From the last decade, researches on human facial emotion recognition disclosed that computing models built on regression modelling can produce applicable performance. However, many systems need extensive computing power to be run that prevents its wide applications such as robots and smart devices. In this proposed system, a real-time automatic facial expression system was designed, implemented and tested on an embedded device such as FPGA that can be a first step for a specific facial expression recognition chip for a social robot. The system was built and simulated in MATLAB and then was built on FPGA and it can carry out real time continuously emotional state recognition at 30 fps with 47.44% accuracy. The proposed graphic user interface is able to display the participant video and two dimensional predict labels of the emotion in real time together.

Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

Predictive preventive personalized medicine Liver cancer is the fifth most common form of cancer worldwide [1], with an incidence rate almost equals the mortality rate and ranks 3 rd among causes of cancer related death [2]. The coexistence of two life threatening conditions, cancer and liver cirrhosis makes the staging challenging. However, there are some staging systems, e.g. the Barcelona staging system for Hepatocellular carcinoma (HCC) [3], that suggest treatment options and management. Whereas diagnosis in early stages gives hope for a curative outcome, the treatment regime for around 80 % [2] of the patients classified as severe stages only gears towards palliation [4]. An intra-arterial radiation approach, radioembolisation (RE) is ubiquitously applied as one of palliative approaches. Although, in general RE shows promising results in intermediate and advanced stage HCC [5], individual treatment outcomes are currently unpredictable. Corresponding stratification criteria are still unclear. We hypothesised that individual radioresistance/radiosensitivity may play a crucial role in treatment response towards RE strongly influencing individual outcomes. Further, HCC represents a highly heterogeneous group of patients which requires patient stratification according to clear criteria for treatment algorithms to be applied individually. Multilevel diagnostic approach (MLDA) is considered helpful to set-up optimal predictive and prognostic biomarker panel for individualised application of radioembolisation. Besides comprehensive medical imaging, our MLDA includes non-invasive multi-omics and sub-cellular imaging. Individual patient profiles are expected to give a clue to targeting shifted molecular pathways, individual RE susceptibility, treatment response. Hence, a dysregulation of the detoxification pathway (SOD2/Catalase) might indicate possible adverse effects of RE, and highly increased systemic activities of matrix metalloproteinases indicate an enhanced tumour aggressiveness and provide insights into molecular mechanisms/targets. Consequently, an optimal set-up of predictive and prognostic biomarker panels may lead to the changed treatment paradigm from untargeted "treat and wait" to the cost-effective predictive, preventive and personalised approach, improving the life quality and life expectancy of HCC patients. Keywords: Market access, Value, Strategy, Companion diagnostics, Cost-effectiveness, Reimbursement, Health technology assessment, Economic models, Predictive preventive personalized medicine Achieving and sustaining seamless "drug -companion diagnostic" market access requires a sound strategy throughout a product life cycle, which enables timely creation, substantiation and communication of value to key stakeholders [1, 2]. The study aims at understanding the root-cause of market access inefficiencies of companies by gazing at the "Rx-CDx" co-development process through the prism of "value", and developing a perfect co-development scenario based on the literature review and discussions with the ...

Abstract:Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM -stereo camera module. The model was built on the Atlys TM Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant's video in real-time and two-dimensional predict labels of the emotion at the same time.

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