Reviewerly/exHarmony huggingface dataset:
Adversarial Subword Regularization for Robust Neural Machine Translation

Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates.However, the diversification of subword segmentations mostly relies on the pre-trained subword language models from which erroneous segmentations of unseen words are less likely to be sampled.In this paper, we present adversarial subword regularization (ADVSR) to study whether gradient signals during training can be a substitute criterion for exposing diverse subword segmentations.We experimentally show that our model-based adversarial samples effectively encourage NMT models to be less sensitive to segmentation errors and improve the performance of NMT models in low-resource and out-domain datasets.
A fuzzy process of individuation

It is shown that an aspect of the process of individuation may be thought of as a fuzzy set. The process of individuation has been interpreted as a two-valued problem in the history of philosophy. In this work, I intend to show that such a process in its psychosocial aspect is better understood in terms of a fuzzy set, characterized by a continuum membership function. According to this perspective, species and their members present different degrees of individuation. Such degrees are measured from the membership function of the psychosocial process of individuation. Thus, a social analysis is suggested by using this approach in human societies.
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors

Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for complex relations. Besides, PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition. Given simple constraints on relation representations, PairRE can encode subrelation further. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.
Sequent calculi of first-order logics of partial predicates with extended renominations and composition of predicate complement

We study new classes of program-oriented logical formalisms – pure first-order logics of quasiary predicates with extended renominations and a composition of predicate complement. For these logics, various logical consequence relations are specified and corresponding calculi of sequent type are constructed. We define basic sequent forms for the specified calculi and closeness conditions. The soundness, completeness, and counter-model existence theorems are proved for the introduced calculi.
Progressive Refinement Imaging

Abstract This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory‐efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras.
Method Evaluation for Software Testability on Object Oriented Code

In software testability, there are various software measurement methods and metrics available for selection. To conduct the software testability evaluation, the combination of software expert tester judgement and appropriate metrics selection has reflected the of software quality accuracy. However, due to perspective of software expert tester, the software measurement and metric selection can lead to an inaccurate software testability result. Moreover, time and budget waste in conduct the software testability evaluation. Thus, this research aims to develop a testability measurement on model on Object-oriented programming software that will facilitate the accurate evaluation result. The investigation on how the factors of classes in a system influent the test effort. The research covers the gap analysis, automatic model creation with ANNs of AI Technology, and testability evaluation result comparison.
An Improved Method for Data Storage Based on Blockchain Smart Contract

With the development of blockchain technology, more and more blockchain systems have emerged. Traditional applications based on Ethereum smart contracts have the problem of requiring the off-chain database to assist in data operations and smart contract execution, which consumes a large amount of gas and causes waste of resources. This paper proposes an improved method for storage of smart contract data based on blockchain to optimize the data upload format of off-chain systems. Use solidity language to design smart contract storage variables and implement the function of adding, deleting, checking and modifying data on the chain. This method is a combination of general frameworks and gives detailed algorithm design and implementation methods. In this paper, the functional and performance tests of the proposed method are carried out through experiments to verify the integrity and feasibility of the system functions. The experimental results show that the improved system implements the function of adding, deleting, and modifying data on the chain. Compared with the traditional system, the improved system reduces the gas consumption. The more obvious with the increase of uploading data to reduce gas consumption.
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition

Classical learning theory suggests that the optimal generalization performance of a machine learning model should occur at an intermediate model complexity, with simpler models exhibiting high bias and more complex models exhibiting high variance of the predictive function. However, such a simple trade-off does not adequately describe deep learning models that simultaneously attain low bias and variance in the heavily overparameterized regime. A primary obstacle in explaining this behavior is that deep learning algorithms typically involve multiple sources of randomness whose individual contributions are not visible in the total variance. To enable fine-grained analysis, we describe an interpretable, symmetric decomposition of the variance into terms associated with the randomness from sampling, initialization, and the labels. Moreover, we compute the high-dimensional asymptotic behavior of this decomposition for random feature kernel regression, and analyze the strikingly rich phenomenology that arises. We find that the bias decreases monotonically with the network width, but the variance terms exhibit non-monotonic behavior and can diverge at the interpolation boundary, even in the absence of label noise. The divergence is caused by the \emph{interaction} between sampling and initialization and can therefore be eliminated by marginalizing over samples (i.e. bagging) \emph{or} over the initial parameters (i.e. ensemble learning).
A Variational Approach for Learning from Positive and Unlabeled Data

Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally. Most recent PU learning methods are developed based on the conventional misclassification risk of the supervised learning type, and they require to solve the intractable risk estimation problem by approximating the negative data distribution or the class prior. In this paper, we introduce a variational principle for PU learning that allows us to quantitatively evaluate the modeling error of the Bayesian classifier directly from given data. This leads to a loss function which can be efficiently calculated without any intermediate step or model, and a variational learning method can then be employed to optimize the classifier under general conditions. In addition, the discriminative performance and numerical stability of the variational PU learning method can be further improved by incorporating a margin maximizing loss function. We illustrate the effectiveness of the proposed variational method on a number of benchmark examples.
On the Dynamics of Training Attention Models

The attention mechanism has been widely used in deep neural networks as a model component. By now, it has become a critical building block in many state-of-the-art natural language models. Despite its great success established empirically, the working mechanism of attention has not been investigated at a sufficient theoretical depth to date. In this paper, we set up a simple text classification task and study the dynamics of training a simple attention-based classification model using gradient descent. In this setting, we show that, for the discriminative words that the model should attend to, a persisting identity exists relating its embedding and the inner product of its key and the query. This allows us to prove that training must converge to attending to the discriminative words when the attention output is classified by a linear classifier. Experiments are performed, which validate our theoretical analysis and provide further insights.
Archiving Development in Bashkiria in the 1920s: Pages of History

The article examines problems of archival documents protection in the Bashkir ASSR in the first years of the Soviet power. History of the State Archival Service of Bashkiria goes back to August 11, 1919, when the Ufa Gubernia Division of the Glavarchive (Main Directorate for Archiving) was founded by resolution of the Collegium of the Glavarchive of the RSFSR. Alongside with the Ufa Gubernia Archive there was the Archive of the BASSR in Sterlitamak, capital of Small Bashkiria. It was established upon initiative of the Head of the Bashkir Revolutionary Committee A. Z. Validov on March 18, 1920. The article reviews the historiography of the issue. Its novelty is based on using unpublished sources from the fonds of the State Archive of the Russian Federation, National Archive of the Republic of Bashkortostan, and State Archive of the Orenburg Region (one of major and oldest regional archives), that are being thus introduced into scientific use. The article reviews main episodes, concerning documents protection: loss of archival materials in the days of the Civil War, first attempts of acquisition and archive fonds systematization. It focuces on issues of preservation of archival fonds of the Ufa Gubernia Government, Ufa District Court, Ufa Religious Consistory, Ufa Gendarmery Department, Orenburg Mohammedan Religious Assembly, Orenburg Education District, Ufa Directorate for Agriculture and Public Property, Ufa Gubernia Zemstvo, Ufa Land Use Commission. It also describes identification of the 1920s volost and country manorial archives of the Usergan, Burzyan-Tangaur, Tabyn, and Yurmaty cantons of Bashkiria. The article addresses the loss of archival documents due to events of the Civil War, absence of archival repositories, insufficient funding of archiving, and stealage. Nevertheless, the analysis of regional archive activities in the documents preservation sphere demonstrates that invaluable archival documentary heritage of Bashkiria has mostly survived, despite poor conditions of archival records storage.
Effect of Interest Rates on Deposit Money Banks’ Profitability in Nigeria

Introduction: Internship is the period when a medical graduates acquires skills which enables him/her to become an effective physician of first contact of the community.However, new graduates go through this period only preparing for the post graduate entrance exam without acquiring many skills and practical knowledge.We conducted a cross-sectional study to document the self-perceived competence of the interns in performing the basic medical skills.Materials and Methods: This was a cross-sectional study conducted on 70 medical interns graduating from a private medical college in North India.The interns who were willing to participate in the study were asked to state the level of their competence on a 3-point scale in a 95 point questionnaire .The questionnaire was divided as per clinical specialties and further sub-divided into competence in examination skills, core skills and knowledge, attitude and performance skills.Results: All the interns self-rated that they were competent in recording basic clinical variables, 60% percent students self-rated that they were fully competent in examination skills and core competencies of internal medicine but only 37.14% felt competent in knowledge and attitudinal skills.In surgery, more than 60% interns rated themselves to have good core competencies, 37.14% rated themselves to be competent in examination of surgical patients whereas only 28% interns rated themselves as competent in knowledge and attitudinal skills of surgery.Only one third interns selfrated that they had good examination skills pertaining to ENT and Ophthalmology.Similarly less than 20% interns selfrated that they were good at core competencies related to Ophthalmology and ENT departments.Conducting a normal vaginal delivery was considered a core competency of Ob-Gyn (Obstetrics and Gynaecology) department.Only 12 interns (27.8%) self-rated to be fully competent to do so, 51.4% interns self-rated that they had good examination skills and only 10% interns felt competent in knowledge and attitudinal skills of Ob-Gyn.Majority of the students rated themselves as having good examination skills and core competencies in pediatrics.Discussion: In the present study less than 60% interns rated themselves as competent in examination skills and core competencies in major clinical specialities except in Ob-gyn, while less than ten percent (9.3%) self evaluated their examination skills as poor.This implies that many of the graduates did not feel competent to carry out the role of a junior doctor with confidence.A variety of studies from other countries have found that many graduates feel inadequately prepared for the role of junior doctor [11][12][13] and criticisms that medical schools do not prepare graduates for early medical practice have been voiced many times.Conclusions: All the graduate medical doctors are not fully competent to perform the basic medical and surgical procedures.They are especially lacking in knowledge and attitudinal skills so medical educators need to stress on training the graduates in knowledge application and critical thinking.
SECOND LANGUAGE ACQUISITION ON KNOWLEDGE TRANSFER IN SOCIAL LEARNING DAYAKNESE BAKATI' RARA IN 3T REGION

In the globalization era, there are various forms of modern life, in Indonesia, especially in Bengkayang. It is still found an isolated and disadvantaged village named Tampe village which is located far from residential city Bengkayang. The research was conducted in Indonesian language, English, technology, and so forth, so the research funded by the Direktorat Riset dan Pengabdian Masyarakat Diirektorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset, Teknologi, dan Pendidikan Tinggi Anggaran 2018 Research Contract no. 115 / SP2H / LT / DRPM / 2018 using grounded theory method, a method that greatly influences sociological perspectives on human activities in terms of talking and communicating. The data collecting was conducted by observing three children of Dayaknese Bakati’ Rara 11-14 years old. They are 5th grader at SDN 15 Tampe Atas. The results of this study have been used as a language for childhood. However, it is known that these three children are unfavorable influences on knowledge transfer in social learning.
A Compact Representation of Histopathology Images Using Digital Stain Separation and Frequency-Based Encoded Local Projections

In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool for classification and recognition of histopathology images. We build on the success of the ELP algorithm as a means for image classification and recognition by proposing a modified algorithm which captures the local frequency information of the image. The proposed algorithm estimates local frequencies by quantifying the changes in multiple projections in local windows of greyscale images. By doing so we remove the need to store the full projections, thus significantly reducing the histogram size, and decreasing computation time for image retrieval and classification tasks. Furthermore, we investigate the effectiveness of applying our method to histopathology images which have been digitally separated into their hematoxylin and eosin stain components. The proposed algorithm is tested on the publicly available invasive ductal carcinoma (IDC) data set. The histograms are used to train an SVM to classify the data. The experiments showed that the proposed method outperforms the original ELP algorithm in image retrieval tasks. On classification tasks, the results are found to be comparable to state-of-the-art deep learning methods and better than many handcrafted features from the literature.
Motion-Constrained Generative Adversarial Network for Anomaly Detection

Increased volume of video data made human monitoring impossible and necessitated automated abnormal event detection system. Abnormal event detection involves learning normal patterns and identifying any deviation as abnormal. In this paper, we propose a generative adversarial network (GAN) based abnormal event detection system. GAN is trained to generate normal frames, and fails to generate the frames with abnormal events. Usually adversarial loss and appearance loss are used to train GANs. In this paper, we propose to impose motion constraint using optical flow and acceleration during generator training. Our experiments on standard datasets prove that motion is an important cue for abnormal event detection.
Evaluation of the Impact of Random Computing Hardware Faults on the Performance of Convolutional Neural Networks

Artificial Intelligence (AI) rapidly spreads across high-tech industries and enters almost every safety-critical area such as automotive, aerospace, and medical industries.However, like any other software, AI-based applications are prone to random hardware faults such as a random bit flip in CPU, RAM, or network.Therefore, it is essential to understand how various hardware faults affect the performance and accuracy of AI applications.This paper provides a general description and particular conceptual and implementational features of our recently introduced Fault Injection (FI) framework InjectTF2.InjectTF2 is developed using the TensorFlow 2 API and allows the user to specify fault parameters and perform layer-wise fault injection into the TensorFlow 2 neural networks.It enables the automated injection of random bitflips.The paper describes the software architecture of the framework.The framework is open source and freely available on the GitHub.The application of InjectTF2 is demonstrated with extensive fault injection experiments on a Convolutional Neural Network (CNN) trained using the GTSRB dataset.The experiments' results show how random bitflips in the outputs of the CNNs layers affect the classification accuracy.Such results support not only numerical analysis of reliability and safety characteristics but also help to identify the most critical CNN layers for more robust and faulttolerant design.
Recursive Elimination Method in Moving Horizon Estimation for a Class of Nonlinear Systems and Non-Gaussian Noise

This paper proposes a recursive elimination method for optimal filtering problems of a class of discrete-time nonlinear systems with non-Gaussian noise. By this method, most of the computations to solve an optimal filtering problem can be carried out off-line by using symbolic computation based on the results from algebraic geometry. This property is suitable for moving horizon estimation, where a certain optimal filtering problem must be solved for different measurement sequences in each sampling interval. A numerical example is provided to compare the proposed method with other state estimation methods including the particle filter, and the efficiency of the proposed method is shown.
Analysis of Area-Efficiency vs. Unrolling for eSTREAM Hardware Portfolio Stream Ciphers

The demand for low resource devices has increased rapidly due to the advancements in Internet-of-things applications. These devices operate in environments that have limited resources. To ensure security, stream ciphers are implemented on hardware due to their speed and simplicity. Amongst different stream ciphers, the eSTREAM ciphers stand due to their frugal implementations. This work probes the effect of unrolling on the efficiency of eSTREAM ciphers, including Trivium, Grain (Grain 80 and Grain 128) and MICKEY (MICKEY 2.0 and MICKEY-128 2.0). It addresses the question of optimal unrolling for designing high-performance stream ciphers. The increase in the area consumption is also bench-marked. The analysis is conducted to identify efficient design principles for ciphers. We experimentally show that the resulting performance after unrolling may disagree with the theoretical prediction when the effects of technology library are considered. We report pre-layout synthesis results on 65 and 130 nm ASIC technology as well as synthesis results for Xilinx FPGA platform in support of our claim. Based on our findings, cipher design and implementation suggestions are proposed to aid hardware designers. Furthermore, we explore why and where area-efficiency for these ciphers saturate.
Reduction of entropy uncertainty for qutrit system under non-Markov noisy environment*

We explore the entropy uncertainty for qutrit system under non-Markov noisy environment and discuss the effects of the quantum memory system and the spontaneously generated interference (SGI) on the entropy uncertainty in detail. The results show that, the entropy uncertainty can be reduced by using the methods of quantum memory system and adjusting of SGI. Particularly, the entropy uncertainty can be decreased obviously when both the quantum memory system and the SGI are simultaneously applied.