Reviewerly/exHarmony huggingface dataset:
Layer-Wise Multi-View Learning for Neural Machine Translation

Traditional neural machine translation is limited to the topmost encoder layer's context representation and cannot directly perceive the lower encoder layers.Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions.In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure.We regard each encoder layer's off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence.In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view.We feed the two views to a partially shared decoder to maintain independent prediction.Consistency regularization based on KL divergence is used to encourage the two views to learn from each other.Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines.As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model.
Generalized far-field integral

The propagation of light in homogeneous media is a crucial technology in optical modeling and design as it constitutes a part of the vast majority of optical systems. Any improvements in accuracy and speed are therefore helpful. The far-field integral is one of the most widely used tools to calculate diffraction patterns. As a general rule, this approximate method requires the observation plane located in the far-field region, i.e., a very considerable propagation distance. Only in the well-designed (namely aberration-free) optical system does the far-field integral not suffer from the limitation of the large distance. Otherwise, the far-field integral cannot provide accurate results. In the present work, we generalize the far-field integral to a more general concept with a much more flexible application scope, which allows for the inclusion of aberrations as well. Finally, as an essential part of this generalization, the propagation to arbitrarily oriented planes is also taken into account.
Design Considerations for Autonomous Cargo Transportation Multirotor UAVs

Unmanned aerial vehicles (UAVs) have proven to be an advanced tool for a variety of applications in the civilian and military sectors. Different categories of UAVs are used in various missions and are also the subject of numerous researches. Due to their characteristics and potential in specific conditions, multirotor UAVs imposes itself as a solution for many tasks, including transport. This chapter presents a conceptual solution of autonomous cargo transportation where the primary research objective is the design of a heavy lift multirotor UAV system. The process of designing a multirotor UAV that can carry heavy lift cargo is quite challenging due to many parameters and constraints. Five selected series of electric propulsion systems are analyzed, with different multirotor configurations, and results are graphically displayed for payloads from 10 kg up to 100 kg.
A Comprehensive Study of Machine Learning algorithms for Predicting Leukemia Based on Biomedical Data

Leukemia is known as the cancers of the blood or bone marrow. Bone marrow which is known as the spongy tissue creates blood cells. So for blood cell productivity problems, Leukemia can boost. It generally impacts the leukocytes or white blood cells. It grows gradually in our bodies. In this paper our goal is to detect leukemia applying Machine Learning which can help in medical science to detect a patient is a cancer patient or not. Therefore we needed a system for decision making in the Leukemia detection system. That’s why we propose some well-known algorithms to get better accuracy to detect Leukemia in our paper. We have used some different types of machine learning algorithms like Naive Bayes, Support Vector Machine(SVM), Decision Tree and K-nearest neighbor (KNN) algorithm to get the result that a patient is a cancer patient or not. We collect our data from Z H Shikder Medical College and Hospital which include 401 data in our dataset. Among them, the Decision tree algorithm gives the best result with 100% accuracy of prediction. In this paper, we have also shown the comparative results analyzing different algorithms precision, recall and F1 score for our all data samples.
Tokens and Accounts in the Context of Digital Currencies

<ns3:p>This report provides an overview of two concepts that come up frequently in discussions of digital currencies: tokens and accounts. The meaning of each concept differs depending on context. For cryptocurrencies, the term "token" has a relatively straightforward, technology-specific definition. For central bank digital currencies (CBDC), the term "token" takes on more of a conceptual or analytical meaning. To further complicate matters, "tokens" and "accounts" are not always mutually exclusive ideas. This paper identifies areas where this terminology has caused confusion and explains why the terminology is problematic in an effort to improve the technical and conceptual discourse on digital currencies and the "tokenization" of financial assets.</ns3:p>
Research Data Management (RDM) at the University of Ghana (UG)

This article explores Research Data Management (RDM) at the University of Ghana (UG). It emphasises on institutional awareness and attitudes, and whether the University Library is officially supporting this emerging strategic interest in research focused Higher Education Institutions (HEIs). Purposive sampling was used to select information-rich respondents from across the University (i.e. Librarians, Research Administrators, ICT Managers and Senior Researchers) who were interviewed on a range of issues about RDM. Institutional documents were also reviewed to corroborate the primary data and get a deeper understanding of the research problem. The study shows that while RDM is recognised at the institutional level as good research practice and integrity issue, the concept is tenuously understood in the local community. Unsurprisingly, however, there was a general appreciation and awareness of the need for RDM and the implications for such critical concerns as security, integrity, continuity and institutional reputation. The library is yet to take a strategic approach to RDM issues and there is clearly a dearth in RDM expertise within the library system. The study recommends that the library must be proactive in advocating and promoting RDM issues at UG, but first, the Librarians must take advantage of numerous existing opportunities to build their capacity.&#x0D;
Interpreting Galaxy Deblender GAN from the Discriminator’s Perspective

In large galaxy surveys it can be difficult to separate overlapping galaxies, a process called deblending. Generative adversarial networks (GANs) have shown great potential in addressing this fundamental problem. However, it remains a significant challenge to comprehend how the network works, which is particularly difficult for non-expert users. This research focuses on understanding the behaviors of one of the network’s major components, the Discriminator, which plays a vital role but is often overlooked. Specifically, we propose an enhanced Layer-wise Relevance Propagation (LRP) algorithm called Polarized-LRP. It generates a heatmap-based visualization highlighting the area in the input image that contributes to the network decision. It consists of two parts i.e. a positive contribution heatmap for the images classified as ground truth and a negative contribution heatmap for the ones classified as generated. As a use case, we have chosen the deblending of two overlapping galaxy images via a branched GAN model. Using the Galaxy Zoo dataset we demonstrate that our method clearly reveals the attention areas of the Discriminator to differentiate generated galaxy images from ground truth images, and outperforms the original LRP method. To connect the Discriminator’s impact on the Generator, we also visualize the attention shift of the Generator across the training process. An interesting result we have achieved is the detection of a problematic data augmentation procedure that would else have remained hidden. We find that our proposed method serves as a useful visual analytical tool for more effective training and a deeper understanding of GAN models.
Multi-feature automatic abstract based on LDA model and redundant control

Abstract With the continuous popularization of computer application technology and the rapid development of Internet technology, there has been an explosion of information in every field, and more and more information is transmitted and stored on the Internet in the form of electronic text. Automatic summarization can solve the problem that the speed of traditional hand-written summaries cannot keep up with the speed of electronic text information generation. Automatic summarization can use a computer to generate a comprehensive and accurate coherent short essay on one or more documents, so people can quickly understand the main content of the text, which greatly improves the efficiency of people’s learning and work. Nowadays, automatic summarization technology has achieved rich results, but Chinese document summarization is still in the shallow semantic analysis of text, and there are problems such as redundancy of summarization and poor quality of abstract content. In view of the above problems, this article is in the research and implementation of Chinese automatic summarization technology. On the one hand, LDA is used to mine the topic distribution of documents to obtain high-quality abstracts; on the other hand, the similarity between the sentences to be extracted is calculated to remove redundant information.
Comparison of the efficiency of neural network algorithms in recognition and classification problems

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.
More than words: Novel visualizations for evidence synthesis

Data sharing is not applicable to this article as no new data were created or analyzed in this study.
HW-TSC’s Participation in the WAT 2020 Indic Languages Multilingual Task

This paper describes our work in the WAT 2020 Indic Multilingual Translation Task. We participated in all 7 language pairs (En Bn/Hi/Gu/Ml/Mr/Ta/Te) in both directions under the constrained condition—using only the officially provided data. Using transformer as a baseline, our Multi->En and En->Multi translation systems achieve the best performances. Detailed data filtering and data domain selection are the keys to performance enhancement in our experiment, with an average improvement of 2.6 BLEU scores for each language pair in the En->Multi system and an average improvement of 4.6 BLEU scores regarding the Multi->En. In addition, we employed language independent adapter to further improve the system performances. Our submission obtains competitive results in the final evaluation.
A Bayesian hierarchical copula model

Dependent data with hierarchical structures arises commonly from a variety of application, and analysis of such data is often challenging due to the complexity in modeling dependence structures and the computation intensity. In this paper, we propose a Bayesian hierarchical copula model (BHCM) to accommodate hierarchical structures of dependent data, where the subject-level dependence is modeled by the copula-based model and the hierarchical structure is described using random dependence parameters. We introduce a layer-by-layer sampling scheme for conducting Bayesian inferences. Our proposed BHCM enjoys the flexibility of modeling various complex association structures, while retaining manageable computation. Extensive simulation studies show that our proposed estimators outperform conventional likelihood-based estimators in a variety of finite sample settings. We apply the BHCM to analyze the Vertebral Column dataset arising from UCI Machine Learning Repository.
A modification of a stopping method for text recognition in a video stream with best frame selection

One of the most important problem in constructing computer vision systems for embedded and mobile devices is offline recognition of text strings. In this paper, we analyze the problem of text strings recognition process in a video stream using best frame selection. This method allows to incorporate the information from multiple views of the same target object, thus increasing the overall extraction accuracy. A stopping method is proposed, which allows to make an automatic stopping decision, i.e. to terminate the process at the optimal time in order to maximize the responsiveness of the system. Experimental evaluation on open identity document datasets MIDV-500 and MIDV-2019 show that the proposed stopping rule allows to decrease mean error level of the text recognition results in comparison with a baseline approach which stops after a fixed amount of processed frames.
Incremental semantic restriction and subjectivity-based adjective ordering

Adjective ordering preferences (e.g., big blue box vs. blue big box) are robustly attested in many unrelated languages (Dixon, 1982). Scontras et al. (2017) showed that adjective subjectivity is a robust predictor of ordering preferences in English: less subjective adjectives occur closer to the modified noun. In a follow-up to this finding, several authors have claimed that pressures from successful reference resolution and the hierarchical structure of modification explain subjectivity-based ordering preferences (Simonicˇ, 2018; Franke et al., 2019; Scon- tras et al., 2019). In cases of restrictive modification, adjectives that compose with the nominal later will classify a smaller set of potential referents (e.g., the set of boxes vs. the set of blue boxes). To avoid alignment errors where a listener might mis-characterize the intended referent, speakers introduce the more error-prone (i.e., more subjective) adjectives later in the hierarchical construction of nominal structure; the structure linearizes such that subjectivity decreases the closer you get to the modified noun. The current study explores the predictions of this reference-resolution story by examining adjective ordering cross-linguistically: when adjectives incrementally restrict a nominal denotation, there should be pressure toward subjectivity- based orderings, but, in the absence of incremental restriction, such pressures should not obtain.
Information Networking and its Application in the Digital Era with Illustration from the University of Port Harcourt Library

This paper discussed the factors that necessitated information networking, types of networks, benefits of information networking, library information networking and the University of Port Harcourt library network initiatives. Information networking is a process of communication, exchange of ideas, resource sharing and collaboration between individuals, organizations, institutions and libraries and it is facilitated by ICTs and the internet for improved accessibility. It has been brought about by information explosion, rapid advancement in information communication technologies, inadequate funding and increased demand for quality information. Networks can be classified into local, national, regional, and international networks and are formed to serve different categories of user communities. Benefits of information networking include resource sharing, on-line conferences and participation in programmes at distant centers, collaboration among scholars in different countries, and communication flow through the internet, social media, and electronic mail. Library information networking started with the interlibrary loan which has metamorphosed into library consortia in which groups of libraries partner to coordinate activities, share resources and combine expertise. The University of Port Harcourt Library network initiatives started with an e-granary (a CD ROM) and the establishment of a local area network. It had transited from Card Catalogue to Online Public Access Catalogue (OPAC) and now has a website devoted to library services. The library has subscribed to more than 15 electronic databases and has updated to include automated circulation and cataloguing services. Information networking has greatly improved the sharing of resources in information acquisition and dissemination since no single institution can acquire the overwhelming number of information resources in their various formats in the reality of the current information explosion.
The distributed ledgers ensuring privacy-preserving transactions

The paper is devoted to the actual problem of ensuring privacy during performing transactions in distributed ledgers. We discuss various aspects of transaction privacy, as well as the specifics of setting the problem for distributed ledgers with two main models for representing participants' balances: the UTXO-model and the account model. Based on these results, we outline definitions and consider security properties of the main cryptographic primitives used for preserving the privacy of transactions: mixers, ring signatures, homomorphic encryption, and zero-knowledge proofs. We analyze well-known solutions for distributed ledgers based on the UTXO-model, such as Zcash, Monero, Zcoin, Dash, CoinShuffle, Verge, Grin, and others, as well as for systems based on the account model: DSC, Zether, Zeth, BlockMaze. Based on the comparison of advantages, disadvantages, and limitations for existing solutions, conclusions are drawn about the future development of distributed ledgers that ensure the privacy of transactions, and new research tasks are outlined.
A Sentiment Analysis Algorithm of Danmaku Based on Building a Mixed Fine-grained Sentiment Lexicon

The Danmaku is a form of instant video text commentary that reflects the viewer's sentiment orientation. Currently, most of sentiment analysis algorithms based on the sentiment lexicon are using manual construction of the lexicon. However, this kind of method has strong subjectivity and low accuracy, and there is no mature sentiment analysis algorithm for the linguistic characteristics of the Danmaku. In order to solve these problems, we propose a sentiment analysis algorithm of Danmaku based on the construction of a mixed fine-grained sentiment lexicon. This paper realizes the sentiment analysis in three parts: the construction of a basic lexicon, the construction of a fine-grained sentiment lexicon, and the calculation of sentiment values. The basic lexicon includes a stopword list with Danmakus' characteristics and a link-word lexicon, while the fine-grained sentiment lexicon is constructed by using the way of combining word embedding and word co-occurrence. The final experimental results show that our method proposed in this paper is effective in analyzing the sentiment of the Danmaku texts.
Characterizing bias due to differential exposure ascertainment in electronic health record data

Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients’ healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and 5-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions.
Realization of a Robust Watermarking System in Spatial Domain

In this paper, we have proposed a watermarking system that is based on the spatial domain and is blind and robust in nature. This scheme is developed to withstand most of the image processing attacks. The watermark has been embedded in the cover image by modification of the DC coefficients calculated in the spatial domain. This method directly calculates DC coefficients in the spatial domain. The values of pixels can be changed/modified in the spatial domain in accordance with available watermark information. Since we have avoided the time-consuming transform operation, i.e., Discrete Cosine Transform, the computational efficiency is very high. For embedding a watermark bit, a particular image is disintegrated into 8 × 8 blocks, followed by further division of each block into two 4 × 4 blocks. After calculation of the DC coefficient of each 4 × 4 block, the watermark bit is embedded by modifying the DC values such that the DC coefficient of one block becomes greater than the other. The output results prove our proposed technique is highly robust to commonly occurring signal processing attacks. Experimental results obtained against numerous signal processing attacks are represented in terms of quality measuring parameters like PSNR, SSIM, and BER to check the efficiency and execution of our scheme.