RECRUITMENT APP – A CUSTOM APPLICATION ON SALESFORCE.COM

Cloud Computing is one of the upcoming Internet based technology. It is been considered as the next generation computing model for its advantages. It is the latest computational model after distributed computing, parallel processing and grid computing. To be effective they need to tap all available sources of supply, both internal and external. The system has facilities where prospective candidates can upload their CV’s and other academic achievements. Earlier recruitment was done manually and it was all at a time-consuming work. Now it is all possible in a fraction of second. Better recruitment and selection strategies result in improved organizational outcomes. With reference to this context, the research paper entitled Recruitment and Selection has been prepared to put a light on Recruitment and Selection process.

A Formal Framework for Complex Event Recognition

Complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees. In this article, we embark on the task of giving a rigorous and efficient framework to CER. We propose a formal language for specifying complex events, called complex event logic (CEL), that contains the main features used in the literature and has a denotational and compositional semantics. We also formalize the so-called selection strategies, which had only been presented as by-design extensions to existing frameworks. We give insight into the language design trade-offs regarding the strict sequencing operators of CEL and selection strategies. With a well-defined semantics at hand, we discuss how to efficiently process complex events by evaluating CEL formulas with unary filters. We start by introducing a formal computational model for CER, called complex event automata (CEA), and study how to compile CEL formulas with unary filters into CEA. Furthermore, we provide efficient algorithms for evaluating CEA over event streams using constant time per event followed by output-linear delay enumeration of the results.

A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling

Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output , Instance Relations , Measure Functionality , and Measure Valence . We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.

Material Methods; Considering Ceramic Raw Materials and the Spread of the Potter‘s Wheel in Early Iron Age Southern Iberia

This paper discusses the role of clay selection and preparation in the production of wheel-made pottery in Early Iron Age southern Iberia. The first systematic use of potter’s wheels in the production of Early Iron Age ceramics in southern Iberia corresponds to the establishment of pottery workshops associated with Phoenician trade colonies, dating to the period between the end of the 10th and 7th century BCE. There are still many gaps in our understanding of how technological knowledge was transmitted between the Phoenician workshops and “indigenous’ communities that adopted the potter’s wheel. This paper draws upon a growing body of archaeometric and ceramic technological research to consider clay selection strategies in these new workshops. Secondly, this paper will consider the role of ceramic raw materials in the development of new “hybrid’ ceramic forms, particularly grey-ware. It will hereby provide theoretical considerations surrounding the significance of material cultural hybridity in answering questions raised by postcolonial archaeologists about identity, cultural transmission and hybridisation in the context of the Phoenician colonial system.

Revisiting the Role of the Medial Temporal Lobe in Motor Learning

Classic taxonomies of memory distinguish explicit and implicit memory systems, placing motor skills squarely in the latter branch. This assertion is in part a consequence of foundational discoveries showing significant motor learning in amnesics. Those findings suggest that declarative memory processes in the medial temporal lobe (MTL) do not contribute to motor learning. Here, we revisit this issue, testing an individual (L. S. J.) with severe MTL damage on four motor learning tasks and comparing her performance to age-matched controls. Consistent with previous findings in amnesics, we observed that L. S. J. could improve motor performance despite having significantly impaired declarative memory. However, she tended to perform poorly relative to age-matched controls, with deficits apparently related to flexible action selection. Further supporting an action selection deficit, L. S. J. fully failed to learn a task that required the acquisition of arbitrary action–outcome associations. We thus propose a modest revision to the classic taxonomic model: Although MTL-dependent memory processes are not necessary for some motor learning to occur, they play a significant role in the acquisition, implementation, and retrieval of action selection strategies. These findings have implications for our understanding of the neural correlates of motor learning, the psychological mechanisms of skill, and the theory of multiple memory systems.

Entropy-Driven Global Best Selection in Particle Swarm Optimization for Many-Objective Software Package Restructuring

Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution. It has been observed that these kinds of many-objective optimization problems (MaOPs) often pose several performance challenges to the traditional multi-objective optimization algorithms. To address the performance issue caused by the different types of MaOPs, recently, a variety of many-objective particle swarm optimization (MaOPSO) has been proposed. However, external archive maintenance and selection of leaders for designing the MaOPSO to real-world MaOPs are still challenging issues. This work presents a MaOPSO based on entropy-driven global best selection strategy (called EMPSO) to solve the many-objective software package restructuring (MaOSPR) problem. EMPSO makes use of the entropy and quality indicator for the selection of global best particle. To evaluate the performance of the proposed approach, we applied it over the five MaOSPR problems. We compared it with eight variants of MaOPSO, which are based on eight different global best selection strategies. The results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches.

Study on Population Dynamics and Effects of Inbreeding on Performance Traits in Indian Murrah Buffalo

Background: A population is continuously facing the changing environment and its directly influencing the production of animal so to adopt these changes population must be flexible and have sufficient variability to overcome the adverse affects of environment. The evaluation of animals in terms of production performance traits along with impact of inbreeding coefficient is essential to formulate breeding and selection strategies for higher genetic improvement. Methods: Genealogy data of 6429 animals maintained at ICAR-NDRI, Karnal, India was analyzed by web-based POPREP application tool (http:// poprep.tzv.fal.de) and ENDOG V5.8 used to study the population structure and genetic diversity and regression model to study the effect of inbreeding on first lactation productive traits in Murrah buffaloes. Result: The result indicated that 91.91% of the individuals had known pedigree. The maximum generation traced was 13 with mean, full and equivalent complete generation as 5.93, 1.67 and 3.25 respectively. The average generation interval was 8.28 years and longer for the sire-son pathway and 2.16% was average inbreeding in whole population. The average genetic diversity loss was 2.10% indicated that the population has been stable with sufficient diversity. The study also revealed non significant effect of inbreeding on all first lactation traits. The low inbreeding was firstly due to introduction of new genetic variant and culling of animals avoiding mating of related ones and secondly due to incompleteness of pedigree in earlier years. This can be used as a base line information of phenomic needs to be generated before applying genomics tools in particular herd to be used as reference population in future for genomic selection.