The Construction of Firm’s IT Capability and Its Impact on IT Assimilation: An Empirical Investigation in China

The paper’s research purpose is to discuss the key firm-specific IT capability and its impact on the business value of IT. In the context of IT application in China, the paper builds research model based on Resource-Based View, this model describes how the partnership between business and IT management partially mediates the effects of IT infrastructure capability and managerial IT skills on the organization-level of IT assimilation(as proxy for business value of IT ). This research releases 105 questionnaires to part-time MBA in the Renmin University of China and gets 70 valid questionnaires, then analyzed the measurement and structural research model by PLS method. The result of the structural model shows the investment in infrastructure capability and managerial IT skills should be transformed into the partnership between IT and business, and then influence the IT assimilation. The paper can give suggestions to the firms about how to identify and improve IT capability, which will help organization to get superior business value from IT investment

Understanding Users’ Continuance Usage for Social Network Services: A Theoretical Model And Empirical Assessment

As a newly emerged e-business model, Social Network Services (SNS) has encouraged new ways of communication and relationship building, also viewed as “the next big thing after Google”. This study draws attention to the substantive differences between adoption and continuance behaviors in the context of SNS, develops the usage continuance model of SNS to investigate continued usage behavior and underlying factors through literature review and theoretical analysis, verified by an empirical test that involved structural equation modeling. This study will advance the theory of IS continuance, provide a comprehensive research model to investigate the post-adoptive behaviors of SNS and help SNS-empowered businesses and organizations to identify critical factors fundamental to long-term viability and the eventual success of their businesses

Polymeric implant of methylprednisolone for spinal injury: preparation and characterization

Purpose: To improve the effectiveness and reduce the systemic side effects of methylprednisolone in traumatic spinal injuries, its polymeric implants were prepared using chitosan and sodium alginate as the biocompatible polymers.Methods: Implants of methylprednisolone sodium succinate (MPSS) were prepared by molding the drug-loaded polymeric mass obtained after ionotropic gelation method. The prepared implants were evaluated for drug loading, in vitro drug release and in vivo performance in traumatic spinal-injury rat model with paraplegia.Results: All the implant formulations were light pale solid matrix with smooth texture. Implants showed 86.56 ± 2.07 % drug loading. Drug release was 89.29 ± 1.25 % at the end of 7 days. Motor function was evaluated in traumatic spinal injury-induced rats in terms of its movement on the horizontal bar. At theend of 7 days, the test group showed the activity score (4.75 ± 0.02) slightly higher than that of standard (4.62 ± 0.25), but the difference was not statistically different (p > 0.05).Conclusion: MPSS-loaded implants produces good recovery in traumatic spinal injury rats.Keywords: Spinal injury, Spinal column, Methylprednisolone, Implant, Activity score, Motor functio

Analytical Studies on a Modified Nagel-Schreckenberg Model with the Fukui-Ishibashi Acceleration Rule

We propose and study a one-dimensional traffic flow cellular automaton model of high-speed vehicles with the Fukui-Ishibashi-type (FI) acceleration rule for all cars, and the Nagel-Schreckenberg-type (NS) stochastic delay mechanism. By using the car-oriented mean field theory, we obtain analytically the fundamental diagrams of the average speed and vehicle flux depending on the vehicle density and stochastic delay probability. Our theoretical results, which may contribute to the exact analytical theory of the NS model, are in excellent agreement with numerical simulations.Comment: 3 pages previous; now 4 pages 2 eps figure

A physical study of the LLL algorithm

This paper presents a study of the LLL algorithm from the perspective of statistical physics. Based on our experimental and theoretical results, we suggest that interpreting LLL as a sandpile model may help understand much of its mysterious behavior. In the language of physics, our work presents evidence that LLL and certain 1-d sandpile models with simpler toppling rules belong to the same universality class. This paper consists of three parts. First, we introduce sandpile models whose statistics imitate those of LLL with compelling accuracy, which leads to the idea that there must exist a meaningful connection between the two. Indeed, on those sandpile models, we are able to prove the analogues of some of the most desired statements for LLL, such as the existence of the gap between the theoretical and the experimental RHF bounds. Furthermore, we test the formulas from the finite-size scaling theory (FSS) against the LLL algorithm itself, and find that they are in excellent agreement. This in particular explains and refines the geometric series assumption (GSA), and allows one to extrapolate various quantities of interest to the dimension limit. In particular, we predict the empirical average RHF converges to $\approx 1.02265$ as dimension goes to infinity.Comment: Augmented version of 1804.03285; expect some overlap

A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks

Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances drop when imperceptible perturbations are made to the original inputs, which may further degrade the following visual tasks or introduce new problems such as data and privacy security. Hence, metrics for evaluating the robustness of deep models against adversarial attacks are desired. However, previous metrics are mainly proposed for evaluating the adversarial robustness of shallow networks on the small-scale datasets. Although the Cross Lipschitz Extreme Value for nEtwork Robustness (CLEVER) metric has been proposed for large-scale datasets (e.g., the ImageNet dataset), it is computationally expensive and its performance relies on a tractable number of samples. In this paper, we propose the Adversarial Converging Time Score (ACTS), an attack-dependent metric that quantifies the adversarial robustness of a DNN on a specific input. Our key observation is that local neighborhoods on a DNN's output surface would have different shapes given different inputs. Hence, given different inputs, it requires different time for converging to an adversarial sample. Based on this geometry meaning, ACTS measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset using state-of-the-art deep networks. Extensive experiments show that our ACTS metric is an efficient and effective adversarial metric over the previous CLEVER metric.Comment: ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM