Entire Object Research Articles

Tinjauan Hukum Progresif terhadap Kesepakatan Partial Settlement

The The partial settlement provision is a new provision in Supreme Court Regulation (PERMA) No 1 of 2016 which has not been regulated in previous regulations, which is an interesting discussion that must be examined from a progressive legal perspective. The aim of this research is to test progressive legal theory regarding the renewal of partial settlement provisions in Supreme Court Regulation (PERMA) Number 1 of 2016. The research method in this research is normative juridical research with a conceptual approach using library data collection techniques or library research. The results of this research show that the update to Perma Number 1 of 2016 concerning partial settlement provisions has fulfilled the basic ideas of progressive law. Partial Settlement Provisions for a portion of the entire object of a case or lawsuit can create ideal law. The parties can resolve the legal consequences that arise peacefully, after the main lawsuit and will reach a happy ending. Partial settlement provisions will realize the function of mediation, namely as a peaceful settlement and provide ample space for the parties to reach a wise and fair settlement.

ShapeBench: A new approach to benchmarking local 3D shape descriptors

The ShapeBench evaluation methodology is proposed as an extension to the popular Area Under Precision-Recall Curve (PRC/AUC) for measuring the matching performance of local 3D shape descriptors. It is observed that the PRC inadequately accounts for other similar surfaces in the same or different objects when determining whether a candidate match is a true positive. The novel Descriptor Distance Index (DDI) metric is introduced to address this limitation. In contrast to previous evaluation methodologies, which identify entire objects in a given scene, the DDI metric measures descriptor performance by analysing point-to-point distances. The ShapeBench methodology is also more scalable than previous approaches, by using procedural generation. The benchmark is used to evaluate both old and new descriptors. The results produced by the implementation of the benchmark are fully replicable, and are made publicly available.

Reverse cross-refinement network for camouflaged object detection

Due to the high intrinsic similarity between camouflaged objects and the background, camouflaged objects often exhibit blurred boundaries, making it challenging to distinguish the boundaries of objects. Existing methods still focus on the overall regional accuracy but not on the boundary quality and are not competent to identify camouflaged objects from the background in complex scenarios. Thus, we propose a novel reverse cross-refinement network called RCR-Net. Specifically, we design a diverse feature enhancement module that simulates the correspondingly expanded receptive fields of the human visual system by using convolutional kernels with different dilation rates in parallel. Also, the boundary attention module is used to reduce the noise of the bottom features. Moreover, a multi-scale feature aggregation module is proposed to transmit the diverse features from pixel-level camouflaged edges to the entire camouflaged object region in a coarse-to-fine manner, which consists of reverse guidance, group guidance, and position guidance. Reverse guidance mines complementary regions and details by erasing already estimated object regions. Group guidance and position guidance integrate different features through simple and effective splitting and connecting operations. Extensive experiments show that RCR-Net outperforms the existing 18 state-of-the-art methods on four widely-used COD datasets. Especially, compared with the existing top-1 model HitNet, RCR-Net significantly improves the performance by ∼16.4% (Mean Absolute Error) on the CAMO dataset, showing that RCR-Net could accurately detect camouflaged objects.

Multi-scale feature correspondence and pseudo label retraining strategy for weakly supervised semantic segmentation

Recently, the performance of semantic segmentation using weakly supervised learning has significantly improved. Weakly supervised semantic segmentation (WSSS) that uses only image-level labels has received widespread attention, it employs Class Activation Maps (CAM) to generate pseudo labels. Compared to traditional use of pixel-level labels, this technique greatly reduces annotation costs by utilizing simpler and more readily available image-level annotations. Besides, due to the local perceptual ability of Convolutional Neural Networks (CNN), the generated CAM cannot activate the entire object area. Researchers have found that this CNN limitation can be compensated for by using Vision Transformer (ViT). However, ViT also introduces an over-smoothing problem. Recent research has made good progress in solving this issue, but when discussing CAM and its related segmentation predictions, it is easy to overlook their intrinsic information and the interrelationships between them. In this paper, we propose a Multi-Scale Feature Correspondence (MSFC) method. Our MSFC can obtain the feature correspondence of CAM and segmentation predictions at different scales, re-extract useful semantic information from them, enhancing the network's learning of feature information and improving the quality of CAM. Moreover, to further improve the segmentation precision, we design a Pseudo Label Retraining Strategy (PLRS). This strategy refines the accuracy in local regions, elevates the quality of pseudo labels, and aims to enhance segmentation precision. Experimental results on the PASCAL VOC 2012 and MS COCO 2014 datasets show that our method achieves impressive performance among end-to-end WSSS methods.

Feature-Driven Next View Planning for Cutting Path Generation in Robotic Metal Scrap Recycling

Metal recycling in scrapyards, where workers cut decommissioned structures using gas torches, is labor-intensive, difficult, and dangerous. As global metal scrap recycling demands are rising, robotics and automation technologies could play a significant role to address this demand. However, the unstructured nature of the scrap cutting problem—due to highly variable object shapes and environments—poses significant challenges to integrate robotic solutions. We propose a novel collaborative workflow for robotic metal cutting that combines worker expertise with robot autonomy. In this workflow, the skilled worker studies the scene, determines an appropriate cutting reference, and marks it on the object with spray paint. The robot, then, autonomously explores the surface of the object for identifying and reconstructing the drawn reference, converts it to a cutting trajectory, and finally executes the cut. This paper focuses on the surface exploration and cutting reference reconstruction tasks, which require appropriate next view planning (NVP) algorithms. We devise three NVP algorithms enabling the robot to explore and extract desired features from the scene, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the drawn reference, without requiring any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> object model. Contrasting with global or feature-agnostic NVP algorithms, our approaches guide the robot via desired local features to increase the efficiency of the exploration. We evaluate our NVP algorithms against six categories of objects both in simulation and in physical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the need of extracting a desired cutting reference determined and drawn on the object by scrap yard workers. From the robot’s perspective, it must explore and reconstruct the drawing, starting from an unknown scene containing an unknown object featuring an unknown drawing. We assume that an RGB-D camera is attached to the tool-tip of the robot, and the color of the drawn path is significantly different from the object’s color. The goal of the robotic system is to explore the object surface to uncover the drawn path entirely without colliding with the object. The exploration algorithm must overcome complex object shapes and must be fast enough for practical use in scrap yards. This means conventional exploration (active vision) techniques are insufficient since they focus on exploring the entirety of the object, which is unnecessary for our task and is time-consuming. Our methods exploit the drawing information to guide the exploration for quickly determining a suitable viewpoint, which results in an efficient extraction of the entire cutting reference, without needing to explore the entire object’s surface. Our algorithms are robust against adversarial features such as discontinuous, non-smooth, or self-occluded object surfaces. Our feature-driven strategies are not limited to robotic scrap cutting as they are applicable to any viewpoint planning problem requiring high performance while extracting the local features in the scene.

Evaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis

Image segmentation method is extensively used in the fields of computer vision, machine learning, and artificial intelligence. The task of segmentation is to distinguish objects in images either by their boundaries or as entire objects from the entire image. Image segmentation methods are implemented as instance, semantic, and panoptic segmentation. In this article, the panoptic segmentation method, seen as an advanced stage of instance and semantic segmentation, has been applied to three datasets and compared with the instance segmentation method. Experimental results are presented visually. Numerical results have been analyzed with the Panoptic Quality (PQ) and Semantic Quality (SQ) metrics. It has been observed that the segmentation outcome was best for the CityScapes dataset for panoptic segmentation.

Steel ribbed dome structural performance with different node connections and bracing system

The conventional design of steel structure objects relies on a first-order elastic analysis, where the entire object is treated as a set of individual structural elements requiring time-consuming, semi-empirical design calculations. Such an approach leads to inefficient design time and excessive material consumption and may additionally result in designing on the verge of structural safety. The AEC sector's technological and digitization advancement process forces designers to use advanced design methods. Hence, it is necessary to indicate the benefits of using effective optimization. The paper presents a comparative analysis of steel domes using two design approaches: traditional first-order analysis and an advanced second-order analysis. The latter method considers the influence of structural deformation on the magnitude of internal forces. Eight models were developed, varying in terms of the connection's stiffness. The work results identify the differences between the two selected design approaches and present opportunities for further structural performance of steel structures.

Pengembangan Potensi Ekonomi dan Identifikasi Sektor Unggulan dalam Pembangunan Daerah di Kabupaten Magetan dan Kabupaten Ngawi

This study aims to identify the leading sectors in Magetan Regency and Ngawi Regency, and evaluate whether there are differences between the leading sectors in the two districts. In addition, this study also aims to understand the role of leading sectors in developing economic potential in the two districts. This research is a descriptive research that uses a quantitative approach to analyze data. The population of this study is economic sectors in Magetan Regency, Ngawi Regency, and East Java Province in the 2017-2022 period. There is no use of samples in this study because the entire object of research can be reached. Statistical analysis includes LQ, Shift Share, and Klassen typology. The results showed that Magetan and Ngawi districts were formed based on Law 12/1950. The area of Magetan is 688.84 km2 with a population from 629,609 people (2017) to 678,343 people (2022). Ngawi 1,298.58 km2 with a population from 829,899 people (2017) to 877,432 people (2022). The GDP of both increased significantly for 6 years, although it fell in 2020 due to Covid-19. Economic growth in both districts fluctuated but tended to slow down, with several sectors experiencing significant positive growth, such as the Transportation and Warehousing, Water Procurement, Waste Management, Waste and Recycling, and Other Services sectors.

Transparent Concrete: A Brief Review

Abstract: World is actively engaged in the creation of unique materials and technologies since the dawn of human civilization. Humans are different from other animals in that we want to live easier, more sophisticated lives. Concrete is nothing short of a miracle for human evolution in this era of constant advancement. One of those cutting-edge varieties of concrete is transparent concrete, which not only dispels the stereotype of concrete as a lifeless, colorless substance but also contributes to energy conservation, one of the main issues facing the modern world. Concrete that transmits light, sometimes referred to as translucent concrete. Due to incorporated light optical elements, the material has light-transmissive qualities. The concrete block allows light to pass through it from one end to the other. Therefore, in order to get maximal light penetration, fibers must travel through the entire object. Natural light in buildings is obstructed by the construction of skyscrapers and high-rise buildings. The use of artificial light has significantly increased as a result of this issue.

HVConv: Horizontal and Vertical Convolution for Remote Sensing Object Detection

Generally, the interesting objects in aerial images are completely different from objects in nature, and the remote sensing objects in particular tend to be more distinctive in aspect ratio. The existing convolutional networks have equal aspect ratios of the receptive fields, which leads to receptive fields either containing non-relevant information or being unable to fully cover the entire object. To this end, we propose Horizontal and Vertical Convolution, which is a plug-and-play module to address different aspect ratio problems. In our method, we introduce horizontal convolution and vertical convolution to expand the receptive fields in the horizontal and vertical directions, respectively, to reduce redundant receptive fields, so that remote sensing objects with different aspect ratios can achieve better receptive fields coverage, thereby achieving more accurate feature representation. In addition, we design an attention module to dynamically aggregate these two sub-modules to achieve more accurate feature coverage. Extensive experimental results on the DOTA and HRSC2016 datasets show that our HVConv achieves accuracy improvements in diverse detection architectures and obtains SOTA accuracy (mAP score of 77.60% with DOTA single-scale training and mAP score of 81.07% with DOTA multi-scale training). Various ablation studies were conducted as well, which is enough to verify the effectiveness of our model.

Exploring the Role of Video Playback Visual Cues in Object Retrieval Tasks.

Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object's last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users' search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object's destination to the object's final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object's last appearance further reduced search time and cognitive load.

3D scanning of daguerreotypes

Daguerreotypes are historical photographic images made on mirror-like metallic plates. These are heritage objects whose shape cannot be measured with invasive techniques, like contact probes, but the high reflectivity of their surfaces makes the use of non-invasive, 3D-measuring optical techniques challenging. Moreover, the dark areas resulting from their degradation produce a very high contrast, which add extra difficulties to their measurement. In the last few years, several strategies have been developed to overcome the limitations of optical techniques when measuring reflective metallic surfaces. Many of these solutions are not applicable to the study of cultural heritage artifacts, as they are invasive. We attempted the use of conoscopic holography in a 3D-scanning system using a double-exposure strategy. This is a promising option for 3D measuring of daguerreotypes, as we experimentally demonstrated in this work. We present the results obtained from the analyses of two 19th-century daguerreotypes with different superficial conditions. The double-exposure allowed us to obtain high-quality data from the entire object surface. This enabled the measurement of micro-scale details related to the manufacturing process and/or to the corrosion deposits. The proposed methodology can be exploited to monitor the overall health of highly reflective metallic objects but also the outcomes of some conservation treatments, such as cleaning.

Pemanfaatan Media Sosial dan Pengaruhnya terhadap Prestasi Belajar PAI di SMA Kabupaten Pidie Jaya

Pemanfaatan Media Sosial dan Pengaruhnya terhadap Prestasi Belajar PAI di SMA Kabupaten Pidie Jaya

Improving weakly-supervised object localization using adversarial erasing and pseudo label

Weakly-supervised learning approaches have gained significant attention due to their ability to reduce the effort required for human annotations in training neural networks. This paper investigates a framework for weakly-supervised object localization, which aims to train a neural network capable of predicting both the object class and its location using only images and their image-level class labels. The proposed framework consists of a shared feature extractor, a classifier, and a localizer. The localizer predicts pixel-level class probabilities, while the classifier predicts the object class at the image level. Since image-level class labels are insufficient for training the localizer, weakly-supervised object localization methods often encounter challenges in accurately localizing the entire object region. To address this issue, the proposed method incorporates adversarial erasing and pseudo labels to improve localization accuracy. Specifically, novel losses are designed to utilize adversarially erased foreground features and adversarially erased feature maps, reducing dependence on the most discriminative region. Additionally, the proposed method employs pseudo labels to suppress activation values in the background while increasing them in the foreground. The proposed method is applied to two backbone networks (MobileNetV1 and InceptionV3) and is evaluated on three publicly available datasets (ILSVRC-2012, CUB-200-2011, and PASCAL VOC 2012). The experimental results demonstrate that the proposed method outperforms previous state-of-the-art methods across all evaluated metrics.

МЕТОДОЛОГИЯ ПРИНЯТИЯ ОПТИМАЛЬНЫХ РЕШЕНИЙ ПО УПРАВЛЕНИЮ ЖИЗНЕННЫМ ЦИКЛОМ ОБЪЕКТА КАПИТАЛЬНОГО СТРОИТЕЛЬСТВА С УЧЕТОМ КРИТЕРИЯ ПЫЛЕВОГО ЗАГРЯЗНЕНИЯ АТМОСФЕРНОГО ВОЗДУХА

Abstract. The existing approaches to decision-making in construction have two significant drawbacks. Firstly, they allow us to calculate the effectiveness of the options under consideration only for the object construction phase, without giving an idea of the decisions effectiveness taken at other life cycle stages. Secondly, the absence of an environmental safety criterion for a particular technology in the model reduces its universality and the adequacy of the decisions taken.&#x0D; The article proposes a new approach to management decision-making in construction as to the management of a capital construction object life cycle. A methodology has been developed for making optimal decisions on life cycle management, taking into account the criterion of atmospheric air dust pollution. In order to build the methodology, a block diagram of the formation of dust emissions into the atmosphere from temporary pollution sources at various stages was developed. The dust pollution values for works performed at various life cycle stages of a capital construction object were obtained experimentally. Based on the data obtained, the values of the given optimization criterion are determined for choosing the optimal solution for life cycle management, taking into account the criterion of atmospheric air dust pollution.&#x0D; Unlike the existing ones, the developed methodology allows, firstly, to calculate the effectiveness of the options under consideration not only for the construction stage, but also for the entire object life cycle. Secondly, the model introduces a criterion of environmental safety of the control solutions under consideration, quantified by the degree of dust pollution of atmospheric air.&#x0D; Subject of research: The processes of atmospheric air dust pollution when performing work at various stages of the capital construction object life cycle. &#x0D; Materials and methods: Theoretical and experimental methods for determining the concentration of dust pollution using the Ecologist program, based on the results of field dust pollution measurements and aerosol dispersed composition analysis.&#x0D; Results: A methodology for making optimal decisions on managing the capital construction object life cycle has been developed, including 5 stages.&#x0D; 1. Identification of work types that cause atmospheric air dust pollution.&#x0D; 2. Determination of fine dust concentration from each type of work.&#x0D; 3. Determination of the atmospheric air pollution indicator total value for the implementation of all control solutions (technologies) variants and for all capital construction object life cycle stages according to the proposed model.&#x0D; 4. Determination of given optimization criterion values for the implementation of all control solutions (technologies) variants and for all life cycle stages according to the proposed expression.&#x0D; 5. Selection of the control solution (technology) with the lowest given optimization criterion value.&#x0D; Conclusions: As a result of theoretical and practical research, a methodology has been created that allows, firstly, to calculate the effectiveness of the options under consideration not only for the construction stage, but also for the entire object life cycle. Secondly, the model introduces a criterion of environmental safety of the control solutions under consideration, quantified by the atmospheric air dust pollution degree.

Comparative Analysis of RGB and CIELAB Colour Spaces in Quality Assessment of Brick Products

Methods for assessing the colour (quality) of manufactured products, as a rule, are aimed at analysing the data obtained by colorimeters or spectrometers for the objects under study. The problematic side of such measurements is the small area of the object affected by the measurements. The introduction to the practice of evaluating the colour of the produced image products makes it possible to evaluate the colour of the entire object, while leaving the traditional measurement methods for verifying the obtained colour characteristics from the image. The typical colour space for images is RGB, and for colorimeters LAB. Naturally, when applying the image method, it becomes necessary to convert from the RGB to LAB colour space in order to obtain colour indicators that can be compared with colorimeters. The article provides a description of the obtained colour characteristics when assessing the quality of brick products.

Exploring the dynamics of collisionless spherical stellar systems using the matrix method: Insights from the dilation mode

Context. Analytical solutions to the perturbed equations that govern self-gravitating collisionless stellar systems are crucial for both code testing and theoretical insights. For spheres, a solution has been known for years that corresponds to the entire object’s shift from the origin. We recently introduced a new exact stationary solution, relevant for models with a single length parameter. This solution, referred to as the scale-invariant or dilation mode, has led to insights regarding the concept of perturbation energy within the linear theory framework. Aims. Our aim is to use Hénon’s isochrone model as an example to verify the ability of the standard matrix method to successfully predict the existence of a dilation mode, and to explore its potential application as a test disturbance. Methods. We used the standard matrix method for radial perturbations and applied Clutton-Brock potential-density pairs to determine the properties of the perturbations. Results. In this particular case of stationary radial perturbations, the typical relationship between the perturbations of the distribution function and the potential fails. This discrepancy poses a challenge when attempting to use the dilation mode as a test. When using Clutton-Brock pairs with the matrix method, a mass conservation equation as an additional equation to the ordinary set of linear equations is required. With this added equation, it’s possible to obtain the needed test: identical vanishing of the determinant of this modified set of equations with an increasing number of included basis functions.

IDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging. While a few previous studies have attempted to leverage semi-automatic methods for cost-effective bounding box annotation, such methods have limitations in efficiently handling numerous multi-class objects. To effectively accelerate 3D annotation pipelines, we propose iDet3D, an efficient interactive 3D object detector. Supporting a user-friendly 2D interface, which can ease the cognitive burden of exploring 3D space to provide click interactions, iDet3D enables users to annotate the entire objects in each scene with minimal interactions. Taking the sparse nature of 3D point clouds into account, we design a negative click simulation (NCS) to improve accuracy by reducing false-positive predictions. In addition, iDet3D incorporates two click propagation techniques to take full advantage of user interactions: (1) dense click guidance (DCG) for keeping user-provided information throughout the network and (2) spatial click propagation (SCP) for detecting other instances of the same class based on the user-specified objects. Through our extensive experiments, we present that our method can construct precise annotations in a few clicks, which shows the practicality as an efficient annotation tool for 3D object detection.

A Novel Boundary-Guided Global Feature Fusion Module for Instance Segmentation

The task of instance segmentation is widely acknowledged as being one of the most formidable challenges in the field of computer vision. Current methods have low utilization of boundary information, especially in dense scenes with occlusion and complex shapes of object instances, the boundary information may become ineffective. This results in coarse object boundary masks that fail to cover the entire object. To address this challenge, we are introducing a novel method called boundary-guided global feature fusion (BGF) which is based on the Mask R-CNN network. We designed a boundary branch that includes a Boundary Feature Extractor (BFE) module to extract object boundary features at different stages. Additionally, we constructed a binary image dataset containing instance boundaries for training the boundary branch. We also trained the boundary branch separately using a dedicated dataset before training the entire network. We then input the Mask R-CNN features and boundary features into a feature fusion module where the boundary features provide shape information needed for detection and segmentation. Finally, we use a global attention module (GAM) to further fuse features. Through extensive experiments, we demonstrate that our approach outperforms state-of-the-art instance segmentation algorithms, producing finer and more complete instance masks while also improving model capability.

The first spatio-spectral Bayesian imaging of SN1006 in X-rays

Supernovae (SNs) are an important source of energy in the interstellar medium. Young remnants of supernovae (SNRs) exhibit peak emission in the X-ray region, making them interesting objects for X-ray observations. In particular, the supernova remnant SN1006 is of great interest due to its historical record, proximity, and brightness. Thus, it has been studied with a number of X-ray telescopes. Improving X-ray imaging of this and other remnants is an important but challenging task, as it often requires multiple observations with different instrument responses to image the entire object. Here, we use Chandra observations to demonstrate the capabilities of Bayesian image reconstruction using information field theory (IFT). Our objective is to reconstruct denoised, deconvolved, and spatio-spectral resolved images from X-ray observations and to decompose the emission into different morphologies, namely, diffuse and point-like. Further, we aim to fuse data from different detectors and pointings into a mosaic and quantify the uncertainty of our result. By utilizing prior knowledge on the spatial and spectral correlation structure of the diffuse emission and point sources, this method allows for the effective decomposition of the signal into these two components. In order to accelerate the imaging process, we introduced a multi-step approach, in which the spatial reconstruction obtained for a single energy range is used to derive an informed starting point for the full spatio-spectral reconstruction. We applied this method to 11 Chandra observations of SN1006 from 2008 and 2012, providing a detailed, denoised, and decomposed view of the remnant. In particular, the separated view of the diffuse emission ought to provide new insights into the complex, small-scale structures in the center of the remnant and at the shock front profiles. For example, our analysis reveals sharp X-ray flux increases by up to two orders of magnitude at the shock fronts of SN1006.