Reviewerly/exHarmony huggingface dataset:
A Strategy for Referential Problem in Low-Resource Neural Machine Translation

This paper aims to solve a series of referential problems in sequence decoding, which caused by corpus sparsity in low-resource Neural Machine Translation (NMT), including pronoun missing, reference error, gender bias. It is difficult to find the essential reason of these problems because they are only shown in the prediction results and all aspects of the model training. Different from the usual solutions based on complex mathematical rule setting and adding artificial features, we expect to turn the problems in the predictions into noise as much as possible. On this basis, we further use adversarial training to make the model find the balance between the noise and the golden samples, instead of exploring the reason of the problem during the complex training. In this paper, a noise-based preprocessing operation and a slight modification of the adversarial training can help the model to better generalize a series of referential problems in low-resource NMT tasks. Experiments show that the evaluation of BLEU score and the accuracy of pronouns in sequence on Korean-Chinese, Mongolian-Chinese and Arabic-Chinese task have been significantly improved.
Time to Get Conversational: Assessment of the Potential of Conversational User Interfaces for Mobile Banking

Conversational user interfaces have recently gained in popularity, for example in chatbot systems for customer service or as intelligent assistants on Smartphones or other IoT devices. However, conversational banking interfaces have also found their way into the mobile banking sector and allow banks to offer their customers a cost-effective and personalized solution to perform financial services via natural language. Conversational interfaces, in particular for mobile banking applications, are very sensitive to user acceptance and trust. Also, user experience and the perceived security influence their adoption significantly. To explore the influence of these dimensions we have implemented three prototypes for conversational banking and evaluated them in a user study with n=18 subjects concerning the factors mentioned above. Although the results suggest no significant differences between the prototypes, we can draw design implications for conversational interfaces for mobile banking from the collected quantitative and qualitative data.
Open Set Authorship Attribution Toward Demystifying Victorian Periodicals

Existing research in computational authorship attribution (AA) has primarily focused on attribution tasks with a limited number of authors in a closed-set configuration. This restricted set-up is far from being realistic in dealing with highly entangled real-world AA tasks that involve a large number of candidate authors for attribution during test time. In this paper, we study AA in historical texts using a new data set compiled from the Victorian literature. We investigate the predictive capacity of most common English words in distinguishing writings of most prominent Victorian novelists. We challenged the closed-set classification assumption and discussed the limitations of standard machine learning techniques in dealing with the open set AA task. Our experiments suggest that a linear classifier can achieve near perfect attribution accuracy under closed set assumption yet, the need for more robust approaches becomes evident once a large candidate pool has to be considered in the open-set classification setting.
Analysis of Security Issues in Blockchain Wallet

Blockchain is a substantive technology which provide reliable and convenient services by establishing trust in open environment. Blockchain basically relies upon two critical technological areas of cryptography and decentralization to perform transactions of confidential data. This technology is gaining traction since its discovery in 2008 due to its ability to accomplish immutable transactions without third party interfere. Owing to these astonishing features, it has become backbone of all industries and business including digital financing, e-Governance, smart grid, etc. Many banking and financial service providers have started to allot wallets to its customer where they can use money in highly secure environment. However, blockchain is considered the most secure technology available today, but its wallet is still not a temper proof. This paper aims to analyse various issues pertaining to blockchain wallet security. The existing developments in this area are studied in detail to pave a future direction of research in this regime. Section 1 is introduction of blockchain which includes blockchain wallets and their types, security issues in wallets and security practices used to resolve those issues. Section 2 includes wallet security challenges, vulnerabilities and recent review of literature on wallet security practices. Section 3 compare the related work and discuss growth in crypto crimes due to loss of cryptocurrency. Last section concludes the paper.
Limitations of the invertible-map equivalences

Abstract This note draws conclusions that arise by combining two recent papers, by Anuj Dawar, Erich Grädel and Wied Pakusa, published at ICALP 2019, and by Moritz Lichter, published at LICS 2021. In both papers, the main technical results rely on the combinatorial and algebraic analysis of the invertible-map equivalences ${\equiv ^{\text {IM}}_{k, Q}}$ on certain variants of Cai–Fürer–Immerman structures (CFI-structures for short). These ${\equiv ^{\text {IM}}_{k, Q}}$-equivalences, for a natural number $k$ and a set of primes $Q$, refine the well-known Weisfeiler–Leman equivalences used in algorithms for graph isomorphism. The intuition is that two graphs $G{\equiv ^{\text {IM}}_{k, Q}}H$ cannot be distinguished by iterative refinements of equivalences on $k$-tuples defined via linear operators on vector spaces over fields of characteristic $p \in Q$. In the first paper it has been shown, using considerable algebraic machinery, that for a prime $q \notin Q$, the ${\equiv ^{\text {IM}}_{k, Q}}$ equivalences are not strong enough to distinguish between non-isomorphic CFI-structures over the field $\mathbb {F}_q$. In the second paper, a similar but not identical construction for CFI-structures over the rings $\mathbb {Z}_{2^i}$ has, again by rather involved combinatorial and algebraic arguments, been shown to be indistinguishable with respect to ${\equiv ^{\text {IM}}_{k, \{2\}}}$. Together with an earlier work on rank logic, this second result suffices to separate rank logic from polynomial time. We show here that the two approaches can be unified to prove that CFI-structures over the rings $\mathbb {Z}_{2^i}$ are in fact indistinguishable with respect to ${\equiv ^{\text {IM}}_{k, {\mathbb {P}}}}$, for the set ${\mathbb {P}}$ of all primes. In particular, this implies the following two results. First, there is no fixed $k$ such that the invertible-map equivalence ${\equiv ^{\text {IM}}_{k, {\mathbb {P}}}}$ coincides with isomorphism on all finite graphs. Second, no extension of fixed-point logic by linear-algebraic operators over fields can capture polynomial time.
Control of Electric Wheelchair via Eye Gestures for People with Neurological Disorder

Tetraplegia is a total paralysis due to injury at C1 - C5 - T1 of spinal cord. People with tetraplegia have very limited or no muscle function from area below the neck. For mobility, electric wheelchair is a good option. However, since commercial electric wheelchair used joystick as its movement control, this is quite difficult for tetraplegic patients to use it. Facial features such as eyes gestures have the potential to be manipulated as instruction to control the movement of electric wheelchair. Therefore, this work aims to develop a system that can classify different eyes gestures of human subject and convert it into different state of control instructions. Methods for object detection that had been developed by researchers in recent years are suitable to be used to detect faces and eyes. This work proposed the combination use of Haar Cascade classifier and Dlib facial detector for detecting face and eye region, respectively. Next, several image enhancement techniques and morphological operations are performed to detect the iris. Image moments is used to calculate the center coordinate of the iris. Afterward, the iris coordinate is used to determine the classification of eye gestures. The proposed method has been proven to be efficient in detecting eyes gestures. The ratio of detection accuracy is ranged between 73.5% and 99.83% depending on the ambient lighting.
Bayesian analysis of mixture autoregressive models covering the complete parameter space

Abstract Mixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for such models offers the additional advantage of incorporating the uncertainty in the estimated models into the predictions. We introduce a new way of sampling from the posterior distribution of the parameters of MAR models which allows for covering the complete parameter space of the models, unlike previous approaches. We also propose a relabelling algorithm to deal a posteriori with label switching. We apply our new method to simulated and real datasets, discuss the accuracy and performance of our new method, as well as its advantages over previous studies. The idea of density forecasting using MCMC output is also introduced.
Philosophical Reflections on Argument Strength and Gradual Acceptability

This paper proposes a classification of three aspects of argument strength based on philosophical insights, in particular Aristotle’s distinction between logic, dialectic and rhetoric. It is then argued that when developing or evaluating gradual accounts of argument strength it is essential to be explicit about which aspect of argument strength is modelled and about the adopted interpretation of the arguments and their relations in abstract or bipolar argumentation frameworks. The underlying aim is to encourage a principled development and evaluation of (principles for) gradual argumentation semantics.
A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms

Simulating the disease progression of a suspicious finding in mammography (MG) images could assist in the early detection of breast cancer, where the telltale signs of malignancy are subtle and hard to detect. It could also decrease both unnecessary biopsies and treatments of indolent or low-grade disease that might otherwise remain asymptomatic. We propose a novel approach to simulating disease progression, as a significant step towards those goals. Our architecture uses the powerful Wasserstein GAN in combination with a novel component that simulates the progression of the disease in deep feature space. This allows us to learn from unlabeled longitudinal MG pairs of current and prior studies, stabilize the learning procedure, and overcome misalignment between the MG pairs. Our output image replicates an actual MG image, maintains the prior’s shape and general appearance while also containing a finding with characteristics that resemble the current image’s suspicious finding. We simulate a progression of: (i) a full MG prior image in low-resolution, and (ii) a high-resolution patch in suspicious areas of the prior image. We demonstrate the effectiveness of our pipeline in achieving the above goals using quantitative and qualitative metrics and a reader study. Our results show the high quality of our simulation and the promise it holds for early risk stratification.
Arithmetic of Koblitz Curve <i>Secp256k1</i> Used in Bitcoin Cryptocurrency Based on One Variable Polynomial Division

Bitcoin uses a specific Koblitz curve secp256k1 is a type of elliptic curve defined by the Standards for Efficient Cryptography Group (SECG). This paper develops an approach for arithmetic (point addition and doubling) on secp256k1 Koblitz curve over finite fields using one variable polynomial based on Euclidean division. This approach succeeds in computing these operations due to a striking observation about computing the gcd of two polynomials one which represents the elliptic curve as its roots and the other representing the lines which intersect or are tangent to the curve. The resulting algorithm is tested on realistic secp256k1 Koblitz curve and is shown to be scalable to perform the computations.
Automatic Topic Labeling model with Paired-Attention based on Pre-trained Deep Neural Network

The automatic topic labeling model aims at generating a sound, interpretable, and meaningful topic label that is used to interpret an LDA-style discovered topic, intending to reduce the cognitive load of end-users while browsing or investigating the topics. In this study, we first introduced the pre-trained language model BERT to topic labeling tasks. It exploits the contextual embedding of the pre-trained language model to improve the quality of encoding sentences. To generate a topic label with higher Relevance, Coverage, and Discrimination, we propose a novel summarization neural framework. Specifically, it exploits the paired-attention to model the relationship between the candidate sentences first and then decides which sentences should be included in the final summarization topic label. Moreover, we expected that high-quality sentence encoding representation could improve our model's performance. So, for each discovered topic, we trained a specific layer to extract the important topic-related features from the sentence embeddings as well as filter the noise information. The experimental results showed that our model significantly outperforms the state-of-the-art and classic topic labeling models.
Design and Implementation of Annotation System for Character Behavior &amp; Event Based on Spring Boot

To better prevent the spread of Corona Virus Disease 2019 (COVID-19), scientific researchers need the data set to research and predict the trajectories of patients. It is a time-consuming task to extract the data from the web page.
An Improved Optimization Algorithm Based on Leapfrog and Whale Optimization Fusion

Whale optimization algorithm is a new type of swarm intelligence optimization algorithm. Aiming at the problems of low optimization accuracy and easy falling into local optima in the basic whale optimization algorithm, an improved whale optimization algorithm that combines the whale algorithm and the hybrid leapfrog search strategy is proposed. The local search mechanism of the hybrid frog leaping algorithm is used to update the position of the whale to improve the accuracy of convergence; when the individual position of the population is updated, an attenuation factor is introduced to disturb the update position of the individual whale, thereby increasing the diversity of the population and adjusting the search scale of the algorithm . Simulation experiment results show that the overall optimization performance of the improved algorithm is better than the original whale algorithm, particle swarm and gray wolf optimization algorithm.
Private Decision Tree Evaluation with Constant Rounds via (Only) SS-3PC over Ring and Field

Multiparty computation (MPC) is the technology that computes an arbitrary function represented as a circuit without revealing input values. Typical MPC uses secret sharing (SS) schemes, garbled circuit (GC), and homomorphic encryption (HE). These cryptographic technologies have a trade-off relationship for the computation cost, communication cost, and type of computable circuit. Hence, the optimal choice depends on the computing resources, communication environment, and function related to applications. The private decision tree evaluation (PDTE) is one of the important applications of secure computation. There exist several PDTE protocols with constant communication rounds using GC, HE, and SS-MPC over the field. However, to the best of our knowledge, PDTE protocols with constant communication rounds using MPC based on SS over the ring (requiring only lower computation costs and communication complexity) are non-trivial and still missing. In this paper, we propose a PDTE protocol based on a three-party computation (3PC) protocol over the ring with one corruption. We also propose another three-party PDTE protocol over the field with one corruption that is more efficient than the naive construction.
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net

Accurate medical image segmentation is crucial for diagnosis and analysis. However, the models without calibrated uncertainty estimates might lead to errors in downstream analysis and exhibit low levels of robustness. Estimating the uncertainty in the measurement is vital to making definite, informed conclusions. Especially, it is difficult to make accurate predictions on ambiguous areas and focus boundaries for both models and radiologists, even harder to reach a consensus with multiple annotations. In this work, the uncertainty under these areas is studied, which introduces significant information with anatomical structure and is as important as segmentation performance. We exploit the medical image segmentation uncertainty quantification by measuring segmentation performance with multiple annotations in a supervised learning manner and propose a U-Net based architecture with multiple decoders, where the image representation is encoded with the same encoder, and segmentation referring to each annotation is estimated with multiple decoders. Nevertheless, a cross loss function is proposed for bridging the gap between different branches. The proposed architecture is trained in an end-to-end manner and able to improve predictive uncertainty estimates. The model achieves comparable performance with fewer parameters to the integrated training model that ranked the runner-up in the MICCAI-QUBIQ 2020 challenge.
Multi-Agent Cognition Difference Reinforcement Learning for Multi-Agent Cooperation

Multi-agent cooperation is one of the most attractive research fields in multi-agent systems. There are many attempts made by researchers in this field to promote the cooperation behavior. However, in partially-observable environments, a large number of agents and complex interactions among the agents cause huge difficulty for policy learning. Moreover, redundant communication contents caused by many agents make effective features hard to be extracted, which prevents the policy from converging. To address the limitations above, a novel method called multi-agent cognition difference reinforcement learning (MACD-RL) is proposed in this paper. The key feature of MACD-RL lies in cognition difference network (CDN) and a soft communication network (SCN). CDN is designed to allow each agent to choose its neighbors (communication targets) adaptively with its environment cognition difference. SCN is designed to handle the complex interactions among the agents with soft attention mechanism. The results of simulations including mixed cooperative and competitive tasks demonstrate that the effectiveness and robustness of the proposed model.
Face Inpainting Combining Structured Forest Edge Information and Gated Convolution

For the face inpainting under arbitrary shape occlusion, the existing methods are easy to produce edge blur and distortion of the inpainting results. In this paper, an algorithm for face inpainting combining structured forest edge information and gated convolution is proposed. Firstly, the edge contour of the occluded area is reconstructed by prior face knowledge to constrain the process of face inpainting. Secondary, the gated convolution holds the ability to extract accurate local feature when some pixels were missed, then a gated convolution based Generative Adversarial Network (GAN) for face inpainting is designed. The model consists of two parts: edge connection network and image inpainting network. The edge connection network accomplish the automatic completion and connection of the missing edge image. The image inpainting network takes the completed edge image as the guidance information, and combines the occlusion image to repair the missing face area. Compared with others, the experimental results show that the proposed algorithm has more precise detail information and better visual quality.
Mutual calibration training: Training deep neural networks with noisy labels using dual-models

A precise large-scale dataset is crucial for supervising the training of deep neural networks (DNNs) in image classification. However, manually annotating large-scale dataset is time-consuming, which limits the scalability of supervised training. On the other hand, it is relatively easier to obtain a small clean dataset as well as a vast amount of data with noisy labels, but training on a noisy dataset causes the performance of deep networks dropping dramatically. To overcome this problem, this work studies how to effectively and efficiently train deep works on the noisy large-scale dataset in conjunction with a small clean dataset. One problem with transfer learning from a small clean dataset is that the transfer learning technique risks over-fitting on the clean dataset due to more parameters than the training examples. Hence, we propose a new approach, called online easy example mining (OEEM) to train deep network on the entire noisy dataset. OEEM aims to select clean samples to guide the training without human annotation by estimating the confidence of the observed labels with the model prediction. However, the sample-selection bias in the OEEM can trap the model into a locally optimal value. Consequently, we propose a general framework called Mutual Calibration Training (MCT) against different noise levels and noise types using dual-models, which combines the idea of transfer learning and OEEM. Finally, we conduct experiments on synthetic as well as real-world dataset with different noise types and noise rates, respectively. And the results demonstrate effectiveness of our approach.
A Relation-aware Attention Neural Network for Modeling the Usage of Scientific Online Resources

More and more online resources for computer science are introduced, used and released in scientific literature in recent years. Knowledge about the usage of these online resources can help researchers easily find the applicable resources for their works. However, most existing methods ignore the importance of the content of the online resource citations. To this end, we manually create SciR, a dataset that contains 3,012 annotation sentences for this task, and introduce a multi-task learning framework to automatically extract the entities and relations from the context of online resource citations in scientific papers. Furthermore, considering the words in a sentence usually play different roles under different relations. In this paper, we treat different relations as distinctive sub-spaces and model the correlations between words in sentence for each relation type by a supervised biaffine attention network. Based on this relation-aware attention network, our model can not only effectively obtain the word-level correlations under each relation, but also naturally avoid the problem of overlapping relations. To evaluate the effectiveness of our model, we conduct comprehensive experiments on three datasets and the experimental results demonstrate that our model outperforms other state-of-the-art methods on the two tasks of entity recognition and relation extraction.