DER is the primary metric to evaluate diarization performance while facing a
dilemma: the errors in short utterances or segments tend to be overwhelmed by
longer ones. Short segments, e.g., `yes' or `no,' still have semantic
information. Besides, DER overlooks errors in less-talked speakers. Although
JER balances speaker errors, it still suffers from the same dilemma.
Considering all those aspects, duration error, segment error, and
speaker-weighted error constituting a complete diarization evaluation, we
propose a Balanced Error Rate (BER) to evaluate speaker diarization. First, we
propose a segment-level error rate (SER) via connected sub-graphs and adaptive
IoU threshold to get accurate segment matching. Second, to evaluate diarization
in a unified way, we adopt a speaker-specific harmonic mean between duration
and segment, followed by a speaker-weighted average. Third, we analyze our
metric via the modularized system, EEND, and the multi-modal method on real
datasets. SER and BER are publicly available at this https URL.

提出了基于平衡误差率的方法来评估说话人分离，该方法包括段错误率、片段级误差率和说话人加权误差率，其中通过使用连接的子图和自适应 IoU 阈值来获得准确的片段匹配而提出了段级错误率（SER）。

说话人分离的平衡误差率

BER: Balanced Error Rate For Speaker Diarization

The exponential growth of the blockchain size has become a major contributing
factor that hinders the decentralisation of blockchain and its potential
implementations in data-heavy applications. In this paper, we propose segment
blockchain, an approach that segmentises blockchain and enables nodes to only
store a copy of one blockchain segment. We use \emph{PoW} as a membership
threshold to limit the number of nodes taken by an Adversary---the Adversary
can only gain at most $n/2$ of nodes in a network of $n$ nodes when it has
$50\%$ of the calculation power in the system (the Nakamoto blockchain security
threshold). A segment blockchain system fails when an Adversary stores all
copies of a segment, because the Adversary can then leave the system, causing a
permanent loss of the segment. We theoretically prove that segment blockchain
can sustain a $(AD/n)^m$ failure probability when the Adversary has no more
than $AD$ number of nodes and every segment is stored by $m$ number of nodes.
The storage requirement is mostly shrunken compared to the traditional design
and therefore making the blockchain more suitable for data-heavy applications.

本文提出了基于分段的区块链方法，该方法使节点只需存储一个区块链段，并基于工作量证明限制节点数，从而缩减存储需求并解决现有区块链的去中心化和大数据应用问题。

区块链的分段：一种减小存储空间的机制

Segment blockchain: A size reduced storage mechanism for blockchain

In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.

该论文提出了一种基于超图的方法（称为 HF）来适应和分割多结构数据，该方法采用了一种新的超图模型，可以有效地和高效地估计存在于含有离群值且具有复杂关系的数据中的模型实例的数量和参数，并开发了鲁棒的超图分区算法来检测模型拟合的子超图。