Effects of self-service technology on customer value and customer readiness: The case of banking industry

The recent development on internet banking has contributed to this industry, significantly. People could do their banking transactions by clicking a bottom and transfer funds, pay bills, etc. In this paper, we present an empirical investigation to find out the effects of different factors on continuous internet banking. The proposed study of this paper has adopted a questionnaire, which was originally developed by Ho and Ko (2008) [Ho, S. H., & Ko, Y. Y. (2008). Effects of self-service technology on customer value and customer readiness: The case of Internet banking. Internet Research, 18(4), 427-446.]. We have used Pearson correlation test as well as stepwise regression techniques to verify the effect of different factors and the results of our survey show that four variables of easy implementation, usefulness, cost reduction and self-control positively influence continuous internet banking

Near-Optimal Straggler Mitigation for Distributed Gradient Methods

Modern learning algorithms use gradient descent updates to train inferential models that best explain data. Scaling these approaches to massive data sizes requires proper distributed gradient descent schemes where distributed worker nodes compute partial gradients based on their partial and local data sets, and send the results to a master node where all the computations are aggregated into a full gradient and the learning model is updated. However, a major performance bottleneck that arises is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. We propose a distributed computing scheme, called Batched Coupon's Collector (BCC) to alleviate the effect of stragglers in gradient methods. We prove that our BCC scheme is robust to a near optimal number of random stragglers. We also empirically demonstrate that our proposed BCC scheme reduces the run-time by up to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation strategies. We also generalize the proposed BCC scheme to minimize the completion time when implementing gradient descent-based algorithms over heterogeneous worker nodes

Cost Sharing Games for Energy-Efficient Multi-Hop Broadcast in Wireless Networks

We study multi-hop broadcast in wireless networks with one source node and multiple receiving nodes. The message flow from the source to the receivers can be modeled as a tree-graph, called broadcast-tree. The problem of finding the minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing centralized approaches, we propose a decentralized algorithm, based on a non-cooperative cost-sharing game. In this game, every receiving node, as a player, chooses another node of the network as its respective transmitting node for receiving the message. Consequently, a cost is assigned to the receiving node based on the power imposed on its chosen transmitting node. In our model, the total required power at a transmitting node consists of (i) the transmit power and (ii) the circuitry power needed for communication hardware modules. We develop our algorithm using the marginal contribution (MC) cost-sharing scheme and show that the optimum broadcast-tree is always a Nash equilibrium (NE) of the game. Simulation results demonstrate that our proposed algorithm outperforms conventional algorithms for the MPBT problem. Besides, we show that the circuitry power, which is usually ignored by existing algorithms, significantly impacts the energy-efficiency of the network.Comment: 33 pages including references, figures, and table

A dynamic performance evaluation of distress prediction models

YesSo far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models

Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy

We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework to simultaneously provide (1) resiliency against stragglers that may prolong computations; (2) security against Byzantine (or malicious) workers that deliberately modify the computation for their benefit; and (3) (information-theoretic) privacy of the dataset amidst possible collusion of workers. LCC, which leverages the well-known Lagrange polynomial to create computation redundancy in a novel coded form across workers, can be applied to any computation scenario in which the function of interest is an arbitrary multivariate polynomial of the input dataset, hence covering many computations of interest in machine learning. LCC significantly generalizes prior works to go beyond linear computations. It also enables secure and private computing in distributed settings, improving the computation and communication efficiency of the state-of-the-art. Furthermore, we prove the optimality of LCC by showing that it achieves the optimal tradeoff between resiliency, security, and privacy, i.e., in terms of tolerating the maximum number of stragglers and adversaries, and providing data privacy against the maximum number of colluding workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the conventional uncoded implementation of distributed least-squares linear regression by up to $13.43\times$, and also achieves a $2.36\times$-$12.65\times$ speedup over the state-of-the-art straggler mitigation strategies

A New Approach for the Implementation of Binary Matrices Using SLP Applications

In this paper, we propose a method for implementing binary matrices with low-cost XOR. First, using a random-iterative method, we obtain a list S from a binary matrix A. Then, based on the list S, we construct a binary matrix B. Next, we find a relation between the implementations of A and B. In other words, using the implementation of the matrix B, we get a low-cost implementation for the matrix A. Also, we show that the implementation of an MDS matrix M is associated with the form of the binary matrix used to construct the binary form of M. In addition, we propose a heuristics algorithm to implement MDS matrices. The best result of this paper is the implementation of a 8 × 8 involutory MDS matrix over 8-bit words with 408 XOR gates. The Paar algorithm is used as an SLP application to obtain implementations of this paper

Optimal Design of Battery-Ultracapacitor Hybrid Source Light/Heavy Electrified Vehicle

This dissertation contributes to the optimal design of battery-ultracapacitor hybrid sources light/heavy duty electrified vehicle power-train architectures. Electrified vehicle (EV) in automotive technology is one of the major solutions to today’s environmental concerns such as air pollution and greenhouse effects. Light duty and heavy duty EVs can reduce the amount of the pollution effectively. Since, in this area all researches deal with optimal cost of the system and rarely consider the regenerate brake energy, the lack of comprehensive study on other important issues on optimal sizing including size, space, and acceleration time is feeling. Also it is necessary to be comparing with regenerate brake energy for battery and UC or both scenarios. Therefore the first part of this study consists of comprehensive optimization of a hybridized energy storage system including batteries and ultracapacitors considering a multi-objective function of cost, space, weight, and acceleration time. In motor drive part of the power-train, a study on analyzing current topologies is essential and if possible any new design which results in better efficiency and harmonics distortion would be appreciated. So in the nest part of this research which is the DC/AC motor drive, a novel motor drive with stacked matrix converter (SMC) was developed. This new design was compared with two other popular DC/AC inverters and was proved to be more efficient and an optimal match for the EV application. In the last phase of this research, since the DC/DC converter deals with battery/UC hybrid sources and their energy management systems (EMS), it needs to be fast enough that can improve the dynamics of the system, but so far, very rare studies have been done to improve the DC/DC converter dynamics in EV applications. Therefore the need of applying prediction algorithms to modify the controller of DC/DC converter dynamics is feeling. Therefore, three different prediction algorithms were developed to be used as the predictive controller for the DC/DC converter. Linear prediction as one of the fast and precise prediction algorithms were applied and modified

Construction of MDS Matrices from Generalized Feistel Structures

This paper investigates the construction of MDS matrices with generalized Feistel structures (GFS). The approach developed by this paper consists in deriving MDS matrices from the product of several sparser ones. This can be seen as a generalization to several matrices of the recursive construction which derives MDS matrices as the powers of a single companion matrix. The first part of this paper gives some theoretical results on the iteration of GFS. In second part, using GFS and primitive matrices, we propose some types of sparse matrices that are called extended primitive GFS (EGFS) matrices. Then, by applying binary linear functions to several round of EGFS matrices, lightweight $4\times 4$, $6\times 6$ and $8\times 8$ MDS matrices are proposed which are implemented with $67$, $156$ and $260$ XOR for $8$-bit input, respectively. The results match the best known lightweight $4\times 4$ MDS matrix and improve the best known $6\times 6$ and $8\times 8$ MDS matrices. Moreover, we propose $8\times 8$ Near-MDS matrices such that the implementation cost of the proposed matrices are $108$ and $204$ XOR for 4 and $8$-bit input, respectively. Although none of the presented matrices are involutions, the implementation cost of the inverses of the proposed matrices is equal to the implementation cost of the given matrices. Furthermore, the construction presented in this paper is relatively general and can be applied for other matrix dimensions and finite fields as well