Dear Editor,This letter addresses the synchronization problem of a class of delayed stochastic complex dynamical networks consisting of multiple drive and response *** aim is to achieve mean square exponential synchro...
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Dear Editor,This letter addresses the synchronization problem of a class of delayed stochastic complex dynamical networks consisting of multiple drive and response *** aim is to achieve mean square exponential synchronization for the drive-response nodes despite the simultaneous presence of time delays and stochastic noises in node dynamics.
This study summarizes the first ChatGPT4PCG competition held at the 2023 IEEE Conference on Games. The goal of the competition is to explore emergent abilities of publicly available LLMs in performing complex tasks re...
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This study summarizes the first ChatGPT4PCG competition held at the 2023 IEEE Conference on Games. The goal of the competition is to explore emergent abilities of publicly available LLMs in performing complex tasks related to procedural content generation, specifically physics-based level generation for Angry Bird-like games. Participants are tasked with submitting their prompts for ChatGPT to generate Angry Birds-like game structures that resemble English uppercase characters. A structure is a collection of stacked game objects comprising a part of an entire Angry Birds-like level. A prompt is an input for large language models (LLMs) including ChatGPT. Two evaluation metrics, i.e., stability and similarity, are used to evaluate the submitted prompts. Stability measures the sturdiness of a structure to withstand in-game gravity, while similarity measures a structure's resemblance to the target character. With such evaluation, participants are challenged not only to produce character-like but also stable structures by utilizing prompt engineering techniques. Finally, the competition's results are discussed to provide valuable insights for future studies and competitions. IEEE
Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running gra...
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Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.
Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure con...
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Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its *** DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of *** generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and *** at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain *** these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the *** these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the *** this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge *** of pre-designed TFs,our knowledge database contains volumes with SOI *** an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the *** approach largely reduces manual TF navigation and fine *** our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the *** the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature *** the intensity profile,we extend the dynamic time warping technique to address subtle alignment
Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial pr...
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Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar *** a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity *** experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.
Modern strides in autonomous vehicles and embedded advanced driver assistant part systems(ADAS) have forced the need of an efficient in addition to accurate system intended for clear road lane as in addition to vehicl...
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Encrypted traffic plays a crucial role in safeguarding network security and user ***,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic *** metho...
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Encrypted traffic plays a crucial role in safeguarding network security and user ***,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic *** methods for detecting encrypted traffic face two significant ***,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network ***,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential *** address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed *** approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context ***-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate *** select the ISCXVPN and the USTC-TK2016 dataset for our *** results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.
Early diagnose of lung diseases is important as it aids In the treatment of the diseases and increment of the patient's quality of life. Chest X-rays are a widely used and cost-effective diagnostic tool for examin...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in op...
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Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identification also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deficiency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deficiency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sfirst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areflat-tened and given to the dense layer(DL)***,the Maximum Margin clas-sifier(MMC)is deployed and takes the input from DL for the classification process tofind *** dataset used in this work has no visible symptoms of a deficiency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deficiency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.
The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gra...
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The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.
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