Reviewerly/exHarmony huggingface dataset:
Urdu and Hindi Poetry Generation Using Neural Networks

One major problem writers and poets face is writer’s block. It is a condition in which an author loses the ability to produce new work or experiences a creative slowdown. The problem is more difficult in the context of poetry than prose, as in the latter case authors need not be very concise while expressing their ideas, also the aspects such as rhyme, poetic meters are not relevant for prose. One of the most effective ways to overcome this writing block for poets can be, to have a prompt system, which would help their imagination and open their minds for new ideas. A prompt system can generate one-liner, two-liner, or full ghazals. The purpose of this work is to give an ode to the Urdu/Hindi poets and help them start their next line of poetry, a couplet or a complete ghazal considering various factors like rhymes, refrain, and meters. The result will help aspiring poets to get new ideas and help them overcome writer’s block by auto-generating pieces of poetry using Deep Learning techniques. A concern with creative works like this, especially in the literary context, is to ensure that the output is not plagiarized. This work also addresses the concern and makes sure that the resulting odes are not exact matches with input data using parameters like temperature and manual plagiarism check against input corpus. To the best of our knowledge, although the automatic text generation problem has been studied quite extensively in the literature, the specific problem of Urdu/Hindi poetry generation has not been explored much. Apart from developing a system to auto-generate Urdu/Hindi poetry, another key contribution of our work is to create a cleaned and preprocessed corpus of Urdu/Hindi poetry (derived from authentic resources) and make it freely available for researchers.
Blockchain Driven Edge Intelligence

BC-driven EI focuses on addressing the challenges of EI as described in Chap. 4. In this chapter, we present the EI benefits that can be realized with the assistance of BC, including computing-power management, data administration and model optimization.
Motivations for Integrating Edge Intelligence with Blockchain

As shown in Fig. 3.1, the limitations of EI and the complementary advantages of BC are painfully clear. Spontaneously, the appearance of BC-assisted EI would be expected to pave the way for the development of emerging intelligent services. In this chapter, we first discuss the limitations of EI. Then, we elaborate the benefits of BC in EI.
Research Challenges and Future Directions

Although EI has promising prospects with the help of BC, there still exists lots of ongoing challenges that need to be considered in future research, which is quite important. In order to identify unresolved issues, understand existing challenges, and circumvent potential misleading directions, in this chapter we discuss open issues separately, including comprehensive architecture, quantization intelligence and trading intelligence.
Sky Computing Smart Locality Aware Approach for Health Analytics

In this work Sky Computing approach has been proposed for “Health Analytics”. Most of Health data of ailments are stored by hospitals in private clouds. In several cases when a query on an ailment is raised by a doctor it requires extensive querying of data on the internet. This may not be always reliable as obtained from the internet. Cloud Computing provides a solution but in several cases datasets required to address a query may not be always available on the same cloud. In such cases it is necessary to obtain datasets from multiple clouds. The Sky computing approach improves the degree of relevant data sets and reduces query processing time by 68% and 30% respectively, compared to the conventional manual approach.
Towards a Practical Library for Monadic Equational Reasoning in Coq

Functional programs with side effects represented by monads are amenable to equational reasoning. This approach to program verification has been experimented several times using proof assistants based on dependent type theory. These experiments have been performed independently but reveal similar technicalities such as how to build a hierarchy of interfaces and how to deal with non-structural recursion. As an effort towards the construction of a reusable framework for monadic equational reasoning, we report on several practical improvements of Monae, a Coq library for monadic equational reasoning. First, we reimplement the hierarchy of effects of Monae using a generic tool to build hierarchies of mathematical structures. This refactoring allows for easy extensions with new monad constructs. Second, we discuss a well-known but recurring technical difficulty due to the shallow embedding of monadic programs. Concretely, it often happens that the return type of monadic functions is not informative enough to complete formal proofs, in particular termination proofs. We explain library support to facilitate this kind of proof using standard Coq tools. Third, we augment Monae with an improved theory about nondeterministic permutations so that our technical contributions allow for a complete formalization of quicksort derivations by Mu and Chiang.
Multi-task Learning-Driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification

Automatic 3D medical image classification,e.g., brain tumor grading from 3D MRI images, is important in clinical practice. However, direct tumor grading from 3D MRI images is quite challenging due to the unknown tumor location and relatively small size of abnormal regions. One key point to deal with this problem is to learn more representative and distinctive features. Contrastive learning has shown its effectiveness with representative feature learning in both natural and medical image analysis tasks. However, for 3D medical images, where slices are continuous, simply performing contrastive learning at the volume-level may lead to inferior performance due to the ineffective use of spatial information and distinctive knowledge. To overcome this limitation, we present a novel contrastive learning framework from synergistic 3D and 2D perspectives for 3D medical image classification within a multi-task learning paradigm. We formulate the 3D medical image classification as a Multiple Instance Learning (MIL) problem and introduce an attention-based MIL module to integrate the 2D instance features of each slice into the 3D feature for classification. Then, we simultaneously consider volume-based and slice-based contrastive learning as the auxiliary tasks, aiming to enhance the global distinctive knowledge learning and explore the correspondence relationship among different slice clusters. We conducted experiments on two 3D MRI image classification datasets for brain tumor grading. The results demonstrate that the proposed volume- and slice-level contrastive learning scheme largely boost the main classification task by implicit network regularization during the model optimization, leading to a $$10.5\%$$ average AUC improvement compared with the basic model on two datasets.
Dual Image-Based High Quality Digital Image Watermarking

Digital image watermarking techniques are suitable for integrity authentication, watermark security, and copyright protection. Further, the number of embedding bits and the process of watermarking also play a crucial role in deciding the effectiveness of any watermarking process. In this regard, to achieve better security without producing noticeable artifacts in the watermarked image, this paper proposes a dual image-based watermarking technique using an exclusive-or (XOR) operation. The proposed technique utilizes a modified least significant bit (LSB) substitution strategy in the host image (HI) pixel to substitute the watermark bits with a maximum of ±1 modification to each pixel. Each HI pixel embeds two watermark bits by producing two different watermarked pixels each. The proposed technique has better watermark bit embedding capacity, higher perceptual transparency, and security.
Cryptocurrency Price Prediction Using Machine Learning

Globally, the use of cryptocurrencies to purchase goods and services has been rising. They rely on a secure distributed ledger data structure; mining is an integral part of such systems. The rise of cryptocurrencies’ value on the market and the growing popularity around the world open several challenges and concerns for business and industrial economics. Cryptocurrencies have been triggered by the substantial changes in their prices, claims that the market for cryptocurrencies is a bubble without any fundamental value and also concerns about evasion of regulatory and legal oversight. Machine learning is part of artificial intelligence that can make future forecastings based on previous experience. In this paper, methods have been proposed to construct machine learning algorithm-based models such as linear regression, K-nearest neighbour(KNN), and also statistical models like Auto-ARIMA and Facebook’s Prophet (Fbprophet). This paper presents a comparative performance of machine learning and statistical modelling algorithms for cryptocurrency forecasting.
AppBastion: Protection from Untrusted Apps and OSes on ARM

ARM-based (mobile) devices are more popular than ever. They are used to access, process, and store confidential information and participate in sensitive authentication protocols, making them extremely attractive targets. Many attacks focus on compromising the primary operating system – for example, by convincing the user to download OS rootkits concealed within seemingly innocent apps. To partially mitigate the impact, device manufacturers responded by offering hardware-rooted trusted environments (TEEs). Yet, making use of TEEs (e.g., by securely porting existing apps) is not easy. Only a small number of security-critical applications make use of TEEs, leaving all others to run on a potentially vulnerable OS, under the control of users that all too often fall prey to cleverly disguised malware. AppBastion is a general-purpose platform that leverages the now ubiquitous ARM TrustZone TEE to secure application data from untrusted OSes. AppBastion enables applications to maintain confidential data in memory regions protected even from a compromised OS. Only approved, signed applications can access their associated protected memory regions. Data never leaves protected regions unencrypted and applications can communicate or declassify protected data only through explicit AppBastion channels. AppBastion ensures that application confidential data cannot be accessed, spoofed, or leaked by the OS.
Using Machine Learning Techniques for Earthquake Prediction Through Student Learning Styles

The Earthquake is an essential problem in human life, by using machine learning techniques in earthquake prediction, we can save humankind. Using the successful application of machine learning techniques indicates that it would be possible to use them to make accurate forecasts to avoid short-term earthquake damage. In this paper, with the first aim, we have applied seven machine learning techniques, namely, Artificial Neural Network (ANN), Decision Tree (DT), Logistic Regression, Random Forest Classification, Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) to reach the best technique for prediction. The second aim used the methods of two learning styles, surface and deep learning, in training students with programming skills to use the seven techniques. Through two experimental groups, one of them used the method of surface learning (collective), and the other used the method of deep learning (individual).This is to determine the best learning style to teach students programming skills.
Processing Kashmiri Text from a Document Image and Associated Challenges

Text processing in document images has evolved as the most prominent research area these days attracting a lot of interest from researchers across the globe. It encompasses a range of activities from simple text detection to text translation. However, there are a good number of languages for which very little or no research work has been done. Most of these languages lack basic resources to carry out research. Kashmiri language based on Perso-Arabic script is one among such languages, which has been discussed in this paper. This paper presents a brief introduction of text processing in the image domain, a description of the Kashmiri language and related issues for kashmiri text processing in the image domain.
Accurate Breast Tumor Identification Using Computational Ultrasound Image Features

Breast cancer ranks the first noncutaneous malignancy incidence and mortality in women worldwide, and seriously endangers the health and life of women. Ultrasound plays a key role and yet provides an economical solution for breast cancer screening. While valuable, ultrasound is still suffered from limited specificity, and its accuracy is highly related to the clinicians, resulting in inconsistent diagnosis. To address the challenge of limited specificity and inconsistent diagnosis, in this retrospective study, we first develop a learning model based on the computational ultrasound image features and identified a set of clinically relevant features. Then, the abstract spatial interaction patterns of the ultrasound images together with the extracted features were employed for breast malignancy diagnosis. We evaluate the proposed algorithm on the Breast Ultrasound Images Dataset (BUSI). The proposed algorithm achieved a diagnostic accuracy of 89.32% and a significant area under curve (AUC) of 0.9473 with the repeated cross-validation scheme. In conclusion, our algorithm shows superior performance over the existing classical methods and can be potentially applied to breast cancer screening.
Recognizing Similar Relationships Within Ontology to Fine Tune Ontology

Ontologies are the backbone of the knowledge management system and play very important role in semantic web. Ontologies help to reduce the bottleneck of knowledge acquisition. Ontology learning process involves identification of concepts and relationship between these concepts. Many approaches based on machine learning and deep learning are used to learn ontology. Automated Ontology based on machine learning and deep learning efficiently identifies concepts and relationship between them. But automatically learned ontologies may have duplicate and similar relationships. Identification of such duplicate or identical relationships are very important to fine tune ontologies. In this paper we propose a method to Recognizing similar relationship to fine tune Ontologies.
Machine Learning Based Earthquake Early Warning (EEW) System: A Case Study of Himalayan Region

Seismic sensing and generation of earthquake alarm is an important application for society at large. In this paper, we propose the strategy of extracting earthquake event features parameters τc and Pd from fast-arriving P-wave signals. The said features are used to explore the performances of some of the popular machine learning (ML) based classifiers to compare their performances in triggering an alarm for the Earthquake Early Warning (EEW) system. We explored four different ML classifiers namely Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Logistic Regression so that the best can be applied for the EEW alarm generation. We have used publicly available data from the PESMOS platform of IIT-Roorkee in this work.
Stain Based Contrastive Co-training for Histopathological Image Analysis

We propose a novel semi-supervised learning approach for classification of histopathology images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. Co-training relies on multiple conditionally independent and sufficient views of the data. We separate the hematoxylin and eosin channels in pathology images using color deconvolution to create two views of each slide that can partially fulfill these requirements. Two separate CNNs are used to embed the two views into a joint feature space. We use a contrastive loss between the views in this feature space to implement co-training. We evaluate our approach in clear cell renal cell and prostate carcinomas, and demonstrate improvement over state-of-the-art semi-supervised learning methods.
Hypothesis Testing of Tweet Text Using NLP

Natural Language processing applications such as sentiment analysis, spam detection, and stance detection extract the author’s emotions, feelings, and categorizations such as favor or denial from a piece of text sentences or corpus. Various researchers keep on working in these areas. However, in this research work, the relations between target entities (say: climate, population) along with the author’s stance and the country’s economic growth level are used to derive a statistical hypothesis. This hypothesis is being proved, discussed, and concluded as per the result obtained. All the missing country information for each Twitter account is filled up with a technique that uses meta information from tweets. A subset of the data is annotated with predefined seeding features, rules, and then applies the best performed supervised machine learning model to predict the remaining unlabeled tweets. Tweets are labeled as “believer” or “denier” for each country, and a hypothesis is being proved based on the statement made by rich and poor countries. The statistical result also shows that there exists a positive correlation between the GDP growth rate and the number of deniers and believers in each country. These techniques, experimental findings, and statistical analysis are presented in this paper.
Real Time Carbon Emissions Calculator for Personal Computers

Many researchers admonish the burgeoning global warming above pre-industrial levels which has catastrophic consequences for mankind. There are various sectors in the industry that contribute to the increase in carbon emissions. Industries such as textile, mining, steel, etc. emit huge amounts of carbon-di-oxide (CO2) every day. CO2 is termed to be the predominant greenhouse gas in global warming. Its levels are increasing without much flora compensating for it. It ultimately leads to climate change. Similar to other industries, AI has also started increasing its contribution to the deterioration of the environment. In this study, we focus on elucidating the negative aspects of Artificial Intelligence towards climate change. Calculations of carbon emissions have become a necessity to limit the usage of resources and increase awareness among people regarding the implications of global warming. Thus, the paper also discusses a real-time carbon emission calculator used to calculate the carbon emissions of personal computers.
Real Time Data Augmentation Using Fractional Linear Transformations in Continual Learning

Over recent years, deep learning algorithms have gained prominence in medical image analysis research. Like other connectionist systems, such networks have been found to be prone to catastrophic forgetting effects. This makes generalization a challenge as new additions to prediction requirements at runtime would invariably require retraining on not only the new dataset, but also substantial portions of older task data. This is a difficult task in clinical imaging where retention of datasets over extended time is challenged by legal and infrastructure constraints. Thus, there is a requirement of algorithmic designs that address for-getting as a part of base and incremental task learning. This has been cast as an incremental learning problem recently. We propose a novel approach to the incremental class addition problem, where a retention of limited numbers of exemplars of old classes helps reduce forgetting instead of large scale data storage, using a strategy of incremental time augmentation with Mobius transformations and weighted distillation objectives to correct evolving class imbalance effects.
Design Aspects of a Multi-dimensional Hybrid Analytical Processing System

Many recent technologies increase the generation of data and its usages. There are concerns to store, retrieved large data, processing multiple queries and services simultaneously. The cube format of the data and dimensional databases can ease the process of retrieval and modelling the data efficiently and effectively. This study suggests few efficient ways to address the concerns using the concept of a data warehouse and analytical operations. It also offers the design aspect of a Hybrid analytical system by linking different functionalities under a Layered Architecture style. The preferred data are collected from those warehouses, later combined to form incremental successive upper-level data. This style supports a Hybrid system to give confidence by connecting various data suppliers of the distributed warehouse methods. It allows the ELT operations other than the normal ETL operations to handle large data to support the data lake. The suggestive functionalities engine is used to produce data patterns. The merit of the PDC tree is incorporated to provide some possible parallel operations. The findings are applied to a case study of data modelling to predict a potential future epidemic. Such a system generates several reports to help the users or the authority for handling such an epidemic in better efficient ways.
Empirical Analysis of Preprocessing Techniques for Imbalanced Dataset Using Logistic Regression

This paper attempts to examine the performance of preprocessing strategies with logistic regression classifier. The goal of this paper is to see if there is a feasible and efficient strategy to enhance the performance of classification techniques on imbalanced datasets for different training dataset percentages. The experiments were conducted on Cleveland dataset—binary class. Several data preprocessing methods like Smote, Borderline-Smote, and ADAYSN were applied to data in order to classify various training dataset percentages. It was necessary to ascertain how the training dataset percentage affected the final classification for preprocessing methods. The experimental results explained that the ratio of 70–30 datasets performed better or better than other ratios when on train and test datasets, respectively. It was found from experimental results that the algorithms gave better accuracy when the training to testing ratio was 70:30 compared to other ratios.
An Ensemble Sentiment Classification on Multidomain Dataset

With the introduction of web 2.0, individuals now have a perfect platform to communicate their ideas, thoughts, and feelings. Web Opinion Mining / Sentiment Analysis is a text mining job aimed at developing a system that automatically extracts, recognizes, and categorizes user opinions from natural language text, user provided content, or user generated media. We have employed classifiers to analyse data sets from a wide variety of domains. The ensemble algorithm is applied to boost its performance. With an delicacy of 80.93%, the Logical Regression using Ensemble Classifier exceeds the others.