Temporal placement control of video frames in ATM networks

1994 ◽  
Vol 7 (4) ◽  
pp. 329-335 ◽  
Author(s):  
Duan-Shin Lee ◽  
Bhaskar Sengupta
Keyword(s):  
Atm Networks ◽  
Video Frames ◽  
Temporal Placement

scholarly journals Thermal burns in electroconvulsive therapy

2021 ◽  
pp. 201010582110310
Author(s):  
Ernest Weisheng Ho ◽  
Eng Leonard ◽  
Lee Tih-Shih ◽  
Gregory James Meredith
Keyword(s):  
Case Report ◽  
Mood Disorders ◽  
Electroconvulsive Therapy ◽  
Ignition Source ◽  
Thermal Burns ◽  
High Impedance ◽  
Temporal Placement ◽  
Close Proximity ◽  
Life Threatening

Electroconvulsive therapy (ECT) is effective for mood disorders and schizophrenia. Thermal burns, while rare, are potentially sight and life threatening. The three elements necessary for a fire are often in close proximity during a session: an oxidiser (oxygen), an ignition source (faulty electrodes, poor contact with skin producing a spark) and fuel (hair, residual alcohol cleanser). This case report describes one such incident when a patient sustained a burn during ECT, with poor contact of electrode pad with skin, high impedance and an oxygen-rich environment possibly contributing. Given that ECT is conducted relatively frequently (once every 2–3 days) in a usual regimen, we make recommendations for safe application of electrode pads for temporal placement ECT.

scholarly journals A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 87
Author(s):  
Sara Ferreira ◽  
Mário Antunes ◽  
Manuel E. Correia
Keyword(s):  
Machine Learning ◽  
Digital Forensics ◽  
State Of The Art ◽  
Forensic Analysis ◽  
Third Party ◽  
Support Vector ◽  
Multimedia Content ◽  
Digital Forensic ◽  
Video Frames ◽  
Forensic Tools

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

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