Issues In Stock Index Futures Introduction And Trading. Evidence From The Malaysian Index Futures Market.

This paper examines several issues related to the introduction and trading of stock index futures contracts in Malaysia. Issues related to volatility, pricing efficiency, systematic patterns and lead-lag relationships are examined. These issues were studied by way of addressing six research questions. We use two data sets. First, daily price data for 4 years and 2 years respectively for stock and futures markets and second, intraday, 15 minute interval data for 43 days (2 months) of futures trading. Based on our results, we find no evidence of any increase in the volatility of the underlying market following futures introduction. If anything, the one year period following futures introduction had lower volatility. Intermarket comparison showed futures volatility to be higher. No evidence of any expiration day effect was found. We find frequent mispricing, with most of it being underpricing. Including transaction costs showed very little mispricing. Analysis of the 15 minute intraday data showed clear evidence of an overall U-shape in futures volume and volatility. However, a minor third peak at reopening following lunch break was also evident. We find no evidence of a lead-lag relationship, rather a contemporaneous one. Both markets appear to react simultaneously to information arrival.Impact of the introduction of Stock Index Futures Contracts on the underlying equity market

Pruning classification rules with instance reduction methods

Generating classification rules from data often leads to large sets of rules that need to be pruned. A new pre-pruning technique for rule induction is presented which applies instance reduction before rule induction. Training three rule classifiers on datasets that have been reduced earlier with instance reduction methods leads to a statistically significant lower number of generated rules, without adversely affecting the predictive performance. The search strategies used by the three algorithms vary in terms of both type (depth-first or beam search) and direction (general-to-specific or specific-to-general)

Quantifying materials waste in the Egyptian construction industry: a critical analysis of rates and factors

Abstract: Construction and demolition waste (CDW) is a critical challenge facing the construction industry. It leads to deterioration of the triple bottom line of sustainability. Unfortunately, the CDW management research in Egypt lacks studies investigating (1) the variations in CDW generation (CDWG) among different types of construction projects, and (2) the factors affecting CDW reduction (CDWR). Based on a benchmarking approach, this research (1) quantifies CDW in terms of generation rates and costs among different construction project types in Egypt, and (2) investigates the relationship between CDWG and different adopted CDWR factors. Using structured interviews, a comparative case study was conducted to investigate industrial, residential, commercial, and infrastructure projects. Analysis of results demonstrated that CDWG rates and costs differ from one project type to another due to the project's nature, size, and complexity on the one hand, and the applied CDWR factors such as waste-efficient practices, awareness, culture & behaviour, and legislation on the other hand. On average among the four project types, it was found that "timber", "sand", and "bricks/blocks" are the most wasteful materials. It was also found that "practices" and "legislation" are the least applied CDWR factors on average among the four project types, which need to be better applied for better CDWR results

Determination of concentration of heavy metals in fish from sea port of Zanzibar by energy dispersive x-ray fluorescence (EDXRF)

Seafood is the major source of food for a large number of people residing in the coastal areas of Zanzibar. It has been reported that the seafood are a connecting link for the transfer of toxic heavy metals in human beings. The present study assessed the metal concentration upon sample species of fish along the coast Sea Port of Zanzibar. Fish samples (namely changu, sardine, baracout and tuna fish) were the ideal species for the assessment study on effects of heavy metal contamination in aquatic organisms. They were collected at the Sea Port and the concentrations of the assessed metals were determined using Energy Dispersive X-ray Fluorescence (EDXRF). Concentrations of Fe, Pb, Cr, Ni, As, Cu and Zn were found to be higher in sardine whilst Hg was found to be higher in changu specie. Concentrations of Cd and Mn were found to be below the detection limits in all sample species but higher in mussels. Whereas Hg was only detected in changu species. Comparing the data from this study to data from other studies in other regions, the concentrations of Fe, Cr and As in different species of fishes collected was quite higher than the values reported in the literature. The results of this study indicated that As, Hg and Cr were higher in fish than WHO/FAO (2004).Keywords: EDXRF, X-Rays, Fish, Pollution studies, Environmen

Pruning methods for rule induction

Machine learning is a research area within computer science that is mainly concerned with discovering regularities in data. Rule induction is a powerful technique used in machine learning wherein the target concept is represented as a set of rules. The attraction of rule induction is that rules are more transparent and easier to understand compared to other induction methods (e.g., regression methods or neural network). Rule induction has been shown to outperform other learners on many problems. However, it is not suitable to handle exceptions and noisy data in training sets, which can be solved by pruning. This thesis is concerned with investigating whether preceding rule induction with instance reduction techniques can help in reducing the complexity of rule sets by reducing the number of rules generated without adversely affecting the predictive accuracy. An empirical study is undertaken to investigate the application of three different rule classifiers to datasets that were previously reduced with promising instance-reduction methods. Furthermore, we propose a new instance reduction method based on Ant Colony Optimization (ACO). We evaluate the effectiveness of this instance reduction method for k nearest neighbour algorithms in term of predictive accuracy and amount of reduction. Then we compared it with other instance reduction methods.We show that pruning classification rules with instance-reduction methods lead to a statistically significant decrease in the number of generated rules, without adversely affecting performance. On the other hand, applying instance-reduction methods enhances the predictive accuracy on some datasets. Moreover, the results provide evidence that: (1) our proposed instance reduction method based on ACO is competitive with the well-known k-NN algorithm; (2) the reduced sets computed by our method offers better classification accuracy than those obtained by the compared algorithms

Factors that predict fertility desires for people living with HIV infection at a support and treatment centre in Kabale, Uganda

<p>Abstract</p> <p>Background</p> <p>Studies from different contexts worldwide indicate that HIV positive patients manifest high-risk sexual behavior characterized by fertility intentions, multiple sexual partners, non-use of contraceptives and non-disclosure of HIV status to their sex partners. The objective was to analyze fertility desires among persons living with HIV at a treatment centre in Kabale Hospital, Southwestern Uganda.</p> <p>Methods</p> <p>From January to August 2009, we interviewed 400 HIV positive patients seeking care using an interviewer-administered questionnaire. We assessed socio-demographic variables, reproductive history, sexuality and fertility desires. At bivariate and multivariate analysis, characteristics of participants who reported or did not report desire to have a child in the near future were compared.</p> <p>Results</p> <p>Of the 400 respondents, (25.3%) were male, 47.3% were aged 25-34 years, over 85% were currently married or had ever been married, and the 62% had primary level of education or less. Over 17% had produced a child since the HIV diagnosis was made, and 28.6% reported that they would like to have a child in the near future. Age of the respondent, being single (versus being ever-married) and whether any of the respondents' children had died were inversely associated with fertility intentions.</p> <p>Conclusion</p> <p>Factors inversely associated with fertility intentions were age of the respondent, marital status and whether any of the respondents' children had died. Use of antiretroviral therapy was not associated with fertility intentions.</p

Novel Approach for IP-PBX Denial of Service Intrusion Detection Using Support Vector Machine Algorithm.

Recent trends have revealed that SIP based IP-PBX DoS attacks contribute to most overall IP-PBX attacks which is resulting in loss of revenues and quality of service in telecommunication providers. IP-PBX face challenges in detecting and mitigating malicious traffic. In this research, Support Vector Machine (SVM) machine learning detection &amp; prevention algorithm were developed to detect this type of attacks Two other techniques were benchmarked decision tree and Naïve Bayes. The training phase of the machine learning algorithm used proposed real-time training datasets benchmarked with two training datasets from CICIDS and NSL-KDD. Proposed real-time training dataset for SVM algorithm achieved highest detection rate of 99.13% while decision tree and Naïve Bayes has 93.28% &amp; 86.41% of attack detection rate, respectively. For CICIDS dataset, SVM algorithm achieved highest detection rate of 76.47% while decision tree and Naïve Bayes has 63.71% &amp; 41.58% of detection rate, respectively. Using NSL-KDD training dataset, SVM achieved 65.17%, while decision tree and Naïve Bayes has 51.96% &amp; 38.26% of detection rate, respectively.The time taken by the algorithms to classify the attack is very important. SVM gives less time (2.9 minutes) for detecting attacks while decision tree and naïve Bayes gives 13.6 minutes 26.2 minutes, respectively. Proposed SVM algorithm achieved the lowest false negative value of (87 messages) while decision table and Naïve Bayes achieved false negative messages of 672 and 1359, respectively

Principal component analysis for human gait recognition system

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject