How Do Designers Deal With Uncertainty

Uncertainty touches most aspects of life and cannot be avoided, anybody is frequently presented with situations wherein a decision must be made when he/she is uncertain of exactly how to proceed. Narrow down into Information Systems (IS) field, uncertainty could be regarded as a basic but difficult problem that every HCI designer need to deal with within their design process. The purpose of this thesis is to find out how do human-computer interaction (HCI) practitioners deal with the uncertainty in their daily work. Based on this purpose, we assume that design approaches could be the methods for the designers to deal with uncertainty. There is however very few existing research on how to deal with uncertainty. In this study, we firstly categorized the uncertainty into a logical taxonomy, also ranked four design approaches by the extent of user involvement. We interviewed five HCI practitioners in different organizations that are or were working as designers. We found that most uncertainties are resulted from their customers, which can also be the most difficult to handle by them. In order to solve uncertainty, the designers need to make a good communication with others in specific situation, and some of them also proposed other practical solutions, such as “Role Play” and “Instinct Follower”. Additionally, the designers all proposed that the relationship between uncertainty and design approaches can be weak or inexistent. Interestingly, modest user involvement can be a helper for designers to solve or avoid uncertainty in the design process

Grouping Boundary Proposals for Fast Interactive Image Segmentation

Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a segmentation strategy cannot take into account the connectivity of the image edge features, increasing the risk of shortcut problem, especially in the case of complicated scenario. In this work, we introduce a new image segmentation model based on the minimal geodesic framework in conjunction with an adaptive cut-based circular optimal path computation scheme and a graph-based boundary proposals grouping scheme. Specifically, the adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once. The boundary proposals are comprised of precomputed image edge segments, providing the connectivity information for our segmentation model. These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them. Experimental results show that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches

Real Time Object Pose Estimation by Two-Step Crossing Line Fitting

Abstract: Real-time industrial vision system for object detection and pose estimation is a promising area yet posing a challenge for high processing efficiency. This paper presents a fast object detection and pose estimation method which captures the specific but common visual pattern as contained in many objects -the two-line cross. A two-step grid based scheme is designed, being able to fast detect the crossing line on the objects and thus identifying the object location and pose. Superior efficiency -4 milliseconds per frame on a laptop with 2.53 HZ is reported for real image data, without any parallelization or hardware acceleration. Our method outperforms the state-of-the-art line detection method significantly, and has been applied in embedded inspection platform for pipeline object pose estimation

Geodesic Models with Convexity Shape Prior

The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit image features in conjunction with geometric regularization terms, such as Euclidean curve length or curvature-penalized length, for computing geodesic curves. In this paper, we take into account a more complicated problem: finding curvature-penalized geodesic paths with a convexity shape prior. We establish new geodesic models relying on the strategy of orientation-lifting, by which a planar curve can be mapped to an high-dimensional orientation-dependent space. The convexity shape prior serves as a constraint for the construction of local geodesic metrics encoding a particular curvature constraint. Then the geodesic distances and the corresponding closed geodesic paths in the orientation-lifted space can be efficiently computed through state-of-the-art Hamiltonian fast marching method. In addition, we apply the proposed geodesic models to the active contours, leading to efficient interactive image segmentation algorithms that preserve the advantages of convexity shape prior and curvature penalization.Comment: This paper has been accepted by TPAM

Double layer back propagation neural network based on restricted Boltzmann machines for forecasting daily particulate matter 2.5

Particulate matter 2.5 (PM₂.₅) pollution is an actual problem in the modern world and forecasting of the daily concentration of PM₂.₅ is a challenging task for researchers. In this study, a novel neural network model that effec­tively forecasts daily PM₂.₅ in Hangzhou city was developed in the form of a restricted Boltzmann machines double layer back propagation neural net­work model (RBM-DL-BPNN). Air quality index, the air pollutants, e.g., particulate matter 10 (PM10), PM₂.₅, SO₂, CO, NO₂, O₃, and meteorological parameters (temperature, dew point, humidity, pressure, wind speed, and precipitation) of Hangzhou city were used in this study to train and test three models: RBM-DL-BPNN, double layer back propagation neural network (DL-BPNN), and back propagation neural network (BPNN). The results of experiments and analyses performed indicate that RBM-DL-BPNN has a smaller mean absolute percent error (MAPE), smaller overall daily absolute percentage errors, and more results in terms of absolute percentage error within the range 0-50 % than DL-BPNN and BPNN.Загрязнение ультрадисперсными частицами (УДЧ) класса PM₂.₅ является актуальной проб-лемой в современном мире. Прогнозирование их ежедневной концентрации является сложной задачей для исследователей. Разработана новая модель в виде ограниченной машины Больцмана обратной связи с удвоенным слоем (RBM-DL-BPNN). Эффективность предложенной модели показана на примере прогнозирования концентрации PM₂.₅ в городе Ханчжоу. Показатели качества воздуха, его загрязнения (PM10, УДЧ PM₂.₅, SO₂, CO, NO₂, O₃), метеорологические параметры (сред-несуточная температура, точка росы, влажность, атмосферное давление, скорость ветра и количество осадков) в Ханчжоу использованы в работе для обучения и тестирования трех моделей: RBM-DL-BPNN, нейронной сети с обратной связью с двойным слоем (DL-BPNN) и нейронной сети обратного распространения (BPNN). Результаты проведенных исследований показали, что относительная погрешность результатов использования RBM-DL-BPNN является наименьшей среди изученных нейронных сетей, которая заключается в том, что количество значений этой погрешности в диапазоне 0–50 % для RBM-DL-BPNN значительно больше, чем в случаях DL-BPNN и BPNN.Забруднення ультрадисперсними частинками (УДЧ) класу PM₂.₅ є актуальною проблемою у сучасному світі. Прогнозування їхньої щоденної концентрації є складним завданням для дослідників. Розроблено нову модель у вигляді обмеженої машини Больцмана зворотного зв’язку з подвоєним шаром (RBM-DL-BPNN). Ефективність запропонованої моделі показано на прикладі прогнозування концентрації УДЧ РМ₂,₅ у місті Ханчжоу. Показники якості повітря, його забруднення (РМ10, РМ₂,₅, SO₂, CO, NO₂, O₃), метеорологічні параметри (середньодобова температура, точка ро-си, вологість, атмосферний тиск, швидкість вітру та кількість опадів) у Ханчжоу використано в роботі для навчання та тестування трьох моделей: RBM-DL-BPNN, нейронної мережі зі зворотним зв’язком з подвійним шаром (DL-BPNN) і нейронної мережі зворотного поширення (BPNN). Результати проведених досліджень показали, що відносна похибка результатів використання RBM-DL-BPNN є найменшою серед вивчених нейронних мереж, яке полягає в тому, що кількість значень цієї похибки в діапазоні 0–50 % для RBM-DL-BPNN значно більше, ніж для випадків DL-BPNN і BPNN

A Financial Embedded Vector Model and Its Applications to Time Series Forecasting

Inspired by the embedding representation in Natural Language Processing (NLP), we develop a financial embedded vector representation model to abstract the temporal characteristics of financial time series. Original financial features are discretized firstly, and then each set of discretized features is considered as a “word” of NLP, while the whole financial time series corresponds to the “sentence” or “paragraph”. Therefore the embedded vector models in NLP could be applied to the financial time series. To test the proposed model, we use RBF neural networks as regression model to predict financial series by comparing the financial embedding vectors as input with the original features. Numerical results show that the prediction accuracy of the test data is improved for about 4-6 orders of magnitude, meaning that the financial embedded vector has a strong generalization ability

A GNN based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time-consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN) based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds