Visualizing Google Scholar Profile of Dr. S.R. Ranganathan using PoP and VOSviewer: a tribute to Father of Library Science in India

Dr. Shiyali Ramamrita Ranganathan was the well-known librarian and mathematician from India. He was also called the father of Indian librarianship. He made India library conscious in particular and he influenced the thinking of library world in general. It is mainly because of his efforts that library & information science became a subject of study and research. Dr S.R. Ranganathan has recorded 307 publications since 1931 including his contributed books, book chapters, reports, and journal articles, texts of invited speeches or special lecture. He received a total of 5455 citations with h-index 27. Highest citations (306) were received in the year 2017. Also, it was observed, “The Five Laws of Library Science” published in the year 1931 received highest citation 1213. Most of his collaborative works or articles in total, are with Neelameghan, A and Gopinath, M A

Binary Classifier Selection Based on Precision and Recall Metrics

Binary classifiers are used for bug deduplication in software development. However, the precision and recall metrics of individual classifiers may be insufficient for particular use cases. Selecting an appropriate binary classifier can improve bug deduplication, e.g., high precision can ensure that duplicate bugs are identified (and thus not analyzed), while providing sufficient recall (to eliminate redundant analysis of bugs that are duplicates). This disclosure describes automated techniques to identify the most appropriate classification model from a set of models. Two different functions - F-beta score and weighted sum score - are evaluated for different performance thresholds. The model and threshold combination with the highest score is selected and used for bug deduplication

Automated Techniques to Identify Most Relevant Duplicates for Bug Deduplication

During bug deduplication, selecting top bugs based on scores from a binary classification model does not work well if the model tends to return lower scores. Further, the selected top candidate bugs are not ranked based on relevance, which means that there is no mechanism to mark a bug as a duplicate without verifying each candidate. This disclosure describes automated techniques to identify ancestor bugs for a newly reported bug, to retrieve the top candidates for duplicate bugs, and rank the candidates based on a scoring function. The techniques are robust to pairwise matches between a newly reported bug and its ancestor bug failing to meet threshold scores. Rather, by relying on comparisons across the pool of bugs - open bugs as well as prior identified duplicates - along with transitive properties, the techniques automatically identify the ancestor bug in such situations. A scoring function is described that utilizes features such as number of duplicates attached to a bug, number of updates, score, number of incorrect duplicate predictions that the bug was part of, the number of affected users, etc. to rank the identified bugs

On the assessment of cyber risks and attack surfaces in a real-time co-simulation cybersecurity testbed for inverter-based microgrids

The integration of variable distributed generations (DGs) and loads in microgrids (MGs) has made the reliance on communication systems inevitable for information exchange in both control and protection architectures to enhance the overall system reliability, resiliency and sustainability. This communication backbone in turn also exposes MGs to potential malicious cyber attacks. To study these vulnerabilities and impacts of various cyber attacks, testbeds play a crucial role in managing their complexity. This research work presents a detailed study of the development of a real-time co-simulation testbed for inverter-based MGs. It consists of a OP5700 real-time simulator, which is used to emulate both the physical and cyber layer of an AC MG in real time through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human–machine interface (HMI) is used for local/remote monitoring and control. The creation and management of HMI is carried out in ACSELERATOR Diagram Builder SEL-5035 software. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established, which map the interaction among the corresponding nodes of cyber-physical layers and also synchronizes data transmission between the systems. The testbed not only provides a real-time co-simulation environment for the validation of the control and protection algorithms but also extends to the verification of various detection and mitigation algorithms. Moreover, an attack scenario is also presented to demonstrate the ability of the testbed. Finally, challenges and future research directions are recognized and discussed

Integrating Bug Deduplication in Software Development and Testing

A bug deduplicator identifies independently discovered bugs that have the same underlying cause. Deduplication of bugs reduces toil for the software team by reducing the number of bugs that developers need to examine. However, if a bug deduplicator incorrectly classifies a bug as a duplicate, human developers might ignore the bug, allowing it to escape to production. A tradeoff exists between toil reduction and risk tolerance. This disclosure describes techniques that enable a software team to trade off the effort to remove bugs (e.g., auto-close bugs so that humans save toil and time) against the risk of errors in a bug deduplicator. Custom settings and a confidence level that a bug is a duplicate are used to determine whether to log a particular bug, to log it with comments, etc. The techniques enable the embedding of a bug deduplicator at suitable locations within a software development toolchain. The performance of the bug deduplicator can be fine-tuned in real-time by an analysis of its true negative and false positive metrics

Bug Management Using Machine Learning

Automated tests of software can often independently log different bugs for the same underlying problem (root cause). Manually identifying duplicate bugs is a source of toil for engineers. A related problem of bug management is that of bug routing, e.g., determining the right team or person to route a bug report to for the purposes of debugging. This disclosure describes techniques for bug deduplication and bug routing based on machine learning (ML). Per the techniques, a binary machine classification model is trained to aggregate bugs with a common root cause. Bugs in a class of bugs with a common root cause are deduplicated, e.g., represented by just one of the multiple bugs in the class. Further, a multi-class ML model is trained to predict the right team for handling a new (incoming) bug

Machine Learning-based Linear regression way to deal with making data science model for checking the sufficiency of night curfew in Maharashtra, India

The birthplace of the novel Covid-19 sickness or COVID-19 began its spread around Wuhan city, China. The spread of this novel infection sickness began toward the start of December 2019. The Covid-19 illness spreads from one individual to another through hacking, sniffling, etc. To stop the spreading of the novel Covid-19 infection the distinctive nation has presented diverse strategies. Some regularly utilized methods are lockdown, night curfew, etc. The fundamental intention of the systems was to stop the social events and leaving homes without serious issues. Utilizing a diverse system Covid-19 first stage can address for saving individuals. Presently the second influx of this novel Covid illness has begun its top from the mid of April-May. The second convergence of this novel Covid disorder flooded all through the world and in India too. To stop the spread of this novel Covid sickness India's richest state Maharashtra government constrained the decision of night curfew. In this paper, we are taking as a relevant examination the night curfew on a schedule of Maharashtra. Here, we study that this system may or may not be able to stop the spread of pandemics. We are using the Machine learning(ML) approach to managing regulate study this case. ML has various systems yet among all of those here we use Linear Regression for the current circumstance. The reproduced insight that readies the plan orchestrated to learn with no other person. Linear Regression is the affirmed strategy for looking over the connection between two sections. Between the two segments, one is astute and another is a seen variable