Reviewerly/exHarmony huggingface dataset:
UXLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose XLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios. In particular, XLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language task. At its core, XLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on zero-resource cross-lingual transfer tasks for Named Entity Recognition (NER), Natural Language Inference (NLI) and paraphrase identification on Paraphrase Adversaries from Word Scrambling (PAWS). XLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to XLA's success.
Applications of blockchain in healthcare

Since 2016, interest in blockchain has emerged across all sectors of healthcare. This interest has been expressed in the form of collaborative initiatives led by notable organizations within the healthcare industry. Some showed promising results, but none has yet achieved scale in adoption and economic sustainability to be considered an unqualified success. After reading this chapter, the reader will be able to describe what blockchain is and the new business models it can enable. In addition, the reader will have a framework to consider blockchain for a use case and to understand the potential barriers to adoption. Finally, the chapter will review multiple use cases being considered across diverse healthcare sectors, the early industry experiments exploring how blockchain can be used, and what ideal end states could look like. Please note that this chapter has been authored with a focus on healthcare in the United States, and as a result, most examples will be presented with a US perspective.
Centralized and Automated Healthcare Systems : A Essential Smart Application Post Covid-19

At an era of Industry 4.0 revolution, when it is said that the automation is buzzword among the all the devices/applications available.S till automation in Hospital Management S ystem are far from reality in most developing nations.Automation in the field of Healthcare is the need of today's generation.This would help in providing a lot of functionalities to a patient with the help of a mobile application and other devices (Wearable devices to complex devices in testing centers).Here in our framework, patient details, which can be collected regularly from wearable devices and/or collected from Testing Centers(Clinics) and the case history from any hospital visited previously, all these vital details are stored in central repository irrespective of the hospital he/she visits.At the case of emergency, the patients case history which is stored in Central repository(Cloud).These details at any point of time if made available in any hospital, it can fast track the process in handling the patient.Also, understanding the secrecy level to be maintained in the Electronic Health Record and the way the HS M operates nowadays.This research work proposes to have a framework wherein, Patient related data collected from the wearable devices to the data stored in the CR (Cloud), utilizes Audio-S teganography as tool for S ecurity aspect.The framework proposed would be an ideal solution during the current pandemic COVID-19 situations, with Doctors Forum proposed in the framework would be utilized to the most extent possible way to benefit each citizen at large.
A new QPSO based hybrid algorithm for bound-constrained optimisation problem and its application in engineering design problems

The aim of this paper is to introduce a new hybrid algorithm for bound-constrained optimisation problem combining quantum behaved particle swarm optimisation (QPSO) and binary tournamenting technique. Depending on the different options of binary tournamenting process, six diverse forms of hybrid algorithm are introduced. Then the efficiency and performance of these hybrid algorithms are investigated through six well known benchmark bound-constrained optimisation problems. Computational results are compared graphically as well as numerically. Finally, this algorithm is utilised to solve four engineering design problems and results are compared with the recent algorithm available in the literature.
GraphAttacker: A General Multi-Task GraphAttack Framework

Graph neural networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. The competition between attack and defense methods also enhances the robustness of GNNs. In this competition, the development of adversarial training methods put forward higher requirement for the diversity of attack examples. By contrast, most attack methods with specific attack strategies are difficult to satisfy such a requirement. To address this problem, we propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks. GraphAttacker generates adversarial examples through alternate training on three key components: the multi-strategy attack generator (MAG), the similarity discriminator (SD), and the attack discriminator (AD), based on the generative adversarial network (GAN). Furthermore, we introduce a novel similarity modification rate SMR to conduct a stealthier attack considering the change of node similarity distribution. Experiments on various benchmark datasets demonstrate that GraphAttacker can achieve state-of-the-art attack performance on graph analysis tasks of node classification, graph classification, and link prediction, no matter the adversarial training is conducted or not. Moreover, we also analyze the unique characteristics of each task and their specific response in the unified attack framework. The project code is available at https://github.com/honoluluuuu/GraphAttacker.
Bistatic-ISAR cross-range scaling and distortion correction

For bistatic inverse synthetic aperture radar (Bi-ISAR) cross-range scaling (CRS), it needs to estimate the effective rotational velocity (ERV) and correct linear-geometry distortion at the same time. In this paper, the effective rotational velocity (ERV), rotational center (RC) and ratio of linear-geometry distortion (RLGD) are jointly estimated by optimizing the image quality, which is measured by the image entropy. After parameter estimation and phase compensation, the image without linear-geometry distortion is generated by the matched Fourier transform (MFT). Numerical results validate that the proposed method works robust under different signal to noise ratio (SNR) conditions.
An Analysis of Frame-skipping in Reinforcement Learning.

In the practice of sequential decision making, agents are often designed to sense state at regular intervals of $d$ time steps, $d > 1$, ignoring state information in between sensing steps. While it is clear that this practice can reduce sensing and compute costs, recent results indicate a further benefit. On many Atari console games, reinforcement learning (RL) algorithms deliver substantially better policies when run with $d > 1$ -- in fact with $d$ even as high as $180$. In this paper, we investigate the role of the parameter $d$ in RL; $d$ is called the frame-skip parameter, since states in the Atari domain are images. For evaluating a fixed policy, we observe that under standard conditions, frame-skipping does not affect asymptotic consistency. Depending on other parameters, it can possibly even benefit learning. To use $d > 1$ in the control setting, one must first specify which $d$-step open-loop action sequences can be executed in between sensing steps. We focus on action-repetition, the common restriction of this choice to $d$-length sequences of the same action. We define a task-dependent quantity called the price of inertia, in terms of which we upper-bound the loss incurred by action-repetition. We show that this loss may be offset by the gain brought to learning by a smaller task horizon. Our analysis is supported by experiments on different tasks and learning algorithms.
Analyzing Workflows and Improving Communication across Departments: A Quick and Simple Project Using Rapid Contextual Design

Analyzing workflows to identify problems and improve efficiency is a useful exercise, but a workflow analysis takes time and resources, particularly human resources. At the University of California, Irvine, two librarians in Technical Services used an adaptation of the Rapid Contextual Design methodology to create and execute a limited, low-barrier analysis to study the e-resources workflow between the Electronic Resources Unit, operating within the Acquisitions Department, and the Cataloging Department. Although limited in scope and time spent, the analysis provided insights that dispelled unfounded assumptions and pointed out several small, easily instituted changes to improve efficiencies, particularly by improving communication; the process also helped us prepare for gathering data and adjusting workflows for a pending migration to Alma and Primo. This presentation provides an overview of the methodology and describes how it was adapted and executed. It then describes the analysis results and actions taken.
Improved FCM based distance regularization level set algorithm for image segmentation

Considering the traditional contour initialization with high sensitivity to noise limitation in original level-set algorithm, this paper proposes a Distance Regularized Level Set Evolution (DRLSE) evolution model based on the morphological reconstruction FCM algorithm. First, morphological reconstruction (MR) is used to smooth and denoise the image. Under the premise of ensuring anti-noise and image detail preservation, the image is clustered and segmented as the initial contour of DRLSE evolution, and finally the clustering result is segmented by DRLSE model evolution. . The experiment is carried out on multiple images. The segmentation results of the algorithm in this paper are significantly better than the traditional FCM algorithm and DRLSE model, which improves the accuracy of segmentation and is robust to noise.
Hybrid Adversarial Inverse Reinforcement Learning.

Extrapolating beyond-demonstrator (BD) through the inverse reinforcement learning (IRL) algorithm aims to learn from and outperform the demonstrator. In sharp contrast to the conventional reinforcement learning (RL) algorithms, BD-IRL can overcome the dilemma incurred in the reward function design and improve the exploration mechanism of RL, which opens new avenues to building superior expert systems. Most existing BD-IRL algorithms are performed in two stages by first inferring a reward function before learning a policy via RL. However, such two-stage BD-IRL algorithms suffer from high computational complexity, weak robustness, and large performance variations. In particular, a poor reward function derived in the first stage will inevitably incur severe performance loss in the second stage. In this work, we propose a hybrid adversarial inverse reinforcement learning (HAIRL) algorithm that is one-stage, model-free, generative-adversarial (GA) fashion and curiosity-driven. Thanks to the one-stage design, the HAIRL can integrate both the reward function learning and the policy optimization into one procedure, which leads to many advantages such as low computational complexity, high robustness, and strong adaptability. More specifically, HAIRL simultaneously imitates the demonstrator and explores BD performance by utilizing hybrid rewards. In particular, the Wasserstein-1 distance (WD) is introduced into HAIRL to stabilize the imitation procedure while a novel end-to-end curiosity module (ECM) is developed to improve the exploration. Finally, extensive simulation results confirm that HAIRL can achieve higher performance as compared to other similar BD-IRL algorithms. Our code is available at our GitHub website \footnote{\url{this https URL}}.
SorrRS: Social recommendation incorporating rating similarity and user relationships analysis

Recently, collaborative filtering (CF) algorithms have played an important role in recommendation systems. However, the traditional CF algorithms cannot make full use of information (e.g., users' preference, social relationship, and so on) implied in check-ins of users in the recommendation process. In order to address this issue, in this paper, a recommendation framework is proposed by exploiting historical user behavior, the check-in information and user social relationships to improve the precision of recommendation, as follows. First, user reputation can be obtained by iteratively calculating the correlation of historical ratings of the user and intrinsic qualities of items. Second, we employ user reputation and rating similarity in the basic social recommender model. Finally, we use the social connections among users to obtain the social influence of their friends. To evaluate the performance of our method, a series of experiments conducted on Epinions and Ciao data sets show that our approach performs better than several state-of-the-art methods on metrics of RMSE, precision rate, recall rate and F-Measure.
The Printed Book and Electronic Book (Ebook) Experiences of Digital Natives in Indonesia

Information about the use of ebook or printed book that use by the digital natives could assist libraries and school professionals in selecting reading materials for their students. The purpose of this study is to understand the experience in using printed books rather than e-books. This study use qualitative and phenomenological study explored and compared the experiences of digital natives in regards to ebooks and printed books. Data collection was carried out through interviews with 26 digital natives in Indonesia with the criteria of reading book more than ebook and at least reading 2 printed books in one month. The result of tis study are printed books have their own uniqueness, such as experience of holding a book, the texture of the paper that can be touched directly (each publisher has a different type of paper), to the fragrance of the book that wafts out as each page is opened, none of these can be replaced with digital technology and when the digital natives use e-book they often distracted by social media. Moreover, due to emotional attachments, connections to identity, social rituals, and an ease of use related to printed books, the participants would not use ebooks exclusively. This study has limitations, namely the number of samples is small, so that future research can increase the sample so that the results can be generalized.
Safety Case Templates for Autonomous Systems.

This report documents safety assurance argument templates to support the deployment and operation of autonomous systems that include machine learning (ML) components. The document presents example safety argument templates covering: the development of safety requirements, hazard analysis, a safety monitor architecture for an autonomous system including at least one ML element, a component with ML and the adaptation and change of the system over time. The report also presents generic templates for argument defeaters and evidence confidence that can be used to strengthen, review, and adapt the templates as necessary. This report is made available to get feedback on the approach and on the templates. This work was sponsored by the UK Dstl under the R-cloud framework.
PSO-Learned Artificial Neural Networks for Activity Recognition

The purpose of Activity Recognition (AR) is to recognize human activity using a sensor to get the data needed. Then, a machine learning approach is used to determine the type of activity performed. A machine learning technique often used in the classification problem is Artificial Neural Network (ANN), which is trained using a backpropagation algorithm. Although this technique has been significantly developed, it still has a few disadvantages compared to others. One of the disadvantages of the ANN is that the result is not always optimum because of randomized initialization and epoch limit. In this paper, a Particle Swarm Optimization (PSO) is proposed to train the ANN. Some experiments on a dataset of 10 k activities with six imbalanced classes show that the PSO-based ANN produces effectiveness of 100% and an F1 score micro of 0.88, which are much higher than the back propagation-based ANN that gives the effectiveness of 75% and an F1 score micro of 0.87.
RBFACO: A New Feature Selection Algorithm

Medical data sets, including a great amount of disease detection data and a small amount of patient personal data, are always high-dimensional. It is hard to extract potentially useful information from the data sets with low time or memory cost. Feature selection is used to reduce the dimension of data sets by discarding redundant or irrelevant features, and effectively improve the classification performance. Ant colony algorithm, an evolutionary computation algorithm, is a characteristic feature selection algorithm with a good effect on ordinary data sets. However, it cannot work so well in a high-dimensional data set. In this paper, we proposed a Minimum Redundancy Maximum Relevance based ant colony algorithm, which is based on a binary fully-connected graph. In this graph, nodes represent features, and each feature node has two sub-nodes, 0 and 1. Feature nodes are visited randomly by access probability, and the sub-node is chosen by prior knowledge. This method is called RBFACO, and finally it is verified that it can effectively reduce the time cost of feature selection and increase the effect of classification.
ORCID: Issues and concerns about its use for academic purposes and research integrity

ORCID (Open Researcher and Contributor ID) was launched in 2012 as an initiative to fortify the validity and integrity of academic publishing through author name disambiguation. Less than a decade later, this portal is being actively promoted in an attempt to ensure that academics adhere to this permanent identifier. Without a doubt, a complete, up-to-date and authentic ORCID has value, not only to a researcher, but to the academic community because it allows facilitated online submissions, and links to funding agencies and other profiles. The mandatory requirement of an ORCID account for the submitting or corresponding author, sometimes for all authors, is becoming more common during the submission of manuscripts to ORCID member journals. Not only are there issues pertaining to academic freedom, or unfair treatment of those without an ORCID, there are other highly pertinent, unpalatable, and contentious issues related to ORCID that need greater attention and debate. These include the inconsistent implementation of ORCID among co-authors, the existence of empty or “ghost” ORCID accounts that are uninformative and thus of limited use, and the plausible abuse of ORCIDs to register potentially fake elements. These issues would not only reduce trust in ORCID, which is actively promoted as a tool for maintaining science’s integrity, they may land up weakening a publishing system that was meant to be fortified by this initiative. They may also hurt the reputation of valid ORCID users who share a platform with “ghost” ORCID accounts or with fake authors, or authors whose identities are unverifiable.
Dynamic prediction model for NOx emission at the outlet of SCR system based on extreme learning machine

Selective catalytic reduction (SCR) system with high efficiency has been widely applied for reducing Nitrogen oxide (NOx) emissions in thermal power plants. Establishing an accurate dynamic prediction model is important for optimal control of SCR system. Firstly, data preprocessing is conducted maximal information coefficient (MIC) is used to estimate the including processing outlets and normalization; Secondly, the delay time of each variable in order to reconstruct the data. Then, develop a new feature selection method by using Lasso combined with partial least squares (PLS) regression; Finally, the dynamic prediction model is established based on extreme learning machine (ELM) with the input variables selected before. Based on the actual historical operating data, the experimental results show that the model established in this study can accurately predict the NOx emission at the outlet of SCR system.
GPS/GIS system for updating capable faults in the Calabrian territory through the use of soft computing techniques

Summary Calabria is crossed by a system of active faults which represents a high seismic risk for the whole territory. In order to provide a contribution to territorial monitoring and control also for the purpose of forecasting catastrophic events and territorial prevention, the Geomatics Lab of the Mediterranean University of Reggio Calabria is working on updating the database of faults capable of the Calabria Region through the creation of territorial topographic networks for the monitoring and control of potentially active faults, to be managed through a GIS. In this regard (thanks to the large amount of displacement data acquired over time by the GPS networks implemented), we are also working on the experimentation of a neural network for the prediction of the probability of occurrence of an earthquake of a given magnitude following others occurring in the surrounding area, relating the data provided by the National Institute of Geophysics and Volcanology, with those obtained from the experimental GPS surveying networks, used to monitor active faults (to date the Castrovillari fault, the fault in the area between Fuscaldo - Falerna, and the Reggio Calabria fault).
Meaning updating of density matrices

The DisCoCat model of natural language meaning assigns meaning to a sentence given: (i) the meanings of its words, and, (ii) its grammatical structure. The recently introduced DisCoCirc model extends this to text consisting of multiple sentences. While in DisCoCat all meanings are fixed, in DisCoCirc each sentence updates meanings of words. In this paper we explore different update mechanisms for DisCoCirc, in the case where meaning is encoded in density matrices---which come with several advantages as compared to vectors. Our starting point are two non-commutative update mechanisms, borrowing one from quantum foundations research, from Leifer and Spekkens. Unfortunately, neither of these satisfies any desirable algebraic properties, nor are internal to the meaning category. By passing to double density matrices we do get an elegant internal diagrammatic update mechanism. We also show that (commutative) spiders can be cast as an instance of the Leifer-Spekkens update mechanism. This result is of interest to quantum foundations, as it bridges the work in Categorical Quantum Mechanics (CQM) with that on conditional quantum states. Our work also underpins implementation of text-level natural language processing on quantum hardware (a.k.a. QNLP), for which exponential space-gain and quadratic speed-up have previously been identified.
Design of DTMF signal experiment system based on MATLAB

Based on the theory of digital signal processing, this paper designs a DTMF signal synthesis and detection system through visual interface interaction by using GUI toolbox in MATLAB, and gives the generation and detection scheme of DTMF signal. The experimental results show that the performance of the system is good, and it can help beginners to understand the process of telephone dialing and decoding.
Cross-Encoded Meta Embedding towards Transfer Learning

In this paper we generate word meta-embeddings from already existing embeddings using cross-encoding. Previous approaches can only work with words that exist in each source embedding, while the arc ...