Healthcare is an important aspect of human life. Use of technologies in healthcare has increased manifolds after the pandemic. Internet of Things based systems and devices proposed in literature can help elders, children and adults facing/experiencing health problems. This paper exhaustively reviews thirty-nine wearable based datasets which can be used for evaluating the system to recognize Activities of Daily Living and Falls. A comparative analysis on the SisFall dataset using five machine learning methods i.e., Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbor, Decision Tree and Naive Bayes is performed in python. The dataset is modified in two ways, in first all the attributes present in dataset are used as it is and labelled in binary form. In second, magnitude of three axes(x,y,z) for three sensors value are computed and then used in experiment with label attribute. The experiments are performed on one subject, ten subjects and all the subjects and compared in terms of accuracy, precision and recall. The results obtained from this study proves that KNN outperforms other machine learning methods in terms of accuracy, precision and recall. It is also concluded that personalization of data improves accuracy.

Deep Reinforcement Learning (DRL) has made tremendous advances in both simulated and real-world robot control tasks in recent years. Nevertheless, applying DRL to novel robot control tasks is still challenging, especially when researchers have to design the action and observation space and the reward function. In this paper, we investigate partial observability as a potential failure source of applying DRL to robot control tasks, which can occur when researchers are not confident whether the observation space fully represents the underlying state. We compare the performance of three common DRL algorithms, TD3, SAC and PPO under various partial observability conditions. We find that TD3 and SAC become easily stuck in local optima and underperform PPO. We propose multi-step versions of the vanilla TD3 and SAC to improve robustness to partial observability based on one-step bootstrapping.

Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor mask-wearing states by utilizing suitable automatic detectors. However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Afterwards, an efficient path aggression network BiFPN is applied as the feature fusion neck. Furthermore, the localization loss is replaced with alpha-CIoU in model training phase to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results on AIZOO face mask dataset show the superiority of the proposed model. Compared with the original YOLOv5, the proposed model increases the inference speed by 28.3% while still improving the precision by 0.58%. It achieves the best mean average precision of 95.2% compared with other seven existing models, which is 4.4% higher than the baseline.

The phenomenon of compounding is ubiquitous in Sanskrit. It serves for achieving brevity in expressing thoughts, while simultaneously enriching the lexical and structural formation of the language. In this work, we focus on the Sanskrit Compound Type Identification (SaCTI) task, where we consider the problem of identifying semantic relations between the components of a compound word. Earlier approaches solely rely on the lexical information obtained from the components and ignore the most crucial contextual and syntactic information useful for SaCTI. However, the SaCTI task is challenging primarily due to the implicitly encoded context-sensitive semantic relation between the compound components. Thus, we propose a novel multi-task learning architecture which incorporates the contextual information and enriches the complementary syntactic information using morphological tagging and dependency parsing as two auxiliary tasks. Experiments on the benchmark datasets for SaCTI show 6.1 points (Accuracy) and 7.7 points (F1-score) absolute gain compared to the state-of-the-art system. Further, our multi-lingual experiments demonstrate the efficacy of the proposed architecture in English and Marathi languages.The code and datasets are publicly available at https://github.com/ashishgupta2598/SaCTI

Artificial intelligence (AI) models have been developed to predict clinically relevant biomarkers for colorectal cancer (CRC), including microsatellite instability (MSI). However, existing deep-learning networks are data-hungry and require large training datasets, which are often lacking in the medical domain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that required relatively small datasets, but achieved a state-of-the-art (SOTA) predictive performance. Our Swin-T workflow substantially outperformed published models in an intra-study cross-validation experiment using the TCGA-CRC-DX dataset (N = 462). It also demonstrated excellent generalizability in cross-study external validation and delivered a SOTA AUROC of 0.90 for MSI, using the MCO dataset for training (N = 1065) and the TCGA-CRC-DX for testing. A similar performance (AUROC = 0.91) was achieved by Echle et al., using ~8000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient when using small training datasets and exhibited robust predictive performance with 200-500 training samples. These data indicate that Swin-T could be 5-10 times more efficient than existing algorithms for MSI based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models showed promise as pre-screening tests for MSI status and BRAF mutation status, which could exclude and reduce the samples before subsequent standard testing in a cascading diagnostic workflow, to allow a reduction in turnaround time and costs.

A social network (SN) is a social structure consisting of a group representing the interaction between them. SNs have recently been widely used and, subsequently, have become suitable and popular platforms for product promotion and information diffusion. People in an SN directly influence each other's interests and behavior. One of the most important problems in SNs is to find people who can have the maximum influence on other nodes in the network in a cascade manner if they are chosen as the seed nodes of a network diffusion scenario. Influential diffusers are people who, if they are chosen as the seed set in a publishing issue in the network, that network will have the most people who have learned about that diffused entity. This is a well-known problem in literature known as influence maximization (IM) problem. Although it has been proven that this is an NP-complete problem and does not have a solution in polynomial time, it has been argued that it has the properties of sub modular functions and, therefore, can be solved using a greedy algorithm. Most of the methods proposed to improve this complexity are based on the assumption that the entire graph is visible. However, this assumption does not hold for many real-world graphs. This study is conducted to extend current maximization methods with link prediction techniques to pseudo-visibility graphs. To this end, a graph generation method called the exponential random graph model (ERGM) is used for link prediction. The proposed method is tested using the data from the Snap dataset of Stanford University. According to the experimental tests, the proposed method is efficient on real-world graphs.

Earlier this year, scientists spotted the building blocks of RNA at the center of the Milky Way. RNA, or ribonucleic acid, a molecule similar to DNA and it is present in all living cells. The team of researchers discovered the building blocks of RNA in a molecular cloud in our galaxy. Such building blocks have also been discovered on asteroids. Most notably, Japanese researchers discovered more than 20 amino acids on the space rock Ryugu, which is more than 200 million miles (320 million kilometers) from Earth. Scientists made the detection by studying samples retrieved from the near-Earth asteroid by the Japan Aerospace Exploration Agency's (JAXA) Hayabusa2 spacecraft, which landed on Ryugu in 2018. According to Kensei Kobayashi, a professor emeritus of astrobiology at Yokohama National University, "Proving amino acids exist in the subsurface of asteroids increases the likelihood that the compounds arrived on Earth from space. This means that amino acids could likely be found on other planets and natural satellites – a clue that "life could have been born in more places in the Universe than previously thought," Building Blocks of Life Were Found on an Asteroid in Space For The Very First Time: ScienceAlert Victoria Meadows, Principal Investigator for NASA's Virtual Planetary Laboratory at the University of Washington has noted that life forms can produce detectable indicators, including the presence of substantial amounts of oxygen, smaller amounts of methane, and a variety of other chemicals. She believes that "upcoming telescopes in space and on the ground will have the capability to observe the atmospheres of Earth-sized planets orbiting nearby cool stars, so it's important to understand how best to recognize signs of habitability and life on these planets," Meadows said, "These computer models will help us determine whether an observed planet is more or less likely to support life."

Earlier this week an artificial intelligence-powered rapper was dropped from its label (yes, it had a label) after its algorithm learned to use racial slurs in its lyrics. More usefully, a recent AI trial at Queensland's Princess Alexandra Hospital was able to give early warnings as much as eight hours before a patient's condition was predicted to decline. Artificial technology is about to send a "tidal wave" of disruption through the way we work, according to a once-in-a-decade forecast by CSIRO, the national science agency. The federal government is being urged to use the upcoming national jobs summit to "double down" on policies set by the former government to ride that tidal wave, or risk being rode over. AI technology is forecast to replace as much as half of the work that is done today by 2030.

Every stage of life is impacted by mental health diseases, which range from dementia to schizophrenia. The World Health Organization estimates that one in eight people worldwide suffer from a mental condition and that poor mental health costs the world economy $1 trillion in lost productivity each year. Effective treatment for mental health illnesses depends on an early and precise diagnosis, just like it does for many illnesses. Nevertheless, unlike, for instance, a heart attack, which can be detected through tests that detect particular signs or "biomarkers" linked with the disorder, no clear-cut biomarkers for mental health issues have yet been identified. This is due to the intricate interplay of factors that causes mental diseases, such as heredity, biological predisposition, and unfavorable living circumstances.

Irony is a ubiquitous figurative language in daily communication. Previously, many researchers have approached irony from linguistic, cognitive science, and computational aspects. Recently, some progress have been witnessed in automatic irony processing due to the rapid development in deep neural models in natural language processing (NLP). In this paper, we will provide a comprehensive overview of computational irony, insights from linguistic theory and cognitive science, as well as its interactions with downstream NLP tasks and newly proposed multi-X irony processing perspectives.