Onward and upward? An empirical investigation of gender and promotions in Information Technology Services

The shaky ascent of women up the organizational ladder is a critical factor that may contribute to the lack of women in information technology (IT). In this study, we examine the effect of gender on the likelihood of employee promotions. We further examine whether women get an equal lift in promotion likelihood from performance improvements, work experience, and training as men. We analyze archival promotion data, as well as demographic, human capital, and administrative data for 7,004 employees at a leading IT services firm located in India for the years 2002–2007 and for multiple levels of promotion. We develop robust econometric models that consider employee heterogeneity to identify the differential effect of gender and performance on promotions. We find that, contrary to expectations, women are more likely to be promoted, on average. However, looking deeper into the heterogeneous main effects using hierarchical Bayesian modeling reveals more nuanced insights. We find that, ceteris paribus, women realize less benefit from performance gains than men, less benefit from tenure within the focal firm, but more benefit from training than men. These results suggest that despite the disparity in returns to performance and experience improvements, women can rely on signaling mechanisms such as training to restore parity in promotions. We find that the effects of gender and performance vary with the level of employee promotion; although not as much as men, women benefit more from performance gains at higher organizational levels. Our findings suggest several actionable managerial insights that can potentially make IT firms more inclusive and attractive to women

Intrusion detection system for wireless sensor network

A Wireless Sensor Network (WSN) is a group of sensor nodes, they monitor a certain environmental information (sound, temperature, motion, pressure, light, etc.), and transmit the information to the base station. Its important to protect the data while information transmitted into the wireless environment. Data can be protected by using cryptographic scheme. a number of attacks can be possible on WSN because of its broadcasting nature, resource restrictions, and remote area of deployment. cryptograpic security can secure network from outside attacks, but fails to protect from inside attack. so we need a second line of defence like Intrusion Detection System.This goal is achieved. PIR motion sensor transmits a signal to base station and the base station trigger an alert message whenever an intruder found into the room. Temperature sensor sends a signal whenever the temperature of room is cross a certain threshold. And light sensor sends a signal whenever intensity of light is cross certain threshold. The attacker node is used to attack on nodes. If IDS found the malicious activity which is done by attacker node, it generates an alert message with the victim nodes information. So we can change the nodes information. As base station is connected to the computer so WSN nodes can be controlled by computer

Virtual Reality for Abstract Data Applications

A considerable interest has emerged in the development of virtual reality applications due to the acceptance of VRML 2.0 as the de facto standard for these applications over the Internet. In this study, we examine and discuss the application of virtual reality for abstract data applications. As a problem domain, we consider monitoring equity stock - a domain that is characterized by abstract data

Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems. This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains. The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment. Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.Comment: 4 pages + 1 reference

IS Perspective of Research Issues in Electronic Commerce an Online Auctions

Online auctions represent a model for the way the Internet is shaping the new economy. In the absence of spatial, temporal and geographic constraints these mechanisms provide many benefits to both buyers and sellers. However, significant research is still needed in designing new and better mechanisms, as well as examining the efficacy of existing ones in the contexts of the markets they serve. Issues of mechanism design, secondary market creation, incentive compatibility, bid taker cheating, simultaneous substitutability, and associated research methodologies are discussed in this review paper. Interestingly, one finds a new and potentially insightful research methodology standard being adopted by IS researchers delving into the area of online auctions. This involves quasi-analytical modeling that is subsequently validated by empirical investigation using data collected by automated agents which track real-world web auctions

Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data

Widespread e-commerce activity on the Internet has led to new opportunities to collect vast amounts of micro-level market and nonmarket data. In this paper we share our experiences in collecting, validating, storing and analyzing large Internet-based data sets in the area of online auctions, music file sharing and online retailer pricing. We demonstrate how such data can advance knowledge by facilitating sharper and more extensive tests of existing theories and by offering observational underpinnings for the development of new theories. Just as experimental economics pushed the frontiers of economic thought by enabling the testing of numerous theories of economic behavior in the environment of a controlled laboratory, we believe that observing, often over extended periods of time, real-world agents participating in market and nonmarket activity on the Internet can lead us to develop and test a variety of new theories. Internet data gathering is not controlled experimentation. We cannot randomly assign participants to treatments or determine event orderings. Internet data gathering does offer potentially large data sets with repeated observation of individual choices and action. In addition, the automated data collection holds promise for greatly reduced cost per observation. Our methods rely on technological advances in automated data collection agents. Significant challenges remain in developing appropriate sampling techniques integrating data from heterogeneous sources in a variety of formats, constructing generalizable processes and understanding legal constraints. Despite these challenges, the early evidence from those who have harvested and analyzed large amounts of e-commerce data points toward a significant leap in our ability to understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Sequential Dialogue Context Modeling for Spoken Language Understanding

Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.Comment: 8 + 2 pages, Updated 10/17: Updated typos in abstract, Updated 07/07: Updated Title, abstract and few minor change

Optimal Investment in Information System Security: A Game Theoretical Approach

In the information age, the scale and scope of cyber attacks on information systems is on the rise. Meanwhile, a new type of terrorism—cyber terrorism has emerged. Cyber terrorists belong to the most dangerous subgroup of hackers. In recent years, many academic researchers have called attention to this hacker group. There is a dearth of research that analyzes and predicts the behavior of cyber terrorists. The aim of this paper is to use game theory to analyze risk in ITbased information systems, predict the behavior of cyber terrorists, and find an optimal investment. This paper proposes a general one-stage static game model that can be applied to all cyber crimes. This model is used to analyze the optimal investment in information systems security from a cyber terrorism perspective

Intelligent Agent-Based Data Mining in Electronic Markets

The advent of web-based electronic commerce has brought a tremendous increase in the volume of “collectable data” that can be mined for valuable managerial knowledge. Utilizing intelligent agents can enhance the data mining procedures that are employed in this process. We focus on the role of data mining and intelligent agent technology in the B2C and B2B e- commerce models. By identifying the complex nature of information flows between the vast numbers of economic entities, we identify opportunities for applying data mining that can lead ultimately to knowledge discovery