Stimulant drug effects on attention deficit/hyperactivity disorder: a review of the effects of age and sex of patients

Objective: As dopamine functioning varies by sex and age it might be expected that the effects of methylphenidate or amfetamine, the psychostimulants used for the treatment of Attention Deficit /Hyperactivity Disorder (ADHD), will also be moderated by these factors. Here we review the published literature on whether stimulant effects in ADHD symptoms vary by age and sex. Method: We searched for studies published from 1989 until October 2009. Databases searched included U. S. National Library of Medicine (PubMed), Medline, EMBASE, PsycINFO and ISI Web of Knowledge. Firstly, we reviewed the effects of stimulant drugs on male and female patients and also patients of pre-school, middle childhood, adolescence and adulthood. Secondly, we reviewed studies that directly tested the moderating effect of age and sex on stimulant treatment outcome. Results: Randomised controlled trials confirm that stimulant medication is efficacious for, and well tolerated by, males and females and patients across the age range; although preschoolers appear to have a less beneficial response and more side effects. Few studies that specifically examined the moderating effect of age and/or sex were identified. For sex, no effects on overall response were found, although one study reported that sex moderated methylphenidate pharmacodynamics. The few effects found for age were small and inconsistent. Conclusions: The available evidence suggests that stimulant medication, when appropriately administered, has efficacy as an ADHD treatment for both sexes and across all ages. There are currently too few published papers examining the effects of sex and age to draw strong conclusions about moderation. Further studies of the pharmacodynamics of stimulants on symptoms measured using objective tests in the laboratory or classroom setting need to be undertaken

Synergy and Group Size in Microbial Cooperation

Microbes produce many molecules that are important for their growth and development, and the consumption of these secretions by nonproducers has recently become an important paradigm in microbial social evolution. Though the production of these public goods molecules has been studied intensely, little is known of how the benefits accrued and costs incurred depend on the quantity of public good molecules produced. We focus here on the relationship between the shape of the benefit curve and cellular density with a model assuming three types of benefit functions: diminishing, accelerating, and sigmoidal (accelerating then diminishing). We classify the latter two as being synergistic and argue that sigmoidal curves are common in microbial systems. Synergistic benefit curves interact with group sizes to give very different expected evolutionary dynamics. In particular, we show that whether or not and to what extent microbes evolve to produce public goods depends strongly on group size. We show that synergy can create an “evolutionary trap” which can stymie the establishment and maintenance of cooperation. By allowing density dependent regulation of production (quorum sensing), we show how this trap may be avoided. We discuss the implications of our results for experimental design

An overview of recent advances in intrusion detection

The intrusion detection system is one of the security defense tools for computer networks. In recent years this research has lacked in direction and focus. In this paper we present a survey on the recent progression of multiagent intrusion detection systems. We survey the existing types, techniques and architectures of Intrusion Detection Systems in the literature. Finally we outline the present research challenges and issue

Malicious code detection architecture inspired by human immune system

Malicious code is a threat to computer systems globally. In this paper, we outline the evolution of malicious code attacks. The threat is evolving, leaving challenges for attackers to improve attack techniques and for researchers and security specialists to improve detection accuracy. We present a novel architecture for an effective defense against malicious code attack, inspired by the human immune system. We introduce two phases of program execution: Adolescent and Mature Phase. The first phase uses a malware profile matching mechanism, whereas the second phase uses a program profile matching mechanism. Both mechanisms are analogous to the innate immune syste

Effects of training datasets on both the extreme learning machine and support vector machine for target audience identification on twitter

The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. This finding is important and may aid researchers in developing a classifier that is capable of identifying a specific group of target audience members. This will assist the account owner to spend resources more effectively, by sending offers to the right audience, and hence maximize marketing efficiency and improve the return on investment

Identifying the high-value social audience from Twitter through text-mining methods

Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of the account owner to segment the followers and identify a group of high-value social audience members. This enables the account owner to spend resources more effectively by sending offers to the right audience and hence maximize marketing efficiency and improve the return of investment

Using support vector machine ensembles for target audience classification on Twitter

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space

Ranking of high-value social audiences on Twitter

Even though social media offers plenty of business opportunities, for a company to identify the right audience from the massive amount of social media data is highly challenging given finite resources and marketing budgets. In this paper, we present a ranking mechanism that is capable of identifying the top-k social audience members on Twitter based on an index. Data from three different Twitter business account owners were used in our experiments to validate this ranking mechanism. The results show that the index developed using a combination of semi-supervised and supervised learning methods is indeed generic enough to retrieve relevant audience members from the three different data sets. This approach of combining Fuzzy Match, Twitter Latent Dirichlet Allocation and Support Vector Machine Ensemble is able to leverage on the content of account owners to construct seed words and training data sets with minimal annotation efforts. We conclude that this ranking mechanism has the potential to be adopted in real-world applications for differentiating prospective customers from the general audience and enabling market segmentation for better business decision making

A data mining approach for detection of self-propagating worms

In this paper we demonstrate our signature based detector for self-propagating worms. We use a set of worm and benign traffic traces of several endpoints to build benign and worm profiles. These profiles were arranged into separate n-ary trees. We also demonstrate our anomaly detector that was used to deal with tied matches between worm and benign trees. We analyzed the performance of each detector and also with their integration. Results show that our signature based detector can detect very high true positive. Meanwhile, the anomaly detector did not achieve high true positive. Both detectors, when used independently, suffer high false positive. However, when both detectors were integrated they maintained a high detection rate of true positive and minimized the false positiv

Evaluating Player Strategies in the Design of a Hot Hand Game

The user’s strategy and their approach to decisionmakingare two important concerns when designing user-centricsoftware. While decision-making and strategy are key factors in awide range of business systems from stock market trading tomedical diagnosis, in this paper we focus on the role these factorsplay in a serious computer game. Players may adopt individualstrategies when playing a computer game. Furthermore, differentapproaches to playing the game may impact on the effectivenessof the core mechanics designed into the game play. In this paperwe investigate player strategy in relation to two serious gamesdesigned for studying the ‘hot hand’. The ‘hot hand’ is aninteresting psychological phenomenon originally studied in sportssuch as basketball. The study of ‘hot hand’ promises to shedfurther light on cognitive decision-making tasks applicable todomains beyond sport. The ‘hot hand’ suggests that playerssometimes display above average performance, get on a hotstreak, or develop ‘hot hands’. Although this is a widely heldbelief, analysis of data in a number of sports has produced mixedfindings. While this lack of evidence may indicate belief in the hothand is a cognitive fallacy, alternate views have suggested thatthe player’s strategy, confidence, and risk-taking may accountfor the difficulty of measuring the hot hand. Unfortunately, it isdifficult to objectively measure and quantify the amount of risktaking in a sporting contest. Therefore to investigate thisphenomenon more closely we developed novel, tailor-madecomputer games that allow rigorous empirical study of ‘hothands’. The design of such games has some specific designrequirements. The gameplay needs to allow players to perform asequence of repeated challenges, where they either fail or succeedwith about equal likelihood. Importantly the design also needs toallow players to choose a strategy entailing more or less risk inresponse to their current performance. In this paper we comparetwo hot hand game designs by collecting empirical data thatcaptures player performance in terms of success and level ofdifficulty (as gauged by response time). We then use a variety ofanalytical and visualization techniques to study player strategiesin these games. This allows us to detect a key design flaw the firstgame and validate the design of the second game for use infurther studies of the hot hand phenomenon