18,317 research outputs found
Temporal naturalism
Two people may claim both to be naturalists, but have divergent conceptions
of basic elements of the natural world which lead them to mean different things
when they talk about laws of nature, or states, or the role of mathematics in
physics. These disagreements do not much affect the ordinary practice of
science which is about small subsystems of the universe, described or explained
against a background, idealized to be fixed. But these issues become crucial
when we consider including the whole universe within our system, for then there
is no fixed background to reference observables to. I argue here that the key
issue responsible for divergent versions of naturalism and divergent approaches
to cosmology is the conception of time. One version, which I call temporal
naturalism, holds that time, in the sense of the succession of present moments,
is real, and that laws of nature evolve in that time. This is contrasted with
timeless naturalism, which holds that laws are immutable and the present moment
and its passage are illusions. I argue that temporal naturalism is empirically
more adequate than the alternatives, because it offers testable explanations
for puzzles its rivals cannot address, and is likely a better basis for solving
major puzzles that presently face cosmology and physics.
This essay also addresses the problem of qualia and experience within
naturalism and argues that only temporal naturalism can make a place for qualia
as intrinsic qualities of matter
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
Video Anomaly Detection (VAD) is an important topic in computer vision.
Motivated by the recent advances in self-supervised learning, this paper
addresses VAD by solving an intuitive yet challenging pretext task, i.e.,
spatio-temporal jigsaw puzzles, which is cast as a multi-label fine-grained
classification problem. Our method exhibits several advantages over existing
works: 1) the spatio-temporal jigsaw puzzles are decoupled in terms of spatial
and temporal dimensions, responsible for capturing highly discriminative
appearance and motion features, respectively; 2) full permutations are used to
provide abundant jigsaw puzzles covering various difficulty levels, allowing
the network to distinguish subtle spatio-temporal differences between normal
and abnormal events; and 3) the pretext task is tackled in an end-to-end manner
without relying on any pre-trained models. Our method outperforms
state-of-the-art counterparts on three public benchmarks. Especially on
ShanghaiTech Campus, the result is superior to reconstruction and
prediction-based methods by a large margin.Comment: Accepted by ECCV'2022; Code is available at
https://github.com/gdwang08/Jigsaw-VA
Stateless Puzzles for Real Time Online Fraud Preemption
The profitability of fraud in online systems such as app markets and social
networks marks the failure of existing defense mechanisms. In this paper, we
propose FraudSys, a real-time fraud preemption approach that imposes
Bitcoin-inspired computational puzzles on the devices that post online system
activities, such as reviews and likes. We introduce and leverage several novel
concepts that include (i) stateless, verifiable computational puzzles, that
impose minimal performance overhead, but enable the efficient verification of
their authenticity, (ii) a real-time, graph-based solution to assign fraud
scores to user activities, and (iii) mechanisms to dynamically adjust puzzle
difficulty levels based on fraud scores and the computational capabilities of
devices. FraudSys does not alter the experience of users in online systems, but
delays fraudulent actions and consumes significant computational resources of
the fraudsters. Using real datasets from Google Play and Facebook, we
demonstrate the feasibility of FraudSys by showing that the devices of honest
users are minimally impacted, while fraudster controlled devices receive daily
computational penalties of up to 3,079 hours. In addition, we show that with
FraudSys, fraud does not pay off, as a user equipped with mining hardware
(e.g., AntMiner S7) will earn less than half through fraud than from honest
Bitcoin mining
On the assumptions that we make about the world around us : a conceptual framework for feature transformation effects
Various phenomena such as halo effects, spontaneous trait inferences, and evaluative conditioning have in common that assumptions about object features (e.g., whether a person is intelligent or likeable) are influenced by other object features (e.g., whether that person is attractive or co-occurs with other liked persons). Surprisingly, these phenomena have rarely been related to each other, most likely because different phenomena are described using different terms. To overcome this barrier, we put forward a conceptual framework that can be used to describe a wide range of these phenomena. After introducing the four core concepts of the framework, we illustrate how it can be applied to various phenomena. Doing so helps to reveal similarities and differences between those phenomena, thus improving communication and promoting interactions between different areas of research. Finally, we illustrate the generative power of the framework by discussing some of the new research questions that it highlights
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics
We address the problem of video representation learning without
human-annotated labels. While previous efforts address the problem by designing
novel self-supervised tasks using video data, the learned features are merely
on a frame-by-frame basis, which are not applicable to many video analytic
tasks where spatio-temporal features are prevailing. In this paper we propose a
novel self-supervised approach to learn spatio-temporal features for video
representation. Inspired by the success of two-stream approaches in video
classification, we propose to learn visual features by regressing both motion
and appearance statistics along spatial and temporal dimensions, given only the
input video data. Specifically, we extract statistical concepts (fast-motion
region and the corresponding dominant direction, spatio-temporal color
diversity, dominant color, etc.) from simple patterns in both spatial and
temporal domains. Unlike prior puzzles that are even hard for humans to solve,
the proposed approach is consistent with human inherent visual habits and
therefore easy to answer. We conduct extensive experiments with C3D to validate
the effectiveness of our proposed approach. The experiments show that our
approach can significantly improve the performance of C3D when applied to video
classification tasks. Code is available at
https://github.com/laura-wang/video_repres_mas.Comment: CVPR 201
"Boring formal methods" or "Sherlock Holmes deduction methods"?
This paper provides an overview of common challenges in teaching of logic and
formal methods to Computer Science and IT students. We discuss our experiences
from the course IN3050: Applied Logic in Engineering, introduced as a "logic
for everybody" elective course at at TU Munich, Germany, to engage pupils
studying Computer Science, IT and engineering subjects on Bachelor and Master
levels. Our goal was to overcome the bias that logic and formal methods are not
only very complicated but also very boring to study and to apply. In this
paper, we present the core structure of the course, provide examples of
exercises and evaluate the course based on the students' surveys.Comment: Preprint. Accepted to the Software Technologies: Applications and
Foundations (STAF 2016). Final version published by Springer International
Publishing AG. arXiv admin note: substantial text overlap with
arXiv:1602.0517
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
- …