Urban Perception: Sensing Cities via a Deep Interactive Multi-task Learning Framework

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-20
Author(s):  
Weili Guan ◽  
Zhaozheng Chen ◽  
Fuli Feng ◽  
Weifeng Liu ◽  
Liqiang Nie
Keyword(s):  
Large Scale ◽  
Pairwise Comparisons ◽  
Specific Information ◽  
Social Scientists ◽  
Learning Framework ◽  
Task Learning ◽  
Highly Correlated ◽  
Image Pairs ◽  
The Given

Social scientists have shown evidence that visual perceptions of urban attributes, such as safe, wealthy, and beautiful perspectives of the given cities, are highly correlated to the residents’ behaviors and quality of life. Despite their significance, measuring visual perceptions of urban attributes is challenging due to the following facts: (1) Visual perceptions are subjectively contradistinctive rather than absolute. (2) Perception comparisons between image pairs are usually conducted region by region, and highly related to the specific urban attributes. And (3) the urban attributes have both the shared and specific information. To address these problems, in this article, we present a Deep inteRActive Multi-task leArning scheme, DRAMA for short. DRAMA comparatively quantifies the perceptions of urban attributes by jointly integrating the pairwise comparisons, regional interactions, and urban attribute correlations within a unified deep scheme. In DRAMA, each urban attribute is treated as a task, whereby the task-sharing and the task-specific information is fully explored. By conducting extensive experiments over a public large-scale benchmark dataset, it is demonstrated that our proposed DRAMA scheme outperforms several state-of-the-art baselines. Meanwhile, we applied the pairwise comparisons of our DRAMA model to further quantify the urban attributes and hence rank cities with respect to the given urban attributes. As a byproduct, we have released the codes and parameter settings to facilitate other researches.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

scholarly journals ФИТОИНДИКАЦИОННОЕ ОЦЕНИВАНИЕ ИЗМЕРЕНИЙ, ПОЛУЧЕННЫХ ПРИ МНОГОМЕРНОМ ШКАЛИРОВАНИИ СТРУКТУРЫ РАСТИТЕЛЬНОГО СООБЩЕСТВА

2015 ◽  
Vol 5 (01) ◽  
pp. 69 ◽  
Author(s):  
A. V. Zhukov
Keyword(s):  
Multidimensional Scaling ◽  
Plant Community ◽  
Large Scale ◽  
Mantel Test ◽  
Specific Information ◽  
Scale Level ◽  
Complementary Information ◽  
Phenomenological Interpretation ◽  
Community Ordination ◽  
The Given

<p>The purpose of our work is to carry out plant community ordination by means of multidimensional scaling to reveal optimum ways of preliminary transformation of data and the similarity/dissimilarity measure, to identify multidimensional dimensions in terms of edafic properties and phytoindicator scales and to reveal character of interrelations of matrixes of plant community, phytoindicator scales and edafic properties. The received results testify that edafic and climatic scales matrixes bear the complementary information on edaphotop properties and possibly climatop. Most possibly that climatic scales at large-scale level bear the specific information on properties of environment. It is difficult to confirm, whether character of this information to adequate nominative properties of a scale at macrolevel is. But with confidence it is possible to say that climatic phytoindicator scales allow to differentiate ecological conditions in biogeocoenosis at large-scale level. Thus, at the given stage we tend to phenomenological interpretation of value of climatic phytoindicator scales at large-scale level.</p> <p><em>Keywords</em><em>: multidimensional scaling, community structure, phytoindicator scales, Mantel test</em></p>

scholarly journals Video Question Answering via Hierarchical Spatio-Temporal Attention Networks

2017 ◽  
Author(s):  
Zhou Zhao ◽  
Qifan Yang ◽  
Deng Cai ◽  
Xiaofei He ◽  
Yueting Zhuang
Keyword(s):  
Visual Information ◽  
Large Scale ◽  
Question Answering ◽  
Temporal Dynamics ◽  
Temporal Attention ◽  
Attention Networks ◽  
Learning Framework ◽  
Spatio Temporal ◽  
The Given ◽  
Video Question Answering

Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoder-decoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the encoder-decoder learning method with reasoning recurrent neural networks for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.

scholarly journals Optimizing schedules for the VLBI global observing system

2020 ◽  
Vol 94 (1) ◽  
Author(s):  
Matthias Schartner ◽  
Johannes Böhm
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Very Long Baseline Interferometry ◽  
Proper Selection ◽  
Long Baseline ◽  
Weight Factors ◽  
Highly Correlated ◽  
Insight Into ◽  
Selection Of

AbstractVery long baseline interferometry (VLBI) scheduling is a challenging optimization problem. With the development of the new VLBI global observing system (VGOS) consisting of smaller but very fast slewing antennas, new opportunities arise. In this work, we give a deep insight into optimized VGOS scheduling using a newly developed VLBI scheduling software called VieSched, and we show how different scheduling parameters and approaches affect the precision of geodetic results. Therefore, the results of over one thousand generated schedules and over one million simulated sessions are analyzed. The simulations reveal that the most important parameters to optimize VGOS schedules with VieSched are the so-called weight factors. A proper selection of individually optimized weight factors can improve the quality of a schedule significantly. It is shown that the values of the weight factors used to generate the schedule are highly correlated with the expected precision of the geodetic parameters. We highlight the benefit of selecting schedules based on large-scale Monte Carlo simulations and show why scheduling statistics like the number of observations or the sky-coverage are not necessarily the best metric to evaluate schedules.

scholarly journals Measuring Taxi Accessibility Using Grid-Based Method with Trajectory Data

2018 ◽  
Vol 10 (9) ◽  
pp. 3187 ◽  
Author(s):  
Shixiong Jiang ◽  
Wei Guan ◽  
Zhengbing He ◽  
Liu Yang
Keyword(s):  
Large Scale ◽  
Grid Cells ◽  
Trajectory Data ◽  
Late Night ◽  
Time Period ◽  
Time Periods ◽  
Highly Correlated ◽  
The Given ◽  
Beijing China ◽  
Grid Based

Accessibility has drawn extensive attention from city planners and transportation researchers for decades. With the benefits of large-scale and varying time, this study aims to combine the taxi global positioning system (GPS) data with a cumulative opportunity measure to calculate taxi accessibility in Beijing, China. As traffic conditions vary significantly over time and space, we select four typical time periods and introduce a grid-based method to divide the study area into grid cells. Both the GPS signals and opportunities that include the constant points of interest, total drop-offs, and dynamic drop-offs, are aggregated in these grid cells. The cumulative opportunity measure counts all reachable grid cells within the given travel time threshold, along with the corresponding opportunities. The results demonstrate that the accessibility varies in the four time periods, with better performance seen in the late-night hours. Although the spatial distributions of the three kinds of opportunities are different, these accessibilities show great similarity. In addition, the relative accessibilities of different measures are highly correlated. In general, grid cells with higher accessibilities in one time period are likely to also have higher accessibilities in other time periods. Moreover, the results suggest that taxi accessibility can be measured from its trajectory data only.

scholarly journals Image Enhancement using Generative Adversarial Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 3492-3495
Keyword(s):  
Large Scale ◽  
Error Function ◽  
Generative Adversarial Networks ◽  
Image Sharpness ◽  
Adversarial Networks ◽  
The Aesthetic ◽  
Artistic Image ◽  
The Given ◽  
Global Parameters

Mobile Photography has been brought to a significantly new level in the last several years. The quality of images taken by the compact lenses of a smartphone have now appreciably increased. Now, even some of the low- end phones of the market spectrum are able to take exceedingly good photos in suitable availability of lighting, due to the advancement in numerous software methods for processing the images post capture. However, despite these tools, these cam- eras still fall behind the aesthetic capabilities of their DSLR counterparts. In the quest to achieve high quality images through a smartphone camera, various image semantics are inadvertently ignored leading to a less artistic image quality than a pro- fessional camera. Although numerous techniques for manual as well as computerized image en- hancement tasks do exist, they are generally only focused on brightness or contrast and other such global parameters of the image and does not go on to improve the content or texture of the image and neither do they take the various semantics of the image into account. Moreover, they are usually based on a predetermined set of rules that never considers the actual device specifics that is capturing the image — the smartphone camera. For our enhancement, we have endeavored to use a unique deep learning technique to transform lower quality images from a smartphone camera into DSLRquality images. To enhance the image sharpness, we have used an error function that combines the three losses - the content, texture and color loss from the given image. By training on the large-scale DSLR Photo Enhancement Dataset, we have optimized the loss function using Generative Adversarial Networks. The end results produced after testing on a number of smartphone images yield enhanced quality images comparable to the DSLR images with an average SSIM score of approximately 0.95.

Professional dilemmas for caregivers in Turkish home care settings in Germany

2014 ◽  
Vol 1 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Jan Basche
Keyword(s):  
Healthcare Services ◽  
Insurance Companies ◽  
Or Education ◽  
Migrant Communities ◽  
Caregiver Relationship ◽  
International Nursing ◽  
Nursing Literature ◽  
Intercultural Care ◽  
The Given

While calling for culturally sensitive healthcare services in migrant communities, the international nursing literature on intercultural care predominantly describes nursing staff as lacking cultural competences and immigrant customers as lacking cleverness to navigate the labyrinths of national healthcare systems. Congruences in language, culture and religion in the customer-caregiver relationship can decisively improve the quality of care. However, they do not automatically guarantee smooth working processes in monocultural in-home settings. On the contrary, new problems occur here for Turkish caregivers which are unknown to the legions of native professionals who feel challenged by migrants and which go beyond differences such as age, sex, income or education. While no cultural or religious brokering is necessary between customers and personnel in the given context in Germany, new challenges arise when caregivers are expected to legally broker between customers and insurance companies or doctors. Conflicting expectations of customers and management as well as their own colliding social and professional roles put the caregivers in a quandary and must be competently managed.

Assessment of quality of life in patients with the effects of transient ischemic stroke

2016 ◽  
pp. 37-39
Author(s):  
A. Babirad
Keyword(s):  
Quality Of Life ◽  
Ischemic Stroke ◽  
Large Scale ◽  
Cerebrovascular Diseases ◽  
Control Measures ◽  
Sociological Research ◽  
Cerebrovascular Events ◽  
Sf 36 ◽  
Angioplasty And Stenting

Cerebrovascular diseases are a problem of the world today, and according to the forecast, the problem of the near future arises. The main risk factors for the development of ischemic disorders of the cerebral circulation include oblique and aging, arterial hypertension, smoking, diabetes mellitus and heart disease. An effective strategy for the prevention of cerebrovascular events is based on the implementation of large-scale risk control measures, including the use of antiagregant and anticoagulant therapy, invasive interventions such as atheromectomy, angioplasty and stenting. In this connection, the efforts of neurologists, cardiologists, angiosurgery, endocrinologists and other specialists are the basis for achieving an acceptable clinical outcome. A review of the SF-36 method for assessing the quality of life in patients with the effects of transient ischemic stroke is presented. The assessment of quality of life is recognized in world medical practice and research, an indicator that is also used to assess the quality of the health system and in general sociological research.

MODIFIKASI MODEL PEMBELAJARAN TEAM ASSISTED INDIVIDUALIZATION DENGAN STRATEGI PEMBELAJARAN TUGAS DAN PAKSA

2019 ◽  
Vol 2 ◽  
Author(s):  
Lea Christy Restu Kinasih ◽  
Dewi Fatimah ◽  
Veranica Julianti
Keyword(s):  
Cooperative Learning ◽  
Learning Strategies ◽  
Learning Process ◽  
Learning Model ◽  
Learning Models ◽  
Classroom Learning ◽  
Quality Of Learning ◽  
The Given

The selection and determination of appropriate learning strategies can improve the results to be obtained from the application of classroom learning models. This writing aims to discipline students to develop individual abilities of students to be more active in the learning process and improve the quality of learning. The learning process in Indonesia in general only uses conventional learning models that make students passive and undeveloped. In order for the quality of learning to increase, the Team Assisted Individualization learning model is combined with the task learning and forced strategies. The Team Assisted Individualization cooperative learning model is one of the cooperative learning models that combines learning individually and in groups. Meanwhile, task and forced learning strategies are strategies that focus on giving assignments that require students to complete them on time so that the learning process can run effectively. Students are required to do assignments according to the given deadline. This makes students become familiar with the tasks given by the teacher. Combining or modifying the learning model of the assisted individualization team with forced and forced learning strategies is expected to be able to make students more active, disciplined, independent, creative in learning and responsible for the tasks assigned. Therefore this method of incorporation is very necessary in the learning process and can be applied to improve the quality of learning in schools.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

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