431,927 research outputs found
Citizen science:A new perspective to advance spatial pattern evaluation in hydrology
Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms. However, in practice, science still largely depends on computer based solutions, which inevitably gives benefits such as speed and the possibility to automatize processes. However, the human vision can be harnessed to evaluate the reliability of algorithms which are tailored to quantify similarity in spatial patterns. We established a citizen science project to employ the human perception to rate similarity and dissimilarity between simulated spatial patterns of several scenarios of a hydrological catchment model. In total, the turnout counts more than 2500 volunteers that provided over 43000 classifications of 1095 individual subjects. We investigate the capability of a set of advanced statistical performance metrics to mimic the human perception to distinguish between similarity and dissimilarity. Results suggest that more complex metrics are not necessarily better at emulating the human perception, but clearly provide auxiliary information that is valuable for model diagnostics. The metrics clearly differ in their ability to unambiguously distinguish between similar and dissimilar patterns which is regarded a key feature of a reliable metric. The obtained dataset can provide an insightful benchmark to the community to test novel spatial metrics
Towards Decoding Brain Activity During Passive Listening of Speech
The aim of the study is to investigate the complex mechanisms of speech
perception and ultimately decode the electrical changes in the brain accruing
while listening to speech. We attempt to decode heard speech from intracranial
electroencephalographic (iEEG) data using deep learning methods. The goal is to
aid the advancement of brain-computer interface (BCI) technology for speech
synthesis, and, hopefully, to provide an additional perspective on the
cognitive processes of speech perception. This approach diverges from the
conventional focus on speech production and instead chooses to investigate
neural representations of perceived speech. This angle opened up a complex
perspective, potentially allowing us to study more sophisticated neural
patterns. Leveraging the power of deep learning models, the research aimed to
establish a connection between these intricate neural activities and the
corresponding speech sounds. Despite the approach not having achieved a
breakthrough yet, the research sheds light on the potential of decoding neural
activity during speech perception. Our current efforts can serve as a
foundation, and we are optimistic about the potential of expanding and
improving upon this work to move closer towards more advanced BCIs, better
understanding of processes underlying perceived speech and its relation to
spoken speech.Comment: 27 pages, 7 figure
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
Detecting Out-of-distribution Objects Using Neuron Activation Patterns
Object detection is essential to many perception algorithms used in modern
robotics applications. Unfortunately, the existing models share a tendency to
assign high confidence scores for out-of-distribution (OOD) samples. Although
OOD detection has been extensively studied in recent years by the computer
vision (CV) community, most proposed solutions apply only to the image
recognition task. Real-world applications such as perception in autonomous
vehicles struggle with far more complex challenges than classification. In our
work, we focus on the prevalent field of object detection, introducing Neuron
Activation PaTteRns for out-of-distribution samples detection in Object
detectioN (NAPTRON). Performed experiments show that our approach outperforms
state-of-the-art methods, without the need to affect in-distribution (ID)
performance. By evaluating the methods in two distinct OOD scenarios and three
types of object detectors we have created the largest open-source benchmark for
OOD object detection
SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
Visual complexity identifies the level of intricacy and details in an image
or the level of difficulty to describe the image. It is an important concept in
a variety of areas such as cognitive psychology, computer vision and
visualization, and advertisement. Yet, efforts to create large, downloadable
image datasets with diverse content and unbiased groundtruthing are lacking. In
this work, we introduce Savoias, a visual complexity dataset that compromises
of more than 1,400 images from seven image categories relevant to the above
research areas, namely Scenes, Advertisements, Visualization and infographics,
Objects, Interior design, Art, and Suprematism. The images in each category
portray diverse characteristics including various low-level and high-level
features, objects, backgrounds, textures and patterns, text, and graphics. The
ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise
comparisons of images using the forced-choice methodology and with more than
1,600 contributors. The resulting relative scores are then converted to
absolute visual complexity scores using the Bradley-Terry method and matrix
completion. When applying five state-of-the-art algorithms to analyze the
visual complexity of the images in the Savoias dataset, we found that the
scores obtained from these baseline tools only correlate well with crowdsourced
labels for abstract patterns in the Suprematism category (Pearson correlation
r=0.84). For the other categories, in particular, the objects and advertisement
categories, low correlation coefficients were revealed (r=0.3 and 0.56,
respectively). These findings suggest that (1) state-of-the-art approaches are
mostly insufficient and (2) Savoias enables category-specific method
development, which is likely to improve the impact of visual complexity
analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table
Multi-Level Visual Alphabets
A central debate in visual perception theory is the argument for indirect versus direct perception; i.e., the use of intermediate, abstract, and hierarchical representations versus direct semantic interpretation of images through interaction with the outside world. We present a content-based representation that combines both approaches. The previously developed Visual Alphabet method is extended with a hierarchy of representations, each level feeding into the next one, but based on features that are not abstract but directly relevant to the task at hand. Explorative benchmark experiments are carried out on face images to investigate and explain the impact of the key parameters such as pattern size, number of prototypes, and distance measures used. Results show that adding an additional middle layer improves results, by encoding the spatial co-occurrence of lower-level pattern prototypes
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