A survey paper on blockchain and its implementation to reduce security risks in various domains

Every technology with its powerful uses has issues connected to it and security is at the top of it. As for the changing environment, the world has been shifting to Virtual Reality, the new coming world seems to be the internet and blockchain technology which is more powerful than others and has its applications in every field, be it quantum computing, internet of things, security or others. This survey paper covers the blockchain and its security in different fields of sciences and technology. We begin with the introduction of blockchain and then discuss its structure. After that security issues have been highlighted which include attacks and their behavior in quantum computing, internet of things, cloud computing. Furthermore, we have discussed the most common types of attacks and the SRM model of blockchain followed by the conclusion

Juvenile dermatomyositis

Juvenile dermatomyositis (JDM) is an important subtype of dermatomyositis characterized by inflammation of muscle, skin and gastrointestinal tract. A 14-year-old girl, with a history of fever, joint pain, easy fatigability and a rash since the age of 3 years is described. Physical examination, laboratory evaluation, electromyography (EMG) and muscle biopsy were suggestive of a chronic inflammatory process involving the muscles, most likely dermatomyositis. The report highlights the importance of a muscle biopsy as the gold standard for diagnosing dermatomyositis

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction

Meta-analysis of the gender outcomes in Pakistan's agricultural evaluations

Presented by Sidra Minhas (DevTrio) on February 28, 2019, as part of the webinar 'Changing gender norms in agriculture projects - What works in Pakistan and Ethiopia'. The webinar was co-organized by the CGIAR Collaborative Platform for Gender Research and the CGIAR Research Program on WHEAT

A Brief Survey on Natural Language Processing Based Text Generation and Evaluation Techniques

Text Generation is a pressing topic of Natural Language Processing that involves the prediction of upcoming text. Applications like auto-complete, chatbots, auto-correct, and many others use text generation to meet certain communicative requirements. However more accurate text generation methods are needed to encapsulate all possibilities of natural language communication. In this survey, we present cutting-edge methods being adopted for text generation. These methods are divided into three broad categories i.e. 1) Sequence-to-Sequence models (Seq2Seq), 2) Generative Adversarial Networks (GAN), and  3) Miscellaneous. Sequence-to-Sequence involves supervised methods, while GANs are unsupervised, aimed at reducing the dependence of models on training data. After this, we also list a few other text generation methods. We also summarize some evaluation metrics available for text generation and their Performanc

Histopathology of the liver, gall bladder and pancreas

Liver biopsies are common in clinical practice for a variety of reasons in the work-up of diverse disorders ranging from congenital disorders like ‘storage diseases’ to inflammatory conditions in particular for grading and staging of ‘chronic hepatitis’ to autoimmune disorders affecting intrahepatic bile ducts. Another common challenge on core needle biopsies of the liver is to differentiate ‘hepatocellular carcinoma’ from liver metastases which may originate from a large number of organs in both genders. As liver transplantation is getting pace biopsies from live allografts are also becoming common. As for gall bladder, almost all biopsy specimens are ‘cholecystectomies’, performed due to gall stones, and only occasionally associated dysplasia or malignancy is seen. Pancreatic biopsies or resections are also not uncommon for both more commonly seen ‘pancreatic adenocarcinoma’ arising from pancreatic ducts to ‘neuroendocrine tumours’. In this chapter all common and relatively uncommon entities from this anatomical region are described with salient light microscopic features and ancillary testing where necessary

The Prospects of Computer-Enabled Voting Systems in Pakistan

Democracy is the power vested in people to choose and elect their representatives. However, theprocess of election and voting is prone to rigging leading to undeserving people leading a nationwhich further causes mistrust and agitation amongst the people. Various methods have beenproposed and implemented towards free and fair elections. In this survey we list and discussdifferent methods proposed and adopted for voting. These include the techniques which wereintroduced in past and can be implied in future, the techniques by which voting system can bemade more secure are, the remote voting, internet/online voting, a RFID tags, a fingerprinttechnology and IOT for updating, two languages the extensible markup language and anotherone is the extensible style language are used to design a unique content and cannot be copied.Other technology like electronic voting machine with battery can be useful in rural areas whereinternet is not available and last one is the blockchain technology by which the voter can casttheir vote in no time, and can have trust on that as this technology is end-to-end encrypted, heredata can be saved in sealing blocks. All this work is to find a better way to make the votingsystem more reliable and trustable for the future. In the end we discuss the efficacy of thesesystems in current infrastructure and requirements of Pakistan

Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey

Mental health is recognized as a non-communicable disease that impairs human lives, sometimes beyond recovery. While everyone is at risk of developing a mental illness, adolescents are more prone to it due to various factors like hormonal changes, study pressure, social pressure, etc. If mental health goes ignored at this stage, it can cause serious, even fatal problems later on in life, which not only impacts a family but also the young workforce of a country. Hence, constant efforts are being made for the early detection of mental disorders so they can be treated better. Early prediction of mental health issues is a classic machine learning problem relying on patient history and data. In this survey, we discuss a total of 22 previous research papers based on machine learning algorithms and other statistical analysis tools employed for the said task and compare their efficacy. The research papers are categorized into different mental health disorders such as 1) Methods for predicting Depression and Anxiety 2) Methods for Suidial Prevalence 3) Methods for Predicting Autism Spectrum Disorder (ASD) 4) Methods for Predicting Substance Abuse among adolescents. On the basis of accuracy, the performance of machine learning prediction models was compared. CNN models, Random Forest, and XGBoost generally performed better than other models. There is centralized research in Pakistan on mental health based on machine learning so SPSS and other tools are mostly used for data analysis. The findings suggest that Machine learning algorithms can be effective for classifying and early predicting high-risk factors among adolescent