Implicit Training of Energy Model for Structure Prediction

Most deep learning research has focused on developing new model and training procedures. On the other hand the training objective has usually been restricted to combinations of standard losses. When the objective aligns well with the evaluation metric, this is not a major issue. However when dealing with complex structured outputs, the ideal objective can be hard to optimize and the efficacy of usual objectives as a proxy for the true objective can be questionable. In this work, we argue that the existing inference network based structure prediction methods ( Tu and Gimpel 2018; Tu, Pang, and Gimpel 2020) are indirectly learning to optimize a dynamic loss objective parameterized by the energy model. We then explore using implicit-gradient based technique to learn the corresponding dynamic objectives. Our experiments show that implicitly learning a dynamic loss landscape is an effective method for improving model performance in structure prediction.Comment: AAA

Incorporating Agile with MDA Case Study: Online Polling System

Nowadays agile software development is used in greater extend but for small organizations only, whereas MDA is suitable for large organizations but yet not standardized. In this paper the pros and cons of Model Driven Architecture (MDA) and Extreme programming have been discussed. As both of them have some limitations and cannot be used in both large scale and small scale organizations a new architecture has been proposed. In this model it is tried to opt the advantages and important values to overcome the limitations of both the software development procedures. In support to the proposed architecture the implementation of it on Online Polling System has been discussed and all the phases of software development have been explained.Comment: 14 pages,1 Figure,1 Tabl

SHUKA DHANYA VARGA (GROUP OF CEREALS): A PREVENTIVE AND CURATIVE PERSPECTIVE

Nutritious diet (Ahara) or we can say balanced diet is the key to follow the first principle of Ayurveda or in order to maintain a healthy life. In Ayurveda Acharya Charak has mentioned regular intake of food articles belongs to different categories of food. Acharya Charak has classified Ahara (diet) in twelve categories. Among these, Shukadhanya is the first one. In modern literature, Shukadhanya has been classified in monocotyledons and energy giving food. Energy giving food mainly includes cereal groups like wheat, rice, maize (corn), oats, Jowar, Ragi, and Bajra. Ancient Acharyas has mentioned some Shukadhanya dravyas with their gunas (qualities) like Shashthika, Vrihi (variety of rice), Yava, wheat, which play an important role in prevention of diseases. These Dravyas are Sheeta (cold in potency), Swadu (sweet in taste), Swadu Vipaka (Sweet in digestion). They are said to be Vatavardhak, Alpavarchasa, Brinhana, Shukrala and Mutral. In modern literature, Shukadhanya Varga has been classified in cereal group. Cereals are enriched with niacin, iron, riboflavin, and thiamine, and most cereals have abundant fibre content, especially barley, oat, and wheat. Cereals also have soluble bran that aids in lowering blood cholesterol level and helps in preventing heart diseases. This article is an attempt to analyze the Shukadhanya Varga mentioned in Ayurveda on a scientific basis

Comparative and Analytical Study towards Mitigation of Gray hole Attacks in VANET

Vehicular Adhoc Network is a type of (MANET) Mobile Adhoc Network that enables vehicles on the road to intelligently interact and communicate with other vehicle and road side infrastructure unit. It is prone to several type of attacks and one such attack is Grayhole attack. Gray hole attack is one of the attack on routing specification in which malicious node selectively drops packets coming from the source. Due to lack of security in Adhoc on Demand Distance Vector (AODV) routing protocol, Grayhole attack disrupts the performance of network and render communication impossible. This paper reviews various attacks in VANET including Grayhole attack on AODV routing protocol and provides a survey of existing defence approaches to mitigate them

Arsenic Contamination of Groundwater: A Review of Sources, Prevalence, Health Risks, and Strategies for Mitigation

Arsenic contamination of groundwater in different parts of the world is an outcome of natural and/or anthropogenic sources, leading to adverse effects on human health and ecosystem. Millions of people from different countries are heavily dependent on groundwater containing elevated level of As for drinking purposes. As contamination of groundwater, poses a serious risk to human health. Excessive and prolonged exposure of inorganic As with drinking water is causing arsenicosis, a deteriorating and disabling disease characterized by skin lesions and pigmentation of the skin, patches on palm of the hands and soles of the feet. Arsenic poisoning culminates into potentially fatal diseases like skin and internal cancers. This paper reviews sources, speciation, and mobility of As and global overview of groundwater As contamination. The paper also critically reviews the As led human health risks, its uptake, metabolism, and toxicity mechanisms. The paper provides an overview of the state-of-the-art knowledge on the alternative As free drinking water and various technologies (oxidation, coagulation flocculation, adsorption, and microbial) for mitigation of the problem of As contamination of groundwater