Achieving Robust Self-Management for Large-Scale Distributed Applications

Autonomic managers are the main architectural building blocks for constructing self-management capabilities of computing systems and applications. One of the major challenges in developing self-managing applications is robustness of management elements which form autonomic managers. We believe that transparent handling of the effects of resource churn (joins/leaves/failures) on management should be an essential feature of a platform for self-managing large-scale dynamic distributed applications, because it facilitates the development of robust autonomic managers and hence improves robustness of self-managing applications. This feature can be achieved by providing a robust management element abstraction that hides churn from the programmer. In this paper, we present a generic approach to achieve robust services that is based on finite state machine replication with dynamic reconfiguration of replica sets. We contribute a decentralized algorithm that maintains the set of nodes hosting service replicas in the presence of churn. We use this approach to implement robust management elements as robust services that can operate despite of churn. Our proposed decentralized algorithm uses peer-to-peer replica placement schemes to automate replicated state machine migration in order to tolerate churn. Our algorithm exploits lookup and failure detection facilities of a structured overlay network for managing the set of active replicas. Using the proposed approach, we can achieve a long running and highly available service, without human intervention, in the presence of resource churn. In order to validate and evaluate our approach, we have implemented a prototype that includes the proposed algorithm

Fiscal space and the procyclicality of fiscal policy: the case for making hay while the sun shines

Utilizing data from 133 countries over the period 1950-2014, we identify fiscal space - the ability to pursue active fiscal policy without undermining fiscal sustainability - as a key factor underlying the cyclicality of fiscal policies. We find that less fiscal space induces greater fiscal procyclicality; and the reduction in fiscal space in high income countries in the post-global financial crisis period prevented these economies from adopting countercyclical fiscal policies. We also show that this relationship is non-linear such that countries in the bottom tail of the fiscal space distribution need to make significant improvements before they are able to perform countercyclical policy. Taken together with the increasingly dominant role of fiscal action in downturns, as is highlighted in the context of the responses to the Covid-19 crisis, these findings clearly indicate the importance of building fiscal space in good times to provide capacity for countercyclical policy in bad times

Screening of Arabidopsis mutants for functional genomic studies

Eight photosynthetic Arabidopsis mutants were screened for co-segregation of a photosynthetic phenotype with the T-DNA insertion. These mutants were selected from 80 photosynthetic mutants with genetic background of Columbia-0. Two different screening approaches were used to study the T-DNA insertion in the genome of mutant Arabidopsis lines. The sulphonamide sulfadiazine was found to be an effective selective agent and a single copy of sulfonamide resistant gene was found to be completely resistant to the optimal concentration i.e., 5mg mL-1. The maximum number of Arabidopsis mutant plants had confirmed insertions. Some of the plants did not show any amplification with gene specific primer combination, and it was assumed that either they were wild type plants or they had random T-DNA insertion and the insertion was not found in the gene under study but it could be found in any where in the genome. Some mutant plants were morphologically different from the wild type plants e.g., ALP105. These plants grew as small in size and dark green in color. After PCR screening with gene specific and T-DNA border primers all such mutant plants were confirmed as heterozygous T-DNA insertion plants

Elevating Code-mixed Text Handling through Auditory Information of Words

With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, different scripts, and a lack of resources. Current language models face difficulty in effectively handling code-mixed data as they primarily focus on the semantic representation of words and ignore the auditory phonetic features. This leads to difficulties in handling spelling variations in code-mixed text. In this paper, we propose an effective approach for creating language models for handling code-mixed textual data using auditory information of words from SOUNDEX. Our approach includes a pre-training step based on masked-language-modelling, which includes SOUNDEX representations (SAMLM) and a new method of providing input data to the pre-trained model. Through experimentation on various code-mixed datasets (of different languages) for sentiment, offensive and aggression classification tasks, we establish that our novel language modeling approach (SAMLM) results in improved robustness towards adversarial attacks on code-mixed classification tasks. Additionally, our SAMLM based approach also results in better classification results over the popular baselines for code-mixed tasks. We use the explainability technique, SHAP (SHapley Additive exPlanations) to explain how the auditory features incorporated through SAMLM assist the model to handle the code-mixed text effectively and increase robustness against adversarial attacks \footnote{Source code has been made available on \url{https://github.com/20118/DefenseWithPhonetics}, \url{https://www.iitp.ac.in/~ai-nlp-ml/resources.html\#Phonetics}}.Comment: Accepted to EMNLP 202

Online Information Searching Techniques: An Investigation from Library Science Professionals

The purpose of this study is to identify the effective information retrieval techniques used by the library and information science professionals/librarians to successfully retrieve the required information from various online information sources. This study also investigates the opinion of information professionals about various useful online information resources which are helpful during research work. It highlights various advanced searching techniques used for retrieving precise results from various online information sources i.e. search engines, databases, repositories, digital libraries, online journals, websites, etc. In this study, a quantitative research method has been used by adopting a survey research design. The population of the study was 370 information professionals working as a librarian in various public and private sector universities of Punjab. Data was successfully obtained from 278 respondents by using a questionnaire and analyzed through SPSS software. The respondents identified various sources of information that are useful to find scholarly information. These sources include e-journals, HEC databases, digital libraries, Google Scholar, digital archives, etc. They identified various useful techniques to effectively formulate a search query and apply advanced searching techniques to retrieve the desired information. This study is highly useful for libraries to increase online information resources which are useful for research students and faculty. The results of this research are also useful for early-career librarians, faculty members, and researchers to understand online search techniques and successfully retrieve the required information for research and educational tasks