5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

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Digital Communication Systems

The theme of IEEE ICCI*CC'16 on Cognitive Informatics CI and Cognitive Computing CC was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel Part I in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

Cognitive Intelligence: Deep Learning, Thinking, and Reasoning by Brain-Inspired Systems

Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.

https://dl.acm.org/doi/pdf/10.1145/3324473

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Towards knowledge modeling and manipulation technologies

Polar code is a low-complexity and capacity-achieving code with applications in channel coding, source coding and secrecy One way to evaluate the error performance of the finite-length polar 'channel' code, for instance, is to use Bonferroni-type (eg, union bound) bounds requiring the Hamming weight distribution Currently, there are no low-complexity algorithms for the polar code's spectra In this paper, we propose a recursive probabilistic weight distribution (PWD) expression for polar codes As this expression is complex, two polynomial-time approximations and a tree-based approach have been presented for a faster implementation of the PWD method with much accuracy

On Probabilistic Weight Distribution of Polar Codes

Fountain codes were introduced to provide high reliability and scalability and low complexities for networks such as the Internet. Luby-transform (LT) codes, which are the first realisation of Fountain codes, achieve the capacity of the binary erasure channel (BEC) asymptotically and universally. Most previous work on single-layer Fountain coding targets the design via the right degree distribution. The left degree distribution of an LT code is left as a Poisson to protect the universality. For finite lengths, this is no longer an issue; thus, the author's focus is on designing better codes for the BEC at practical lengths. Their left degree shaping provides codes outperforming LT codes and all other competing schemes in the literature. At a bit error rate of 10−7 and packet length k = 256, their scheme provides a realised rate of 0.6 which is 23.5% higher than that of Sorensen et al.’s decreasing-ripple-size scheme.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2014.0658

Improved finite-length Luby-transform codes in the binary erasure channel

Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network HCN, which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis ACEA. The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification ARSC. Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.

Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

Formal Properties and Mathematical Rules of Concept Algebra for Cognitive Machine Learning (II)

Fountain codes were introduced to provide higher reliability, lower complexities, and more scalability for networks such as the Internet. Luby-Transform (LT) codes, which are the first realization of Fountain codes, achieve the capacity of the binary erasure channel (BEC) asymptotically and universally. For finite lengths, the search is continued to find codes closer to the capacity limits at even lower encoding and decoding complexities. Most previous work on single-layer Fountain coding targets the design via the right degree distribution. The left degree distribution of an LT code is left as Poisson to protect the universality. For finite lengths, this is no longer an issue; thus, we focus on the design of better codes for the BEC at practical lengths. Our left degree shaping provides codes outperforming LT and all other competing schemes in the literature. At a bit error rate of 10−7 and packet length k = 256, our scheme provides a realized rate of 0.6 which is 23.5% higher than Sorensen et al.'s scheme [1].

Mehrdad Valipour

Papers

On Probabilistic Weight Distribution of Polar Codes

Improved finite-length Luby-transform codes in the binary erasure channel

Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

Formal Properties and Mathematical Rules of Concept Algebra for Cognitive Machine Learning (II)

Left degree distribution shaping for LT codes over the binary erasure channel