Efficient and Secure Multi-party Computation for Heterogeneous Environment

MPC (multi-party computation) is a comprehensive cryptographic concept that can be used to do computations while maintaining anonymity. MPC allows a group of people to work together on a function without revealing the plaintext's true input or output. Privacy-preserving voting, arithmetic calculation, and large-scale data processing are just a few of the applications of MPC. Each MPC party can run on a single computing node from a system perspective. Multiple parties' computing nodes could be homogenous or heterogeneous; nevertheless, MPC protocols' distributed workloads are always homogeneous (symmetric). We investigate the system performance of a representative MPC framework and a collection of MPC applications in this paper. On homogeneous and heterogeneous compute nodes, we describe the complete online calculation workflow of a state-of-the-art MPC protocol and examine the fundamental cause of its stall time and performance limitation. Keywords: Cloud Computing, IoT, MPC, Amazon Service, Virtualization.

Evaluation of the Efficiency of Supinator Tendon Transfer to Wrist Extensor in Brachial Plexus Injuries at C7-T1, A Case Study Based on OpenSIM Modelling Approach

BackgroundThe incidence of brachial plexus injuries (BPI) is increasing due to improvement in transportation technology. Those with BPI have upper limb performance limitation depends on the type of injury. Various surgical treatments have been used in this group of the subjects to restore their performance. The aim of this study was to evaluate the efficiency of supinator transferred to wrist extensors in those with lesion level at C7-T1 based on OpenSim modelling approach.MethodThe motions of the upper limb during vertical reaching, transverse reaching and wrist motion of a normal subject were achieved by use of motion analysis system. The ranges of motion of the upper limb joints were evaluated by inverse kinematic. OpenSim software was used to determine the muscles forces during aforementioned tasks. Tendon of supinator was transferred to extensors in modified models of OpenSim. Forward dynamic approach was used to determine the range of motion in tendon transferred condition. ResultsThe range of wrist motion in normal condition (extensors intake) were 85, 103 and 140 degrees for the first task (transverse reaching), the second task (vertical reaching) and the third task (only flexion/extension of wrist), respectively. Although the force of supinator was significantly less than that of wrist extensors, the pattern of its force is the same as wrist extensors.

Information Cascades Prediction With Graph Attention

The cascades prediction aims to predict the possible information diffusion path in the future based on cascades of the social network. Recently, the existing researches based on deep learning have achieved remarkable results, which indicates the great potential to support cascade prediction task. However, most prior arts only considered either cascade features or user relationship network to predict cascade, which leads to the performance limitation because of the lack of unified modeling for the potential relationship between them. To that end, in this paper, we propose a recurrent neural network model with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features. Specifically, for user spatial feature, we learn potential relationship among users based on social network through graph attention network. Then, for temporal feature, a recurrent neural network is built to learn their structural context in several different time intervals based on timestamp with a time-decay attention. Finally, we predict the next user with the latest cascade representation which obtained by above method. Experimental results on two real-world datasets show that our model achieves better performance than the baselines on the both evaluation metrics of HITS and mean average precision.

PERFORMANCE ISSUES WITH CLOUD COMPUTING

— Cloud computing is key for today's fast world. It serves across all the domains. Be it business, educational, public, or private sector, etc. Its application can be observed anywhere. Its overall concept and its analysis of services and model are presented here. Mainly cloud has 3 delivery models. Saas (Software as a service), Paas(Platform as a service), and Iaas (Infrastructure as a service). We explored these with their performances and interdependencies. Cloud adoption has its performance limitation and solutions to overcome these challenges are provided here. Also, cloud delivery models are discussed. Performance factors are very important for as a whole cloud computing success, where it adds its ideal cost of services, its reliability, and its scalability constitutes as its pillar factors. They require a lot of attentiveness and effort by the cloud computing providers and contributors, integrators, and service consumers

Investigating the Influence of Non-linear Effects in the Bidirectional Fiber System

To investigate the influence of non-linearity in the bidirectional optical link which enables simultaneous uplink and downlink transmission of 16-QAM data signal. We analyze non-linear effects like self-phase modulation (SPM) and stimulated Brillouin scattering (SBS). The performance limitation due to the non-linear effects in the system is analyzed. The analysis shows that the SBS threshold for launch power is 16 dBm for unmodulated carrier signal and 6 dBm for modulated signal. At 6 dBm launch power, the system has an average Q of 44.

Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications

With the ubiquitous diffusion of mobile computing and Internet of Things (IoT), the amount of data exchanged and processed over the internet is increasing every day, demanding secure data communication/storage and new computing primitives. Although computing systems based on microelectronics steadily improved over the past 50 years thanks to the aggressive technological scaling, their improvement is now hindered by excessive power consumption and inherent performance limitation associated to the conventional computer architecture (von Neumann bottleneck). In this scenario, emerging memory technologies are gaining interest thanks to their non-volatility and low power/fast operation. In this chapter, experimental characterization and modeling of spin-transfer torque magnetic memory (STT-MRAM) are presented, with particular focus on cycling endurance and switching variability, which both present a challenge towards STT-based memory applications. Then, the switching variability in STT-MRAM is exploited for hardware security and computing primitives, such as true-random number generator (TRNG) and stochastic spiking neuron for neuromorphic and stochastic computing.