Graphical Operations Research Articles

A proposed reconstruction method of a 3D animation scene based on a fuzzy long and short-term memory algorithm.

With the development of computer technology leading to a broad range of virtual technology implementations, the construction of virtual tasks has become highly demanded and has increased rapidly, especially in animation scenes. Constructing three-dimensional (3D) animation characters utilizing properties of actual characters could provide users with immersive experiences. However, a 3D face reconstruction (3DFR) utilizing a single image is a very demanding operation in computer graphics and vision. In addition, limited 3D face data sets reduce the performance improvement of the proposed approaches, causing a lack of robustness. When datasets are large, face recognition, transformation, and animation implementations are relatively practical. However, some reconstruction methods only consider the one-to-one processes without considering the correlations or differences in the input images, resulting in models lacking information related to face identity or being overly sensitive to face pose. A face model composed of a convolutional neural network (CNN) regresses 3D deformable model coefficients for 3DFR and alignment tasks. The manuscript proposes a reconstruction method for 3D animation scenes employing fuzzy LSMT-CNN (FLSMT-CNN). Multiple collected images are employed to reconstruct 3D animation characters. First, the serialized images are processed by the proposed method to extract the features of face parameters and then improve the conventional deformable face modeling (3DFDM). Afterward, the 3DFDM is utilized to reconstruct animation characters, and finally, high-precision reconstructions of 3D faces are achieved. The FLSMT-CNN has enhanced both the precision and strength of the reconstructed 3D animation characters, which provides more opportunities to be applied to other animation scenes.

Use of modern digital educational resources in geometry lessons in higher educational institutions of the Republic of Kazakhstan

Relevance. In the era of global digitalisation, more and more spheres of public life are switching to the use of innovative technologies, which also applies to the educational process. Especially important is the process of using digital technologies for exact sciences and those requiring modelling and graphical operations, geometry being one of them. This explains the relevance of the issue of student-teacher interaction during the use of digital programmes for studying geometry in higher educational institutions of the Republic of Kazakhstan. Purpose. The purpose of the study is to analyse the impact of innovative technologies on the processes of teaching and assimilation of educational material on geometry by students and to determine the role of digital programmes in the process of establishing communication between a student and a teacher. Methodology. It is necessary to systematically apply various means for scientific research, in particular, dialectical and activity-based methodological approaches, methods of logical analysis, synthesis, comparative analysis, content analysis, modelling, and analysis of scientific literature. Results. The main findings of the paper are the theoretical and practical background to the issue. That is, the theoretical component of the study includes the concept of digital technologies, their features, and types. The practical part includes an analysis of the effectiveness of using digital technologies in the context of studying geometry and determining the level of prevalence of relevant programmes abroad. As for the prospects for the following studies, it would be necessary to conduct a comparative analysis of the use of innovative technologies in higher educational institutions before and after the COVID-19 pandemic. Conclusions. The practical value of the study is quite high since it determines the possibility of using the findings in further developments, in particular, in the construction of geometry curricula in universities, or use as a means for the development and improvement of current curricula. Keywords: digital educational resources; geometry; innovative technologies; students; higher educational institutions

Trinity: High-Performance and Reliable Mobile Emulation through Graphics Projection

Mobile emulation, which creates full-fledged software mobile devices on a physical PC/server, is pivotal to the mobile ecosystem. Unfortunately, existing mobile emulators perform poorly on graphics-intensive apps in terms of efficiency and compatibility. To address this, we introduce graphics projection , a novel graphics virtualization mechanism that adds a small-size projection space inside the guest memory, which processes graphics operations involving control contexts and resource handles without host interactions. While enhancing performance, the decoupled and asynchronous guest/host control flows introduced by graphics projection can significantly complicate emulators’ reliability issue diagnosis when faced with a variety of uncommon or non-standard app behaviors in the wild, hindering practical deployment in production. To overcome this drawback, we develop an automatic reliability issue analysis pipeline that distills the critical code paths across the guest and host control flows by runtime quarantine and state introspection. The resulting new Android emulator, dubbed Trinity, exhibits an average of 97% native hardware performance and 99.3% reliable app support, in some cases outperforming other emulators by more than an order of magnitude. It has been deployed in Huawei DevEco Studio, a major Android IDE with millions of developers.

Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.

Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.

Dorsal Preservation Rhinoplasty in Asian Hump Noses.

Dorsal preservation rhinoplasty, which preserves the bony-cartilaginous junction and the keystone area, has been gaining popularity in Western countries for hump nose correction. The authors aimed to report the feasibility, surgical outcome, and technical considerations of dorsal preservation rhinoplasty in Asian hump nose correction. A retrospective case series study was performed on nine patients who had undergone primary dorsal preservation rhinoplasty for hump nose correction. Rhinoplasty was performed by the senior author (H.R.J.) from March of 2019 to December of 2021. Clinical charts, graphic operation records, and standardized photographs of the patients were retrospectively reviewed and analyzed. Operations were performed using an open approach in all patients. Either the push-down technique ( n = 3) or the let-down technique ( n = 6) was used for dorsal preservation. All patients underwent tip modification, with or without radix grafting, together with dorsal preservation rhinoplasty. Bony step-off camouflage at the transverse osteotomy site was required in three patients. After surgery, both the nasofacial and rhinion angles exhibited significant changes ( P = 0.008). In all cases, hump reduction was successful, without recurrence or saddle nose, and no major complications occurred. All patients were satisfied with the aesthetic and functional results. Dorsal preservation rhinoplasty seems to be a viable option for correcting Asian hump noses. Technical considerations include a preference for the open approach; camouflage of bony step-off deformity; and strategic management of the septal cartilage, in consideration of tip modification.

Image downscaling via co-occurrence learning

Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernel-based convolutional filters could be modified to develop efficient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image filtering concept. We propose to use pairwise co-occurrence similarity of the pixelpairs as the range kernel similarity in the filtering operation. The co-occurrence of the pixel-pair is learned directly from the input image. This co-occurrence learning is performed in a neighborhood based fashion all over the image. The proposed method can preserve the high-frequency structures, which were present in the input image, into the downscaled image. The idea is further extended to the case of fractions factor of downscaling. The resulting images retain visually-important details and do not suffer from edge-blurring artifact. We demonstrate the effectiveness of our proposed approach with extensive experiments on a large number of images downscaled with various downscaling factors.

SA-UBA: Automatically Privileged User Behavior Auditing for Cloud Platforms with Securely Accounts Management

Cloud platforms allow administrators or management applications with privileged accounts to remotely perform privileged operations for specific tasks, such as deleting virtual hosts. When privileged accounts are leaked and conduct dangerous privileged operations, severe security problems will appear on cloud platforms. To solve these problems, researchers focus on auditing privileged users’ behaviors. However, it is difficult to automatically audit fine-grained privileged behaviors for graphical operating systems. Moreover, it is hard to prevent users from bypassing the audit system or to prevent hackers from attacking audit system. In this paper, we propose a Secure and Automatic Behavior Audit system named SA-UBA. It provides advanced deep learning models to automatically achieve fine-grained user behavior audits for graphical operating systems. Furthermore, it adopts cryptography-based account storage and sharing methods to securely manage privileged accounts. In particular, privileged accounts cannot be leaked even if SA-UBA is compromised by attackers. We built a threat model of a cloud platform to evaluate the security of the SA-UBA and conduct extensive experiments with SA-UBA in real scenarios. The results show SA-UBA introduces a small overhead on securely managing privileged accounts and accurately recognizes fine-grained user behaviors.

DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation

3D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structures, in a controllable manner. To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured & geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with disentangled control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner, we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications, but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.

A unifying causal framework for analyzing dataset shift-stable learning algorithms

AbstractRecent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known asdataset shift) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability and accuracy. Taking a causal graphical view, we use a flexible graphical representation to express various types of dataset shifts. Given a known graph of the data generating process, we show that all invariant distributions correspond to a causal hierarchy of graphical operators, which disable the edges in the graph that are responsible for the shifts. The hierarchy provides a common theoretical underpinning for understanding when and how stability to shifts can be achieved, and in what ways stable distributions can differ. We use it to establish conditions for minimax optimal performance across environments, and derive new algorithms that find optimal stable distributions. By using this new perspective, we empirically demonstrate that that there is a tradeoff between minimax and average performance.

Research on Interactive Picking Method of 3D Model Graphics Based on BVH and Qt

In the front and rear graphics operations of finite element analysis software, traditional methods use ray picking and callback functions to complete the graphics picking interaction, which is inefficient and inflexible, which affects the software user experience. The method in this paper adopts the ray picking algorithm based on the BVH structure and the signal-slot data communication mechanism of Qt to realize multi-threaded concurrent communication, supports efficient and free interactions such as single-point picking of 3D models and rectangular frame selection, and improves the interaction friendliness and friendliness of the finite element analysis system. User operation efficiency.

Some More Results on Reciprocal Degree Distance Index and ℱ‐Sum Graphs

A chemical invariant of graphical structure is a unique value characteristic that remains unchanged under graph automorphisms. In the study of QSAR/QSPR, like many other chemical invariants, reciprocal degree distance has played a significant role to estimate the bioactivity of several compounds in chemistry. Reciprocal degree distance is a chemical invariant, which is the degree weighted version of Harary index, i.e., . Eliasi and Taeri proposed four new graphic unary operations: , and , frequently implemented in sum of graphs, symbolized as , i.e., sum of two graphs , is one of the unary graphic operations . This work provides constraints for the above‐mentioned invariant for this binary graphic operation F‐sum of graphs.

Single Channel SAR Ground Moving Target Detection Algorithm Based on Subband Interferometry

Traditional synthetic aperture radar (SAR) ground moving target detection (GMTD) technology mostly relies on multichannel data, which requires higher complexity of the radar system. This letter proposes a subband interferometry (SBI)-based algorithm for single-channel SAR GMTD. First, the echo signal is divided into high and low bands containing overlapping parts before the imaging process, then the subbands are imaged separately, and the SBI algorithm is used to achieve the interferometric phase of the subbands. It can be observed that the moving target has the characteristics of phase reversal in the interferometric phase image, and graphics operations such as median filtering or morphological processing can be used to detect the moving target. In this letter, the relationship between the subbands coincidence, the correlation coefficient (CC) of SBI, and the phase of moving target after SBI processing are derived. The real airborne SAR data verify the feasibility and effectiveness of the algorithm.

Random growth scale-free networked models with an identical degree distribution and a tunable assortativity index.

In this work, we propose two kinds of graphic operations by using triangle configuration, based on which we establish a family of random growth networked models G(t;p) where notations t and p represent time step and probability parameter, respectively. By studying some fundamental structural parameters both analytically and numerically, we show that (1) all the realizations G(t;p) follow the same power-law degree distribution with exponent γ=2+ln⁡3/ln⁡2 regardless of probability p and thus have scale-free feature; (2) each model G(t;p) has a relatively high clustering coefficient; and (3) while network G(t;1) has a small average path length, it is not a unique model possessing small-world property mainly because its diameter D(t;1) does not reach the theoretical lower bound. Next, we make use of assortativity index R to quantify the tendency of forming connection between vertices and observe that (1) model G(t;0) exhibits disassortative mixing because the corresponding index R(t;0) is non-positive, and (2) model G(t;1) is in the opposite direction. As a result, we demonstrate that random model G(t;p) has a tunable quantity R(t;p) controlled by probability p. In addition, we exactly determine the total number of spanning trees of deterministic models G(t;1) and G(t;0) and also calculate the entropy of spanning trees.

Odd-Graceful Total Colorings for Constructing Graphic Lattice

The security of passwords generated by the graphic lattices is based on the difficulty of the graph isomorphism, graceful tree conjecture, and total coloring conjecture. A graphic lattice is generated by a graphic base and graphical operations, where a graphic base is a group of disjointed, connected graphs holding linearly independent properties. We study the existence of graphic bases with odd-graceful total colorings and show graphic lattices by vertex-overlapping and edge-joining operations; we prove that these graphic lattices are closed to the odd-graceful total coloring.

Mean Hitting Time for Random Walks on a Class of Sparse Networks.

For random walks on a complex network, the configuration of a network that provides optimal or suboptimal navigation efficiency is meaningful research. It has been proven that a complete graph has the exact minimal mean hitting time, which grows linearly with the network order. In this paper, we present a class of sparse networks in view of a graphic operation, which have a similar dynamic process with the complete graph; however, their topological properties are different. We capture that has a remarkable scale-free nature that exists in most real networks and give the recursive relations of several related matrices for the studied network. According to the connections between random walks and electrical networks, three types of graph invariants are calculated, including regular Kirchhoff index, M-Kirchhoff index and A-Kirchhoff index. We derive the closed-form solutions for the mean hitting time of , and our results show that the dominant scaling of which exhibits the same behavior as that of a complete graph. The result could be considered when designing networks with high navigation efficiency.

The Second Hyper-Zagreb Coindex of Chemical Graphs and Some Applications

The second hyper-Zagreb coindex is an efficient topological index that enables us to describe a molecule from its molecular graph. In this current study, we shall evaluate the second hyper-Zagreb coindex of some chemical graphs. In this study, we compute the value of the second hyper-Zagreb coindex of some chemical graph structures such as sildenafil, aspirin, and nicotine. We also present explicit formulas of the second hyper-Zagreb coindex of any graph that results from some interesting graphical operations such as tensor product, Cartesian product, composition, and strong product, and apply them on a q-multiwalled nanotorus.

HOTCAP: a new software package for high-speed oscilloscope-based three-dimensional bunch charge and position measurement

A software package to be used in high-speed oscilloscope-based three-dimensional bunch-by-bunch charge and position measurement is presented. The software package takes the pick-up electrode signal waveform recorded by the high-speed oscilloscope as input, and it calculates and outputs the bunch-by-bunch charge and position. In addition to enabling a three-dimensional observation of the motion of each passing bunch on all beam position monitor pick-up electrodes, it offers many additional features such as injection analysis, bunch response function reconstruction, and turn-by-turn beam analysis. The software package has an easy-to-understand graphical user interface and convenient interactive operation, which has been verified on the Windows 10 system.

M-polynomials and degree-based topological indices of tadpole graph

Chemical graph theory is a branch of mathematical chemistry which has an important outcome on the development of the chemical sciences. A chemical graph is a graph which is produced from some molecular structure by applying some graphical operations. The demonstration of chemical compounds and chemical networks with M-polynomials is a new idea and the M-polynomial of different molecular structures supports us to calculate many topological indices. A topological index is a numeric quantity that describes the whole structure of a molecular graph of the chemical compound and supports to understand its physical features, chemical reactivates and boiling activities. In this paper, we compute M-polynomial and topological indices of tadpole graph, then we recover numerous topological indices using the M-polynomial.

Part-to-full shape matching of different human subjects

Shape matching is a fundamental operation in digital geometry processing and computer graphics. Challenges in shape matching include finding correspondences of partial shapes with deformations, as well as topological noise and ambiguities. This paper presents a partial shape correspondence algorithm based on the concept of the functional map. An iterative dense matching algorithm, incorporating sparse and guided dense matching, is proposed along with a new objective function including both descriptor matching error and transformation error. Rank estimation with the rank direction is proposed to achieve more accurate slope approximation of the functional map. The slope is beneficial because it directly influences the matching efficiency. The experimental results obtained using FAUST and SHREC′16 datasets demonstrate the effectiveness of our proposed algorithm for matching the shapes of different human subjects and shapes with large missing parts compared with state-of-the art algorithms. The proposed algorithm provides an average geodesic distance of <0.033 even when the missing part is up to 80% of the area.

Language creativity of the protest discourses in Belarus after the 2020 presidential election

The subject of the article is the use of language and linguistic aspects of social behavior within the protest discourses that took place after the results of the presidential elections in Belarus were falsified in August 2020. The author considers the concept of protest discourse, referring to scientific literature and comparing its interpretation by various researchers. The analysis of around 500 posters, chosen from the corpus of a first month of Belarusian numerous and various protesting activities, is focused on both rhetoric and language means of protesting communicative actions, namely on lexical nomination and code switching, wordplay and structural modifications, neologisms, paronomasia, irony, graphic operations and others, which, in the format of peaceful demonstrations (on behand of the demonstrators) actualizes the features of carnivalization and the acratic type of discourse.