Page 6 - Task Analysis News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Stay updated with breaking news from Task analysis. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

Top News In Task Analysis Today - Breaking & Trending Today

"Diffusion Kernel Attention Network for Brain Disorder Classification" by Jianjia Zhang, Luping Zhou et al.

Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its effective attention mechanism in modeling complex feature relationships. In this paper, for the first time, we develop Transformer for integrated FBN modeling, analysis and brain disorder classification with rs-fMRI data by proposing a Diffusion Kernel Attention Network to address the specific challenges. Specifically, directly applying Transformer does not necessarily admit optimal performance in this task due to its extensive parameters in the attention module against the limited training samples usually available. Looking into this issue, we propose to use kernel attention to replace the o ....

Diffusion Kernel Attention Network , Attention Network , Rain Disease Classification , Brain Modeling , Drain Network , Diffusion Process , Feature Extraction , Task Analysis , Time Series Analysis ,

"Charging RF-Energy Harvesting Devices in IoT Networks with Imperfect C" by Hang Yu, Kwan Wu Chin et al.

This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC) solution that utilizes Gaussian Mixture Model (GMM) and a so called backoff that is used to tighten probabilistic constraints. The results show that the performance of the MPC based solution is within 8% of the optimal solution with a probability of 90.8%. ....

Integer Linear Program , Hybrid Access Point , Radio Frequency , Channel State Information , Mixed Integer Linear Program , Sample Average Approximation , Model Predictive Control , Gaussian Mixture Model , Internet Of Things , Power System Reliability , Robabilistic Logic , Eceding Horizon , Resource Management , Stochastic Optimization , Task Analysis , Wireless Charging ,

"Orchestrating Virtual Network Functions in Wireless Powered IoT Networ" by Honglin Ren, Kwan Wu Chin et al.

Virtualization of devices operating in Internet of Things (IoTs) networks allows them to host functions or tasks from different users; these devices can thus execute multiple on demand sensing and data processing services concurrently. Devices, however, have limited energy and operational lifetime. To this end, this paper considers supporting Virtual Network Functions (VNFs) in a Radio Frequency (RF)-charging network with a Hybrid Access Point (HAP). Our aim is to minimize the energy used by the HAP to power devices in order to support deployed VNFs. It outlines a Mixed-Integer Linear Program (MILP) to jointly optimize VNFs placement, routing and link scheduling, and also the HAP’s charging duration. Further, it proposes a heuristic, called Decoupled Greedy Algorithm (DGA), that first assigns VNFs onto devices with the highest energy level before optimizing the HAP’s charging, routing and link schedule. Our results show that DGA has a probability higher than 0.95 to successfully se ....

Integer Linear Program , Virtual Network Functions Vnfs , Virtual Network Functions , Radio Frequency , Hybrid Access Point , Mixed Integer Linear Program , Decoupled Greedy Algorithm , Nternet Of Things , Ulti Hop Communications , Radio Frequency , Task Analysis , Virtual Functions , Wireless Communication , Wireless Sensor Networks ,

"Novel Tasks Assignment Methods for Wireless Powered IoT Networks" by Honglin Ren and Kwan Wu Chin

Devices in Internet of Things (IoT) networks are required to execute tasks such as sensing, computation and communication. These devices, however, have energy limitation, which in turn bounds the number of tasks they can execute and their tasks execution time. To this end, this paper considers energy delivery, tasks assignment and execution in a Radio Frequency (RF) IoT network with a Hybrid Access Point (HAP) and RF-powered devices. We outline a novel Mixed-Integer Linear Program (MILP) to assign tasks to devices, and also to optimize the HAP’s charging duration. We also propose a heuristic algorithm called Energy Saving Task Assignment (ESTA), and two Model Predictive Control (MPC) approaches called MPC-MILP and MPC-ESTA; both of which use channel estimates over a given window or time horizon. Our results show that MPC-MILP and MPC-ESTA respectively consume up to 74.27% and 63.71% less energy as compared to competing approaches. Moreover, MPC-MILP with a small window has better per ....

Integer Linear Program , Energy Saving Task Assignment , Radio Frequency , Hybrid Access Point , Mixed Integer Linear Program , Model Predictive Control , Directed Acyclic Graph Dag , Distributed Computing , Mperfect Csi , Nternet Of Things , Radio Frequency , Resource Management , Task Analysis , Wireless Communication , Wireless Sensor Networks ,

"Blockchain-based secure deduplication and shared auditing in decentral" by Guohua Tian, Yunhan Hu et al.

Data deduplication and public auditing are significant for providing secure and efficient network storage services. However, the existing data deduplication schemes supporting auditing not only cannot effectively alleviate the threats of the single point of failure and duplicate-faking attack, but also have to bear the massive waste of computation and storage resources caused by metadata redundancy and repetitive audit tasks. In this paper, we propose a blockchain-based secure deduplication and shared auditing scheme in decentralized storage. Specifically, our scheme utilizes a novel deduplication protocol based on the double-server storage model to achieve efficient space-saving while protecting data users from losing data under a single point of failure and duplicate-faking attack. Besides, it sharply reduces the computation and storage costs of metadata by introducing a lightweight authenticator generation algorithm and update protocol. On this basis, our scheme further adopts a blo ....

Data Deduplication , Decentralized Storage , Maximum Likelihood Estimation , Hared Auditing , Task Analysis ,