This paper proposes a novel method for optimal exposure operation in low quality images. The method uses reinforcement learning coupled with a discriminant loss (from GANs) to learn the optimal sequence of operations (i.e., the different exposures for each subimage component from a semantic segmentation of the input image) that generate, through a blender of all the components, a good quality - better exposed image. The main concern with this paper is the poor clarity of exposition. The formal definition of the image processing problem is lacking. Semantic segmentation is one major component but it's not discussed.

Telemedicine utilization was accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, the quality of photographs sent by patients remains a major limitation. To address this issue, we developed TrueImage 2.0, an artificial intelligence (AI) model for assessing patient photo quality for telemedicine and providing real-time feedback to patients for photo quality improvement. TrueImage 2.0 was trained on 1700 telemedicine images annotated by clinicians for photo quality. On a retrospective dataset of 357 telemedicine images, TrueImage 2.0 effectively identified poor quality images (Receiver operator curve area under the curve (ROC-AUC) =0.78) and the reason for poor quality (Blurry ROC-AUC=0.84, Lighting issues ROC-AUC=0.70). The performance is consistent across age, gender, and skin tone. Next, we assessed whether patient-TrueImage 2.0 interaction led to an improvement in submitted photo quality through a prospective clinical pilot study with 98 patients. TrueImage 2.0 reduced the number of patients with a poor-quality image by 68.0%.

Artificial intelligence can do a lot of things. But can AI predict what you're getting for Christmas? Classifying images is something AI does exceptionally well. A type of model known as a convolutional neural network performs exceptionally on image tasks. The field of computer vision is constantly advancing and improving.

Many believe that cybersecurity is an exciting field to work in, and indeed it is. Yet being responsible for an organization's IT Security is no easy feat. Attackers always seem to be a few steps ahead of defenders. It often feels like a game of one against many – from petty criminals to nation-states. It would be highly advantageous if our cybersecurity tools could automatically adapt to these threats.

Artificial Intelligence is taking the world by storm. It is ingressing into every field of life, as computers did three decades ago, but in more intrusive ways. For the first time in the history of human civilisation, humans have been challenged on their brain power, something they always considered as the differentiator between them and the rest of the animal kingdom. So, there is a threat. Machines will never be able to take on everything humans do with their brains.

The paper introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The paper shows how intentionally false or inaccurate information of agents could be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming and enable the exploration of various strategies that agents can employ in their negotiation