Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. ( 2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts. UEFISCDI, project number 108, COTOHILI, within PNCDI III.
The continuous advance of science often leaves behind devices, and makes their usage obsolete. This can be observed, for example, in the medical domain, where the performance of devices achieved tremendous capabilities, or in the latest increase of power of computing: today's top-ranked smartphones are comparable in performance with the best desktop computers of the previous decade. Therefore, significant research efforts are directed at the reusability of current technologies by updating them according to new discoveries. Among the existing solutions for device reusability, there are two concepts which are highly ranked: the usage of system-on-chip devices and partial reconfigurability implementation. This paper analyzes the benefits of using these solutions both independently and combined.
The functional verification process is one of the most expensive steps in integrated circuit manufacturing. Functional coverage is the most important metric in the entire verification process. By running multiple simulations, different situations of DUT functionality can be encountered, and in this way, functional coverage fulfillment can be improved. However, in many cases it is difficult to reach specific functional situations because it is not easy to correlate the required input stimuli with the expected behavior of the digital design. Therefore, both industry and academia seek solutions to automate the generation of stimuli to reach all the functionalities of interest with less human effort and in less time. In this paper, several approaches inspired by genetic algorithms were developed and tested using three different designs. In all situations, the percentage of stimulus sets generated using well-performing genetic algorithms approaches was higher than the values that resulted when random simulations were employed. In addition, in most cases the genetic algorithm approach reached a higher coverage value per test compared to the random simulation outcome. The results confirmed that in many cases genetic algorithms can outperform constrained random generation of stimuli, that is employed in the classical way of doing verification, considering coverage fulfillment level per verification test.
Nowadays various neural network algorithms are used in the classification of clinical data for human conditions such as Alzheimer's disease, which can extract low-to-high-level features. Classification of clinical data for Alzheimer's disease has always been challenging as currently there is no clinical test for Alzheimer's disease. Doctors diagnose it by conducting assessments of patients' cognitive decline. But it's particularly difficult for them to identify mild cognitive impairment at an early stage when symptoms are less obvious. Also, it is difficult to predict whether patients will develop Alzheimer's disease or not. The accurate diagnosis of Alzheimer's disease in the early stage is important in order to take preventive measures and to reduce the severity and progression before irreversible brain damages occur. The effectiveness of abnormality detection depends on the accuracy and robustness of the algorithm used. Different machine learning techniques with different levels of sensitivity, efficiency, and accuracy have been developed. In this paper, a feature selection using T-Test method for joint regression and classification via instance based k-Nearest Neighbor classifier is proposed for Alzheimer's disease detection. Also, we compare the accuracy measures and performance of the proposed method with existing techniques in Alzheimer's disease detection. The new method gives a better accuracy results compared to conventional methods.
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