Reviewerly/exHarmony huggingface dataset:
Multicomponent Unitary Coupled Cluster and Equation-of-Motion for Quantum Computation

The variational quantum eigensolver (VQE) algorithm combined with the unitary coupled cluster (UCC) ansatz has been developed for the quantum computation of molecular energies and wave functions within the Born–Oppenheimer approximation. Herein, this approach is extended to multicomponent systems to enable the quantum mechanical treatment of more than one type of particle, such as electrons and positrons or electrons and nuclei, without invoking the Born–Oppenheimer approximation. Specifically, we introduce the multicomponent unitary coupled cluster (mcUCC) method combined with the VQE algorithm for the calculation of ground-state energies and wave functions as well as the multicomponent equation-of-motion (mcEOM) method for the calculation of excitation energies. These methods are developed within the nuclear-electronic orbital (NEO) framework and are formulated in the qubit basis to enable implementations on quantum computers. Moreover, these methods are used to calculate the ground-state energy and excitation energies of positronium hydride, where both electrons and the positron are treated quantum mechanically, as well as the H2 molecule, where both electrons and one proton are treated quantum mechanically. These applications validate the implementation and provide benchmark data for future calculations. The errors due to Trotterization of the mcUCC ansatz are also analyzed. This formalism, as well as the accompanying computer code, will serve as the basis for applications to more complex multicomponent systems, such as simulations of photoinduced nonadiabatic molecular processes, on both classical and quantum computers.
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters

This paper presents an efficient graphenhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model.This model is based on recent advances in pre-training both encoder and decoder on very large text data (Lewis et al., 2019), and it incorporates an efficient encoding mechanism (Beltagy et al., 2020) that avoids the quadratic memory growth typical for traditional Transformers.We show that this powerful combination not only scales to large input documents commonly found when summarizing news clusters; it also enables us to process additional input in the form of auxiliary graph representations, which we derive from the multi-document clusters.We present a mechanism to incorporate such graph information into the encoder-decoder model that was pre-trained on text only.Our approach leads to significant improvements on the Multi-News dataset, overall leading to an average 1.8 ROUGE score improvement over previous work (Li et al., 2020).We also show improvements in a transfer-only setup on the DUC-2004 dataset.The graph encodings lead to summaries that are more abstractive.Human evaluation shows that they are also more informative and factually more consistent with their input documents. 1
Document-level Event Extraction via Parallel Prediction Networks

Document-level event extraction (DEE) is indispensable when events are described throughout a document.We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.It is a challenging task because it requires a holistic understanding of the document and an aggregated ability to assemble arguments across multiple sentences.In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner.Specifically, we first introduce a document-level encoder to obtain the document-aware representations.Then, a multi-granularity non-autoregressive decoder is used to generate events in parallel.Finally, to train the entire model, a matching loss function is proposed, which can bootstrap a global optimization.The empirical results on the widely used DEE dataset show that our approach significantly outperforms current stateof-the-art methods in the challenging DEE task.
Reservoir computing on epidemic spreading: A case study on COVID-19 cases

A reservoir computing based echo state network (ESN) is used here for the purpose of predicting the spread of a disease. The current infection trends of a disease in some targeted locations are efficiently captured by the ESN when it is fed with the infection data for other locations. The performance of the ESN is first tested with synthetic data generated by numerical simulations of independent uncoupled patches, each governed by the classical susceptible-infected-recovery model for a choice of distributed infection parameters. From a large pool of synthetic data, the ESN predicts the current trend of infection in $5%$ patches by exploiting the uncorrelated infection trend of $95%$ patches. The prediction remains consistent for most of the patches for approximately 4 to 5 weeks. The machine's performance is further tested with real data on the current COVID-19 pandemic collected for different countries. We show that our proposed scheme is able to predict the trend of the disease for up to 3 weeks for some targeted locations. An important point is that no detailed information on the epidemiological rate parameters is needed; the success of the machine rather depends on the history of the disease progress represented by the time-evolving data sets of a large number of locations. Finally, we apply a modified version of our proposed scheme for the purpose of future forecasting.
Qualitative exploration of digital chatbot use in medical education: A pilot study

During the coronavirus disease 2019 pandemic, face-to-face teaching has been severely disrupted and limited for medical students internationally. This study explores the views of medical students and academic medical staff regarding the suitability and limitations, of a bespoke chatbot tool to support medical education.Five focus groups, with a total of 16 participants, were recruited using a convenience sample. The participants included medical students across all year groups and academic staff. The pre-determined focus group topic guide explored how chatbots can augment existing teaching practices. A thematic analysis was conducted using the transcripts to determine key themes.Thematic analysis identified five main themes: (1) chatbot use as a clinical simulation tool; (2) chatbot use as a revision tool; (3) differential usefulness by medical school year group; (4) standardisation of education and assessment; (5) challenges of use and implementation.Both staff and students have clear benefits from using chatbots in medical education. However, they documented possible limitations to their use. The creation of chatbots to support the medical curriculum should be further explored and urgently evaluated to assess their impact on medical students training both during and after the global pandemic.
Multi-label feature selection based on fuzzy neighborhood rough sets

Abstract Multi-label feature selection, a crucial preprocessing step for multi-label classification, has been widely applied to data mining, artificial intelligence and other fields. However, most of the existing multi-label feature selection methods for dealing with mixed data have the following problems: (1) These methods rarely consider the importance of features from multiple perspectives, which analyzes features not comprehensive enough. (2) These methods select feature subsets according to the positive region, while ignoring the uncertainty implied by the upper approximation. To address these problems, a multi-label feature selection method based on fuzzy neighborhood rough set is developed in this article. First, the fuzzy neighborhood approximation accuracy and fuzzy decision are defined in the fuzzy neighborhood rough set model, and a new multi-label fuzzy neighborhood conditional entropy is designed. Second, a mixed measure is proposed by combining the fuzzy neighborhood conditional entropy from information view with the approximate accuracy of fuzzy neighborhood from algebra view, to evaluate the importance of features from different views. Finally, a forward multi-label feature selection algorithm is proposed for removing redundant features and decrease the complexity of multi-label classification. The experimental results illustrate the validity and stability of the proposed algorithm in multi-label fuzzy neighborhood decision systems, when compared with related methods on ten multi-label datasets.
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection

As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods are promising when training and test data are well aligned, but perform poorly on independent tests. Moreover, due to the absence of authentic test images, their image-level detection specificity is in doubt. The key question is how to design and train a deep neural network capable of learning generalizable features sensitive to manipulations in novel data, whilst specific to prevent false alarms on the authentic. We propose multi-view feature learning to jointly exploit tampering boundary artifacts and the noise view of the input image. As both clues are meant to be semantic-agnostic, the learned features are thus generalizable. For effectively learning from authentic images, we train with multi-scale (pixel / edge / image) supervision. We term the new network MVSS-Net and its enhanced version MVSS-Net++. Experiments are conducted in both within-dataset and cross-dataset scenarios, showing that MVSS-Net++ performs the best, and exhibits better robustness against JPEG compression, Gaussian blur and screenshot based image re-capturing.
EaaS infrastructure disruptor for MSE

Equipping Malaysia's $3 billion markets worth medium size enterprise with an optimise information technology infrastructure is a major challenge during the volatile business environment. This paper advances beyond traditional cloud-based infrastructure which relies heavily on internet service provider while our solution focuses on LAN based virtual grid to provide reliable services. Our pessimistic discussion into legacy Harvard University theories on the commodity of infrastructure and technology disruptor reveals the need for an updated strategic viewpoints on the possible consolidation available yet hidden infrastructure features while sustaining the economic uncertainty. The research is supported using a test-retest data of 100 samples and 122 sample from the Peninsular Malaysia. The compiled information using Spearman correlation gave alarming contribution from the unproductive 29% investment into building a much needed dynamic infrastructure to storm thru the economic turbulence, yet boost competitive difference when market bull run. Our client-server grid solution reshapes workload into the existing and available desktop to form a virtual centralised server that can grow or shrink with the business size. The revised exostructure as a service framework concludes the importance to revisit back local infrastructure disruptor.
Diffusion Deformable Model for 4D Temporal Medical Image Generation

Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively studied, approaches for temporal medical image generation such as 4D cardiac volume data are limited. In this work, we present a novel deep learning model that generates intermediate temporal volumes between source and target volumes. Specifically, we propose a diffusion deformable model (DDM) by adapting the denoising diffusion probabilistic model that has recently been widely investigated for realistic image generation. Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path. Once our model is trained, the latent code estimated from the diffusion module is simply interpolated and fed into the deformation module, which enables DDM to generate temporal frames along the continuous trajectory while preserving the topology of the source image. We demonstrate the proposed method with the 4D cardiac MR image generation between the diastolic and systolic phases for each subject. Compared to the existing deformation methods, our DDM achieves high performance on temporal volume generation.
Surrogate losses in passive and active learning

Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objective is to produce a classifier of relatively low risk, as measured under the $0$-$1$ loss, ideally using fewer label requests than the number of random labeled data points sufficient to achieve the same. This work investigates the potential uses of surrogate loss functions in the context of active learning. Specifically, it presents an active learning algorithm based on an arbitrary classification-calibrated surrogate loss function, along with an analysis of the number of label requests sufficient for the classifier returned by the algorithm to achieve a given risk under the $0$-$1$ loss. Interestingly, these results cannot be obtained by simply optimizing the surrogate risk via active learning to an extent sufficient to provide a guarantee on the $0$-$1$ loss, as is common practice in the analysis of surrogate losses for passive learning. Some of the results have additional implications for the use of surrogate losses in passive learning.
Development of Socio-Professional Activity of Personality in Conditions of Distance Education During Covid-19 Pandemic

In the article, peculiarities of researching socio-professional activity have been studied.For understanding the peculiarities of socio-professional activity development, three levels of person's socio-professional activity have been proposed.Experimental data and theoretical generalizations have allowed determining certain criteria for the formation of socio-professional activity of personality.Criteria and levels of socio-professional activity have been analysed.The following criteria of socio-professional activity: personal self-realization, socio-professional orientation and socioprofessional motivation have been defined.While analyzing the development of socio-professional activity in accordance with a certain level, the research has been focused on the features of different influences of educational activities on the growth of socio-professional activity.It has been substantiated that the formation of socio-professional activity of the personality is carried out in the process of activity, communication and interaction with the educational environment through active influence on the development of socio-professional environment, through the development of interpersonal communication and improvement of psychological and pedagogical training.
Banking 4.0: ‘The Influence of Artificial Intelligence on the Banking Industry & How AI Is Changing the Face of Modern Day Banks’

Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by machines, in contrast to the natural knowledge demonstrated by humans. From Siri to self-driving cars, AI is progressing at a rapid pace.Artificial intelligence consists of generally two fundamental ideas. First it involves studying human brains like how their thought process works and secondly it helps representing those processes through machine learning. Artificial Intelligence in finance is more than about chat bots. Artificial Intelligence has taken over numerous sectors including banking industry. The principal thought behind this investigation was to comprehend the impact of AI on present day banking.This research mainly focuses on the concept of AI in the field of banking, how it has brought revolutionary changes in banking and its impact on human manpower. As we are aware that humans tend to commit errors, but the world is evolving so does the innovations, there is lack of skilled talents required to handle the automation. Several routine and manual tasks which were earlier performed by the humans are now being replaced by the automated machines with advance technology. Given that the business is experiencing noteworthy change at a quick pace, this research is a preview of the current applications of AI in the banking industry and how it is changing the face of banking in India.
Library Research Guides: Open Access Business Resources: Demographics and Markets

Select open access resources that will help business owners and entrepreneurs make smart, data-driven decisions.
Developing reading fluency of students with intellectual disabilities

Children with intellectual disabilities normally have significantly reduced intellectual capacities and adaptation skills. Their cognitive development is delayed, affecting the development of their reading literacy. These children develop a low level of reading, which means that their reading is not fluent enough to ensure reading comprehension. In these cases, reading is not one of the methods of how to acquire new knowledge. Continuous and intensive implementation of carefully selected activities can significantly contribute to the improvement of reading. In the master's thesis, we focused on how to encourage the development of reading skills in three individuals with intellectual disabilities, attending the fourth grade of separate special education with a lower education standard. We drew up a program, designed to determine to what extent reading of these three individuals, who have problems with reading, will improve after a three-month continuous program, encompassing everyday intensive implementation of activities, aimed at improving reading literacy. Prior to the implementation of our activities, we examined the children's reading skills with individual tasks taken from the Reading and Writing Disorder Test (T-MBP) (Sali, 1975) and with texts from the CBM Reading Fluency Test (Kosir, 2011a), based on which a reading program was set up. In order to improve reading fluency, we selected some activities within the program and performed them at our meetings. As we wanted to monitor the achieved goals set in the program, we kept a record of results related to individual activities and noted any particularities. Once we conducted all the meetings, we tested the pupils' reading fluency with the same tasks as at the beginning. By comparing the initial and final results of the tests, we confirmed that everyday reading activities have a significant impact on the reduction of reading errors, on the improvement of reading fluency and, consequently, on the comprehension of the read content in individuals with reduced intellectual capacities.
Tips system in module education

This article is about the essence of the pedagogical process and its direction, the tasks to be solved in the formation and upbringing of the individual. There were opportunities for communication and feedback with any student, wherever he was. The proliferation of high-speed Internet made it possible to move to a system of “online reading seminars” and the result was a distance learning system. Distance learning explains two main approaches to teaching - the expansion and transformation models. The teaching technology in the expansion model is almost no different from the current traditional method.
Behavior and Cognition Processing of Educational Tabletop Coding Games

Many countries have been actively promoting computational thinking since it was put forward, and visual programming language, robots and educational tabletop games are commonly used in its promotion. To understand the relationship between students’ learning of coding principles in educational tabletop games and players’ cognitive processing at key points in time, students’ behavior while playing the Interstellar Explorer tabletop game was observed by a brainwave EEG (electroencephalography) instrument and video and analyzed with cognitive processing. The results showed that a continuous increase in attention and meditation reflected a low level of knowledge and understanding as well as high-level application, analysis and evaluation of cognitive processing. This study explores the relationship between the state of students playing tabletop games and their behavior during play and then derives the relationship between their behavior and cognitive processing. It is recommended that the design of tabletop games consider how the learning content matches the game mechanics and examine whether cognitive processing is present according to Bloom’s taxonomy. In addition, to enhance players’ cognitive ability, it is recommended that tabletop games provide the ability to use contextual content and multiple strategies.
Self-Attention is Not Only a Weight: Analyzing BERT with Vector Norms

Self-attention modules are essential building blocks of Transformer-based language models and hence are the subject of a large number of studies aiming to discover which linguistic capabilities these models possess (Rogers et al., 2020). Such studies are commonly conducted by analyzing correlations of attention weights with specific linguistic phenomena. In this paper, we show that attention weights alone are only one of two factors determining the output of self-attention modules and propose to incorporate the other factor, namely the norm of the transformed input vectors, into the analysis, as well. Our analysis of self-attention modules in BERT (Devlin et al., 2019) shows that the proposed method produces insights that better agree with linguistic intuitions than an analysis based on attention-weights alone. Our analysis further reveals that BERT controls the amount of the contribution from frequent informative and less informative tokens not by attention weights but via vector norms.
MODIFICATION FACTORS IN THE SYSTEM OF HIGHER PEDAGOGICAL EDUCATION: GLOBAL AND NATIONAL LEVELS

"A number of factors that determine the introduction of competencies in professional education is presented in this article. The analysis of competence features contained in the European Qualifications Framework, communiqué materials, the Tuning project, the National Qualifications Framework and the professional standard «Teacher» of the Republic of Kazakhstan is carried out. The article provides a definition of the model of a specialist, as well as the norms of requirements for his or her qualifications and competencies. Moreover, the need to include employers in educational systems of all levels is considered, but not only as an order for personnel training, but also as carriers of initiatives in education. A holistic set of competencies is presented, tied to certain tasks, which reflects the competence model of a university graduate. The study identifies professional and personal requirements for a modern teacher. Such areas of the teacher’s activity as the development and planning of educational activities and training programs, teaching, assessment and reflection, professional development, the use of appropriate learning technologies, showing respect for certain students and promoting equality of opportunities for students are considered. "