Workday (United States)

This paper critically examines existing modes of participation in design practice and machine learning. Cautioning against 'participation-washing', it suggests that the ML community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context-independent scalability.

Observability is a necessary capability of modern distributed systems as it allows us to gain actionable insights about reliability, availability, performance, etc., of the system.

Infrastructure-as-a-service cloud environment provides infrastructure services per the user's workflow requirements using the pay-you-go model on demand. Workflow applications are highly used in IT industries, project management, supply chain management, large distributed management, and many more. Load-balanced workflow allocation to solve all the requirements of the cloud users is already proven NP-hard. In this scenario, meta-heuristic algorithms are one of the best candidates to solve the load balancing problem in the cloud to achieve an optimal allocation schedule. In this paper, we proposed a load-balanced workflow allocation method using stochastic fractal search (SFS) algorithms to maximize the resource utilization of the virtualized cloud resources. In this way, the load imbalance on virtual machines can be minimized. SFS is an evolutionary optimization method that uses the natural growth behavior in the search process. The performance analysis is conducted by implementing the workflow load balancer in MATLAB. The study shows that the solutions achieved by the proposed SFS strategy are far better than those obtained by particle swarm optimization (PSO).

This article serves as an introduction to the practice of project management for an IAM project, describing basic project management terminology and practices. Given the number of systems an IAM project generally impacts, excellent project management is essential for the stakeholders involved.

IaaS Cloud provides infrastructural serviceon rental basis on demand by pooling of dynamically provisioned heterogeneous resources to cater the user's workflow applications. Workflows occur in business and industrial applications such as project management, supply chain management, molecular structure, blockchain, business process and require large distributed resource requirement. Workflow mapping while meeting the user's requirements in cloud environment has been proven as NP-Hard. In this work, we proposed a multi-objective workflow allocation (MOWA) strategy cloud to minimize makespan and flowtime simultaneously for IaaS. Level attributes has been taken to preserve precedence constraints. An experimental study has been conducted in MATLAB by producing a set of tradeoff solutions for MOWA and MOHEFT. Further, Pareto front performance measures of the tradeoff solutions are computed and compared. Study shows that the achieved solutions for MOWA are better Pareto-optimal solutions.

Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph construction, and question answering. Traditionally, this work has focused on text that has been well-formed, like news articles, but in common real world datasets such as messaging, resumes, or short-form social media, non-grammatical, loosely-structured text adds a new dimension to this problem. This paper presents Pangloss, a production system for entity disambiguation on noisy text. Pangloss combines a probabilistic linear-time key phrase identification algorithm with a semantic similarity engine based on context-dependent document embeddings to achieve better than state-of-the-art results (>5% in F1) compared to other research or commercially available systems. In addition, Pangloss leverages a local embedded database with a tiered architecture to house its statistics and metadata, which allows rapid disambiguation in streaming contexts and on-device disambiguation in low-memory environments such as mobile phones.