Securing information became essential to exchange multimedia information safely. The exchanged data need to be transformed in a well-managed, secure, and reliable manner. In this paper, we will focus on securing RGB images via cryptography during transmission among users using our effective proposal of utilizing appropriate Pseudo Random Number Generator (PRNG). We implement many techniques of PRNG involved in two consecutive crypto-processes of substitution and transposition to present secure image transformation. Our technique proposal of PRNGs selection is based on testing to encrypt RGB images to be compared with current related used approaches. The work experimentation aims to identify suitability and reliability through security measures standard parameters. The research justifies its proper PRNG selection to model our approach as attractive effective work worth remarking.

Remodeling randomness prioritization to boost-up security of RGB image encryption

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

/pdf/artificial-intelligence-for-automatic-pain-assessment-14fjd8pr.pdf

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Current state-of-the-art image captioning systems generally produce a sentence from left to right, and every step is conditioned on the given image and previously generated words. Nevertheless, such autoregressive nature makes the inference process difficult to parallelize and leads to high captioning latency. In this paper, we propose a non-autoregressive approach for faster image caption generation. Technically, low-dimension continuous latent variables are shaped to capture semantic information and word dependencies from extracted image features before sentence decoding. Moreover, we develop an iterative back modification inference algorithm, which continuously refines the latent variables with a look back mechanism and parallelly generates the whole sentence based on the updated latent variables in a constant number of steps. Extensive experiments demonstrate that our method achieves competitive performance compared to prevalent autoregressive captioning models while significantly reducing the decoding time on average.

Iterative Back Modification for Faster Image Captioning

Abstract Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented. 

/pdf/a-systematic-review-of-neurophysiological-sensing-for-the-3m2uofit.pdf

A systematic review of neurophysiological sensing for the assessment of acute pain

Deep image captioning: A review of methods, trends and future challenges

Pain is a complex subjective unpleasant experience that can potentially cause tissue damage. In clinical practice, the main method used for assessing pain is self-report; however, it is not possibly adopted in a huge number of vulnerable populations or by non-communicative patients such as those with disorders of speech and consciousness. Thus, the availability of an objective measure of the subjective pain’s perception that complements the self-report pain assessments is a great significant demand in several clinical applications. The aim of this paper is to propose a novel approach to objectively quantify the subjective perception of pain. We integrated signal processing techniques and machine learning principles to learn brain signals associated with pain and classify them into one of four pain intensities (no pain, low, moderate, and high). We found that the signal processing revealed a direct correlation between Alpha frequency band power and the pain intensity, and the classifier could achieve an accuracy of 94.83%. This study provides a clue for the betterment of the collective scientific understanding of the brain’s activities inflicted by the physical pain and helps in building a reliable automated prediction of pain.

/pdf/a-novel-approach-to-objectively-quantify-the-subjective-1wz96xnchd.pdf

A Novel Approach to Objectively Quantify the Subjective Perception of Pain Through Electroencephalogram Signal Analysis

Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to modify existing captions from a given framework by modeling the residual information, where at each timestep the model learns what to keep, remove or add to the existing caption allowing the model to fully focus on "what to modify" rather than on "what to predict". We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.

Look and Modify: Modification Networks for Image Captioning

Look and modify: Modification networks for image captioning

The measurement of image blurriness is a very important issue in image quality assessment. A number of algorithms have been developed by other researchers and used to calculate image blurriness. In this paper a new method is proposed and developed based on the full reference concept and demonstrated to show excellent results in measuring the blurriness in any image with a reference. We then compared our proposed method with previously developed methods and, based on the experimental results, the proposed method was shown to offer significant improvement in term of accuracy.

A New Method for Full Reference Image Blur Measure

The complexity of the electroencephalogram makes it a significant challenge for physicians and engineers to extract useful information from, process, and classify the electroencephalogram signals. Moreover, the difficulty in conducting clinical experimentation limits the collection of a sufficient number of electroencephalogram data samples for further processing using advanced computational techniques such as deep learning. This complexity and difficulty together with the inflexibility and the subtle linearity of the traditional signal processing techniques motivate us to find innovative techniques to address the problem of insufficient electroencephalogram data. In this paper, a number of computational and statistical techniques to generate electroencephalogram data from a previously done experiment on 30 healthy participants experiencing painful stimuli are applied. We believe this application will benefit the research in the field of biomedical signal processing.

Effective Computational Techniques for Generating Electroencephalogram Data

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