Theoretical Analysis for Scale-down-Aware Service Allocation in Cloud Storage Systems

Servcie allocation algorithms have been drawing popularity in cloudcomputing research community. There has been lots of research onimprovingservice allocation schemes for high utilization, latency reductionand VM migration enfficient, but few work focus on energy consumptionaffected by instance placement in data centers. In this paper we propose an algorithm in which to maximize the number of freed-up machines in data centers, machines that host purely scale-down instances, which are reuiqred to be shut down for energy saving at certain points of time. We intuitively employ a probability partitioning mechanism to schedule services such that the goal of the maximization can be achieved. Furthermore we perform a set of experiments to test the partitioning rules, which show that the proposed algorithms can dynamically increase the number of freed-up machines substantially.DOI:http://dx.doi.org/10.11591/ijece.v3i1.179

Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., \emph{person} or \emph{location}) and have equipped language models with access to an external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.Comment: NAACL 2019; updated to cite Zhou et al. (2018) EMNLP as a piece of related wor

On Stochastic Analysis of the Quantities of Information in Economics

In this paper, the definition of information and data are presented with examples illustrating the differences between them. To articulate the value of information, the definition of quantities of information, based on information theory, generally considered to be founded by Claude Shannon, has been quoted. In addition, with corresponding mathematical derivations, specific examples are employed to manifest the factors that make information valuable. At last, we have a multifold discussion on the importance of information to economics.DOI:http://dx.doi.org/10.11591/ijece.v2i6.179

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201

Detection of Thrombin with an Aptamer-Based Macromolecule Biosensor Using Bacterial Ghost System

A rapid on-site detection of exogenous proteins without the need for equipped laboratories or skilled personnel would benefit many areas. We built a rapid protein detection platform based on aptamer-induced inner-membrane scaffolds dimerization by virtue of bacterial ghost system. When the detection platform was coincubated with two kinds of aptamers targeting two different sites of thrombin, green fluorescence or beta-lactamase activity were yielded with two different designs. The latter was detected by commercially available testing strips

Detection of Thrombin with an Aptamer-Based Macromolecule Biosensor Using Bacterial Ghost System

A rapid on-site detection of exogenous proteins without the need for equipped laboratories or skilled personnel would benefit many areas. We built a rapid protein detection platform based on aptamer-induced inner-membrane scaffolds dimerization by virtue of bacterial ghost system. When the detection platform was coincubated with two kinds of aptamers targeting two different sites of thrombin, green fluorescence or β-lactamase activity were yielded with two different designs. The latter was detected by commercially available testing strips