The "Advanced Chat Application Integrating Machine Learning for Sentimental Analysis" is a progressive interface that utilizes machine learning (ML) algorithm to analyze user's interaction and offer comp...
详细信息
With the increasing number of Web services, how to provide developers with Web APIs that meet their Mashup requirements accurately and efficiently has become a challenge. Even though the existing methods show improvem...
详细信息
Bagging is an essential skill that humans perform in their daily ***,deformable objects,such as bags,are complex for robots to manipulate.A learning-based framework that enables robots to learn bagging is *** novelty ...
详细信息
Bagging is an essential skill that humans perform in their daily ***,deformable objects,such as bags,are complex for robots to manipulate.A learning-based framework that enables robots to learn bagging is *** novelty of this framework is its ability to learn and perform bagging without relying on *** learning process is accomplished through a reinforcement learning(RL)algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state *** framework utilises a set of primitive actions and represents the task in five *** our experiments,the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states,***,the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.
An important part of a country's infrastructure are its roads, which provide a vast transportation network necessary for the movement of people and goods. Road transport is the most cost-effective means of transpo...
详细信息
In cloud platforms, large amount of energy is consumed by execution of scientific workflow. So, VMs has to be deployed in energy efficient manner. Throughout the world, wide attention is attracted by cloud platform’s...
详细信息
Unsupervised feature selection attempts to select a small number of discriminative features from original high-dimensional data and preserve the intrinsic data structure without using data labels. As an unsupervised l...
详细信息
Unsupervised feature selection attempts to select a small number of discriminative features from original high-dimensional data and preserve the intrinsic data structure without using data labels. As an unsupervised learning task, most previous methods often use a coefficient matrix for feature reconstruction or feature projection, and a certain similarity graph is widely utilized to regularize the intrinsic structure preservation of original data in a new feature space. However, a similarity graph with poor quality could inevitably afect the final results. In addition, designing a rational and efective feature reconstruction/projection model is not easy. In this paper, we introduce a novel and efective unsupervised feature selection method via multiple graph fusion and feature weight learning（MGF2WL） to address these issues. Instead of learning the feature coefficient matrix, we directly learn the weights of diferent feature dimensions by introducing a feature weight matrix, and the weighted features are projected into the label space. Aiming to exploit sufficient relation of data samples, we develop a graph fusion term to fuse multiple predefined similarity graphs for learning a unified similarity graph, which is then deployed to regularize the local data structure of original data in a projected label space. Finally, we design a block coordinate descent algorithm with a convergence guarantee to solve the resulting optimization problem. Extensive experiments with sufficient analyses on various datasets are conducted to validate the efficacy of our proposed MGF2WL.
作者：
Zhong, WenjieSun, TaoZhou, Jian-TaoWang, ZhuoweiSong, XiaoyuInner Mongolia University
College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology
School of Computer Science and Technology Guangzhou510006 China Portland State University
Department of Electrical and Computer Engineering PortlandOR97207 United States
Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design...
详细信息
Blockchain technology has emerged as a promising solution for enhancing the security, transparency, and efficiency of electronic voting systems. This review paper presents a comprehensive analysis of recent research p...
详细信息
Estimating solar radiation is important for designing a solar photovoltaic or thermal system. In the present work, multiple linear regression model is used to estimate global horizontal irradiance, direct normal irrad...
详细信息
Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DC...
详细信息
Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DCN),short and long flows are sensitive to low latency and high throughput,*** traditional security processing approaches,however,neglect these characteristics and *** paper proposes a fine-grained security enhancement mechanism(SEM)to solve the problem of heterogeneous traffic and reduce the traffic completion time(FCT)of short flows while ensuring the security of multi-tenant traffic ***,for short flows in DCN,the lightweight GIFT encryption method is *** Intra-DCN long flows and Inter-DCN traffic,the asymmetric elliptic curve encryption algorithm(ECC)is *** NS-3 simulation results demonstrate that SEM dramatically reduces the FCT of short flows by 70%compared to several conventional encryption techniques,effectively enhancing the security and anti-attack of traffic transmission between DCNs in cloud computing ***,SEM performs better than other encryption methods under high load and in largescale cloud environments.
暂无评论