Bucking the trend : Indian print media

At a time when circulation of newspapers in the USA, Europe and other developed countries, making some media expert declare that ‘newspapers are dying? (Greenslade 2008), the Indian print media industry has been bucking the trend in the past decade. According to the World Association of Newspapers’ report Indian newspaper sales increased by 35.51 per cent in the five year period between 2003 and 2007. The Indian Media and Entertainment sector is said to be twice as profitable as its global counterparts, according to an analysis of 37 publicly traded Indian companies whose gross profits grew by 31 per cent between 2003 and 2007 (Press Council of India 2008). Overall, in 2008, about 100 million copies of newspapers were sold in India (WAN 2008), whereas according to National Readership Survey as many as 222 million readers read an Indian newspaper in 2006 (Press Council of India 2008). This expansion of newspaper readership is at a time when television viewership and radio listener numbers too are rapidly multiplying in India. This paper takes a critical look at the reasons for this expansion in India, at a time when online media seems to be threatening the survival of newspapers in more advanced economies. The paper discusses current trends and strategies employed by media proprietors to maintain and expand their market share in a competitive environment. The paper also raises questions about the quality of journalism, and whether it is being compromised in these times of boom, in a rush to make money from this ‘sunrise industry’ in India.

Learning Bayesian networks in Semi-deterministic systems

In current constraint-based (Pearl-style) systems for discovering Bayesian networks, inputs with deterministic relations are prohibited. This restricts the applicability of these systems. In this paper, we formalize a sufficient condition under which Bayesian networks can be recovered even with deterministic relations. The sufficient condition leads to an improvement to Pearl’s IC algorithm; other constraint-based algorithms can be similarly improved. The new algorithm, assuming the sufficient condition proposed, is able to recover Bayesian networks with deterministic relations, and moreover suffers no loss of performance when applied to nondeterministic Bayesian networks.

A dynamical deterministic packet marking scheme for DDoS traceback

DDoS attack source traceback is an open and challenging problem. Deterministic packet marking (DPM) is a simple and relatively effective traceback scheme among the available traceback methods. However, the existing DPM schemes inheret a critical drawback of scalability in tracing all possible attack sources, which roots at their static mark encoding and attempt to mark all Internet routers for their traceback purpose. We find that a DDoS attack session usually involves a limited number of attack sources, e.g. at the thousand level. In order to achieve the traceback goal, we only need to mark these attack related routers. We therefore propose a novel Marking on Demand (MOD) scheme based on the DPM mechanism to dynamical distribute marking IDs in both temporal and space dimensions. The proposed MOD scheme can traceback to all possible sources of DDoS attacks, which is not possible for the existing DPM schemes. We thoroughly compare the proposed MOD scheme with two dominant DPM schemes through theoretical analysis and experiments. The the results demonstrate that the MOD scheme outperforms the existing DPM schemes. © 2013 IEEE.

Evaluating non-deterministic retrieval systems

The use of sampling, randomized algorithms, or training based on the unpredictable inputs of users in Information Retrieval often leads to non-deterministic outputs. Evaluating the effectiveness of systems incorporating these methods can be challenging since each run may produce different effectiveness scores. Current IR evaluation techniques do not address this problem. Using the context of distributed information retrieval as a case study for our investigation, we propose a solution based on multivariate linear modeling. We show that the approach provides a consistent and reliable method to compare the effectiveness of non-deterministic IR algorithms, and explain how statistics can safely be used to show that two IR algorithms have equivalent effectiveness. Copyright 2014 ACM.

Statistical comparisons of non-deterministic IR systems using two dimensional variance

Retrieval systems with non-deterministic output are widely used in information retrieval. Common examples include sampling, approximation algorithms, or interactive user input. The effectiveness of such systems differs not just for different topics, but also for different instances of the system. The inherent variance presents a dilemma - What is the best way to measure the effectiveness of a non-deterministic IR system? Existing approaches to IR evaluation do not consider this problem, or the potential impact on statistical significance. In this paper, we explore how such variance can affect system comparisons, and propose an evaluation framework and methodologies capable of doing this comparison. Using the context of distributed information retrieval as a case study for our investigation, we show that the approaches provide a consistent and reliable methodology to compare the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non-deterministic IR system has equivalent effectiveness to another IR system, and how to avoid the common pitfall of misusing a lack of significance as a proof that two systems have equivalent effectiveness.

Rethinking the univariate approach to panel unit root testing: using covariates to resolve the incidental trend problem

In an influential article, Hansen showed that covariate augmentation can lead to substantial power gains when compared to univariate tests. In this article, we ask if this result extends also to the panel data context? The answer turns out to be yes, which is maybe not that surprising. What is surprising, however, is the extent of the power gain, which is shown to more than outweigh the well-known power loss in the presence of incidental trends. That is, the covariates have an order effect on the neighborhood around unity for which local asymptotic power is negligible.

A feasible IP traceback framework through dynamic deterministic packet marking

DDoS attack source traceback is an open and challenging problem. Deterministic packet marking (DPM) is a simple and effective traceback mechanism, but the current DPM based traceback schemes are not practical due to their scalability constraint. We noticed a factor that only a limited number of computers and routers are involved in an attack session. Therefore, we only need to mark these involved nodes for traceback purpose, rather than marking every node of the Internet as the existing schemes doing. Based on this finding, we propose a novel marking on demand (MOD) traceback scheme based on the DPM mechanism. In order to traceback to involved attack source, what we need to do is to mark these involved ingress routers using the traditional DPM strategy. Similar to existing schemes, we require participated routers to install a traffic monitor. When a monitor notices a surge of suspicious network flows, it will request a unique mark from a globally shared MOD server, and mark the suspicious flows with the unique marks. At the same time, the MOD server records the information of the marks and their related requesting IP addresses. Once a DDoS attack is confirmed, the victim can obtain the attack sources by requesting the MOD server with the marks extracted from attack packets. Moreover, we use the marking space in a round-robin style, which essentially addresses the scalability problem of the existing DPM based traceback schemes. We establish a mathematical model for the proposed traceback scheme, and thoroughly analyze the system. Theoretical analysis and extensive real-world data experiments demonstrate that the proposed traceback method is feasible and effective.

Evaluation Of Reservoir Model Integration With Deterministic And Probabilistic Streamflow Forecasts

Due to the increase in water demand and hydropower energy, it is getting more important to operate hydraulic structures in an efficient manner while sustaining multiple demands. Especially, companies, governmental agencies, consultant offices require effective, practical integrated tools and decision support frameworks to operate reservoirs, cascades of run-of-river plants and related elements such as canals by merging hydrological and reservoir simulation/optimization models with various numerical weather predictions, radar and satellite data. The model performance is highly related with the streamflow forecast, related uncertainty and its consideration in the decision making. While deterministic weather predictions and its corresponding streamflow forecasts directly restrict the manager to single deterministic trajectories, probabilistic forecasts can be a key solution by including uncertainty in flow forecast scenarios for dam operation. The objective of this study is to compare deterministic and probabilistic streamflow forecasts on an earlier developed basin/reservoir model for short term reservoir management. The study is applied to the Yuvacık Reservoir and its upstream basin which is the main water supply of Kocaeli City located in the northwestern part of Turkey. The reservoir represents a typical example by its limited capacity, downstream channel restrictions and high snowmelt potential. Mesoscale Model 5 and Ensemble Prediction System data are used as a main input and the flow forecasts are done for 2012 year using HEC-HMS. Hydrometeorological rule-based reservoir simulation model is accomplished with HEC-ResSim and integrated with forecasts. Since EPS based hydrological model produce a large number of equal probable scenarios, it will indicate how uncertainty spreads in the future. Thus, it will provide risk ranges in terms of spillway discharges and reservoir level for operator when it is compared with deterministic approach. The framework is fully data driven, applicable, useful to the profession and the knowledge can be transferred to other similar reservoir systems.

Measuring core inflation as the common trend of prices

In recent years, many central banks have adopted inflation targeting policies starting an intense debate about which measure of inflation to adopt. The literature on core inflation has tried to develop indicators of inflation which would respond only to "significant" changes in inflation. This paper defines a measure of core inflation as the common trend of prices in a multivariate dynamic model, that has, by construction, three properties: it filters idiosyncratic and transitory macro noises, and it leads the future leveI of headline inflation. We also show that the popular trimmed mean estimator of core inflation could be regarded as a proxy for the ideal GLS estimator for heteroskedastic data. We employ an asymmetric trimmed mean estimator to take account of possible skewness of the distribution, and we obtain an unconditional measure of core inflation.

Trend following performance with size and liquidity: evidence from US, Brazil and Portugal

Neste trabalho, eu analiso a eficiência de se aplicar estratégias que identificam tendências em mercados de capitais, em três países diferentes, usando um conjunto de variáveis macroeconómicas. Em cada país, a estratégia é testada contra os índices de grande capitalização, pequena capitalização e o índice principal. Eu concluo que, ao combinar os sinais diários obtidos pela estratégia, é possível alcançar retornos ajustados ao risco superiores e reduzir as perdas possíveis do portfólio. No geral, enfatizo os benefícios de usar estratégias que exploram tendências para investidores avessos ao risco, obtendo retornos característicos de capitais próprios com a volatilidade característica de obrigações.