Reviewerly/exHarmony huggingface dataset:
A Scalable Block Chain Framework for User Identity Management in a Decentralized Network

In a decentralized network every user makes use of personal identity details at different places for various services and these details are shared with third-parties without their consent and stored at an unknown location. Organizations like government, banks and social platforms are considered to be the weakest area in the current identity management system as they are vulnerable which in turn leads to compromising billions of user identity data. Block chain based User Identity Management is a solution which provides a decentralized environment that manages the user identity data and their related Know-Your-Customer documents in a distributed ledger. All the transactions of the network are stored in the block, which is a type of a data structure and these blocks are validated using the powerful consensus algorithms and linked to form a block chain. Smart contracts will act as an interface between the client and the block chain network. User’s information cannot be provided to any third party vendors without the explicit consent of the user. This paper proposes a framework for User Identity Management using Block chain technology in a decentralized network. The proposed framework ensures a high level privacy and security for the personal identity details and the documents. In addition to that the performance analysis of the framework is presented in terms of Transaction immutability, Mining Resource, Memory utilization and Difficulty Variation. The results of the analysis proves that the proposed framework performs better in terms of all these factors.
Complexity and Performance Evaluation of Segmented and Recursive Reinforcement Learning

Artificial Intelligent systems play very crucial role in current advanced technology with complex Machine Learning and Deep Learning algorithms. Reinforcement Machine Learning models directly learn from the environment and very useful for solving many complex problems in absence of prior datasets. Existing reinforcement algorithms like Q-learning solve complex problems in game theory and data science with high accuracy. But most of the reinforcement learning algorithms store the reward, parameters and state-data at each state, require huge storage and computational resources and results in large training periods. Lower training period is very important in real-time continual and transfer learning systems to reduce the latency. In this paper, novel Segmented and Recursive Reinforcement Learning algorithm is proposed to meet the time and resource constraints of the intelligent systems with very less training period. The performance of proposed algorithm is evaluated by comparing it with the existing Q-learning and Markov decision Process based algorithms in terms of accuracy, complexity and training periods. Both proposed and existing reinforcement algorithms are used for Parameter tuning in wavelet based image noise reduction application and accuracy of the algorithms are analyzed in terms of the noise reduction performance.
ON-OFF Privacy in the Presence of Correlation

We formulate and study the problem of ON-OFF privacy. ON-OFF privacy algorithms enable a user to continuously switch his privacy between ON and OFF. An obvious example is the incognito mode in internet browsers. But beyond internet browsing, ON-OFF privacy can be a desired feature in most online applications. The challenge is that the statistical correlation over time of a user’s online behavior can lead to leakage of information. We consider the setting in which a user is interested in retrieving the latest message generated by one of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> sources. The user’s privacy status can change between ON and OFF over time. When privacy is ON the user wants to hide his request. Moreover, since the user’s requests depend on personal attributes such as age, gender, and political views, they are typically correlated over time. As a consequence, the user cannot simply ignore privacy when privacy is OFF. We model the correlation between user’s requests by an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> state Markov chain. The goal is to design query schemes with optimal download rate, that preserve privacy in an ON-OFF privacy setting. In this paper, we present inner and outer bounds on the achievable download rate for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> sources. We also devise an efficient algorithm to construct an ON-OFF privacy scheme achieving the inner bound and prove its optimality in the case <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=2$ </tex-math></inline-formula> sources. For <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N &gt; 2$ </tex-math></inline-formula> , finding tighter outer bounds and efficient constructions of ON-OFF privacy schemes that would achieve them remains an open question.
Explaining Multimodal Errors in Autonomous Vehicles

Complex machines, such as autonomous vehicles, are unable to reconcile conflicting behaviors between their underlying subsystems, which leads to accidents and other negative consequences. Existing approaches to error and anomaly detection are not equipped to detect and mitigate inconsistencies among parts. In this paper, we present “Anomaly Detection through Explanations” or ADE, a multimodal monitoring architecture to reconcile critical discrepancies under uncertainty. ADE uses symbolic explanations as a debugging language, by examining underlying reasons for those decisions. Further, when decisions conflict, our method uses a synthesizer, along with a priority hierarchy, to process subsystem outputs along with their underlying reasons and transparently judges the conflicts. We show the accuracy and performance of ADE on autonomous vehicle scenarios and data, and discuss other error evaluations for future work.
Automatic Lifting System for Crane Safety using Machine Learning

Crane is extensively used to carry loads. But many safety measures need to be considered while operating a crane. One of these is material overloading and not lifting a load at an appropriate safety angle, leading to accidents. In this paper, a methodology is introduced to automate the safety lifting system in a crane by calculating the safety angle to prevent workplace hazards. A crane model is developed with a servo motor, sensor, and buzzer. The boom of a crane is moved to the desired safety angle with the help of the servo motor MG90S. MPU6050 sensor is used to read the current boom position of a crane. Calculation of the safety angle for the given load is automated with the help of machine learning. The load chart of a rotary crane is used as the dataset for training. The machine learning model is trained using Gaussian process regression in MATLAB with statistics and machine learning toolbox. The trained model predicts the boom radius for the given weight. With the help of mathematical modeling safety angle is obtained. If the boom's current position equals the safety angle predicted with the model, then the buzzer will ring, indicating the appropriate safety angle to lift the given load. Otherwise, it will move the boom of a crane model to the safety angle. For this implementation, the MATLAB support package for Arduino hardware is used.
Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles

Sensor uncertainty significantly affects the performance of autonomous vehicles (AVs). Sensor uncertainty is predominantly linked to sensor specifications, and because sensor behaviors change dynamically, the machine learning approach is not suitable for learning them. This paper presents a novel learning approach for predicting sensor performance in challenging environments. The design of our approach incorporates both epistemic uncertainties, which are related to the lack of knowledge, and aleatoric uncertainties, which are related to the stochastic nature of the data acquisition process. The proposed approach combines a state-based model with a predictive model, where the former estimates the uncertainty in the current environment and the latter finds the correlations between the source of the uncertainty and its environmental characteristics. The proposed approach has been evaluated on real data to predict the uncertainties associated with global navigation satellite systems (GNSSs), showing that our approach can predict sensor uncertainty with high confidence.
FedFly: Toward Migration in Edge-Based Distributed Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since devices may be resource-constrained, offloading can be used to improve FL performance by transferring computational workload from devices to edge servers. However, due to mobility, devices participating in FL may leave the network during training and need to connect to a different edge server. This is challenging because the offloaded computations from an edge server need to be migrated. In line with this assertion, we present FedFly, which is, to the best of our knowledge, the first work to migrate a deep neural network (ONN) when devices move between edge servers during FL training. Our empirical results on the CIFAR-10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33 percent when a device moves after 50 percent of the training is completed, and by up to 45 percent when 90 percent of the training is completed when compared to the state-of-the-art offloading approach in FL. FedFly has negligible overhead of up to two seconds and does not compromise accuracy. Finally, we highlight a number of open research issues for further investigation. FedFly can be downloaded from https://github.com/qub-bless-on/FedFly.
Full interpretable machine learning in 2D with inline coordinates

This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. Specifically, it can be done with the inline based coordinates in different modifications, including static and dynamic ones. The classification and regression algorithms based on these inline coordinates were introduced. A successful case study based on a benchmark data demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning as a promising ML methodology. It has advantages of abilities to involve actively the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.
A Tourist Recommendation System: A Study Case in Mexico

The present work deals with implementing tourist recommendation systems designed to predict the user preferences about a place or tourist activity in Mexico. Three recommendation systems have been proposed: two based on collaborative filtering (user and items) and the other based on demographic issues. To this aim, a corpus has been built by collecting 2,263 ratings from TripAdvisor.com about eighteen tourist places in Mexico. Experimental results show that the demographic-based recommendation system outperforms those based on collaborative filtering, obtaining a mean absolute error of 0.67 and a mean square error of 1.2980. These results also show significant improvement over a majority class baseline based on a sizeable unbalanced corpus.
NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish

Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish.
Securing mHealth Applications with Grid-Based Honey Encryption

Mobile healthcare (mHealth) application and technologies have promised their cost-effectiveness to enhance healthcare quality, particularly in rural areas. However, the increased security incidents and leakage of patient data raise the concerns to address security risks and privacy issues of mhealth applications urgently. While recent mobile health applications that rely on password-based authentication cannot withstand password guessing and cracking attacks, several countermeasures such as One-Time Password (OTP), grid-based password, and biometric authentication have recently been implemented to protect mobile health applications. These countermeasures, however, can be thwarted by brute force attacks, man-in-the-middle attacks and persistent malware attacks. This paper proposed grid-based honey encryption by hybridising honey encryption with grid-based authentication. Compared to recent honey encryption limited in the hardening password attacks process, the proposed grid-based honey encryption can be further employed against shoulder surfing, smudge and replay attacks. Instead of rejecting access as a recent security defence mechanism in mobile healthcare applications, the proposed Grid-based Honey Encryption creates an indistinct counterfeit patient's record closely resembling the real patients' records in light of each off-base speculation legitimate password.
Domain Extension and Ideal Elements in Mathematics

Abstract Domain extension in mathematics occurs whenever a given mathematical domain is augmented so as to include new elements. Manders argues that the advantages of important cases of domain extension are captured by the model-theoretic notions of existential closure and model completion. In the specific case of domain extension via ideal elements, I argue, Manders’s proposed explanation does not suffice. I then develop and formalize a different approach to domain extension based on Dedekind’s Habilitationsrede, to which Manders’s account is compared. I conclude with an examination of three possible stances towards extensions via ideal elements.
Image Adaptive Contrast Enhancement for Low-illumination Lane Lines Based on Improved Retinex and Guided Filter

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Multidimensional indexing technique for medical images retrieval

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines.
Jamming Identification for GNSS-based Train Localization based on Singular Value Decomposition

Train localization based on the Global Navigation Satellite System (GNSS) is an important feature of the novel train control systems. Considering the complicated railway operation conditions, jamming signals from the environment may pose a severe threat to the GNSS-based train localization. Therefore, the accurate detection and perception of GNSS jamming will play a significant role in ensuring the safe operation of the trains. In this paper, a jamming identification method for GNSS-based train localization using singular value decomposition (SVD) is proposed. By extracting feature values from the singular value sequence, and modeling the mapping relationship between the feature values and the jamming characteristics, the discrimination of jamming characteristics, including the type and the power of the jamming signal, is achieved. A satellite signal-level test platform with the jamming signal injection capability is built to verify the proposed solution. Results of the tests demonstrate the jamming recognition performance of the proposed solution under the Continuous Wave Interference (CWI), Linear Frequency Modulation (LFM) and the Band-limited White Noise (BLWN) jamming conditions.
IR From Bag-of-words to BERT and Beyond through Practical Experiments

The task of adhoc search is undergoing a renaissance, sparked by advances in natural language processing. In particular, pre-trained contextualized language models (such as BERT and T5) have consistently shown to be a highly-effective foundation upon which to build ranking models. These models are equipped with a far deeper understanding of language than the capabilities of bag-of-words (BoW) models. Applying these techniques to new tasks can be tricky, however, as they require knowledge of deep learning frameworks, and significant scripting and data munging. In this full-day tutorial, we build up from foundational retrieval principles to the latest neural ranking techniques. We first provide foundational background on classical bag-of-words methods. We then show how feature-based Learning to Rank methods can be used to re-rank these results. Finally, we cover contemporary approaches, such as BERT, doc2query, and dense retrieval. Throughout the process, we demonstrate how these can be easily experimentally applied to new search tasks in a declarative style of conducting experiments exemplified by the PyTerrier and OpenNIR search toolkits.
Nondestructive Assessment of Elastomeric Bridge Bearings Using 3D Digital Image Correlation

Elastomeric bridge bearings are installed between the bridge superstructure and substructure to accommodate translational and rotational deformations. The manufacturing quality of elastomeric bridge bearings is of high significance because manufacturing defects (such as variations in rubber layer thickness and nonparallel steel laminates) may jeopardize their short- and long-term structural behavior and integrity. Current quality control procedures involve destructive testing of samples of bearings from a lot. This type of testing is time consuming and costly, and thus limited to a relatively small sample size, which may undermine confidence in the quality of remaining bearings in the lot. This paper presents an alternative, vision-based assessment methodology for the nondestructive identification of the internal structure of elastomeric bridge bearings. The methodology capitalizes on the high deformability of rubber and the near inextensibility of the steel laminates, which together result in a unique deformation pattern on the vertical surfaces of a bearing when it is subjected to axial load. This deformation pattern features local extrema in in-plane strain and horizontal displacement fields on the vertical surfaces. These local extrema are analyzed to deduce the thicknesses of the rubber layers and rubber side covers. The methodology is developed based on three-dimensional finite-element analyses (3D-FEA). Then, three-dimensional digital image correlation (3D-DIC) is used in experimental tests to evaluate its capability to identify manufacturing defects in elastomeric bridge bearings. Finally, the methodology is validated against destructive tests. The nondestructive method presented in this study is conducted in conjunction with compressive tests that departments of transportation carry out routinely and is therefore expected to facilitate rapid and cost-effective qualification of elastomeric bridge bearings.