Reviewerly/exHarmony huggingface dataset:
Overview of Edge Intelligence and Blockchain

In this chapter, we introduce and explain the basic aspects in EI and BC such as the concept, architecture, background, characteristics, classification, working principle, development, and application. From these aspects, it can be conscious of the motivation and necessity of integration of EI and BC.
DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures

Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain. Furthermore, obtaining labeled medical data is difficult and expensive since it requires expert annotators like doctors, radiologists, etc. Active learning (AL) is a well-known method to mitigate labeling costs by selecting the most diverse or uncertain samples. However, current AL methods do not work well in the medical imaging domain with OOD data. We propose Diagnose (avoiDing out-of-dIstribution dAta usinG submodular iNfOrmation meaSurEs), an active learning framework that can jointly model similarity and dissimilarity, which is crucial in mining in-distribution data and avoiding OOD data at the same time. Particularly, we use a small number of data points as exemplars that represent a query set of in-distribution data points and another set of exemplars that represent a private set of OOD data points. We illustrate the generalizability of our framework by evaluating it on a wide variety of real-world OOD scenarios. Our experiments verify the superiority of Diagnose over the state-of-the-art AL methods across multiple domains of medical imaging.
Textual adversarial attacks by exchanging text‐self words

Adversarial attacks expose the vulnerability of deep neural networks. Compared to image adversarial attacks, textual adversarial attacks are more challenging due to the discrete nature of texts. Recent synonym-based methods achieve the current state-of-the-art results. However, these methods introduce new words against the original text, leading to that humans easily perceive the difference between the adversarial example and the original text. Motivated by the fact that humans are usually unaware of chaotic word order in some cases, we propose exchange-attack (EA), a concise and effective word-level textual adversarial attack model. Specifically, the EA model generates adversarial examples by exchanging words of the original text itself according to the contributions that these words make regarding classification results. Intuitively, the smaller the distance between the two exchanged words, the more difficult the chaotic word order to be perceived by humans. We thus take the word distance into consideration when generating the chaotic word orders. Extensive experiments on several text classification data sets show that the EA model consistently outperforms the selected baselines in terms of averaged after-attack accuracy, modification rate, query number, and semantic similarity. And human evaluation results reveal that humans difficultly perceive the adversarial examples generated by the EA model. In addition, quantitative and qualitative analyses further validate the effectiveness of the EA model, including that the generated adversarial examples are grammatically correct and semantically preserved.
Disentangling a Single MR Modality

Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks. Current methods learn disentangled representations using either paired multi-modal images with the same underlying anatomy or auxiliary labels (e.g., manual delineations) to provide inductive bias for disentanglement. However, these requirements could significantly increase the time and cost in data collection and limit the applicability of these methods when such data are not available. Moreover, these methods generally do not guarantee disentanglement. In this paper, we present a novel framework that learns theoretically and practically superior disentanglement from single modality magnetic resonance images. Moreover, we propose a new information-based metric to quantitatively evaluate disentanglement. Comparisons over existing disentangling methods demonstrate that the proposed method achieves superior performance in disentanglement and cross-domain image-to-image translation tasks.
Object Detection Using Peak, Balanced Division Point and Shape Based Features

In this paper, numerous techniques have been presented based upon the structure or geometrical shape of an object. By extracting the features of an object, we can detect and recognize an object. In this work, we firstly detect and count the number of objects available within an image. Each object is cropped and resized, and boundary values of an object are detected, which further helps extract the relevant features of an object. The various features extracted in this work are contiguous horizontal and vertical peak extent features, non-connected and connected contour segment features, vertical and horizontal balanced division point, chord features, etc. These features further assist in finding shape of a given object. For object detection and recognition of an object, the Linear-SVM and k-NN classifiers are used during classification. In this work, we have taken total 1020 images from MPEG dataset; these images include both, i.e. training and testing. The dataset consists of a total of 51 classes, and each class contains 20 images. In this, we achieve the accurateness of 91 and 90% by the use of Linear-SVM Classifier for object recognition using the proposed vertical and horizontal peak extent feature extraction methods.
Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment

Assessing prostate cancer grade from whole slide images (WSIs) is a challenging task. While both slide-wise and pixel-wise annotations are available, the latter suffers from noise. Multiple instance learning (MIL) is a widely used method to train deep neural networks using WSI annotations. In this work, we propose a method to enhance MIL performance by deriving weak supervisory signals from pixel-wise annotations to effectively reduce noise while maintaining fine-grained information. This auxiliary signal can be derived in various levels of hierarchy, all of which have been investigated. Comparisons with strong MIL baselines on the PANDA dataset demonstrate the effectiveness of each component to complement MIL performance. For 2,097 test WSIs, accuracy (Acc), the quadratic weighted kappa score (QWK), and Spearman coefficient were increased by 0.71%, 5.77%, and 6.06%, respectively, while the mean absolute error (MAE) was decreased by 14.83%. We believe that the method has great potential for appropriate usage of noisy pixel-wise annotations.
Topic Modelling Based Semantic Search

In today’s world, information is needed to be retrieved from plethora of publications and papers for scientific paper writing, paper reviewing. Retrieval of relevant data has become a very tedious job. The novelty of this paper is that we are providing a holistic approach to retrieve information or abstracts from a corpus of published papers and artifacts, by improving the conventional methods of searching abstracts. To get a better search result, reviewer can provide phrases, sentences and even a complete abstract. This brings to the reviewer the most relevant abstracts in any huge database of publications. To accomplish this, we have implemented topic modelling based semantic search. Various AI algorithms are used to find the closest and nearest abstracts to any given search abstract. For this paper we have extensively used the PUBMED data. Along with search abstracts we are also providing other inherent features which may get unnoticed in huge corpus, like similar words, relation between words and phrases.
Survey on Accent Correction and Region Prediction

Background: In recent years, speech recognition technology has become a dominant part of our everyday lives, and as most of the future technology being developed can easily be integrated with the help of speech recognition. To make a digital future, technological advancement of everyone is necessary, and to make this technological advancement not so technical, speech recognition serves its role. Although speech recognition has made significant advances at certain languages, what has been achieved is a drop and what is left is an ocean. This technology has failed miserably in recognizing different accents of a single language or a voice disorder, and this has led to various questions on the authenticity of progress of the process. This paper documents the drawbacks of this technology and the areas where its immediate progress is possible. It talks about the limitations of various existing and popular and under radar ASR technologies with insights of their flaws which need to be considered immediately to avoid various social dilemmas and insecurities.
User-Based Cloud Service Recommendation System

Recommender system is one of the emerging niches of machine learning. They are being used widely in a variety of fields. Netflix, IMDb, and Amazon have been using it commercially for their interests. With the increase in usage of cloud services, cloud service recommendation has attained significant attention in recent years. Cloud service recommendation system aims at helping the user to find services he might prefer. This paper proposes a user-based collaborative filtering method to enhance it. Here, we take the preferences of an active user first and then try to compute users with similar preferences using a popular similarity measure like cosine similarity which computes the similarity scores by computing the cosine distance between users. Then, we recommend the services the active users did not invoke. Finally, we found the root-mean-square error with the various number of recommendations of our proposed approach thirty times and noted the average. We observed that the cosine similarity measure works better than a few other popular measures like Euclidean distance by 14.99% when the number of top recommendations is five.
Performance and Application of Digital Forensic Tools: A Comparative Study

Currently, computers and the Internet are used to conduct the majority of business transactions, communications and the automated control of industrial equipment, among other things. Working online makes the process more efficient and convenient. The risk of cyber-attacks has also increased significantly as a result of devices being exposed to the Internet on a daily basis. The Internet’s speed, ease of use and invisibility, lack of geographical boundaries cyber financial crimes, stalking and bullying are becoming more commonplace, according to the FBI. A digital forensic investigation carried out with the assistance of software tools yields evidence against cybercriminals that can be presented in court. This review work aimed to evaluate and compare the performance and applications of ten online digital forensic tools. The conclusions, limitations of these tools and how after moral improvement, they can be used to assist digital forensics professionals in discovering digital evidence are presented.
Literature Review on Machine Translation Systems for Sign Language Generation

Every human being has the fundamental right to equal opportunity. The world's deaf society, like every other person, deserves access to all forms of information. Gestures have long served as a mode of communication for people hard of hearing worldwide. There should be a way for hearing and deaf people to communicate directly in order for this to happen. A human translator can help in this situation but this will violate the privacy of the conversation. In this twenty-first century, it has become imperative to automate this communication so that deaf people are not reliant on human translators. This paper deals with the Sign Language generation systems based on different machine translation techniques.
A Survey on Chatbot in Devanagari Language

Chatbots is the current trending topic in machine learning. It processes the user's queries and gives appropriate response. Chatbots are kind of virtual assistant where we talk with a computer bot not with a real human being. But it feels like we are taking with a real human being. Chatbots are being used heavily in various sectors like in banking, ticket reservations, customer enquiry desks, etc. Chatbots are helping businesses to give quick responses to the user queries, and most importantly, it is saving both time and resources of the businesses and customer. In future, chatbot will play major role for the communication between business and consumers for sure. Most of the chatbots are developed in English language by using various frameworks. This paper explains various techniques and architectures which can be used for developing a real-time chatbot.
Anomaly Detection of Unstructured Logs Generated from Complex Micro-service Based Architecture Using One-Class SVM

With the increase of micro-service-based architecture, detection of anomalies in enormous, complex production infrastructure has become complicated. In this work, authors provide a solution for finding anomalies using the structured event objects in a complex micro-service-based productions environment, where the flow of data is assumed to be periodic, deterministic and predictable in nature. As these objects are multivariate and multidimensional in nature, the number of features and dimensions is reduced without losing the quality of data. Next, a method to find anomalies has been proposed with the obtained dataset. The proposed method uses an unsupervised anomaly detection model using Tax–Duin approach for one-class support vector machine (SVM) to classify outliers. The basic assumption of this model includes classifying the first occurrence of any event as anomaly by default. The experimental results obtained indicate an accuracy of 88% by applying one-class SVM on the considered dataset.
Augmented Feature Generation Using Maximum Mutual Information Minimum Correlation

With size of datasets varying un-uniformly in sample size and feature length, to optimize the feature set usually different methods such as filter, wrapper methods are used. However, with different machine learning techniques though either feature reduction is used, or feature extraction is used, both have its own merits and de-merits. The proposed work proposes a hybrid model that tries to combine the feature extraction and feature reduction techniques thereby using both linear and non-linear techniques to take the best parts of both methods. After the initial ensemble is created still the feature set is further optimized by using the concept of entropy and information gain. Using mutual information, on further analysis the best non-redundant feature sets are selected after considering a specific threshold and using this as a testing tool the datasets are again analyzed to check the working accuracy. The model performance is found to be effective even using reduced feature sub-set. Also, it has been found apart from excelling in classification accuracy, the model has been successful in maintaining the range of the metric irrespective of the input size.
Exploratory and Sentiment Analysis of Classified Drug Groups

Health issues in this pandemic situation were most challenging factor for the people where the only path was Internet and the trust worthy Web sites. This research uses a drug review dataset from UCI machine learning repository and filtered a part of dataset based on some psychiatric conditions and identified fourteen drug groups considering suffixes of drugs. By applying exploratory data analysis (EDA) and sentiment analysis (SA) on the drug groups, we have identified the best drug group as well as the less efficient drug group according to its rating and sentiment polarity. From the experimental results on the considered dataset, it was found that barbiturate is the best drug group by customer rating with mean rating of 9.625, and antipsychotics drug group is found to have less rating as per customer suggestion.
The moving target tracking and segmentation method based on space-time fusion

Abstract At present, the target tracking method based on the correlation operation mainly uses deep learning to extract spatial information from video frames and then performs correlations on this basis. However, it does not extract the motion features of tracking targets on the time axis, and thus tracked targets can be easily lost when occlusion occurs. To this end, a spatiotemporal motion target tracking model incorporating Kalman filtering is proposed with the aim of alleviating the problem of occlusion in the tracking process. In combination with the segmentation model, a suitable model is selected by scores to predict or detect the current state of the target. We use an elliptic fitting strategy to evaluate the bounding boxes online. Experiments demonstrate that our approach performs well and is stable in the face of multiple challenges (such as occlusion) on the VOT2016 and VOT2018 datasets with guaranteed real-time algorithm performance.
Generation of Indian Sign Language Animation from Audio and Video Content Using Natural Language Processing

The hearing impaired population in India forms around 1–2% of the Indian population. The standard medium of communication among this community is the Indian Sign Language (ISL). For a person who wants to communicate with the hearing impaired individual, it becomes challenging since general people don’t really know ISL. Also, for a hearing impaired individual it is difficult for one to understand the content of normal audio or video by own self as the communication is carried out using regular language in most of the audios and videos. To overcome these difficulties, in this paper we have discussed the design and implementation of an application—Sign Savvy, a system for Audio and Video conversion to ISL. This application allows the user to provide inputs in distinct media forms such as text (English/Hindi), audio and video. The application then converts the corresponding inputs into ISL format and generates animation for the Indian Sign Language. The application also has a provision of recording of the ISL animations generated that can be downloaded if needed by the user. Further in the paper, the system performance results are discussed and conclusion is presented.
A Softcomputing Approach for Predicting and Categorising Learner’s Performance Using Fuzzy Model

Covid-19: a pandemic situation in the world gives a turning point to education system and it becomes e-education. E-learning is emerging trend in digital era and empowerment of this trend is necessary. Traditional education system trying to adopt this new method of teaching and learning. But only teaching and learning is not sufficient in education system. We have to focus on learner and the environmental impact on them. Traditional education system unable to resolve all the issues arises due to obstacles such as understanding ability, thinking, mood, concentration etc. Proposed research work focusing on designing, developing and modelling of soft computing decision making model for solving real life problems and learners capability in education system. This research work uses Fuzzy Inference System (FIS) which is one of the applications in MATLAB software, for analysing learners result from obtained score and other factors related to environment. It also predict the learner which is helpful in e-learning.
Asymmetry and Architectural Distortion Detection with Limited Mammography Data

Detection of the asymmetry (AS) and architectural distortion (AD) on mammograms is important for early breast cancer diagnosis. However, this is a challenging task because there are very limited mammography data containing these two lesions. In this paper, we tackle this problem by presenting a novel transfer learning framework of Supervised mass-Transferred Pre-training (STP) followed by Supervised Constrained Contrastive Fine-tuning (S $$\mathrm C^2$$ F). While STP can leverage the commonly available mass data to help with detecting the rarely available AS and AD as pre-training, S $$\mathrm C^2$$ F can depart the mass, AS, and AD in the embedding space as far as possible with a carefully designed constrained contrastive loss. In addition, a novel detection network - AsAdNet, is proposed for the AS and AD detection. The validation results on the largest-so-far AS and AD dataset show state-of-the-art (SOTA) detection performance.
Generating Adversarial Robust Defensive CAPTCHA (GARD-CAPTCHA) in Convolutional Neural Networks

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has been crucial for distinguishing normal users from malicious requests. However, DNNs (Deep Learning Models) have been exploited to recognize symbols and objects in CAPTCHA challenges. To combat the threat, a number of works have been conducted on enhancing the robustness against DNN models. Nonetheless, most existing defensive schemes have been focusing on text character used in the CAPTCHA. Meanwhile, image CAPTCHAs consisting of various objects have not received sufficient attention. To fill the gap, we investigate the defense of object image CAPTCHAs against DNN-based solvers. In our proposed approach, we generate perturbation noises with significant pixels extracted using the DeConvolution and UnPooling operations in CNNs (Convolutional Neural Networks). In our evaluation, we demonstrate that our generated images are capable of misclassifying popular pre-trained DNN model solvers and can maintain the visuality for users to recognize. Moreover, we show the transferability since CAPTCHA solvers are likely to use other DNN models to crack the CAPTCHA protection.
Tailoring Blockchain to Edge Intelligence

Despite the great remarkably benefits brought by BC to edge EI, BC is still in the initial stage, and also facing critical challenges. To bring the gap between immature BC and EI amicable BC, it is necessary to acclimatize BC to EI. In this chapter, we probe into how to tailor BC to EI. from four perspectives, including flexible consensus protocol, effective incentive mechanism, intellectuality smart contract, and scalability.
A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data

Gliomas are one of the most widespread and aggressive forms of brain tumors. Accurate brain tumor segmentation is crucial for evaluation, monitoring and treatment of gliomas. Recent advances in deep learning methods have made a significant step towards a robust and automated brain tumor segmentation. However, due to the variation in shape and location of gliomas, as well as their appearance across different tumor grades, obtaining an accurate and generalizable segmentation model is still a challenge. To alleviate this, we propose a cascaded segmentation pipeline, aimed at introducing more robustness to segmentation performance through data stratification. In other words, we train separate models per tumor grade, aided with synthetic brain tumor images generated through conditional generative adversarial networks. To handle the variety in size, shape and location of tumors, we utilize a localization module, focusing the training and inference in the vicinity of the tumor. Finally, to identify which tumor grade segmentation model to utilize at inference time, we train a dense, attention-based 3D classification model. The obtained results suggest that both stratification and the addition of synthetic data to training significantly improve the segmentation performance, whereby up to 55% of test cases exhibit a performance improvement by more than 5% and up to 40% of test cases exhibit an improvement by more than 10% in Dice score.