ACM Transactions on Management Information Systems最新文献

For any successful business endeavor, recruitment of required number of appropriately qualified employees in proper positions is a key requirement. For effective utilization of human resources, reorganization of such workforce assignment is also a task of utmost importance. This includes situations when the under-performing employees have to be substituted with fresh applicants. Generally, the number of candidates applying for a position is large and hence, the task of identifying an optimal subset becomes critical. Moreover, a human resource manager would also like to make use of the opportunity of retirement of employees to improve manpower utilization. However, the constraints enforced by the security policies prohibit any arbitrary assignment of tasks to employees. Further, the new employees should have the capabilities required to handle the assigned tasks. In this article, we formalize this problem as the Optimal Recruitment Problem (ORP), wherein the goal is to select the minimum number of fresh employees from a set of candidates to fill the vacant positions created by the outgoing employees, while ensuring satisfiability of the specified security conditions. The model used for specification of authorization policies and constraints is Attribute Based Access Control (ABAC), since it is considered to be the de facto next generation framework for handling organizational security policies. We show that the ORP problem is NP-hard and propose a greedy heuristic for solving it. Extensive experimental evaluation shows both the effectiveness as well as efficiency of the proposed solution.

Many applications use data that are better represented in the binary matrix form, such as click-stream data, market basket data, document-term data, user-permission data in access control, and others. Matrix factorization methods have been widely used tools for the analysis of high-dimensional data, as they automatically extract sparse and meaningful features from data vectors. However, existing matrix factorization methods do not work well for the binary data. One crucial limitation is interpretability, as many matrix factorization methods decompose an input matrix into matrices with fractional or even negative components, which are hard to interpret in many real settings. Some matrix factorization methods, like binary matrix factorization, do limit decomposed matrices to binary values. However, these models are not flexible to accommodate some data analysis tasks, like trading off summary size with quality and discriminating different types of approximation errors. To address those issues, this article presents weighted rank-one binary matrix factorization, which is to approximate a binary matrix by the product of two binary vectors, with parameters controlling different types of approximation errors. By systematically running weighted rank-one binary matrix factorization, one can effectively perform various binary data analysis tasks, like compression, clustering, and pattern discovery. Theoretical properties on weighted rank-one binary matrix factorization are investigated and its connection to problems in other research domains are examined. As weighted rank-one binary matrix factorization in general is NP-hard, efficient and effective algorithms are presented. Extensive studies on applications of weighted rank-one binary matrix factorization are also conducted.

Bacterial spoilage of food products is regulated by density dependent communication system called quorum sensing (QS). QS control biofilm formation in numerous food pathogens and Biofilms formed on food surfaces act as carriers of bacterial contamination leading to spoilage of food and health hazards. Agents inhibiting or interfering with bacterial QS and biofilm are gaining importance as a novel class of next-generation food preservatives/packaging material. In the present study, Zinc nanostructures were synthesised using Nigella sativa seed extract (NS-ZnNPs). Synthesized nanostructures were characterized hexagonal wurtzite structure of size ~24 nm by UV-visible, XRD, FTIR and TEM. NS-ZnNPs demonstrated broad-spectrum QS inhibition in C. violaceum and P. aeruginosa biosensor strains. Synthesized nanostructures inhibited QS regulated functions of C. violaceum CVO26 (violacein) and elastase, protease, pyocyanin and alginate production in PAO1 significantly. NS-ZnNPs at sub-inhibitory concentrations inhibited the biofilm formation of four-food pathogens viz. C. violaceum 12472, PAO1, L. monocytogenes, E. coli. Moreover, NS-ZnNPs was found effective in inhibiting pre-formed mature biofilms of the four pathogens. Therefore, the broad-spectrum inhibition of QS and biofilm by biogenic Zinc oxide nanoparticles and it is envisaged that these nontoxic bioactive nanostructures can be used as food packaging material and/or as food preservative.

We present an integrated interactive modeling environment to support public health epidemiology. The environment combines a high resolution individual-based model with a user-friendly web-based interface that allows analysts to access the models and the analytics back-end remotely from a desktop or a mobile device. The environment is based on a loosely-coupled service-oriented-architecture that allows analysts to explore various counter factual scenarios. As the modeling tools for public health epidemiology are getting more sophisticated, it is becoming increasingly hard for non-computational scientists to effectively use the systems that incorporate such models. Thus an important design consideration for an integrated modeling environment is to improve ease of use such that experimental simulations can be driven by the users. This is achieved by designing intuitive and user-friendly interfaces that allow users to design and analyze a computational experiment and steer the experiment based on the state of the system. A key feature of a system that supports this design goal is the ability to start, stop, pause and roll-back the disease propagation and intervention application process interactively. An analyst can access the state of the system at any point in time and formulate dynamic interventions based on additional information obtained through state assessment. In addition, the environment provides automated services for experiment set-up and management, thus reducing the overall time for conducting end-to-end experimental studies. We illustrate the applicability of the system by describing computational experiments based on realistic pandemic planning scenarios. The experiments are designed to demonstrate the system's capability and enhanced user productivity.