Stacked filters

2020 ◽  
Vol 14 (4) ◽  
pp. 600-612
Author(s):  
Kyle Deeds ◽  
Brian Hentschel ◽  
Stratos Idreos
Keyword(s):  
False Positive ◽  
The Core ◽  
End To End ◽  
Core Idea ◽  
Better Than

We present Stacked Filters, a new probabilistic filter which is fast and robust similar to query-agnostic filters (such as Bloom and Cuckoo filters), and at the same time brings low false positive rates and sizes similar to classifier-based filters (such as Learned Filters). The core idea is that Stacked Filters incorporate workload knowledge about frequently queried non-existing values. Instead of learning, they structurally incorporate that knowledge using hashing and several sequenced filter layers, indexing both data and frequent negatives. Stacked Filters can also gather workload knowledge on-the-fly and adaptively build the filter. We show experimentally that for a given memory budget, Stacked Filters achieve end-to-end query throughput up to 130x better than the best alternative for a workload, either query-agnostic or classifier-based filters, and depending on where data is (SSD or HDD).

Let’s Try Real Democracy

2021 ◽  
pp. 135-141
Author(s):  
Jason Brennan
Keyword(s):  
Deliberative Democracy ◽  
Legislative Process ◽  
Political Decision ◽  
The Core ◽  
The World ◽  
Laws And Policies ◽  
Core Idea ◽  
Decision Power ◽  
Better Than

This chapter lays out a general theoretical case for democracy, specifically the kind of democracy that democratic theorists call “deliberative democracy,” which traces the legitimacy of laws and policies to the reasoned exchange of arguments among free and equal citizens. The chapter shows the benefits of distributing political decision-power in an inclusive and egalitarian manner, especially in the deliberative phase of the legislative process. The core idea is that many minds deliberating together are better than few when it comes to dealing with the uncertainty and complexity of the world and figuring out solutions that work for all within it.

Sustainable Development is a Dead-End: The Logic of Modernity and Ecological Crisis

2020 ◽  
Author(s):  
Simon Lumsden
Keyword(s):  
Sustainable Development ◽  
Ecological Crisis ◽  
Self Determination ◽  
Ecological Sustainability ◽  
Planetary Boundaries ◽  
Western Modernity ◽  
Conceptual Frame ◽  
The Core ◽  
Dead End ◽  
Core Idea

This paper examines the theory of sustainable development presented by Jeffrey Sachs in The Age of Sustainable Development. While Sustainable Development ostensibly seeks to harmonise the conflict between ecological sustainability and human development, the paper argues this is impossible because of the conceptual frame it employs. Rather than allowing for a re-conceptualisation of the human–nature relation, Sustainable Development is simply the latest and possibly last attempt to advance the core idea of western modernity — the notion of self-determination. Drawing upon Hegel’s account of historical development it is argued that Sustainable Development and the notion of planetary boundaries cannot break out of a dualism of nature and self-determining agents.

Beyond response output: More logical than we think

2009 ◽  
Vol 32 (1) ◽  
pp. 87-88 ◽  
Author(s):  
Wim De Neys
Keyword(s):  
Bayesian Model ◽  
Response Selection ◽  
Data Fitting ◽  
Output Data ◽  
The Core ◽  
Response Output ◽  
Exclusive Focus ◽  
Better Than

AbstractOaksford & Chater (O&C) rely on a data fitting approach to show that a Bayesian model captures the core reasoning data better than its logicist rivals. The problem is that O&C's modeling has focused exclusively on response output data. I argue that this exclusive focus is biasing their conclusions. Recent studies that focused on the processes that resulted in the response selection are more positive for the role of logic.

DS-kNN

2021 ◽  
Vol 15 (2) ◽  
pp. 131-144
Author(s):  
Redha Taguelmimt ◽  
Rachid Beghdad
Keyword(s):  
Intrusion Detection ◽  
False Positive ◽  
Detection Rate ◽  
Nearest Neighbors ◽  
The Other ◽  
Intrusion Detection Systems ◽  
K Nearest Neighbors ◽  
Detection Systems ◽  
Knn Classifier ◽  
Better Than

On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.

scholarly journals Semi-Supervised Noisy Label Learning for Chinese Medical Named Entity Recognition

2021 ◽  
pp. 1-10
Author(s):  
Zhucong Li ◽  
Zhen Gan ◽  
Baoli Zhang ◽  
Yubo Chen ◽  
Jing Wan ◽  
...  
Keyword(s):  
Hybrid System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Final Test ◽  
Named Entity ◽  
The Core ◽  
Category Labels ◽  
Adversarial Training ◽  
Core Idea ◽  
The Impact

Abstract This paper describes our approach for the Chinese Medical named entity recognition(MER) task organized by the 2020 China conference on knowledge graph and semantic computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We construct a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule postprocessing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we use post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

scholarly journals Automatic Inference of Sound Correspondence Patterns Across Multiple Languages

2018 ◽  
Author(s):  
Johann-Mattis List
Keyword(s):  
Pattern Recognition ◽  
Graph Theory ◽  
Crucial Role ◽  
Phylogenetic Reconstruction ◽  
Automatic Method ◽  
Network Approach ◽  
The Core ◽  
Cover Problem ◽  
Core Idea ◽  
Multiple Languages

Sound correspondence patterns play a crucial role for linguistic reconstruction. Linguists use them to prove language relationship, to reconstruct proto-forms, and for classical phylogenetic reconstruction based on shared innovations. Cognate words which fail to conform with expected patterns can further point to various kinds of exceptions in sound change, such as analogy or assimilation of frequent words. Here we present an automatic method for the inference of sound correspondence patterns across multiple languages based on a network approach. The core idea is to represent all columns in aligned cognate sets as nodes in a network with edges representing the degree of compatibility between the nodes. The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique. The resulting partitions represent all correspondence patterns which can be inferred for a given dataset. By excluding those patterns which occur in only a few cognate sets, the core of regularly recurring sound correspondences can be inferred. Based on this idea, the paper presents a method for automatic correspondence pattern recognition, which is implemented as part of a Python library which supplements the paper. To illustrate the usefulness of the method, we present how the inferred patterns can be used to predict words that have not been observed before.

scholarly journals Asymmetric Distribution Measure for Few-shot Learning

2020 ◽  
Author(s):  
Wenbin Li ◽  
Lei Wang ◽  
Jing Huo ◽  
Yinghuan Shi ◽  
Yang Gao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Asymmetric Distribution ◽  
Query Image ◽  
Local Descriptor ◽  
Innovative Design ◽  
Feature Representations ◽  
The Core ◽  
Measure Function ◽  
Asymmetric Relation ◽  
Core Idea

The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings. Previous work mainly focuses on image-level feature representations, which actually cannot effectively estimate a class's distribution due to the scarcity of samples. Some recent work shows that local descriptor based representations can achieve richer representations than image-level based representations. However, such works are still based on a less effective instance-level metric, especially a symmetric metric, to measure the relation between a query image and a support class. Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning. To that end, we propose a novel Asymmetric Distribution Measure (ADM) network for few-shot learning by calculating a joint local and global asymmetric measure between two multivariate local distributions of a query and a class. Moreover, a task-aware Contrastive Measure Strategy (CMS) is proposed to further enhance the measure function. On popular miniImageNet and tieredImageNet, ADM can achieve the state-of-the-art results, validating our innovative design of asymmetric distribution measures for few-shot learning. The source code can be downloaded from https://github.com/WenbinLee/ADM.git.

scholarly journals Keadilan Hukum Jeremy Bentham dan Relevansinya bagi Praktik Hukum Kontemporer

Humaniora ◽  
2012 ◽  
Vol 3 (1) ◽  
pp. 299
Author(s):  
Frederikus Fios
Keyword(s):  
Global Politics ◽  
Jeremy Bentham ◽  
Ideal Model ◽  
The Core ◽  
The Subject ◽  
Core Idea ◽  
The Universe ◽  
Universe Of Discourse ◽  
British Philosopher ◽  
The Ideal

Fair punishment for a condemned has been long debated in the universe of discourse of law and global politics. The debate on the philosophical level was no less lively. Many schools of thought philosophy question, investigate, reflect and assess systematically the ideal model for the subject just punishment in violation of the law. One of the interesting and urgent legal thought Jeremy Bentham, a British philosopher renowned trying to provide a solution in the middle of the debate was the doctrine or theory of utilitarianism. The core idea is that the fair punishment should be a concern for happiness of a condemned itself, and not just for revenge. Bentham thought has relevance in several dimensions such as dimensions of humanism, moral and utility.  

scholarly journals Analisis Optimasi Kinerja Protokol Routing AODV dan AOMDV dengan Menggunakan Metode RFAP untuk Mencegah RREQ Flooding Attacks pada Jaringan MANET

2019 ◽  
Vol 1 (1) ◽  
pp. 9-18
Author(s):  
Andy Hidayat Jatmika ◽  
I Made Windra Yudistiana ◽  
Ariyan Zubaidi
Keyword(s):  
Destination Node ◽  
Malicious Nodes ◽  
End To End Delay ◽  
Flooding Attacks ◽  
End To End ◽  
Prevention Method ◽  
Prevention Methods ◽  
Bandwidth Capacity ◽  
Route Request ◽  
Better Than

One sector that greatly influences it is in terms of network security. This is due to the characteristics of the MANET network that are dynamic so that the MANET network is very easily disturbed by irresponsible parties. One of the attacks that can occur in MANET network is Route Request (RREQ) Flooding Attacks. In RREQ flooding attacks in the form of fake nodes that are outside the area of the network and broadcast RREQ to the destination node in the network, so that it meets the bandwidth capacity which results in a decrease in quality in determining the route of sending data or information to the destination node. To prevent the occurrence of RREQ flooding attacks, a prevention method for these attacks is required, namely the RREQ Flooding Attacks Prevention (RFAP). This method works by finding nodes that are likely to be malicious nodes then isolated from the network to be restored to normal nodes. This research will optimize the AODV and AOMDV routing protocols by adding RFAP prevention methods and knowing the performance of the two protocols in terms of throughput, average end-to-end delay and normalized routing load. Based on the results of the simulation, that the application of the method RFAP on AODV routing protocol can produce network quality is better than AOMDV protocol, both in terms of throughput, average end-to-end delay and normalized routing load.

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