Entire Object Research Articles

Recognition In Asean Free Trade

The purpose of this paper is to analyze the implementation of the ASEAN Free Trade Agreement and to analyze the principle of recognition in ASEAN free trade. The research method in this paper is using normative legal research methods. This research is a study that uses secondary materials as the main reference material in research. Secondary materials are library materials in the form of documents and scientific papers as sources. The documents used are in the form of legal materials such as laws and regulations, agreements, legal theories, and potential scientific papers as reference material for writing. This research is a normative research on the principle of recognition in ASEAN free trade which will be described analytically the entire object systematically according to the data obtained.

All-optical optoacoustic micro-tomography in reflection mode.

High-resolution optoacoustic imaging at depths beyond the optical diffusion limit is conventionally performed using a microscopy setup where a strongly focused ultrasound transducer samples the image object point-by-point. Although recent advancements in miniaturized ultrasound detectors enables one to achieve microscopic resolution with an unfocused detector in a tomographic configuration, such an approach requires illuminating the entire object, leading to an inefficient use of the optical power, and imposing a trans-illumination configuration that is limited to thin objects. We developed an optoacoustic micro-tomography system in an epi-illumination configuration, in which the illumination is scanned with the detector. The system is demonstrated in phantoms for imaging depths of up to 5mm and in vivo for imaging the vasculature of a mouse ear. Although image-formation in optoacoustic tomography generally requires static illumination, our numerical simulations and experimental measurements show that this requirement is relaxed in practice due to light diffusion, which homogenizes the fluence in deep tissue layers.

Adaptive sampling for accelerating neutron diffraction-based strain mapping * *This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive,paid-up, irrevocable, world-wide license to publish or reproduce the published form of

Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. The technique works by placing an object in the path of a neutron beam, measuring the diffracted signals and inferring the local lattice strain values from the measurement. In order to map the strains across the entire object, the object is stepped one position at a time in the path of the neutron beam, typically in raster order, and at each position a strain value is estimated. Typical dwell times at neutron diffraction instruments result in an overall measurement that can take several hours to map an object that is several tens of centimeters in each dimension at a resolution of a few millimeters, during which the end users do not have an estimate of the global strain features and are at risk of incomplete information in case of instruments outages. In this paper, we propose an object adaptive sampling strategy to measure the significant points first. We start with a small initial uniform set of measurement points across the object to be mapped, compute the strain in those positions and use a machine learning technique to predict the next position to measure in the object. Specifically, we use a Bayesian optimization based on a Gaussian process regression method to infer the underlying strain field from a sparse set of measurements and predict the next most informative positions to measure based on estimates of the mean and variance in the strain fields estimated from the previously measured points. We demonstrate our real-time measure-infer-predict workflow on additively manufactured steel parts—demonstrating that we can get an accurate strain estimate even with 30%–40% of the typical number of measurements—leading the path to faster strain mapping with useful real-time feedback. We emphasize that the proposed method is general and can be used for fast mapping of other material properties such as phase fractions from time-consuming point-wise neutron measurements.

TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) with only image-level supervision is a challenging task. Most existing methods exploit Class Activation Maps (CAM) to generate pixel-level pseudo labels for supervised training. However, due to the local receptive field of Convolution Neural Networks (CNN), CAM applied to CNNs often suffers from partial activation — highlighting the most discriminative part instead of the entire object area. In order to capture both local features and global representations, the Conformer has been proposed to combine a visual transformer branch with a CNN branch. In this paper, we propose TransCAM, a Conformer-based solution to WSSS that explicitly leverages the attention weights from the transformer branch of the Conformer to refine the CAM generated from the CNN branch. TransCAM is motivated by our observation that attention weights from shallow transformer blocks are able to capture low-level spatial feature similarities while attention weights from deep transformer blocks capture high-level semantic context. Despite its simplicity, TransCAM achieves competitive performance of 69.3% and 69.6% on the respective PASCAL VOC 2012 validation and test sets, showing the effectiveness of transformer attention-based refinement of CAM for WSSS.

ПРИНЦИПИ ПОБУДОВИ ОПЕРАТОРНОЇ МОДЕЛІ ПРОЦЕСУ ТЕХНІЧНОГО ОБСЛУГОВУВАННЯ ОЗБРОЄННЯ ТА ВІЙСЬКОВОЇ ТЕХНІКИ

Maintenance is an important component of the life cycle of a sample of weapons and military equipment (OMT), and the high-quality organization of the maintenance system is a guarantee of the durability of the intended use of the OMT. The operational properties of combat vehicles (BM) are quite multifaceted and require increased attention considering the functional features of use. Studying the inherent features of operation is a preventive measure to prevent premature failure of the object, in the technical sense. Practice indicates that the best means for determining the properties of objects is a natural experiment, that is, a study of the properties and condition of the object under appropriate conditions and operating factors of influence. However, in most cases, a full-scale experiment is not possible or appropriate. Some tests may be close to extreme conditions, may proceed so violently that accidents and destruction of components or the entire object are possible. That is why process modeling and control calculations are used to determine individual reliability standards. At the same time, reliability indicators (RP) are usually carried out for unique objects, the calculation is based on the RP of similar elements (objects as a whole) and, if necessary, these indicators are extrapolated. If the control calculations are very cumbersome or significantly distort the actual indicators, then probabilistic modeling is carried out on computers (computers) using test data of individual elements or on the operational data of prototypes of the necessary objects. A prerequisite for various types of simulations should be the construction of operator models for depicting the processes that accompany the actual life cycle of the object, as a result of which there is a clear understanding of the factor stages of influence on the technical condition, failure forecasting, prevention of the appearance of negative or critical factors affecting the survivability of the object in in turn, the analysis of the need for technical maintenance, its sequence, volumes or even expediency. Keywords: operational model, failure, maintenance, operation of combat vehicles, technical condition, restoration.

Module extraction and DLL hijacking detection via single or multiple memory dumps

A memory dump contains the current state of a system's physical memory at the time of its acquisition. Among other things, it contains the processes that were running at the time of acquisition. These processes can share certain functionalities provided by shared object files, which are internally represented by modules in Windows. However, each process only maps in its address space the functionalities it needs, and not the entire shared object file. In this way, the current tools for extracting modules from existing processes in a memory dump from a Windows system obtain the partial content of a shared object file instead of the entire file. In this paper we present two tools, dubbed Modex and Intermodex, which are built on top of the Volatility 3 framework. These tools allow a forensic analyst to extract a 64-bit module from one or more Windows memory dumps as completely as possible. To achieve this, they aggregate the contents of the same module loaded by multiple processes that were running in the same memory dump or in different dumps (we called it intradump and interdump, respectively). Additionally, we also show how our developed tools are useful to detect dynamic-link library (DLL) hijacking attacks, a widely used attack on Windows where attackers trick processes into loading a malicious DLL instead of the benign one.

A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection

Automatic identification of key points within objects is crucial in various application domains. This paper presents a novel framework for accurately estimating the key point within an object by leveraging deep neural network-based object detection. The proposed framework is built upon a training dataset annotated with four non-overlapping bounding boxes, one of which shares a coordinate with the key point. These bounding boxes collectively cover the entire object, enabling automatic annotation if region annotations around the key point exist. The trained object detector is then utilized to generate detection results, which are subsequently post-processed to estimate the key point. To validate the effectiveness of the framework, experiments were conducted using two distinct datasets: cross-sectional images of a parawood log and pupil images. The experimental results demonstrate that our proposed framework surpasses previously proposed approaches in terms of precision, recall, F1-score, and other domain-specific metrics. The improvement in performance can be attributed to the unique annotation strategy and the fusion of object detection and key point estimation within a unified deep learning framework. The contribution of this study lies in introducing a novel framework for closely estimating key points within objects based on deep neural network-based object detection. By leveraging annotated training data and post-processing techniques, our approach achieves superior performance compared to existing methods. This work fills a critical gap in the field by integrating object detection and key point estimation, which has received limited attention in previous research. Our framework provides valuable insights and advancements in key point estimation techniques, offering potential applications in precise object analysis and understanding. Doi: 10.28991/HIJ-2023-04-01-08 Full Text: PDF

Classifying metal-binding sites with neural networks.

To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal-binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine-tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe-2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub-classifications of the [2Fe-2S] cofactor: (1) a standard [2Fe-2S] cofactor and (2) a Rieske [2Fe-2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal-binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal-binding site identification in proteins.

Measurement in Scientific Research

The purpose of this paper is to highlight the importance of measurement in scientific research, with an emphasis on measurement in social research and the progress it has made, as well as, the differences in views in the social sciences on the need for measurement. To explain measurement, scale-related concepts such as reality, validity, certainty, accuracy, objectivity and intersubjectivity are highlighted, as these concepts directly affect the measurement process and scale creation. The understanding of measurement in the natural sciences differs from the social sciences. Measurement in the natural sciences was found to be more advanced than in the social sciences. While the purpose of measurement is to produce some kind of truth, but it may sometimes not be able to measure the entire object or component intended to measure, therefore, we may reach wrong conclusions, and the measurement may appear at a lower level than expected.

Simultaneous fabrication of multiple tablets within seconds using tomographic volumetric 3D printing

3D printing is driving a shift in patient care away from a generalised model and towards personalised treatments. To complement fast-paced clinical environments, 3D printing technologies must provide sufficiently high throughputs for them to be feasibly implemented. Volumetric printing is an emerging 3D printing technology that affords such speeds, being capable of producing entire objects within seconds. In this study, for the first time, rotatory volumetric printing was used to simultaneously produce two torus- or cylinder-shaped paracetamol-loaded Printlets (3D printed tablets). Six resin formulations comprising paracetamol as the model drug, poly(ethylene glycol) diacrylate (PEGDA) 575 or 700 as photoreactive monomers, water and PEG 300 as non-reactive diluents, and lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) as the photoinitiator were investigated. Two printlets were successfully printed in 12 to 32 s and exhibited sustained drug release profiles. These results support the use of rotary volumetric printing for efficient and effective manufacturing of various personalised medicines at the same time. With the speed and precision it affords, rotatory volumetric printing has the potential to become one of the most promising alternative manufacturing technologies in the pharmaceutical industry.

On the robustness of inverse scattering for penetrable, homogeneous objects with complicated boundary

The acoustic inverse obstacle scattering problem consists of determining the shape of a domain from measurements of the scattered far field due to some set of incident fields (probes). For a penetrable object with known sound speed, this can be accomplished by treating the boundary alone as an unknown curve. Alternatively, one can treat the entire object as unknown and use a more general volumetric representation, without making use of the known sound speed. Both lead to strongly nonlinear and nonconvex optimization problems for which recursive linearization provides a useful framework for numerical analysis. After extending our shape optimization approach developed earlier for impenetrable bodies, we carry out a systematic study of both methods and compare their performance on a variety of examples. Our findings indicate that the volumetric approach is more robust, even though the number of degrees of freedom is significantly larger. We conclude with a discussion of this phenomenon and potential directions for further research.

A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection

Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, APS (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects.

Multiple object tracking with appearance feature prediction and similarity fusion

Object tracking is a crucial research area within the field of intelligent transportation, providing a vital foundation for anomalous behavior analysis and traffic statistics. Although pedestrian detectors have shown impressive results, leading to the advancement of detection-based tracking methods, target association in complex scenarios remains a difficult and less efficient task due to the lack of feature robustness in the presence of partial occlusions. In the proposed tracking method, we extract convolutional features on each entire object and its local blocks, segmented by the superpixel algorithm. Aiming to emphasize the global and local information respectively, the global features for each entire object are extracted from the last layer of the backbone network, while local features are derived from a specific intermediate layer of the backbone network. The association between tracked targets and detected pedestrian candidates relies on fused similarity degrees. Furthermore, we use the transformer’s self-attention mechanism to predict features for the current frame based on the information within past frames, aiming to eliminate the effects of target appearance variations. Additionally, we remove redundant background pixels in the detected rectangles of pedestrian candidates by using a background modeling algorithm. Experimental results demonstrate that the tracker proposed in this paper outperforms other trackers across five publicly available datasets, indicating its effectiveness and potential for further development.

Weakly Supervised Instance Segmentation by Exploring Entire Object Regions

Weakly supervised instance segmentation with image-level class supervision is a challenging task as it associates the highest-level instances to the lowest-level appearance. Previous approaches for the task utilize classification networks to obtain rough discriminative parts as seed regions and use distance as a metric to cluster pixels of the same instances. Unlike previous approaches, we provide a novel self-supervised joint learning framework as the basic network and consider the clustering problem as calculating the probability that pixels belong to each instance. To this end, we propose our self-supervised joint learning two-stream network (SJLT Net) to finish this task. In the first stream, we leverage a joint learning framework to implement image-level supervised semantic segmentation with self-supervised saliency detection. In the second stream, we propose a Center Detection Network to detect different instances’ centers with the gaussian loss function to cluster instances pixels. Besides, an integration module is utilized to combine information of both streams and get precise pseudo instances labels. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. Our model achieves excellent performance on the PASCAL VOC 2012 dataset, surpassing the best baseline trained with the same labels by 4.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP^r_{50}$</tex-math></inline-formula> on the train set and 2.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AP^r_{50}$</tex-math></inline-formula> on the validation set.

Selecting High-Quality Proposals for Weakly Supervised Object Detection With Bottom-Up Aggregated Attention and Phase-Aware Loss.

Weakly supervised object detection (WSOD) has received widespread attention since it requires only image-category annotations for detector training. Many advanced approaches solve this problem by a two-phase learning framework, that is, instance mining that classifies generated proposals via multiple instance learning, and instance refinement that iteratively refines bounding boxes using the supervision produced by the preceding stage. In this paper, we observe that the detection performance is usually limited by imprecise supervision, including part domination and untight boxes. To mitigate their adverse effects, we focus on selecting high-quality proposals as the supervision for WSOD. To be specific, for the issue of part domination, we propose bottom-up aggregated attention which incorporates low-level features from shallow layers to improve location representation of top-level features. In this manner, the proposals corresponding to entire objects can get high scores. Its advantage is that it can be flexibly plugged into the WSOD framework since there is no need to attach learnable parameters or learning branches. As regards the problem of untight boxes, we propose a phase-aware loss, which is the first work to measure supervision quality by the loss in the instance mining phase, to highlight correct boxes and suppress untight ones. In this work, we unify the proposed two modules into the framework of online instance classifier refinement. Extensive experiments on the PASCAL VOC and the MS COCO demonstrate that our method can significantly improve the performance of WSOD and achieve the state-of-the-art results. The code is available at https://github.com/Horatio9702/BUAA_PALoss.

A Review: 3D Printing

Abstract: 3D printing or additive manufacturing is a method of creating three dimensional solid matters from a digital file. The design of a 3D printed object is accomplish using additive processes it is also called as RAPID PROTOTYPING. In an additive process an object is manufactured by dozing consecution layers of material as far as the entire object is created.3D concept has the capabilities to furnish benefits for patients, pharmacists and the pharmaceutical industry alike by empower the on-demand design and production of various formulations with individualized dosages, shapes, sizes, drug release and multi-drug combinations. This article criticizing the major benefits for using 3D printing in pharmaceuticals, highlighting the crucial role that healthcare staff play, and will continue to play, in the future implementation of 3D printing into the pharmaceutical sector.

ВЕРБАЛІЗАЦІЯ КОНЦЕПТУ REMARK В ПРОМОВАХ КОРОЛЕВИ ЄЛИЗАВЕТИ ІІ

The article investigates the reflection of the concept REMARK in English linguistic culture. Archetypal characterіs of the speech image of Queen Elizabeth II dominate in the public consciousness of recipients and form the basis of a unified speech of a politician. The disposition of the REMARK concept in the speeches of Queen Elizabeth II is inherent in functional ambivalence: on the one hand, it allows to attract and hold the attention of the recipients, on the other hand, to facilitate the perception of what is heard and to encourage the necessary actions. The investigation used the method of quantitative analysis, which showed the partial authorial intention of 170 verbalizers, their relative and absolute frequencies, clarification of the structure of the entire object on the basis of the developed system of indicators. The linguistic observation was to identify the minimum and maximum changes in the linguistic representation of the concept REMARK that took place from 1952 to 2022. The observation of the speech of Queen Elizabeth II allowed us to find out the dynamics of language changes in general, on a universal scale. During this process, a sample was formed, according to which the linguistic design and expression of the thoughts of Queen Elizabeth II. The important role of the sample of verbalizers is proved, which makes it possible to monitor the transformation of the language standart which initially originated in the speech of Queen Elizabeth II, later showed a wide manifestation of the analyzed concept in various functional speeches, statements and appeals. As a result of the analysis, the initial assumption that the quantitative method provides a mechanism for selecting research units in the dynamics of quantitative changes was confirmed.

Dense Attentive Feature Enhancement for Salient Object Detection

Attention mechanisms have been proven highly effective for salient object detection. Most previous works utilize attention as a self-gated module to reweigh the feature maps at different levels independently. However, they are limited to certain-level guidance and could not satisfy the need of both accurately detecting intact objects and maintaining their detailed boundaries. In this paper, we build dense attention upon features from multiple levels simultaneously and propose a novel Dense Attentive Feature Enhancement (DAFE) module for efficient feature enhancement in saliency detection. DAFE stacks several attentional units and densely connects attentive feature output from current unit to its all subsequent units. This allows feature maps at deep units to absorb attentive information from shallow units, thus more discriminative information can be efficiently selected at the final output. Note that DAFE is plug and play, which can be effortlessly inserted into any saliency or video saliency models for their performance improvements. We further instantiate a highly effective Dense Attentive Feature Enhancement Network (DAFE-Net) for accurate salient object detection. DAFE-Net constructs DAFE over the aggregation feature that contains both semantics and saliency details, the entire salient objects and their boundaries can be well retained through dense attentions. Extensive experiments demonstrate that the proposed DAFE module is highly effective, and the DAFE-Net performs favorably compared with state-of-the-art approaches.

Modeling and optimizing of complex electrodynamic objects

This paper is connected with the necessity of problem modeling and optimizing complex electrodynamic objects. A hollow structure is considered as such object. Its shape and main dimensions are set. It is required to determine the value of the size of the selected element to ensure the maximum average values of the characteristics of the object under study. In the course of modeling the scattering of electromagnetic waves, the method of integral equations was used. The collocation method was used to solve integral equations. The algorithm for determining the dimensions of an element of a hollow structure was developed. The approximation of the dependence of the element size on the length of the entire object was carried out.

Self-supervised transfer learning framework driven by visual attention for benign–malignant lung nodule classification on chest CT

Lung cancer is one of the most fatal malignant diseases, which poses an acute menace to human health and life. The accurate differential diagnosis of lung nodules is a vital step in the computed tomography (CT)-based noninvasive screening of lung cancer. Though deep learning-based methodologies have achieved good results in the task of nodule malignancy prediction, there are still two fundamental challenges that are required to be overcome, including insufficient labeled samples and the interferences of background tissues. Motivated by the above facts, a self-supervised transfer learning framework driven by visual attention (STLF-VA) is presented for benign–malignant identification of nodules on chest CT, which advocates using volumes containing the entire nodule objects as inputs to obtain discriminative features. Compared with traditional models that designed 2D natural image-based transfer learning models or learning from scratch 3D models, the proposed STLF-VA method can effectively alleviate the dependence on labeled samples by exploring the valuable information from 3D unlabeled CT scans in a coarse-to-fine self-supervised transfer learning fashion. Unlike the single attention mechanism, the multi-view aggregative attention (MVAA) module embedded in the STLF-VA architecture fully recalibrates the multi-layer feature maps from multiple attention angles, and can strengthen the anti-interference ability on background information. Moreover, a new dataset CQUCH-LND is constructed for evaluating the effectiveness of the STLF-VA model in clinical practice. Experimental results on the clinical dataset CQUCH-LND and the public dataset LIDC-IDRI indicate that the proposed STLF-VA framework achieves more competitive performance than some state-of-the-art nodule classification approaches.