229 research outputs found
Interview with Scott Barretta, freelance researcher and author
No full textThis interview was conducted as a teaching interview to instruct field school participants in interviewing technique; Interviewer: Michael Taft, Recordist: Guha Shankar. Recorded at Barnard Observatory (University of Mississippi)
Women’s ‘Defence-Narrative’ and its Role in the Formation of the Novel
No full textThe practice of women defending themselves in writing, which is often called the “women’s defence-narrative,” is a tradition that emerged in the late medieval period and continued as a dominant strain in women’s writing through the early modern period. There have been studies on how Daniel Defoe, usually considered to be the first major English novelist, relied heavily on latter-day authors of the women’s defence-narrative, such as Mary Carleton. But there still remains room for detailed studies (as far as the history of the novel as a genre is concerned) critically examining the role played by the women’s defence-narratives in the formation of the novel. This article attempts to outline its history, with an emphasis on the seventeenth-century examples that contributed so importantly to the formation of the novel
Exploring Football as a Site for Discursive Construction and Disciplinary Power in Colonial India
No full textThis paper will use the body as an analytical tool in approaching the early history of football in India. It will demonstrate that football here acted as a means for the colonizers to construct and transform the Indian body. However and perhaps more importantly, it will show that the game also became implicated in Indian attempts to resist these colonial corporeal politics. The paper uses the twin themes of discourse and power from Said and Foucault respectively in an attempt to place the body at the centre of analysis in colonial politics with respect to football. The game provided the British an idiom for the discursive construction of the inferiority of the Indian body and the superiority of the European body that was so important to legitimizing colonial rule. The paper also points to football as an idiom in which local resistance to colonial discourses and interventions took place. The ironic evocation of the ‘beef-eating John Bull’ and the ‘rice-eating Bengali’ in the wake of the 1911 Mohun Bagan cup final victory demonstrates that Indians thought of colonialism in corporeal terms and used football at certain moments to challenge British representations of them as inferior
Survivin as a global target of intrinsic tumor suppression networks
No full textCo-author Minakshi Guha is a student in the Cancer Biology program in the Graduate School of Biomedical Sciences (GSBS) at UMass Medical School.Despite the constant exposure to genomic insults that may lead to malignancy, cancer is surprisingly a relatively rare occurrence, and this is largely credited to an elaborate network of endogenous tumor suppression. Many effectors of tumor suppression have been identified, and their functions when activated in damaged cells have in large part been elucidated. What is less clear is whether there are common target gene(s) of tumor suppression, whose expression must be ablated in order to block transformation and preserve cellular homeostasis. Fresh experimental evidence suggests that silencing of the mitotic regulator and cell death inhibitor, survivin, is a universal requirement for successful tumor suppression in humans
Business rule learning using data mining of GUHA association rules
No full textV současném vysoce-konkurenčním prostředí je pro podniky velmi důležité, aby jejich informační systémy nejen co nejefektivněji podporovaly stávající podnikové procesy, ale aby se zároveň byly schopny se dynamicky přizpůsobovat měnícímu se prostředí. Stále více se prosazují snahy vzájemně oddělit aplikační a business logiku v rámci informačních systémů, přičemž jedním z vhodných prostředků pro záznam business logiky je využití business rules. Business rules jakožto jednoduchá a srozumitelná pravidla je možné využívat nejen pro shromažďování znalostí v rámci podniku, ale také pro aktivní rozhodování a řízení podnikových procesů. Ačkoliv je business rule přístup využíván již téměř 20 let, jednotlivé specifikace a možné aplikace business rules jsou stále předmětem aktivního výzkumu i praktického vývoje. Nevýhodou business rules je velká náročnost jejich získávání - pravidla jsou obvykle zadávána ručně prostřednictvím doménových expertů. Jedním z problémů, na které je zaměřen aktuální výzkumu v této oblasti, je možnost (polo)automatického získávání business rules z jiných zdrojů - podnikových dokumentů či historických dat. Získávání business pravidel z historických podnikových dat je věnována také tato práce. Hlavním cílem této disertační práce je navrhnout a ověřit metodu (polo)automatického učení business rules za využití dolování asociačních pravidel. Asociační pravidla jsou známou data miningovou metodou objevování zajímavých vztahů v datech, přičemž nalezené vztahy jsou srozumitelné a vysvětlitelné. Tato srozumitelnost napomáhá možnosti využívat je pro učení business pravidel. K učení business pravidel lze využívat nejen jednoduchá asociační pravidla získávaná pomocí algoritmu Apriori či FP Growth, ale také složitější asociační pravidla získávaná za využití metody GUHA. V rámci práce je využívána procedura 4ft-Miner data miningového systému LISp-Miner. V rámci této práce je nejprve popsána problematika business pravidel a jejich využívání pro modelování podniků i praktické zapojení do podnikových procesů a také problematika dolování asociačních pravidel. S ohledem na roztříštěnost specifikací a standardů pro definici business rules je v rámci práce definován a následně prakticky aplikován proces pro výběr odpovídající specifikace business rules pro konkrétní praktické využití. Následně jsou v rámci práce navrženy tři způsoby zapojení dolování asociačních pravidel pro učení business rules. V rámci těchto modelů byl též definován model pro transformaci GUHA asociačních pravidel do business pravidel ve formátu DRL (pro systém Drools). Pro možnost získávání business pravidel za využití většího množství zdrojů, zejména za využití dolování asociačních pravidel z většího množství data setů, je v další části práce navržena struktura báze znalostí vhodné pro propojení business rules a asociačních pravidel z většího množství zdrojů, přičemž z pohledu business rules slouží jako terminologický slovník, pro dolování asociačních pravidel pak plní úlohu báze doménových znalostí pro předzpracování dat. Navržené modely byly ověřeny za využití praktických implementacích v systémech EasyMiner (v kombinaci se systémem Drools) a Erian. V rámci práce jsou kromě popisů praktických implementací definovány také dva modelové příklady praktického využití učení business pravidel z výsledků dolování GUHA asociačních pravidel, založené na reálných datech. Jeden v oblasti marketingu, druhý v oblasti kontrol ve zdravotních pojišťovnách.In the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance
Correction to: The Small GTPase RAC1/CED-10 Is Essential in Maintaining Dopaminergic Neuron Function and Survival Against α-Synuclein-Induced Toxicity
No full textWith the author(s)’ decision to opt for Open Choice the copyright of the article changed on March 2018 to © The Author(s) 2018 and the name of one of the author was changed to “Sanjib Guha”.</jats:p
Business rule learning using data mining of GUHA association rules
No full textV současném vysoce-konkurenčním prostředí je pro podniky velmi důležité, aby jejich informační systémy nejen co nejefektivněji podporovaly stávající podnikové procesy, ale aby se zároveň byly schopny se dynamicky přizpůsobovat měnícímu se prostředí. Stále více se prosazují snahy vzájemně oddělit aplikační a business logiku v rámci informačních systémů, přičemž jedním z vhodných prostředků pro záznam business logiky je využití business rules. Business rules jakožto jednoduchá a srozumitelná pravidla je možné využívat nejen pro shromažďování znalostí v rámci podniku, ale také pro aktivní rozhodování a řízení podnikových procesů. Ačkoliv je business rule přístup využíván již téměř 20 let, jednotlivé specifikace a možné aplikace business rules jsou stále předmětem aktivního výzkumu i praktického vývoje. Nevýhodou business rules je velká náročnost jejich získávání - pravidla jsou obvykle zadávána ručně prostřednictvím doménových expertů. Jedním z problémů, na které je zaměřen aktuální výzkumu v této oblasti, je možnost (polo)automatického získávání business rules z jiných zdrojů - podnikových dokumentů či historických dat. Získávání business pravidel z historických podnikových dat je věnována také tato práce. Hlavním cílem této disertační práce je navrhnout a ověřit metodu (polo)automatického učení business rules za využití dolování asociačních pravidel. Asociační pravidla jsou známou data miningovou metodou objevování zajímavých vztahů v datech, přičemž nalezené vztahy jsou srozumitelné a vysvětlitelné. Tato srozumitelnost napomáhá možnosti využívat je pro učení business pravidel. K učení business pravidel lze využívat nejen jednoduchá asociační pravidla získávaná pomocí algoritmu Apriori či FP Growth, ale také složitější asociační pravidla získávaná za využití metody GUHA. V rámci práce je využívána procedura 4ft-Miner data miningového systému LISp-Miner. V rámci této práce je nejprve popsána problematika business pravidel a jejich využívání pro modelování podniků i praktické zapojení do podnikových procesů a také problematika dolování asociačních pravidel. S ohledem na roztříštěnost specifikací a standardů pro definici business rules je v rámci práce definován a následně prakticky aplikován proces pro výběr odpovídající specifikace business rules pro konkrétní praktické využití. Následně jsou v rámci práce navrženy tři způsoby zapojení dolování asociačních pravidel pro učení business rules. V rámci těchto modelů byl též definován model pro transformaci GUHA asociačních pravidel do business pravidel ve formátu DRL (pro systém Drools). Pro možnost získávání business pravidel za využití většího množství zdrojů, zejména za využití dolování asociačních pravidel z většího množství data setů, je v další části práce navržena struktura báze znalostí vhodné pro propojení business rules a asociačních pravidel z většího množství zdrojů, přičemž z pohledu business rules slouží jako terminologický slovník, pro dolování asociačních pravidel pak plní úlohu báze doménových znalostí pro předzpracování dat. Navržené modely byly ověřeny za využití praktických implementacích v systémech EasyMiner (v kombinaci se systémem Drools) a Erian. V rámci práce jsou kromě popisů praktických implementací definovány také dva modelové příklady praktického využití učení business pravidel z výsledků dolování GUHA asociačních pravidel, založené na reálných datech. Jeden v oblasti marketingu, druhý v oblasti kontrol ve zdravotních pojišťovnách.In the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance
Measuring Access and Practice: Designing a Survey Methodology for the Hygiene, Sanitation and Water Sector
No full textAccess to safe water and sanitary means of excreta disposal are essential elements of
human development and poverty alleviation. It is estimated that one in four people in
the developing world lacks access to water while over half the population has no
access to sanitation. From the Alma-Ata declaration in 1978 to the recent Millennium
Development Goals, efforts to improve this situation have been hampered by the lack
of meaningful indicators to measure hygiene, sanitation and water coverage and
establish progress towards the goals and targets set out by the international
community.
This thesis aims to determine if measuring prevalence of access to water~ sanitation
and the practice of hygienic behaviour in hous~hold surveys can be.improved. With
no indicators available in current international' laws and targets, various aspects of
access and practice were examined to design indicators for field-testing. By using
- existing data sets, the research established that there is a high geographic clustering of
the measures of interest, which results in large design effects (deff) and rates of
homogeneity (roh) in cluster surveys. Based on the calculated roh optimum numbers
ofcluster and sample size were calculated for the field trials. This requires
introducing survey costs in the sample size calculations. The high clustering of water
and sanitation indicator require large sample sizes, resulting in large amounts of data
which organisations in the four field trials in Kosovo, South Africa, Kenya and Laos
found difficult to handle. Practical problems in the implementation of the survey
method resulted in non-sampling errors and could cause reluctance in adoption the
methodology. The research improved water and sanitation indicators but found that
for individual behaviour such as hygiene the household is not a suitable sampling unit.
It also showed that observation among interviewers have to be better standardised to
reduce the inter-surveyor.variation. Representative sampling is the current bottleneck
in the development of such a survey method. Current method requires a good
understanding of sampling theory as well as reliable sample frames, which are rarely
available to implementing organisations. Alternative sampling methods are
suggested, and recommendations are made for the further development ofthe survey
method designed in this research, which to date may be too complex for widespread
use
Business rule learning using data mining of GUHA association rules
No full textIn the currently highly competitive environment, the information systems of the businesses should not only effectively support the existing business processes, but also have to be dynamically adaptable to the changes in the environment. There are increasing efforts at separation of the application and the business logic in the information system. One of the appropriate instruments for this separation is the business rule approach. Business rules are simple, understandable rules. They can be used for the knowledge externalization and sharing also as for the active control and decisions within the business processes. Although the business rule approach is used for almost 20 years, the various specifications and practical applications of business rules are still a goal of the active research. The disadvantage of the business rule approach is great demands on obtaining of the rules. There has to be a domain expert, who is able to manually write them. One of the problems addressed by the current research is the possibility of (semi)automatic acquisition of business rules from the different resources - unstructured documents, historical data etc. This dissertation thesis addresses the problem of acquisition (learning) of business rules from the historical data of the company. The main objective of this thesis is to design and validate a method for (semi)automatic learning of business rules using the data mining of association rules. Association rule are a known data mining method for discovering of interesting relations hidden in the data. Association rules are comprehensible and explainable. The comprehensibility of association rules is suitable for the use of them for learning of business rules. For this purpose the user can use not only simple rules discovered using the algorithm Apriori or FP-Growth, but also more complex association rules discovered using the GUHA method. Within this thesis is used the procedure 4ft-Miner implemented in the data mining system LISp Miner. The first part of this thesis contains the description of the relevant topics from the research of business rules and association rules. Business rules is not a name of one specification of standard but rather a label of the approach to modelling of business logic. As part of the work there is defined a process of selection of the most appropriate specification of business rules for the selected practical use. Consequently, the author proposed three models of involving of data mining of association rules into business rule sets. These models contain also the definition of a model for the transformation of GUHA association rules in the business rules for the system JBoss Drools. For the possibility of learning of business rules using the data mining results from more than one data set, the author proposed a knowledge base. The knowledge base is suitable for the interconnection of business rules and data mining of association rules. From the perspective of business rules the knowledge base is a term dictionary. From the perspective of data mining the knowledge base contains some background knowledge for data preprocessing and preparation of classification models. The proposed models have been validated using practical implementations in the systems EasyMiner (in conjunction with JBoss Drools) and Erian. The thesis contains also a description of two model use cases based on real data from the field of marketing and the field of health insurance
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