Session Three - Caribou & Human Activity
نویسندگان
چکیده
منابع مشابه
Methaemoglobin Content and NADH-Methaemoglobin Reductase Activity of Three Human Erythrocyte Genotypes
Background: To study methaemoglobin content and NADH-methaemoglobin reductase activity of three human erythrocyte genotypes (HbAA, HbAS and HbSS).Materials and Methods: Studies to ascertain methaemoglobin concentration and level of NADH-methaemoglobin reductase activity of three human erythrocyte genotypes (HbAA, HbAS and HbSS) were carried out in forty-three (43) healthy male participants of c...
متن کاملCaribou: Intelligent Distributed Storage
The ever increasing amount of data being handled in data centers causes an intrinsic inefficiency: moving data around is expensive in terms of bandwidth, latency, and power consumption, especially given the low computational complexity of many database operations. In this paper we explore near-data processing in database engines, i.e., the option of offloading part of the computation directly t...
متن کاملSession 3: Human Language Evaluation
* Cross-system evaluation: This is a mainstay of the periodic ARPA evaluations on competing systems. Multiple sites agree to run their respective systems on a single application, so that results across systems are comparable. This includes evaluations such as message understanding (MUC)[6], information retrieval (TREC)[7], spoken language systems (ATIS)[8], and automated speech recognition (CSR...
متن کاملSession - Key Generation using Human
We present session-key generation protocols in a model where the legitimate parties share only a human-memorizable password. The security guarantee holds with respect to probabilistic polynomial-time adversaries that control the communication channel (between the parties), and may omit, insert and modify messages at their choice. Loosely speaking, the eeect of such an adversary that attacks an ...
متن کاملModelling Session Activity with Neural Embedding
Neural embedding techniques are being applied in a growing number of machine learning applications. In this work, we demonstrate a neural embedding technique to model users’ session activity. Specifically, we consider a dataset collected from Microsoft’s App Store consisting of user sessions that include sequential click actions and item purchases. Our goal is to learn a latent manifold that ca...
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ژورنال
عنوان ژورنال: Rangifer
سال: 2000
ISSN: 1890-6729
DOI: 10.7557/2.20.5.1635