Real-time video life streaming of events over a network continued to gain
more popularity among the populace. However, there is need to ensure the
judicious utilization of allocated bandwidth without compromising the Quality
of Service (QoS) of the system. In this regard, this paper presents an approach
based on spatio-temporal frame indexing that detects and eliminate redundancy
within and across captured frame, prior transmission from the server to
clients. The standard and local low motion videos were the two scenarios
considered in evaluating the performance of the proposed algorithm. Results
obtained showed that the proposed approach achieved an improvement of 5.13%,
15.8% and 5%, 15.6% improvement in terms of the buffer size and compression
ratio. Though with a tradeoff of the frame-built time, where both the standard
and local frame indexing outperforms the proposed scheme with 10.8% and 8.71%
respectively.

基于时空帧索引的方法可以检测和消除视频帧内部和跨帧的冗余，在保证系统的服务质量（QoS）的同时，合理利用分配的带宽。

基于时空的帧索引算法用于提升实时低运动视频流的 QoS

A Spatio-Temporal based Frame Indexing Algorithm for QoS Improvement in  Live Low-Motion Video Streaming

Abductive reasoning generates explanatory hypotheses for new observations
using prior knowledge. This paper investigates the use of forgetting, also
known as uniform interpolation, to perform ABox abduction in description logic
(ALC) ontologies. Non-abducibles are specified by a forgetting signature which
can contain concept, but not role, symbols. The resulting hypotheses are
semantically minimal and each consist of a set of disjuncts. These disjuncts
are each independent explanations, and are not redundant with respect to the
background ontology or the other disjuncts, representing a form of hypothesis
space. The observations and hypotheses handled by the method can contain both
atomic or complex ALC concepts, excluding role assertions, and are not
restricted to Horn clauses. Two approaches to redundancy elimination are
explored for practical use: full and approximate. Using a prototype
implementation, experiments were performed over a corpus of real world
ontologies to investigate the practicality of both approaches across several
settings.

本文研究了如何使用遗忘（uniform interpolation）技术实现描述逻辑（ALC）本体中的 ABox 推导，并探讨了完全和近似两种冗余性消除方法以提高实用性。

基于遗忘的 ALC ABox 引理推断（长版）

ABox Abduction via Forgetting in ALC (Long Version)

In this paper, we study the problem of designing efficient convolutional
neural network architectures with the interest in eliminating the redundancy in
convolution kernels. In addition to structured sparse kernels, low-rank kernels
and the product of low-rank kernels, the product of structured sparse kernels,
which is a framework for interpreting the recently-developed interleaved group
convolutions (IGC) and its variants (e.g., Xception), has been attracting
increasing interests.
Motivated by the observation that the convolutions contained in a group
convolution in IGC can be further decomposed in the same manner, we present a
modularized building block, {IGCV$2$:} interleaved structured sparse
convolutions. It generalizes interleaved group convolutions, which is composed
of two structured sparse kernels, to the product of more structured sparse
kernels, further eliminating the redundancy. We present the complementary
condition and the balance condition to guide the design of structured sparse
kernels, obtaining a balance among three aspects: model size, computation
complexity and classification accuracy. Experimental results demonstrate the
advantage on the balance among these three aspects compared to interleaved
group convolutions and Xception, and competitive performance compared to other
state-of-the-art architecture design methods.

本文研究设计高效卷积神经网络的问题，提出了一种模块化建筑单元 {IGCV$2$:}，这种结构化稀疏卷积消除卷积核冗余，并呈现出计算效率与精度上的平衡。实验证明，相较于现有的架构设计方法，本文提出的方法在模型大小、计算复杂性和分类精度方面都有优势。