Virtual Movement from Natural Language Text

It is a challenging task for machines to follow a textual instruction. Properly understanding and using the meaning of the textual instruction in some application areas, such as robotics, animation, etc. is very difficult for machines. The interpretation of textual instructions for the automatic generation of the corresponding motions (e.g. exercises) and the validation of these movements are difficult tasks. To achieve our initial goal of having machines properly understand textual instructions and generate some motions accordingly, we recorded five different exercises in random order with the help of seven amateur performers using a Microsoft Kinect device. During the recording, we found that the same exercise was interpreted differently by each human performer even though they were given identical textual instructions. We performed a quality assessment study based on the derived data using a crowdsourcing approach. Later, we tested the inter-rater agreement for different types of visualization, and found the RGB-based visualization showed the best agreement among the annotatorsa animation with a virtual character standing in second position. In the next phase we worked with physical exercise instructions. Physical exercise is an everyday activity domain in which textual exercise descriptions are usually focused on body movements. Body movements are considered to be a common element across a broad range of activities that are of interest for robotic automation. Our main goal is to develop a text-to-animation system which we can use in different application areas and which we can also use to develop multiple-purpose robots whose operations are based on textual instructions. This system could be also used in different text to scene and text to animation systems. To generate a text-based animation system for physical exercises the process requires the robot to have natural language understanding (NLU) including understanding non-declarative sentences. It also requires the extraction of semantic information from complex syntactic structures with a large number of potential interpretations. Despite a comparatively high density of semantic references to body movements, exercise instructions still contain large amounts of underspecified information. Detecting, and bridging and/or filling such underspecified elements is extremely challenging when relying on methods from NLU alone. However, humans can often add such implicit information with ease due to its embodied nature. We present a process that contains the combination of a semantic parser and a Bayesian network. In the semantic parser, the system extracts all the information present in the instruction to generate the animation. The Bayesian network adds some brain to the system to extract the information that is implicit in the instruction. This information is very important for correctly generating the animation and is very easy for a human to extract but very difficult for machines. Using crowdsourcing, with the help of human brains, we updated the Bayesian network. The combination of the semantic parser and the Bayesian network explicates the information that is contained in textual movement instructions so that an animation execution of the motion sequences performed by a virtual humanoid character can be rendered. To generate the animation from the information we basically used two different types of Markup languages. Behaviour Markup Language is used for 2D animation. Humanoid Animation uses Virtual Reality Markup Language for 3D animation

Data augmentation enhanced speaker enrollment for text-dependent speaker verification

Data augmentation is commonly used for generating additional data from the available training data to achieve a robust estimation of the parameters of complex models like the one for speaker verification (SV), especially for under-resourced applications. SV involves training speaker-independent (SI) models and speaker-dependent models where speakers are represented by models derived from an SI model using the training data for the particular speaker during the enrollment phase. While data augmentation for training SI models is well studied, data augmentation for speaker enrollment is rarely explored. In this paper, we propose the use of data augmentation methods for generating extra data to empower speaker enrollment. Each data augmentation method generates a new data set. Two strategies of using the data sets are explored: the first one is to training separate systems and fuses them at the score level and the other is to conduct multi-conditional training. Furthermore, we study the effect of data augmentation under noisy conditions. Experiments are performed on RedDots challenge 2016 database, and the results validate the effectiveness of the proposed methods

Green extraction and in vitro anti-mycobacterial activity of Hydrocotyle sibthorpioides Lam. and Carica papaya L. leaves collected from Assam, India

The traditional healers of different parts of India use goat urine for the treatment of tuberculosis. On the basis of ethnomedicinal claims, two plant species namely Hydrocotyle sibthorpioides and Carica papaya were extracted with raw (fresh) and photo-activated goat urine as a menstruum. The present study reports the in vitro antimycobacterial activity of the leaf extracts of H. sibthorpioides and C. papaya against Mycobacterium smegmatis (ATCC 700084 /Mc2155 strain). It was observed that the photo-activated goat urine and raw goat urine leaf extracts could inhibit M. smegmatis. Among all the four extracts, the extract of C. papaya using photo-activated goat urine showed the highest antimycobacterial activity against M. smegmatis

Green extraction and in vitro anti-mycobacterial activity of Hydrocotyle sibthorpioides Lam. and Carica papaya L. leaves collected from Assam, India

60-66The traditional healers of different parts of India use goat urine for the treatment of tuberculosis. On the basis of ethnomedicinal claims, two plant species namely Hydrocotyle sibthorpioides and Carica papaya were extracted with raw (fresh) and photo-activated goat urine as a menstruum. The present study reports the in vitro antimycobacterial activity of the leaf extracts of H. sibthorpioides and C. papaya against Mycobacterium smegmatis (ATCC 700084 /Mc2155 strain). It was observed that the photo-activated goat urine and raw goat urine leaf extracts could inhibit M. smegmatis. Among all the four extracts, the extract of C. papaya using photo-activated goat urine showed the highest antimycobacterial activity against M. smegmatis

The Role of Vitamin D in the Restriction of the Progress and Severity of COVID-19 Infection

SARS-CoV-2 has affected socio-economic activity in every country around the world since its outbreak began in 2019. 3.5 million people have died worldwide as of now, including 3.2 lakh in India. The cytokine storm significantly contributes to COVID mortality. To put it simply, the virus causes an uncontrolled release of cytokines, which results in severe inflammation, multi-organ failure, and death. Vitamin D was discovered to be a significant risk factor for cytokine storm in COVID patients. Numerous studies have demonstrated that those with deficient serum vitamin D levels have a significant mortality rate. The current understanding of the role of vitamin D in immune modulation in the innate and adaptive immune systems and how this may relate to COVID-19 is discussed in this article. Additionally, we evaluated the most recent clinical information about vitamin D deficiency, cytokine storm, and COVID-19 mortality

Virtuelle Bewegung aus natürlichem Sprachtext

It is a challenging task for machines to follow a textual instruction. Properly understanding and using the meaning of the textual instruction in some application areas, such as robotics, animation, etc. is very difficult for machines. The interpretation of textual instructions for the automatic generation of the corresponding motions (e.g. exercises) and the validation of these movements are difficult tasks. To achieve our initial goal of having machines properly understand textual instructions and generate some motions accordingly, we recorded five different exercises in random order with the help of seven amateur performers using a Microsoft Kinect device. During the recording, we found that the same exercise was interpreted differently by each human performer even though they were given identical textual instructions. We performed a quality assessment study based on the derived data using a crowdsourcing approach. Later, we tested the inter-rater agreement for different types of visualization, and found the RGB-based visualization showed the best agreement among the annotatorsa animation with a virtual character standing in second position. In the next phase we worked with physical exercise instructions. Physical exercise is an everyday activity domain in which textual exercise descriptions are usually focused on body movements. Body movements are considered to be a common element across a broad range of activities that are of interest for robotic automation. Our main goal is to develop a text-to-animation system which we can use in different application areas and which we can also use to develop multiple-purpose robots whose operations are based on textual instructions. This system could be also used in different text to scene and text to animation systems. To generate a text-based animation system for physical exercises the process requires the robot to have natural language understanding (NLU) including understanding non-declarative sentences. It also requires the extraction of semantic information from complex syntactic structures with a large number of potential interpretations. Despite a comparatively high density of semantic references to body movements, exercise instructions still contain large amounts of underspecified information. Detecting, and bridging and/or filling such underspecified elements is extremely challenging when relying on methods from NLU alone. However, humans can often add such implicit information with ease due to its embodied nature. We present a process that contains the combination of a semantic parser and a Bayesian network. In the semantic parser, the system extracts all the information present in the instruction to generate the animation. The Bayesian network adds some brain to the system to extract the information that is implicit in the instruction. This information is very important for correctly generating the animation and is very easy for a human to extract but very difficult for machines. Using crowdsourcing, with the help of human brains, we updated the Bayesian network. The combination of the semantic parser and the Bayesian network explicates the information that is contained in textual movement instructions so that an animation execution of the motion sequences performed by a virtual humanoid character can be rendered. To generate the animation from the information we basically used two different types of Markup languages. Behaviour Markup Language is used for 2D animation. Humanoid Animation uses Virtual Reality Markup Language for 3D animation

Fixed Dose Oral Dispersible Tablet of Bitter Drug Using Okra Mucilage: Formulation and Evaluation

Background: The solid oral dosage forms containing bitter drugs need improved palatability for administration. Formulation scientists have given attention to the improvement of taste masking technologies and utilised various strategies. Objective: The present work aimed to mask the bitter taste of Promethazine Hydrochloride by formulating Oral Dispersible Tablets using Okra mucilage as a taste-masking agent.  Methods: The Okra mucilage was extracted from Okra by the aqueous extraction process. An emulsion solvent diffusion technique was used for masking the bitter taste of Promethazine Hydrochloride by using Okra mucilage. The Oral Dispersible Tablet was prepared by the wet granulation method. The mucilage and the formulation were characterized and evaluated by standard methods and protocols. Results: Taste masking of the bitter drug was successfully achieved by Okra mucilage. The DSC and FTIR study revealed that the drug molecule was compatible with okra mucilage and drug entrapment efficacy was found to be 94.76%. The palatability test asserted that masking of the bitter taste of the drug.  The In vitro drug release study showed that the F7 tablet batch has a better drug release rate and followed non- fickian mechanism of drug release. Conclusion: Thus, taste masking with Okra mucilage was successful and this opens opportunities for application of common edible substances in formulation development. Keywords: Fast disintegrating tablet; Natural polymer; Mouth dissolving tablet; Promethazine Hydrochloride; Taste maskin