Reviewerly/exHarmony huggingface dataset:
SDCF: semi-automatically structured dataset of citation functions

Abstract There is increasing research interest in the automatic detection of citation functions, which is why authors of academic papers cite previous works. A machine learning approach for such a task requires a large dataset consisting of varied labels of citation functions. However, existing datasets contain a few instances and a limited number of labels. Furthermore, most labels have been built using narrow research fields. Addressing these issues, this paper proposes a semiautomatic approach to develop a large dataset of citation functions based on two types of datasets. The first type contains 5668 manually labeled instances to develop a new labeling scheme of citation functions, and the second type is the final dataset that is built automatically. Our labeling scheme covers papers from various areas of computer science, resulting in five coarse labels and 21 fine-grained labels. To validate the scheme, two annotators were employed for annotation experiments on 421 instances that produced Cohen’s Kappa values of 0.85 for coarse labels and 0.71 for fine-grained labels. Following this, we performed two classification stages, i.e., filtering, and fine-grained to build models using the first dataset. The classification followed several scenarios, including active learning (AL) in a low-resource setting. Our experiments show that Bidirectional Encoder Representations from Transformers (BERT)-based AL achieved 90.29% accuracy, which outperformed other methods in the filtering stage. In the fine-grained stage, the SciBERT-based AL strategy achieved a competitive 81.15% accuracy, which was slightly lower than the non-AL strategy. These results show that the AL is promising since it requires less than half of the dataset. Considering the number of labels, this paper released the largest dataset consisting of 1,840,815 instances.
Implications of Quantum Science on Industry 4.0: Challenges and Opportunities

The classical computers are structured based on of digital logic; information is encoded by operating on bits and performs operations using logical gates. Contrary to such traditional computing, the quantum computers exploit quantum theory and behavior of atomic and sub-atomic particles. In quantum this bit of data is represented by a single atom qubit. Qubits utilizes two states and the superposition of these two states and its quantum correlations. In Quantum mechanics the qubits work together to solve a particular problem. The quantum superposition and entanglement principle gives qubits the ability to perform with much faster computations than bits in classical computers. Quantum computers are disrupting every industry. They can solve problems that are intractable for current classical computers. This is the reason for increasing investment by Government and players like Google, Microsoft and IBM, in recent past. Advances in quantum computing, have implications in the future world and will change the way the world functions today. The present work outline various futuristic and practical examples in various fields such as online security, artificial intelligence, drug development, weather forecasting and climate change, where, the current capability of quantum on one hand, leave the several processes vulnerable, and on the other hand, its immense power of computation, gives opportunity to open doors to amazing developments in the field. In the present survey, the discussion is developed around the implications in these fields and a broad road map of futuristic events is sketched.
On the Effects of Properties of the Minibatch in Reinforcement Learning

Neural networks are typically trained on large amounts of data using a gradient descent optimization algorithm. With large quantities of data it is infeasible to calculate the gradient over the entire dataset, and the gradient is therefore estimated over smaller minibatches of data. Conventional wisdom in deep learning dictates that best performance is achieved when each minibatch is representative of the whole dataset, which is typically approximated by uniform random sampling from the dataset. In deep reinforcement learning the agent being optimized and the data are intimately linked, as the agent often chooses its own traversal of the problem space (and therefore data generation), and further the objective is not necessarily to perform optimally over the whole problem space but rather to identify the high rewarding regions and how to reach them. In this paper we hypothesize that one can train specifically for subregions of the problem space by constructing minibatches with data exclusively from this subregion, or conversely that one can avoid catastrophic forgetting by ensuring that each minibatch is representative of the whole dataset. We further investigate the effects of applying such a strategy throughout the training process in the offline reinforcement learning setting. We find that specific training in this sense is not possible with the suggested approach, and that simple random uniform sampling performs comparable or better than the suggested approach in all cases tested.
CMIC: an efficient quality score compressor with random access functionality

Abstract Background Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access. Results In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores. Conclusions CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic .
Design and Analysis of Adversarial Samples in Safety–Critical Environment: Disease Prediction System

Deep learning is a part of machine learning applied in many applications, from object detection to disease prediction. In 2013, deep neural networks were vulnerable to perturbed samples during the testing/deployment stage. Deep learning becomes a significant issue when we apply such algorithms in designing safety–critical applications. Deep neural networks are fooled by adding some noise intentionally in the image such that the changes are sometimes not noticeable to the human eye and are known as an adversarial mechanism. The noise adds in such a direction that the difference between the original and perturbed images should be minimal. This work demonstrates the designing of adversarial images and analyzing them on the disease prediction using a chest X-ray prototype system. This work illustrates the design of adversarial samples using three different methods: an image augmentation scheme, filtering, and patches and analysis of how adversarial samples affect the result in real-world clinical settings. The change in the clinical setting not only affects the healthcare economy but also raises technical vulnerability. This work attempts to design and improve a more robust medical learning system. The analysis shows if patches applied in specific regions give expected results to the adversary. In this work, after the successful attack model shows 71% of confidence.
Social consequences of the education digital transformation in the conditions of the coronacrisis

A bioinspired quasi-molecular scenario analysis algorithm has been developed, on the basis of which seven scenarios of the social consequences of the digital transformation of education, as well as five scenarios of the development of the social consequences of the COVID-19 pandemic, have been proposed. The scenario approach is an effective tool for system analysis and forecasting of various variants of social activity of Internet users in the conditions of the coronacrisis.
Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform

Text interpretation of public English vocabulary is a critical task in the subject of natural language processing, which uses technology to allow humans and computers to communicate effectively using natural language. Text feature extraction is one of the most fundamental and crucial elements in allowing computers to effectively grasp and read text. This paper proposes a text feature extraction method based on wavelet analysis that performs fast discrete wavelet transform and inverse discrete wavelet transform on the feature vectors under the traditional TF-IDF vector space model to address the problem of low feature differentiation of high-dimensional data in text feature extraction. In particular, due to the design of the Mallat algorithm, there is frequency aliasing in the signal decomposition process. This phenomenon is a problem that cannot be ignored when using wavelet analysis for feature extraction. Therefore, this paper proposes an improved inverse discrete wavelet transform method, in which the signal is decomposed by Mallat algorithm to obtain wavelet coefficients at each scale and then reconstructed to the required wavelet space coefficients according to the reconstruction method, and the reconstructed coefficients are used to analyze the signal at that scale instead of the wavelet coefficients obtained at the corresponding scale. Experiments on the public English vocabulary dataset reveal that the wavelet transform-based strategy suggested in this research outperforms existing feature extraction methods while maintaining greater classification accuracy while reducing the dimensionality of the TF-IDF vector space model.
Clean or Annotate: How to Spend a Limited Data Collection Budget

Crowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling.There are two common strategies to manage the impact of such noise: The first involves aggregating redundant annotations, but comes at the expense of labeling substantially fewer examples.Secondly, prior works have also considered using the entire annotation budget to label as many examples as possible and subsequently apply denoising algorithms to implicitly clean the dataset.We find a middle ground and propose an approach which reserves a fraction of annotations to explicitly clean up highly probable error samples to optimize the annotation process.In particular, we allocate a large portion of the labeling budget to form an initial dataset used to train a model.This model is then used to identify specific examples that appear most likely to be incorrect, which we spend the remaining budget to relabel.Experiments across three model variations and four natural language processing tasks show our approach outperforms or matches both label aggregation and advanced denoising methods designed to handle noisy labels when allocated the same finite annotation budget.1 Training a large language model (such as RoBERTa-Large) until convergence can easily take a day or longer.Doing so each time for 12k annotations would take 30+ years.
The Effect of Knowledge Management in Healthcare Services: A Systematic Review

The quality of service at health facilities needs to be improved, one of the main keys is the service provided by health workers. The aim of this systematic review was to analyze the effectiveness of knowledge management in healthcare. The systematic review was carried out in March-April 2020 with a range of article search times from 2016-2020 using 5 electronic databases (Scopus, PubMed, Science Direct, CINAHL and ProQuest). The Center for Review and Dissemination and the Joanna Briggs Institute Guideline were used to assess the quality and PRISMA's checklist for this review guide. The literature search described four keyword groups based on Medical Subject Heading (MeSH) and the search description was (knowledge management*' OR 'SECI knowledge management') AND ('health care' OR hospital). The article searched was found 13 articles suitable with the eligibility criteria, the results of the systematic review found that knowledge management had the effectiveness to provide information and knowledge processes, improve decision-making abilities, improve performance and quality of health services and increase organizational effectiveness. As health service providers, hospitals and other health care institutions must continue to improve the quality of services. By improving the quality of services, it is expected to be able to answer the demands of the community to always provide optimal health services. With the application of good knowledge management, ideas and creativity from the workforce will be created in creating health service innovations.
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation

Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework named C riterion-guided H eterogeneous C ollaborative F iltering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.
Learning Optical Flow from a Few Matches

State-of-the-art neural network models for optical flow estimation require a dense correlation volume at high resolutions for representing per-pixel displacement. Although the dense correlation volume is informative for accurate estimation, its heavy computation and memory usage hinders the efficient training and deployment of the models. In this paper, we show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it. Based on this observation, we propose an alternative displacement representation, named Sparse Correlation Volume, which is constructed directly by computing the k closest matches in one feature map for each feature vector in the other feature map and stored in a sparse data structure. Experiments show that our method can reduce computational cost and memory use significantly, while maintaining high accuracy compared to previous approaches with dense correlation volumes. Code is available at https://github.com/zacjiang/scv .
Augmentation Strategies for Learning with Noisy Labels

Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the "learning with noisy labels" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset Clothing1M. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the Clothing1M dataset. (K. Nishi and Y. Ding contributed equally to this work)
Application and Integration of VIAF and OCLC FAST with Koha, VuFind, and Google CSE

The purpose of Digital Library system is collection of electronic objects. The “authorities” module runs the entire mechanism for fetching the authority data from the remote server. The study explores mechanisms for integration activities behind the authority control and practical evaluation. This study has been done on the publicly accessible online library OPAC through Google Form tools. It demonstrates that all authority control tools can manage several headings than just one or a few. The research seeks explanation through systematically searching observations of Online OPAC databases for studies of authority control. According to the findings, AuthorityBox is a Koha add-on that enhances the system's functionality. It complies with VIAF identifiers and other MARC21 authority data to compliance with RDA Cataloguing Guidelines. The open source integrated library system Koha enables easy authority import into the ILS-OPAC search engine. Statistically, Haringhata Mahavidyalaya, West Bengal, India used the most authority terms, with 24,820 personal names and 53,256 topical terms. This study reviews the many recommendations and techniques for designing a library discovery authority framework. It also focuses on the software and standards for integrating VIAF with Koha, VuFind, and Google Custom Search Engine for importing authority data from other libraries via the Z39.50 server.
Visual Place Recognition Using Region of Interest Extraction with Deep Learning Based Approach

Visual Place Recognition (VPR) is process of properly identifying a formerly visited place under varying viewpoints conditions. VPR becomes a challenging task due to the variations in lighting conditions (day time or night time), shadows, weather conditions, view points, or even different seasons. Therefore, it is needed to develop VPR algorithms which can handle the variation in visual appearances. This article designs a visual place recognition using a novel region of interest (RoI) extraction with deep learning based (VPR-ROIDL) technique on changing environments. The proposed VPR-ROIDL technique initially enables the extraction of RoIs using saliency map then features of RoIs using local diagonal extrema patterns (LDEP) respectively. Besides, the extracted features of RoIs are passed into the convolutional neural network based residual network (ResNet) model and the computed deep features are stored in the database. Upon providing a query image (QI), the RoIs and features of RoIs are extracted as same as reference images. Moreover, the deep features from the ResNet model are generated and Euclidean distance based similarity measurement is used to finally recognize the places. By the use of RoI and descriptors matching process, the VPR-ROIDL model has the ability to recognize places irrespective of changing illuminations, seasons, and viewpoints. The simulation results pointed out the supremacy of the ROIDL-VPR technique over the recent state of art approaches.
Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization Techniques

It’s been around two years from the outbreak of the coronavirus, thus labeled as Covid-19, and there has been an explosion of literature being published by research scholars related to work done on Covid-19. Covid-19 as a keyword has been mentioned in the titles of most of these papers. It was thought to analyse the number of papers and the titles of papers which include Covid-19 in the title of the research papers. The various combinations of other words like, prefixes, suffixes, N-gram combinations with the keyword Covid- 19 in the titles of these papers were also analysed. The research publication repositories analysed were: IEEE Explore, ACM Digital Library, Semantic Scholar, Google Scholar, Cornel University etc. The domains of research publication title analysis were restricted to computer science/computer engineering related papers. As the term labeling the corona virus outbreak as Covid-19 was labeled in 2020, the timeline of title analysis was restricted from 2019 till December 2021. The term Covid-19 is also one of the most searched terms in most of these research repositories as is evident from the search suggestions offered by them. Considering the usefulness of Bag of Words and N Gram algorithm in analytics and data visualization, a methodology is proposed and implemented based on bag of words algorithm to do prefix and suffix words analysis. This methodology is working correctly to state different prefix and suffix words used by various researchers to demonstrate significance of their titles. Methodology based on N Gram analysis is found effective to find topic on which most of the researchers have done work. Word Clouds are generated to demonstrate different buzz words used by researchers in their respective paper titles. These are useful for providing visualization of the data if it is in big size.
Quantum simulations of molecular systems with intrinsic atomic orbitals

Quantum simulations of molecular systems on quantum computers often employ minimal basis sets of Gaussian orbitals. In comparison with more realistic basis sets, quantum simulations employing minimal basis sets require fewer qubits and quantum gates, but yield results of lower accuracy. A natural strategy to achieve more accurate results is to increase the basis set size, which in turn requires increasing the number of qubits and quantum gates. Here we explore the use of intrinsic atomic orbitals (IAOs) in quantum simulations of molecules, to improve the accuracy of energies and properties at the same computational cost required by a minimal basis. We investigate ground-state energies and one- and two-body density operators in the framework of the variational quantum eigensolver, employing and comparing different Ans\"{a}tze. We also demonstrate the use of this approach in the calculation of ground- and excited-states energies of small molecules by a combination of quantum algorithms, using IBM Quantum computers.