Distress

ObjectivesA sample of women with persistent distress following breast cancer (BC) previously exhibited attentional bias (AB) away from supraliminally presented cancer-or threat-related information, responses consistent with avoidance coping, and showed negative interpretation bias. Here, we attempt to characterize the nature of supraliminal AB and interpretation bias in that sample of women by comparing against healthy controls.MethodsExtending our previous work, we compared AB patterns for supraliminally presented negatively valenced words and cancer-related information (CRI) assessed by modified dot-probe tasks and negative interpretation bias assessed by an ambiguous cue task between 140 BC women previously identified as featuring low-stable or persistent high anxiety and 150 age-matched non-BC healthy controls having HADS-defined low or high anxiety (HADS-anxiety scores = 8).ResultsAttentional avoidance of non-cancer-related negatively valenced words was seen among the anxious BC group, while heightened attention toward such words was seen in anxious healthy controls, F(3, 282) = 3.97, p = 0.009. However, all anxious women in both BC and healthy groups directed attention away from CRI under supraliminal conditions. Interpretation bias scores were significantly higher in BC women with high anxiety vs. healthy controls with high anxiety, F(3, 282) = 13.26, p < 0.001.ConclusionWomen with high anxiety generalized conscious attentional avoidance responses to negatively valenced stimuli, indicating a likely hypersensitivity to potential threat in ambiguous cues and exaggerated threat perception. This may cause (or reflect) maladaptive emotional regulation. Attention focus training, reducing threat salience or modifying threat appraisal, may help women alleviate anxiety levels after BC.

Is Electronic Multitasking Always Viewed as a Counterproductive Meeting Behavior? Understanding the Nature of the Secondary Task.

This project explored social evaluations of electronic multitasking during work meetings, including factors that may affect whether it is seen as a counterproductive meeting behavior. We used an experimental vignette design to test whether social evaluations (norm violating, agency, and communalism) of a hypothetical coworker's electronic multitasking differed by whether the secondary task was relevant to the meeting (Study 1; N = 274) or ambiguous (Study 2; N = 188). Observers evaluated task-irrelevant multitasking as more of a norm violation and less communal compared to task-relevant multitasking, and work-related tasks were evaluated as more agentic than nonwork-related tasks. Ambiguous tasks were also rated as more agentic than task-irrelevant multitasking. Taken together, our results show that the nature of the secondary task reduces negative perceptions of coworkers' electronic multitasking behavior during meetings. However, electronic multitasking for any purpose, even if relevant, was generally judged negatively consistent with expectations for a counterproductive meeting behavior.

Looking for Zebras: Unexpected Solutions to Pattern Tasks

Creating ambiguity in task prompts can encourage students to identify unconventional pattern extensions.

Compensating for processing difficulty in discourse: Effect of parallelism in contrastive relations

ABSTRACT This study aims to establish whether the processing of different connectives (e.g., and, but) and different coherence relations (addition, contrast) can be modulated by a structural feature of the connected segments—namely, parallelism. While but is mainly used to contrast two expressions, and occurs in many different relations and has been shown to come with a processing cost. We report three self-paced reading experiments in which we manipulate whether the connected segments share a common verb phrase. Such parallel constructions frequently occur in contrastive relations, although they are typically treated as additive in comprehension research. We expect that parallelism will compensate for the cognitive complexity of contrast and for the ambiguity of and by further signaling the coherence relation. Our results indicate that parallelism speeds up processing and provides further evidence for priming in comprehension. However, parallelism interacted with connective ambiguity in an overt disambiguation task (Experiment 3) but not in a more natural reading task (Experiment 2). We argue that the processing of contrast remains shallow unless disambiguation is explicitly required.

Evaluation of the Factor Structure and Content Specificity of the Interpretation Bias Task (IBT)

BackgroundTheories suggest that interpretation biases play a role in the aetiology of a range of psychopathology including depression, anxiety and psychosis. We evaluate the psychometric properties of an adapted version of an ambiguous scenario task (i.e., Interpretation Bias Task [IBT]) that assesses benign and negative interpretations in four domains: immediate bodily injury; long-term illness; social rejection; and, performance failure.MethodsThe factor structure of the IBT was evaluated in a student sample (N = 237) in Study 1, and subsequently confirmed in a community sample with a wider age range (N = 1103) in Study 2. Correlations between interpretation biases and health and social anxiety symptoms were tested in both studies.ResultsThe four IBT domains were differentiable and each was represented by two factors (i.e., benign vs. negative). In Study 1, higher health anxiety was characterised by fewer benign interpretations for injury- and illness-related scenarios, whereas higher social anxiety was associated with more negative and fewer benign interpretations for social rejection and performance failure scenarios. Correlational results were replicated in Study 2 for social anxiety, but not health anxiety.ConclusionsThe IBT is suitable for measuring interpretation biases in Asian adults. The content specificity of interpretation biases was partially supported.

Utilitarian welfare and representation guarantees of approval-based multiwinner rules

To choose a suitable multiwinner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an “optimal” subset of alternatives. In this paper, we provide a quantitative analysis of multiwinner voting rules using methods from the theory of approximation algorithms—we estimate how well multiwinner rules approximate two extreme objectives: a representation criterion defined via the Approval Chamberlin–Courant rule and a utilitarian criterion defined via Multiwinner Approval Voting. With both theoretical and experimental methods, we classify multiwinner rules in terms of their quantitative alignment with these two opposing objectives. Our results provide fundamental information about the nature of multiwinner rules and, in particular, about the necessary tradeoffs when choosing such a rule.

Impact of Culture Medium on Cellular Interactions in in vitro Co-culture Systems.

Co-culturing of cells in in vitro tissue models is widely used to study how they interact with each other. These models serve to represent a variety of processes in the human body such as development, homeostasis, regeneration, and disease. The success of a co-culture is dependent on a large number of factors which makes it a complex and ambiguous task. This review article addresses co-culturing challenges regarding the cell culture medium used in these models, in particular concerning medium composition, volume, and exchange. The effect of medium exchange on cells is often an overlooked topic but particularly important when cell communication via soluble factors and extracellular vesicles, the so-called cell secretome (CS) is being studied. Culture medium is regularly exchanged to supply new nutrients and to eliminate waste products produced by the cells. By removing medium, important CSs are also removed. After every medium change, the cells must thus restore their auto- and paracrine communication through these CSs. This review article will also discuss the possibility to integrate biosensors into co-cultures, in particular to provide real-time information regarding media composition. Overall, the manner in which culture medium is currently used will be re-evaluated. Provided examples will be on the subject of bone tissue engineering.

Stacked auto-encoder based tagging with deep features for content-based medical image retrieval

Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images is very convenient in large and balanced datasets. Still, it is generally not possible to find such datasets in the medical domain. In this study, a four-step and effective hash code generation technique is presented to reduce the semantic gap between low-level features and high-level semantics for unbalanced medical image datasets. In the first stage, the convolutional neural network (CNN) architecture, the most effective feature representation method available today, is employed to extract discriminative features from images automatically. The features obtained in the last fully connected layer (FCL) at the output of the CNN architecture are used for hash code generation. In the second stage, using the Synthetic Minority Over-sampling Technique (SMOTE), the imbalance between the classes in the dataset is reduced. The solution to the unbalanced problem increases performance by almost 3%. In the third stage, balanced features are converted to a code of 13 symbols by using deep stacked auto-encoder. Finally, this code is translated to the standard 13-character labeling and retrieval code used by the 'Image retrieval in the medical application' (IRMA) dataset, since this is the database with which experiments have been done. IRMA error parameter, classification performance, and retrieval performance of the proposed method are more successful than other state-of-the-art methods.

Image Outpainting and Harmonization using GANs

Although the inherently ambiguous task of predicting what resides on the far side all four edges of a image has rarely been explored before, we have a tendency to demonstrate that GANs hold powerful potential in manufacturing reasonable extrapolations. Two outpainting ways square measure projected that aim to instigate this line of research: the primary approach uses a context encoder inspired by common inpainting architectures and paradigms, whereas the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the design of the original image.

3D-ReConstnet: A Single-View 3D-Object Point Cloud Reconstruction Network

Object 3D reconstruction from a single-view image is an ill-posed problem. Inferring the self-occluded part of an object makes 3D reconstruction a challenging and ambiguous task. In this paper, we propose a novel neural network for generating a 3D-object point cloud model from a single-view image. The proposed network named 3D-ReConstnet, an end to end reconstruction network. The 3D-ReConstnet uses the residual network to extract the features of a 2D input image and gets a feature vector. To deal with the uncertainty of the self-occluded part of an object, the 3D-ReConstnet uses the Gaussian probability distribution learned from the feature vector to predict the point cloud. The 3D-ReConstnet can generate the determined 3D output for a 2D image with sufficient information, and 3D-ReConstnet can also generate semantically different 3D reconstructions for the self-occluded or ambiguous part of an object. We evaluated the proposed 3D-ReConstnet on ShapeNet and Pix3D dataset, and obtained satisfactory improved results.

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Data Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using two datasets such as Pima Indians Diabetes dataset and heart disease dataset. Based on the World Health Organization (WHO), diabetes is the one of the biggest health concerns so mining the diabetes data is an ambiguous task. Diabetic patients may also have to suffer from other diseases like heart disease, eye complications, kidney disease, nerve damage, foot problems, skin complications and dental diseases. However, the existing techniques faced the difficulty to identify inconsistent and redundant features. In this research work, the Random Forest Feature Selection Technique (RFS) is developed to identify the significant features and eliminate irrelevant features which are used to improve the predicting accuracy of Cardio Vascular (CV) disease for diabetic patients. According to the output from the extensive experiments, this research categorizes that whether the diabetic patients having CV or not by using True Positive as diabetes and True Negative as no diabetes.