Reviewerly/exHarmony huggingface dataset:
New Dataset and Strong Baselines for the Grammatical Error Correction of Russian

Motivated by recent advancements in grammatical error correction in English and existing issues in the field, we describe a new resource, an annotated learner corpus of Russian, extracted from the Lang-8 language learning website.This new dataset is benchmarked against two grammatical error correction models that use state-of-the-art neural architectures.Results are provided on the newlycreated corpus and are compared against performance on another, existing resource.We also evaluate the contribution of the Lang-8 training data to the grammatical error correction of Russian and perform type-based analysis of the models.The expert annotations are available for research purposes.
Reconstruction for Indoor Scenes Based on an Interpretable Inference

Autonomous robots for medical and emergency supplies are a potential way to avoid contact with people in quarantine and control the spread of contagious diseases in an indoor scene. However, scene understanding and reconstruction through a single low-cost camera remains a challenge. It is known that absolute precise depth cannot be calculated accurately from a single image, but the relative pose of different planes, which can be inferred from geometric features in a 2-D image, are more likely to be used in understanding scenes and its reconstruction. In this article, we present an interpretable model to bridge the gap between 2-D scene understanding and three-dimensional (3-D) reconstruction without prior training or any precise depth data. Based on 2-D semantic information in our previous works, the 3-D relative pose of estimated planes can be estimated. At that point, indoor scenes are approximated in the reconstruction. The approach behaves as an interpretable characteristic and requires no prior training or knowledge of the camera's internal parameters. We compare the quantitative performance on the percentage of incorrectly reconstructed planes by relative pose estimation. The results demonstrated that the method can successfully understand and reconstruct indoor scenes including both Manhattan and curved non-Manhattan structures.
Overview of Cyber Threat Intelligence Description

The description of cyber threat information is the basis of threat intelligence sharing. Focusing on the description of threat information, this paper analyzes the threat intelligence standards at home and abroad, summarizes the description and analysis methods of threat intelligence and their advantages and disadvantages. Finally, the direction and development trend of cyber threat intelligence research are prospected and summarized.
A CNN Approximation Method Based on Low-bit Quantization and Random Forests

In recent years, the use of image recognition technology in edge devices has been increasing. To achieve low-power and low-latency inference of convolutional neural networks (CNNs) in edge devices, methods that reduce the number of operations, such as pruning, have been actively researched. However, even after applying these existing methods, we still need to calculate many multiply-accumulate (MAC) operations. In this paper, we propose a hardware-friendly CNN approximation method based on low-bit quantization and random forests to reduce the number of operations and operation cost of CNN inference. In our experiments, we reduce the number of operations by 30.8% for LeNet and by 27.1% for ResNet18 while maintaining high image classification accuracy.
Abel inversion method using Chebyshev wavelets for tomographic reconstruction of an axisymmetric medium

We describe an Abel inversion method for tomographic reconstruction of an axisymmetric medium. The essence of this method is the approximation of data by Chebyshev wavelets. A numerical method and operational matrices are proposed. The accuracy of the method is tested in terms of the noise coefficient. The studied method is compared with the Legendre wavelets method. The results show that the proposed method is less sensitive to noise. We also demonstrate the efficiency of the developed method by obtaining good and expected results for the inversion of interferometric data.
Debiased Subjective Assessment of Real-World Image Enhancement

In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhancement. Conventional subjective testing requires manually pre-selecting a small set of visual examples, which may suffer from three sources of biases: 1) sampling bias due to the extremely sparse distribution of the selected samples in the image space; 2) algorithmic bias due to potential overfitting the selected samples; 3) subjective bias due to further potential cherry-picking test results. This eventually makes the field of real-world image enhancement more of an art than a science. Here we take steps towards debiasing conventional subjective assessment by automatically sampling a set of adaptive and diverse images for subsequent testing. This is achieved by casting sample selection into a joint maximization of the discrepancy between the enhancers and the diversity among the selected input images. Careful visual inspection on the resulting enhanced images provides a debiased ranking of the enhancement algorithms. We demonstrate our subjective assessment method using three popular and practically demanding image enhancement tasks: dehazing, super-resolution, and low-light enhancement.
Orienting Students to Course Recommendations Using Three Types of Explanation

An emerging challenge in course recommender systems is explaining to students why they have been recommended particular courses. In the context of a university, it can be valuable for a recommender system to introduce students to courses they may have not otherwise taken but which are still relevant to them. However, there is a tension between these goals as students have less ability to judge the relevance of courses, the less familiar they are with them. In this paper, we explore ways of familiarizing students with recommendations with three types of explanations designed with varying levels of personalization. We conduct a 67 student randomized controlled experiment using two course recommendation engines, content and context-based, and augment recommendations with three different types of explanation. Students rated each course recommended in terms of novelty, unexpectedness, and successfulness (i.e., intent to enroll). We find several statistically significant results, including an increase in serendipity (i.e., unexpectedness + successfulness) when explaining new course recommendations using keywords from courses a student has previously taken.
Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder

Abstract Objective We propose an interpretable disease prediction model that efficiently fuses multiple types of patient records using a self-attentive fusion encoder. We assessed the model performance in predicting cardiovascular disease events, given the records of a general patient population. Materials and Methods We extracted 798111 ses and 67 623 controls from the sample cohort database and nationwide healthcare claims data of South Korea. Among the information provided, our model used the sequential records of medical codes and patient characteristics, such as demographic profiles and the most recent health examination results. These two types of patient records were combined in our self-attentive fusion module, whereas previously dominant methods aggregated them using a simple concatenation. The prediction performance was compared to state-of-the-art recurrent neural network-based approaches and other widely used machine learning approaches. Results Our model outperformed all the other compared methods in predicting cardiovascular disease events. It achieved an area under the curve of 0.839, while the other compared methods achieved between 0.74111 d 0.830. Moreover, our model consistently outperformed the other methods in a more challenging setting in which we tested the model’s ability to draw an inference from more nonobvious, diverse factors. Discussion We also interpreted the attention weights provided by our model as the relative importance of each time step in the sequence. We showed that our model reveals the informative parts of the patients’ history by measuring the attention weights. Conclusion We suggest an interpretable disease prediction model that efficiently fuses heterogeneous patient records and demonstrates superior disease prediction performance.
Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction

Chinese Spelling Error Correction is a hot subject in the field of natural language processing. Researchers have already produced many great solutions, from the initial rule-based solution to the current deep learning method. At present, SpellGCN, proposed by Alibaba’s team, achieves the best results of which character level precision over SIGHAN2013 is 98.4%. However, when we apply this algorithm to practical error correction tasks, it produces many false error correction results. We believe that this is because the corpus used for model training contains significantly more errors than the text used for model correcting. In response to this problem, we propose performing a post-processing operation on the error correction tasks. We employ the initial model’s output as a candidate character, obtain various features of the character itself and its context, and then use a classification model to filter the initial model’s false error correction results. The post-processing idea introduced in this paper can apply to most Chinese Spelling Error Correction models to improve their performance over practical error correction tasks.
Test Data-Driven Machine Learning Models for Reliable Quantum Circuit Output

While current quantum computers, referred to as Noisy Intermediate-Scale Quantum (NISQ) computers, are expected to be beneficial for different applications, they are prone to different types of errors. In order to enhance the reliability of quantum systems, noise-aware quantum compilers are used to generate physical quantum circuits to be executed on NISQ computers. The quantum hardware is calibrated very frequently and its error rates are computed accordingly. Based on the hardware error rates, a quantum compiler allocates physical qubits and schedules quantum operations. However, error rates may change post-calibration. To incorporate dynamic error rates into quantum circuit compilation with minimum cost, we propose a Machine Learning (ML)-based scheme to detect the incorrect output of the quantum circuit and predict the Probability of Successful Trials (PST) with high accuracy. Our approach can verify the error rates of the quantum hardware and validate the correctness of the extracted quantum circuit output. We provide a case study of our ML-based reliability models using IBM Q16 Melbourne quantum computer. Our results show that the proposed scheme achieves a very high prediction accuracy.
Oracle-based process automation in DLT dominated ecosystems with an application to German waterway transportation

Distributed ledger technology (DLT) has been designed to ensure the correctness and integrity of transaction and process management. A ledger maintains records of data transactions as well as assures the correct execution of processes attached to data. Consistency across processes and parties is achieved by means of consensus building. However, scalability is a major concern when and significant amounts of data need confirmation. When digitalizing existing business processes with serious amounts of data, scalability becomes a pivotal objective. Hence, technical means for information compression become a vital asset. This paper presents a novel approach for data aggregation. On the one hand, data are aggregated in order to merely maintain an audit certificate of the aggregate on the ledger. On the other hand, provenance of raw data aggregated can be traced and proven correctly. Hence, audit services for information exchange in business relationships can scale, while raw data of supplier parties must not be unveiled in detail. We present a novel architecture for efficient data aggregation, while ensuring smart contracts and their main functionality of processing relevant process data. German waterway transportation serves as evaluation scenario. Moreover, decentral identity management is introduced as anchor element of our DLT-enabled ecosystem for business collaboration. Hence, preserving sovereignty and safety of personal data can be assured for M2M economies.
An Intelligent Decision Support System for Aircraft Landing Based on the Runway Surface

This paper presents a decision support system based on fuzzy logic as assistance for aircraft pilots of Boeing 747-100. The designed Fuzzy Logic Control model in the MATLAB concentrates on analysis of the external data, which are received from radar, Global Positioning System and various sensors. The purpose of the research is to reduce the amount of accidents due to aircraft landing assistance system and speed analysis of Boeing 747-100. To avoid an aviation accident, the landing speed of aircraft, the braking force, the rolling resistance, distance to obstacles and runway length are taken in to consideration. As a result of fuzzy logic model based on Mamdani Fuzzy Inference System the aircraft landing assistance provides the output data of acceptable braking force and stopping distance.
Real-Time Facial Emotion Recognition Using Deep Learning

Facial emotion analysis is a useful task and can be used as a basis for many real-time applications such as safety monitoring, care of patients in the medical sector, marketing analysis and e-learning. This paper proposes a system which is capable of processing/recognizing the captured image and as a result predicts the facial emotion in real time. With the advancement in deep learning in image processing, the proposed model for the evaluation of human facial emotions is based on deep convolutional neural network (CNN). The proposed model uses the Xception model CNN architecture and is trained on FER2013 benchmark database. The model shows comparable emotion recognition accuracy for identifying seven emotions in real time from the images captured using webcam.
The Matrix Reloaded: Multiplication Strategies in FrodoKEM

Lattice-based schemes are promising candidates to replace the current public-key cryptographic infrastructure in wake of the looming threat of quantum computers. One of the Round 3 candidates of the ongoing NIST post-quantum standardization effort is FrodoKEM. It was designed to provide conservative security, which comes with the caveat that implementations are often bigger and slower compared to alternative schemes. In particular, the most time-consuming arithmetic operation of FrodoKEM is the multiplication of matrices with entries in \(\mathbb {Z}_q\).
View-dependent Scene Appearance Synthesis using Inverse Rendering from Light Fields

In order to enable view-dependent appearance synthesis from the light fields of a scene, it is critical to evaluate the geometric relationships between light and view over surfaces in the scene with high accuracy. Perfect diffuse reflectance is commonly assumed to estimate geometry from light fields via multiview stereo. However, this diffuse surface assumption is invalid with real-world objects. Geometry estimated from light fields is severely degraded over specular surfaces. Additional scene-scale 3D scanning based on active illumination could provide reliable geometry, but it is sparse and thus still insufficient to calculate view-dependent appearance, such as specular reflection, in geometry-based view synthesis. In this work, we present a practical solution of inverse rendering to enable view-dependent appearance synthesis, particularly of scene scale. We enhance the scene geometry by eliminating the specular component, thus enforcing photometric consistency. We then estimate spatially-varying parameters of diffuse, specular, and normal components from wide-baseline light fields. To validate our method, we built a wide-baseline light field imaging prototype that consists of 32 machine vision cameras with fisheye lenses of 185 degrees that cover the forward hemispherical appearance of scenes. We captured various indoor scenes, and results validate that our method can estimate scene geometry and reflectance parameters with high accuracy, enabling view-dependent appearance synthesis at scene scale with high fidelity, i.e., specular reflection changes according to a virtual viewpoint.
An Improved Method for Underwater Image Super-Resolution and Enhancement

Underwater object detection and recognition are still challenging because of the degradation caused by the complex underwater imaging environment and lighting conditions. Aiming at the problems of the simultaneous enhancement and super-resolution (SESR), a method with single image super-resolution and enhancement of underwater imagery is proposed. Firstly, an underwater image super-resolution algorithm is proposed to remove blurring, and then an end-to-end underwater image enhancement network is used to compensate for color casts and produce natural color enhancement images. In addition, we verify the effectiveness of the proposed model through qualitative analysis and quantitative experiments and compare the performance with several state-of-the-art models, which show that the proposed method performs better.
Impact of Classifiers to Drift Detection Method: A Comparison

Drift Detection Method (DDM) emerges as a critical problem faced in the Industry 4.0 era, especially for the identification of concept drift in continuously-gathered data streams. In this study, a small-scale comparison with regard to the impact of different classifiers in DDM is presented. Six well-established classifiers are introduced in order to compare their performance and evaluate their impact on DDM, namely Naïve Bayes (NB), Hoeffding Tree (HT), k-Nearest Neighbors (KNN), Passive Aggressive (PAC), Stochastic Gradient Descent (SGD) and Very Fast Decision Rules (VFDRC) classifiers. Streaming Ensemble Algorithm (SEA dataset) was selected to validate our simulations due to its suitability regarding concept drift detection and handling, incorporating abrupt concept changes. Confusion matrices were used to effectively juxtapose the performance of the considered algorithms in the SEA dataset. The findings suggest that KNN exhibits the best overall accuracy (87%) and seems to be the most stable algorithm compared to NB (86%), HT (86%) and VFDRC (84%) classifiers. Overall, these algorithms show increased performance, while the presence of noise in the data has a significant impact on the model accuracy.
Research on Bootstrapping Algorithm for Health Insurance Data Fraud Detection Based on Decision Tree

After years of development, the traditional decision tree algorithm implementations represented by LightGBM has been very mature and widely used in various classification problems. However, when applying LightGBM to detecting fraud in health insurance data, we find that the performance of LightGBM is not ideal due to the large imbalance between the number of fraud examples and normal examples. To solve this problem, we propose a simple and effective LightGBM-based hard example mining algorithm (LHEM) for detecting health insurance fraud. Our motivation is to detect the large number of simple examples and the small number of hard examples in the dataset. Selecting these hard examples and discarding simple examples can balance the ratio of fraud and normal examples, thus improving the performance of the original model. We use the health insurance data collected in Zhejiang Jinhua to test our new method, and prove that the performance of our new method is better than LightGBM.
Efficient Face Alignment Network For Masked Face

The sudden outbreak and global spread of the COVID-19 have severely disrupted normal life of everyone. Masks have become an effective way to suppress the transmission of the COVID-19. The widespread use of masks has brought sever challenges to many face-based algorithms, which would severely damage the accuracy of model. As the basis of various facial analysis algorithms, the performance of facial landmark detection could directly affect the precision of them. However, the existing methods cannot effectively locate facial landmarks of masked faces. In order to improve the robustness to masked face, we propose an efficient masked face alignment network named MaskFAN. Specifically, we introduce depthwise separable convolution and group operation to build a lightweight backbone. A novel loss function and data augmentation module are also used to boost the performance. Extensive experiments are conducted on masked face dataset, and the results show that our approach surpasses the state-of-the-art methods by a significant margin with small model size and limited computation cost.
Blockchain Based Authentication and Trust Evaluation Mechanism for Secure Routing in Wireless Sensor Networks

In this paper, a blockchain based authentication model is proposed where the identity of each node is stored on the blockchain. The public and private blockchains are used for authentication. The authentication of Sensor Nodes (SNs) is performed at the private blockchain, whereas the public blockchain authenticates the cluster heads. The existing malicious node detection methods do not guarantee the authentication of the entities in Wireless Sensor Networks (WSNs). The unregistered nodes can easily access the resources of the network and perform malicious activities. Moreover, the malicious nodes broadcast wrong route information that increases packet delay and lowers packet delivery ratio. In the proposed model, the trust value is calculated in order to remove the malicious nodes. The secure routing is performed on the basis of the most trustworthy nodes in the network. The aim is to reduce the packet delay and increase the packet delivery ratio. The simulation results show that the high throughput and packet delivery ratio is achieved due to the presence of highly trusted nodes. Moreover, our proposed model detects the malicious nodes effectively.
Perfect quantum-state synchronization

We investigate the most general mechanisms that lead to perfect synchronization of the quantum states of all subsystems of an open quantum system starting from an arbitrary initial state. We provide a necessary and sufficient condition for such "quantum-state synchronization", prove tight lower bounds on the dimension of the environment's Hilbert space in two main classes of quantum-state synchronizers, and give an analytical solution for their construction. The functioning of the found quantum-state synchronizer of two qubits is demonstrated experimentally on an IBM quantum computer and we show that the remaining asynchronicity is a sensitive measure of the quantum computer's imperfection.
Analysis of the Smart Library Construction in Colleges Based Big Data and Artificial Intelligence

Abstract With the rapid development of artificial intelligence in recent years, colleges and universities committed to information construction are seeking to transform from digital to intelligent. Among them, the smart library is an important part of university information construction. However, set up smart libraries face such problems as low rate of library entry and poor sense of experience. This paper analyses the status and difficulties of research and practices in smart libraries by crawler technology and questionnaire, and puts forward a smart library information construction scheme. The results show setting up a smart campus is in line with the new trend of education.