Refinement Stage Research Articles

PENGEMBANGAN MODUL PEMBELAJARAN BAHASA INGGRI ESP PROGRAM STUDY TEKNIK LISTRIK JURUSAN ELEKTRO POLINEMA

Changes in the implementation of English learning from semester to semester is a separate problem considering that the content of the material also changes in its emphasis pedagogically. Therefore, research on the development of teaching materials is very relevant to the conditions that occur so that the output later can provide a real contribution to the teaching and learning process. The purpose of this research is to develop English teaching materials that suit the needs of students as well as input from teaching staff as well as available references and documents. This development is very urgent due to changes in the semester of the implementation of the teaching and learning process. Thus, this product is very helpful not only for lecturers, but also students and consequently the availability of a teaching material product. Researchers adapted the Research and Development (R&D) model from Borg and Gall which consisted of 6 stages, namely: (1) initial needs analysis, (2) module development design, (3) creating learning modules, (4) assessment/feasibility testing stage, (5) product trials and (6) learning module refinement stages that produce the final product of development. In conclusion, the availability of teaching modules that can be used in the teaching and learning process.

Physical and Mathematical Modeling and Computer Analysis of Radial Impellers for Chemical and Power Engineering, taking into account Ecology

This article is devoted to mathematical models for the study of natural frequencies, static stresses of radial impellers with cover disk for chemical and power engineering, taking into account ecology and their modeling by the finite element method. General form and mathematical model based on the finite element method of a radial impeller with a covering disc from Schiele & Co (Germany). For a numerical study, this wheel is accepted in the technical field in different computing software systems. For example, commercial systems such as ANSYS WORKBENCH, SOLIDWORKS and initiative software packages (BLADIS+) [1] for solving problems of statics, vibrations and calculating the resource of different technical systems. Calculations of natural frequencies and static stresses of a radial impeller of 10 blades for chemical engineering are presented. The results obtained in this work can be safely assumed the study of changes in natural frequencies and static stresses of turbomachines are relevant at the design and refinement stages. These computer calculations make it possible to significantly reduce the volume of expensive experimental studies that pollute the environment, reduce design time and improve ecology.

Development of depth map from stereo images using sum of absolute differences and edge filters

This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.

Go hand in hand: Showcasing lecturers’ online collaborative teaching practices

Collaborative teaching has been performed by special educators in K-12 schools for decades, and myriads of studies have witnessed its promising impacts on both students and teachers. Notwithstanding its vogue, only a few studies have primarily dealt with its implementation in higher education. Furthermore, empirical attempts accentuating lecturers’ stepwise practices during collaborative teaching implementation remain under-explored, especially when the practices are undertaken online. This study, henceforth, intends to fill the void. In total, the experiences of two collaborating lecturers, leading Teaching English for Foreign Language (TEFL) and Technology-Enhanced Language Learning (TELL) course, were scrutinized. Their practices were documented for 20 weeks and their views were also assembled to grasp how they perceived their online co-teaching practices. In addition, the views from six representative students were assembled at the end of the term through focused group discussion. Through Joint Practice Development (JPD) framework, lecturers’ online collaborative teaching practices were captured and conceptually grouped into five developed stages – collaborative planning, collaborative instruction, peer feedback and evaluation, follow up, and refinement stage. Admitting lecturers’ online co-teaching approach, students contended that its implementation was opportune in bridging their needs of pedagogical knowledge and related technology, facilitating learning efficiency, and fostering their collaboration awareness. Albeit its effectiveness, students are also concerned with collaboration appearance clarity, learning proficiency, and autonomy. Thus far, this teaching approach is worthy of continuing in the higher education, with clear format to adopt.

MC-Calib: A generic and robust calibration toolbox for multi-camera systems

In this paper, we present MC-Calib, a novel and robust toolbox dedicated to the calibration of complex synchronized multi-camera systems using an arbitrary number of fiducial marker-based patterns. Calibration results are obtained via successive stages of refinement to reliably estimate both the poses of the calibration boards and cameras in the system. Our method is not constrained by the number of cameras, their overlapping field-of-view (FoV), or the number of calibration patterns used. Moreover, neither prior information about the camera system nor the positions of the checkerboards are required. As a result, minimal user interaction is needed to achieve an accurate and robust calibration which makes this toolbox accessible even with limited computer vision expertise. In this work, we put a strong emphasis on the versatility and the robustness of our technique. Specifically, the hierarchical nature of our strategy allows to reliably calibrate complex vision systems even under the presence of noisy measurements. Additionally, we propose a new strategy for best-suited image selection and initial parameters estimation dedicated to non-overlapping FoV cameras. Finally, our calibration toolbox is compatible with both, perspective and fisheye cameras. Our solution has been validated on a large number of real and synthetic sequences including monocular, stereo, multiple overlapping cameras, non-overlapping cameras, and converging camera systems. Project page: https://github.com/rameau-fr/MC-Calib

Inventory Application Design for Optimizing Business Processes for MSMEs Nyong Group Yogyakarta

Nyong Group is one of the Micro, Small and Medium Enterprises (MSME) located in Sleman, Yogyakarta. Nyong group has several business units, namely nyong shoes and care Yogya, shoes and care Cilacap, nyong donuts, and nyong stalls (food, snacks and chili sauce). The problems by IbM partners include; First, (1) There is no Point of Sales application that uses inventory to meet the needs of the production process or is sold again, (2) Not yet has this Inventory covering raw materials, in-process goods, and goods so (finished goods). The output targets in this program are (1) the application of point of sales applications for recording sales transactions, (2) Adding an inventory application to the created point of sales applications. The service program is carried out in 2 (two) forms; First, (1) design of a point of sales application for Nyong Group's sales transactions. Second, making an inventory application to be able to determine whether to record goods based on arrival time or the amount of stock you can determine to record goods based on arrival time or stock amount. The implementation of this program is designed in four stages of activity, namely: (1) Coordination and preparation stage (2) Design stage (3) Science and technology implementation stage (4) Evaluation and refinement stage.

Two-Stage Edge Reuse Network for Salient Object Detection of Strip Steel Surface Defects

Recently, researchers have paid more attention to salient object detection of strip steel surface defects, but there are lots of difficulties, such as a mass of noise, blurry defect boundaries, complex backgrounds, and various types of defects. Existing image salient object detection methods fail to overcome the above challenging scenes. To address them, in this paper, we propose a novel Two-Stage Edge Reuse Network (TSERNet), which consists of two stages, <i>i.e</i>., prediction and refinement. In the first prediction stage, we construct a primary net based on the encoder-decoder architecture. The encoder not only extracts multi-scale features but also generates an edge map. In the decoder, we propose a novel Edge-aware Foreground-Background Integration (EFBI) module to distinguish foreground and background through edge features and reverse attention mechanism, and exploit the decoder to generate an initial saliency map. In the second refinement stage, we construct a sub-net based on the same architecture as that of the first stage. Its encoder extracts features from the initial saliency map, and its decoder deploys the Edge-aware Refinement Module (ERM), which reuses the edge map generated from the first stage, to enhance these features for purifying the initial saliency map, resultting in the final saliency map. Comprehensive experiments on the public dataset show that our proposed TSERNet is consistently superior to 22 relevant state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/TSERNet.

Unpaired Image Dehazing With Physical-Guided Restoration and Depth-Guided Refinement

Most existing single image dehazing methods aim to learn supervised models from paired synthetic data, which often limits their generalization ability in real-world applications. Besides, due to ignoring the merits of physical model in visibility restoration and the properties of depth features in clarity improvement, we observe that only relying on the transfer capability of unpaired adversarial learning will suffer from low-quality recovery. To this end, we develop an effective end-to-end unpaired image dehazing method by integrating a physical-guided restoration stage and a depth-guided refinement stage in a GAN framework, named as PDR-GAN. Specifically, the dark channel prior is embedded in the restoration stage to provide constraints for the network, and the preliminary dehazed image is first generated. For the refinement stage, we excavate the potential relationship between the depth and transmission map to better refine the results of the previous stage and further recover the distant area details. Our framework benefits from the stage-wise learning strategy of model-based restoration and feature-based reconstruction, which is especially helpful for image dehazing when paired data is not available. Experimental results show that our method is superior to the current unpaired dehazing approaches in terms of both quantitative and qualitative.

Novel multilevel particle swarm optimization algorithm for graph partitioning

Graph partitioning is crucial step in resolving real time applications in the field of image analysis, smart city designing, wireless communications, data analysis etc. Though considerable research has been done for getting an optimal partitioning of graphs still it demands enhancement for diverse application problems. Hybrid graph partitioning approaches are promising and possess ability to partition graphs with large number of vertices. In our research we have developed multilevel particle swarm optimization algorithm for graph partitioning. Size of the graph is reduced by heavy edge matching algorithm and then greedy graph growing partitioning is used to divide the graph. Discrete particle swarm optimization used at the most important stage of refinement. Performance is evaluated by using Walshaw’s Benchmark graphs and from analysis it has been observed that proposed algorithm generates optimal partitioning with reduced cut values and computational cost.

Prior-Induced Information Alignment for Image Matting

Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image. However, most existing deep learning-based methods still suffer from the coarse-grained details. In general, these algorithms are incapable of felicitously distinguishing the degree of exploration between deterministic domains (certain FG and BG pixels) and undetermined domains (uncertain in-between pixels), or inevitably lose information in the continuous sampling process, leading to a sub-optimal result. In this paper, we propose a novel network named Prior-Induced Information Alignment Matting Network (PIIAMatting), which can efficiently model the distinction of pixel-wise response maps and the correlation of layer-wise feature maps. It mainly consists of a Dynamic Gaussian Modulation mechanism (DGM) and an Information Alignment strategy (IA). Specifically, the DGM can dynamically acquire a pixel-wise domain response map learned from the prior distribution. The response map can present the relationship between the opacity variation and the convergence process during training. On the other hand, the IA comprises an Information Match Module (IMM) and an Information Aggregation Module (IAM), jointly scheduled to match and aggregate the adjacent layer-wise features adaptively. Besides, we also develop a Multi-Scale Refinement (MSR) module to integrate multi-scale receptive field information at the refinement stage to recover the fluctuating appearance details. Extensive quantitative and qualitative evaluations demonstrate that the proposed PIIAMatting performs favourably against state-of-the-art image matting methods on the Alphamatting.com, Composition-1K and Distinctions-646 dataset.

An Efficient Cascaded Model for Ship Segmentation in Aerial Images

In this work, we propose a new method for real-time ship segmentation during maritime surveillance missions using aircrafts with onboard video cameras. We propose a cascade model with a detection stage followed by a segmentation stage. The detection stage selects candidate regions (bounding boxes) likely to contain ships. These bounding boxes are passed to the segmentation stage, where the ship segmentation mask is then obtained. By focusing the segmentation effort only in the image regions recommended by the previous detection stage, it is possible to improve the overall image processing time and, simultaneously, to obtain a better segmentation score than the one obtained by monolithic segmentation models that are trained in an end-to-end way. Additionally, we test the viability of using a Conditional Random Field model as final boundary refinement stage: although such model can improve the segmentation results when a full segmentation approach is used, our experiments did not show any significant improvements when using our proposed cascade model. We trained the detection and segmentation models with aerial ship images from publicly available maritime datasets. We tested the cascade model on the Airbus ship detection challenge, showing real-time performance and accurate maritime ship segmentation, comparable to state-of-the-art results.

A Dual-Branch Self-Boosting Framework for Self-Supervised 3D Hand Pose Estimation.

Although 3D hand pose estimation has made significant progress in recent years with the development of the deep neural network, most learning-based methods require a large amount of labeled data that is time-consuming to collect. In this paper, we propose a dual-branch self-boosting framework for self-supervised 3D hand pose estimation from depth images. First, we adopt a simple yet effective image-to-image translation technology to generate realistic depth images from synthetic data for network pre-training. Second, we propose a dual-branch network to perform 3D hand model estimation and pixel-wise pose estimation in a decoupled way. Through a part-aware model-fitting loss, the network can be updated according to the fine-grained differences between the hand model and the unlabeled real image. Through an inter-branch loss, the two complementary branches can boost each other continuously during self-supervised learning. Furthermore, we adopt a refinement stage to better utilize the prior structure information in the estimated hand model for a more accurate and robust estimation. Our method outperforms previous self-supervised methods by a large margin without using paired multi-view images and achieves comparable results to strongly supervised methods. Besides, by adopting our regenerated pose annotations, the performance of the skeleton-based gesture recognition is significantly improved.

Upaya Penanganan Khusus Narapidana Lansia sebagai Kelompok Rentan

LAPAS or Correctional Institution under the auspices of the Directorate General of Corrections is the final place of the criminal justice system process that fosters and integrates the social of convicts, so that when returning to society they are able to live a normal life. Elderly (according to Law No. 13 of 1998: Elderly) is a person whose age reaches the age of 60 years and above. In its efforts, Correctional Services interpret this understanding by regulating the rights of inmates contained in Law no. 12 of 1995 article 14 paragraph 1.The increasing age and declining body condition of the elderly inmates need special treatment both in health, treatment, and public services. Elderly inmates as much as possible are treated as people usually are, it's just that the place and application are different. This is for the implementation of part of Human Rights (HAM) as a national instrument. This special handling effort involves other stakeholders be it medical or health workers and the government to support the infrastructure needed. The elderly as one of the vulnerable groups become important objects in terms of how decent handlers are so that they feel cared for in their twilight years. Although until now still in the process and stages of refinement of special handling for them. The fact is still not optimal and this study illustrates how well the effort is.

An Electronic Patient-Reported Outcome Mobile App for Data Collection in Type A Hemophilia: Design and Usability Study.

BackgroundThere is currently limited evidence on the level and intensity of physical activity in individuals with hemophilia A. Mobile technologies can offer a rigorous and reliable alternative to support data collection processes but they are often associated with poor user retention. The lack of longitudinal continuity in their use can be partly attributed to the insufficient consideration of stakeholder inputs in the development process of mobile apps. Several user-centered models have been proposed to guarantee that a thorough knowledge of the end user needs is considered in the development process of mobile apps.ObjectiveThe aim of this study is to design and validate an electronic patient-reported outcome mobile app that requires sustained active input by individuals during POWER, an observational study that aims at evaluating the relationship between physical activity levels and bleeding in patients with hemophilia A.MethodsWe adopted a user-centered design and engaged several stakeholders in the development and usability testing of this mobile app. During the concept generation and ideation phase, we organized a need-assessment focus group (FG) with patient representatives to elicit specific design requirements for the end users. We then conducted 2 exploratory FGs to seek additional inputs for the app’s improvement and 2 confirmatory FGs to validate the app and test its usability in the field through the mobile health app usability questionnaire.ResultsThe findings from the thematic analysis of the need-assessment FG revealed that there was a demand for sense making, for simplification of app functionalities, for maximizing integration, and for minimizing the feeling of external control. Participants involved in the later stages of the design refinement contributed to improving the design further by upgrading the app’s layout and making the experience with the app more efficient through functions such as chatbots and visual feedback on the number of hours a wearable device had been worn, to ensure that the observed data were actually registered. The end users rated the app highly during the quantitative assessment, with an average mobile health app usability questionnaire score of 5.32 (SD 0.66; range 4.44-6.23) and 6.20 (SD 0.43; range 5.72-6.88) out of 7 in the 2 iterative usability testing cycles.ConclusionsThe results of the usability test indicated a high, growing satisfaction with the electronic patient-reported outcome app. The adoption of a thorough user-centered design process using several types of FGs helped maximize the likelihood of sustained retention of the app’s users and made it fit for data collection of relevant outcomes in the observational POWER study. The continuous use of the app and the actual level of engagement will be evaluated during the ongoing trial.Trial RegistrationClinicalTrials.gov NCT04165135; https://clinicaltrials.gov/ct2/show/NCT04165135

Data-driven placemaking: Public space canopy design through multi-objective optimisation considering shading, structural and social performance

In the context of ongoing densification of cities and aging urban populations, public spaces are a crucial infrastructure to support the physical and mental wellbeing of urban residents. The design of public space furniture elements is often standardised, and not considered in relation to environmental conditions and mechanisms of social interaction. This article presents a digital workflow to generate site-specific designs for shaded public seating, considering the relationships of local public places to their surroundings. A strategy for customised and site-specific design is developed through the use of multiple software tools, employing evolutionary algorithms and multi-objective optimisation. The method is applied to a small public space canopy prototype installed within a public housing estate in Hong Kong, incorporating additional criteria to achieve a low-cost and light-weight structure. Through multiple stages of refinement and optimisation, a material, structural and social performance-driven outcome was achieved that creates a shaded space for public seating, people watching and social interaction. As part of a larger research agenda exploring architectural form-finding and environmental psychology, the project represents potential new applications in the emerging field of socially driven computational design.

Развитие на механизираното и автоматизираното администриране на местните данъци и такси в България

The article presents in a historical plan the results from an investigation of the Informational systems for mechanized and automated admission of local taxes and fees in Bulgaria. Presented is the change of the technical, software and communicational means used in the different stages of development and refinement of the Informational systems for local taxes and fees. A period of 50 years is reviewed starting from 1970 until today. The historical development is bound with the development of regulatory base and information technology.

SYNTHESIS AND CHARACTERIZATION OF GRAPHENE SHEETS DECORATED WITH CARBON BLACK BY DIRECT PYROLYSIS OF A MOLASSES–CARBON BLACK MIXTURE AS A POTENTIAL VERSATILE FILLER FOR RUBBER

ABSTRACT A mixture of molasses and carbon black was pyrolyzed in an inert atmosphere, which resulted in graphene of high quality, comprising of three to four layers on average with carbon black particles distributed over the graphene sheets. Molasses is the viscous dark colored slurry which is obtained at the last stage of refinement of sugar from sugarcane, in which sucrose is present as the major chemical component. Carbon black was also used as a substrate for the growth of graphene. The carbon black decorated graphene hybrid nanostructure was thoroughly characterized by different techniques and improved the failure properties of cured styrene butadiene rubber when incorporated into the rubber matrix. Bound rubber content increased by 50% with the hybrid filler compared to carbon black at 45 phr filler loading. The hybrid filler displayed 63% enhancement in the tensile strength at 2 phr filler loading and 86% increase at 45 phr filler loading, compared to the carbon black filled rubbers at the same loading. The vulcanizates containing the novel filler also exhibited improved abrasion resistance, ice traction, and wet traction and decreased rolling resistance compared to the carbon black filled systems. The new filler exhibited fair value of specific capacitance, 127 F/g when incorporated in an uncured rubber latex matrix. The hybrid filler synthesized, characterized, and studied thus can be classified as a versatile smart filler for rubber nanocomposites with a range of functionalities from mechanical reinforcement to electrochemical properties.

Innovation in Aesthetic Plastic Surgery: An International Collaborative Effort.

An overview of the importance of international contributions to the advancement of aesthetic plastic surgery is presented, with emphasis on why, how and where in our specialty does innovation take place.The reason for innovating is clear: to improve on the quality of care patients receive today, through research and education. In order to do so, innovators must be either visionaries, advancers or popularizers of a new technique and they must have an exhaustive knowledge ina specific field, an intrinsic motivation to pursue work in that field and a capacity to solve problems in an original way. The four areas of advancement in aesthetic plastic surgery are outlined, including better aesthetic results, better and faster patient recovery, increased patient safety and better surgeon experience. The three stages of development of our specialty are described as well, beginning with the creationstage in the late 1870s, in which the correction of evidently altered structures was undertaken; the establishment stage, which beganinthe late1960s with the study and correction of surgical stigmata, the detailed analysis of surface aesthetics and later with the rise of minimally invasive procedures; and finally,the refinement stage thatwe have witnessed overthe past few years, in which natural movement and feel are also considered essential in order to achieve successful aesthetic outcomes. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine Ratings, please refer to Table of Contents or online Instructions to Authors www.springer.com/00266 .

Culturally Relevant Health Education: A Foundation for Building Cultural Competence of Health Professionals

Background: Professionals educating ethnic minority populations should employ a cultural focus during development, training, refinement and implementation stages of an intervention. Purpose: This manuscript posits that the skill of developing a culturally relevant curriculum supports the increase of cultural competence proficiency of professionals, while promoting health equity. Methods: A community-based participatory research-trained staff, recruited 378 families with 2 to 8-year-old children. Eight intergenerational focus groups were conducted at neighborhood facilities. Graduate fellows conducted reviews of literature and health directives to conceptualize the curriculum. Spanish-speaking students and promotoras (community health workers) having participants’ confidence, presented healthy lifestyle information and taught practical skills to each group of 12-16 low-income Latino families from Long Beach, CA. With attention to participants’ preferred language, educational level, cultural beliefs, practices and food preferences, the intervention demonstrates a culturally relevant curriculum. Hands-on activities and motivational interviewing questions enriched the 4-hour intervention delivered in Spanish. Results: Graduate fellows’ cultural competence increased. More than 97% of participants reported sessions beneficial to their families’ health with 84% attending all sessions/assessments. Participants found the content easy to understand and helpful to eat healthier and be more active. Conclusion: Sanos y Fuertes is a model for developing a culturally relevant, family-based healthy lifestyle educational curriculum and building culturally competent health professionals.

TSRRNet: two-stage reflection removal network with reflective guidance.

Due to the shortage of paired images, the training of reflection removal networks relies heavily on synthesized samples, for which the ground truths of transmission and reflection are both known. But most existing CNN-based models cannot fully utilize the reflection information, which may cause performance limitations. In this paper, our goal is to design a novel, to the best of our knowledge, network that can take the reflection layer to refine the transmission layer. To this end, we propose a two-stage generative-adversarial-network-based network, where the first stage is used to obtain the coarse estimation of transmission and reflection, and the second stage is used to achieve the refinement. In addition, instead of just applying two penalty terms on the two coarse predictions in previous works, we consider the coarse reflection as a soft mask overlapped on the transmission and apply the recently proposed gated convolution into the second stage for further refinement. The network is trained with an adversarial frame using WGAN. The experimental results with benchmark datasets indicate that our method outperforms several state-of-the-art networks.