4,098 research outputs found
Automated Knowledge Generation with Persistent Surveillance Video
The Air Force has increasingly invested in persistent surveillance platforms gathering a large amount of surveillance video. Ordinarily, intelligence analysts watch the video to determine if suspicious activities are occurring. This approach to video analysis can be a very time and manpower intensive process. Instead, this thesis proposes that by using tracks generated from persistent video, we can build a model to detect events for an intelligence analyst. The event that we chose to detect was a suspicious surveillance activity known as a casing event. To test our model we used Global Positioning System (GPS) tracks generated from vehicles driving in an urban area. The results show that over 400 vehicles can be monitored simultaneously in real-time and casing events are detected with high probability (43 of 43 events detected with only 4 false positives). Casing event detections are augmented by determining which buildings are being targeted. In addition, persistent surveillance video is used to construct a social network from vehicle tracks based on the interactions of those tracks. Social networks that are constructed give us further information about the suspicious actors flagged by the casing event detector by telling us who the suspicious actor has interacted with and what buildings they have visited. The end result is a process that automatically generates information from persistent surveillance video providing additional knowledge and understanding to intelligence analysts about terrorist activities
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Large language models with instruction-following capabilities open the door
to a wider group of users. However, when it comes to information extraction - a
classic task in natural language processing - most task-specific systems cannot
align well with long-tail ad hoc extraction use cases for non-expert users. To
address this, we propose a novel paradigm, termed On-Demand Information
Extraction, to fulfill the personalized demands of real-world users. Our task
aims to follow the instructions to extract the desired content from the
associated text and present it in a structured tabular format. The table
headers can either be user-specified or inferred contextually by the model. To
facilitate research in this emerging area, we present a benchmark named
InstructIE, inclusive of both automatically generated training data, as well as
the human-annotated test set. Building on InstructIE, we further develop an
On-Demand Information Extractor, ODIE. Comprehensive evaluations on our
benchmark reveal that ODIE substantially outperforms the existing open-source
models of similar size. Our code and dataset are released on
https://github.com/yzjiao/On-Demand-IE.Comment: EMNLP 202
Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding
In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)
Automatic object classification for surveillance videos.
PhDThe recent popularity of surveillance video systems, specially located in urban
scenarios, demands the development of visual techniques for monitoring purposes.
A primary step towards intelligent surveillance video systems consists on automatic
object classification, which still remains an open research problem and the keystone
for the development of more specific applications.
Typically, object representation is based on the inherent visual features. However,
psychological studies have demonstrated that human beings can routinely categorise
objects according to their behaviour. The existing gap in the understanding
between the features automatically extracted by a computer, such as appearance-based
features, and the concepts unconsciously perceived by human beings but
unattainable for machines, or the behaviour features, is most commonly known
as semantic gap. Consequently, this thesis proposes to narrow the semantic gap
and bring together machine and human understanding towards object classification.
Thus, a Surveillance Media Management is proposed to automatically detect and
classify objects by analysing the physical properties inherent in their appearance
(machine understanding) and the behaviour patterns which require a higher level of
understanding (human understanding). Finally, a probabilistic multimodal fusion
algorithm bridges the gap performing an automatic classification considering both
machine and human understanding.
The performance of the proposed Surveillance Media Management framework
has been thoroughly evaluated on outdoor surveillance datasets. The experiments
conducted demonstrated that the combination of machine and human understanding
substantially enhanced the object classification performance. Finally, the inclusion
of human reasoning and understanding provides the essential information to bridge
the semantic gap towards smart surveillance video systems
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