10,857 research outputs found
The Neurocognitive Process of Digital Radicalization: A Theoretical Model and Analytical Framework
Recent studies suggest that empathy induced by narrative messages can effectively facilitate persuasion and reduce psychological reactance. Although limited, emerging research on the etiology of radical political behavior has begun to explore the role of narratives in shaping an individualâs beliefs, attitudes, and intentions that culminate in radicalization. The existing studies focus exclusively on the influence of narrative persuasion on an individual, but they overlook the necessity of empathy and that in the absence of empathy, persuasion is not salient. We argue that terrorist organizations are strategic in cultivating empathetic-persuasive messages using audiovisual materials, and disseminating their message within the digital medium. Therefore, in this paper we propose a theoretical model and analytical framework capable of helping us better understand the neurocognitive process of digital radicalization
Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective
Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as âA.I. neuroprediction,â and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed
Investigative Simulation: Towards Utilizing Graph Pattern Matching for Investigative Search
This paper proposes the use of graph pattern matching for investigative graph
search, which is the process of searching for and prioritizing persons of
interest who may exhibit part or all of a pattern of suspicious behaviors or
connections. While there are a variety of applications, our principal
motivation is to aid law enforcement in the detection of homegrown violent
extremists. We introduce investigative simulation, which consists of several
necessary extensions to the existing dual simulation graph pattern matching
scheme in order to make it appropriate for intelligence analysts and law
enforcement officials. Specifically, we impose a categorical label structure on
nodes consistent with the nature of indicators in investigations, as well as
prune or complete search results to ensure sensibility and usefulness of
partial matches to analysts. Lastly, we introduce a natural top-k ranking
scheme that can help analysts prioritize investigative efforts. We demonstrate
performance of investigative simulation on a real-world large dataset.Comment: 8 pages, 6 figures. Paper to appear in the Fosint-SI 2016 conference
proceedings in conjunction with the 2016 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining ASONAM 201
Fear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentration
Affective computing through physiological signals monitoring is currently a hot topic in
the scientific literature, but also in the industry. Many wearable devices are being developed for
health or wellness tracking during daily life or sports activity. Likewise, other applications are being
proposed for the early detection of risk situations involving sexual or violent aggressions, with the
identification of panic or fear emotions. The use of other sources of information, such as video or audio
signals will make multimodal affective computing a more powerful tool for emotion classification,
improving the detection capability. There are other biological elements that have not been explored
yet and that could provide additional information to better disentangle negative emotions, such
as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands
located above the kidneys. These hormones are released in the body in response to physical or
emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have
been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood
volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work
presents a comparison of the results provided by the analysis of physiological signals in reference to
catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli
through an immersive environment in virtual reality. Artificial intelligence algorithms for fear
classification with physiological variables and plasma catecholamine concentration levels have been
proposed and tested. The best results have been obtained with the features extracted from the
physiological variables. Adding catecholamineâs maximum variation during the five minutes after
the video clip visualization, as well as adding the five measurements (1-min interval) of these levels,
are not providing better performance in the classifiers.This research has been supported by the Madrid Governement (Comunidad de Madrid,
Spain) under the ARTEMISA-UC3M-CM research project (reference 2020/00048/001), the EMPATIACM
research project (reference Y2018/TCS-5046) and the Multiannual Agreement with UC3M in the
line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional
Programme of Research and Technological Innovation)
Vision-based Fight Detection from Surveillance Cameras
Vision-based action recognition is one of the most challenging research
topics of computer vision and pattern recognition. A specific application of
it, namely, detecting fights from surveillance cameras in public areas,
prisons, etc., is desired to quickly get under control these violent incidents.
This paper addresses this research problem and explores LSTM-based approaches
to solve it. Moreover, the attention layer is also utilized. Besides, a new
dataset is collected, which consists of fight scenes from surveillance camera
videos available at YouTube. This dataset is made publicly available. From the
extensive experiments conducted on Hockey Fight, Peliculas, and the newly
collected fight datasets, it is observed that the proposed approach, which
integrates Xception model, Bi-LSTM, and attention, improves the
state-of-the-art accuracy for fight scene classification.Comment: 6 pages, 5 figures, 4 tables, International Conference on Image
Processing Theory, Tools and Applications, IPTA 201
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