Reviewerly/exHarmony huggingface dataset:
Learning to Expand

Information-seeking conversation systems are increasingly popular in real-world applications, especially for e-commerce companies. To retrieve appropriate responses for users, it is necessary to compute the matching degrees between candidate responses and users' queries with historical dialogue utterances. As the contexts are usually much longer than responses, it is thus necessary to expand the responses (usually short) with richer information. Recent studies on pseudo-relevance feedback (PRF) have demonstrated its effectiveness in query expansion for search engines, hence we consider expanding response using PRF information. However, existing PRF approaches are either based on heuristic rules or require heavy manual labeling, which are not suitable for solving our task. To alleviate this problem, we treat the PRF selection for response expansion as a learning task and propose a reinforced learning method that can be trained in an end-to-end manner without any human annotations. More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as a reward to guide the selector to extract useful PRF terms, which boosts the overall task performance. Extensive experiments on both standard benchmarks and commercial datasets prove the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both case studies and quantitative analysis show that our model is capable of selecting meaningful PRF terms to expand response candidates and also achieving the best results compared with all baselines on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system.
Support vector machine prediction model based on fractional particle swarm algorithm

The support vector machine algorithm is widely used to solve nonlinear classification problems with its good generalization ability. This paper mainly explores the parameter optimization problem of the algorithm in detail. The method is proposing an improved fractional particle swarm algorithm, that is, set a linear decrease strategy for the inertia weight, and randomly adopt the single point mutation operation of the genetic algorithm during the particle update process. The improved particle swarm algorithm is utilized to optimize the parameters of the support vector machine to build a heart disease prediction model. The new algorithm can effectively avoid falling into the local optimal solution. The convergence speed, stability, and accuracy of the algorithm have been significantly improved, and further improving the ability to find the global optimal solution. The simulation experiment also proved the improvement of the diagnostic efficiency and accuracy of the predictive model. It significantly reduces the diagnosis errors and makes the prediction results have certain practical significance.
Temporal Kernel Consistency for Blind Video Super-Resolution

Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accurately estimate the unknown downscaling kernel from a given low-resolution (LR) image in order to leverage the kernel during restoration. Although these approaches have largely been successful, they are predominantly image-based and therefore do not exploit the temporal properties of the kernels across multiple video frames. In this paper, we investigated the temporal properties of the kernels and highlighted its importance in the task of blind video super-resolution. Specifically, we measured the kernel temporal consistency of real-world videos and illustrated how the estimated kernels might change per frame in videos of varying dynamicity of the scene and its objects. With this new insight, we revisited previous popular video SR approaches, and showed that previous assumptions of using a fixed kernel throughout the restoration process can lead to visual artifacts when upscaling real-world videos. In order to counteract this, we tailored existing single-image and video SR techniques to leverage kernel consistency during both kernel estimation and video upscaling processes. Extensive experiments on synthetic and real-world videos show substantial restoration gains quantitatively and qualitatively, achieving the new state-of-the-art in blind video SR and underlining the potential of exploiting kernel temporal consistency.
Few-shot Object Detection for Plateau Wildlife Images

For few-shot object detection has attracted wide attention. Plateau wildlife detection is a typical problem of few-shot object detection. The solution of this problem can reduce the manpower and material resources of collecting and marking large-scale wildlife data sets. Deep neural network can significantly improve the efficiency of wildlife detection, and the key step is to obtain a large amount of data with bounding box annotations. However, there are few plateau wildlife pictures with labeling, which is insufficient to support the training of deep neural network. In this paper, we propose a target detection method based on few-shot learning to detect plateau wildlife images, where each animal category has only a small number of annotated bounding boxes (no more than 10). we use two-stage training method to solve the task of small samples detection task. Based on the two-stage target detector Faster R-CNN, we established a few shot target detection model. The training is divided into two stages. The first stage is to train the model on the basic set, and the second stage is to adjust the balance set of the new class and the base class on the last layer. In order to further improve the detection effect, the two-stage training adopts data enhancement methods to expand the sample data. Experimental results show that this model can achieve better detection effect on plateau wildlife pictures with a small number of labeled samples.
Grammatical Error Correction with Dependency Distance

Grammatical Error Correction (GEC) task is always considered as low resource machine translation task which translates a sentence in an ungrammatical language to a grammatical language. As the state-of-the-art approach to GEC task, transformer-based neural machine translation model takes input sentence as a token sequence without sentence's structure information, and may be misled by some strange ungrammatical contexts. In response, to lay more attention on a given token's correct collocation rather than the misleading tokens, we propose dependent self-attention to relatively increase the attention score between correct collocations according to the dependency distance between tokens. However, as the source sentence is ungrammatical in GEC task, the correct collocations can hardly be extracted by normal dependency parser. Therefore, we propose dependency parser for ungrammatical sentence to get the dependency distance between tokens in the ungrammatical sentence. Our method achieves competitive results on both BEA-2019 shared task, CoNLL-2014 shared task and JFLEG test sets.
A Lattice Reduction Algorithm Based on Sublattice BKZ

We present m-SubBKZ reduction algorithm that outputs a reduced lattice basis, containing a vector shorter than the original BKZ. The work is based on the properties of sublattices and the Gaussian Heuristic of the full lattice and sublattices. By theoretical analysis and simulation, we suggest a BKZ call on the sublattice is possible to produce a short vector close to the shortest vector in the full lattice. The key idea of our algorithm is to extract multiple sublattices from the preprocessed lattice, restricting the context in which a lattice reduction solver is called. The full basis is then updated with vectors from the reduced basis of each sublattice. The new algorithm improves on the efficiency of the original BKZ algorithm and the BKZ 2.0 variant. We show the experimental results on random lattices to compare the length of vectors produced by our algorithm and original BKZ and BKZ 2.0. On the 180-dimension basis, the m-SubBKZ reaches 47% of the output of BKZ and 46% of BKZ 2.0. The ratio drops with the dimension increasing. The effect is more oblivious with smaller blocks. The results show that the new algorithm is able to produce a shorter vector at a relatively low cost compared with previous algorithms, and the improvements are especially explicit for lattices of high dimensions.
Locality and Relative Distance-Aware Non-local Networks for Hand-Raising Detection in Classroom Video

Detecting and understanding interactions between students and teachers in classroom is an important criterion for computer vision-based educational assistive systems. Recently, deep long-range spatial dependencies modeling techniques, such as non-local networks, have been proven to be very effective for such tasks. Yet, regarding global context generation, we analyze that the non-local operation only compares pixels using their values, which cannot pertain to structural information. In this paper, we first extend the non-local module to corporate locality attributes. We further observe that each query is treated uniformly to generate the attention map. Hence, we incorporate distance-wise representations with an efficient implementation into the non-local formulas. The proposed locality and relative distance-aware non-local module is integrated into an object detection architecture namely Libra-RCNN and is evaluated through our experiments on a pre-access hand-raising gesture dataset. Our straightforward modification achieves 0.8% and 2.8% higher performance compared to the baseline Libra-RCNN model, in terms of mAP 0.5 and mAP 0.75 respectively.
Using Artificial Intelligence Methods to Predict Student Academic Achievement

This study applies two artificial intelligence methods represented by both the neural network and fuzzy logic to predict student achievement in the exam. The dataset used in this study was taken from an Iraqi engineering college and it represents data of 200 students who have enrolled in the computer science course. Gender, age, resources downloaded, videos viewed, discussion chat joined, exam scores used as the data set. The type of artificial neural network used was pattern neural network. Levenberg-Marquardt's algorithm was used to train the neural networks. On the other hand Sugeno fuzzy inference system was used for the fuzzy logic. The study results showed that the students who spend more time on the learning system have the most success rate. According to the results the neural network accuracy rate 73% and the fuzzy was 88%. This high accuracy rates support that artificial intelligence methods can be used to predict student academic achievement.
An Efficient Gauss-Seidel Cubature Kalman Filter

The Cubature Kalman filter (CKF) is a widely used filter with excellent performance, particularly on large non-linear systems. However, the CKF is computationally challenging to implement. The matrix inversion operation increases the computational burden if exact inversion methods, such as the Cholesky decomposition, are used. To reduce the CKF computational cost without losing accuracy in non-linear systems, in this study, we propose an efficient CKF, called the Gauss-Seidel Cubature Kalman filter (GSCKF), where the traditional Kalman gain calculating operation is replaced with the Gauss-Seidel (GS) method. The GS method is exploited to calculate the Kalman gain without complicated matrix inversion. To choose the optimal number of iterations adaptively, the Euclidean norm is considered as the evaluation criterion, which helps the GSCKF achieve the accuracy similar to CKF with less complexity. The computational complexity analysis and execution time results reveal that the GSCKF computational cost is significantly reduced. Numerical simulation results on different large non-linear systems show that the GSCKF, with only a few iterations and outstanding time efficiency, can provide a performance that is indistinguishable with respect to the performance of the CKF. The proposed algorithm will be useful in a variety of engineering areas, specifically in signal processing issues involving large non-linear systems.
Categorizing Code Review Comments Using Machine Learning

Code review turns into a progressively mainstream method to detect early defects in the codebase. These days experts are rushing towards peer-investigating the codebases written by any co-located team members or other authors from distributed or dispersed teams. Chipping away at a circulated or scattered team, reviewing a codebase is required to inspect the patches before consolidating. Code looking into can likewise be a structure of approving practical and non-useful necessities. In certain circumstances, analysts do not invest enough time to comment in an organized manner, which turns into a bottleneck to other developers for tackling the discoveries or recommendations remarked by the peer-reviewers. To make the review process more progressively successful and well-organized, productive remarks are compulsory. We have extricated 2185 human code review comments of five marketed projects by mining respective projects' repositories. Six machine learning classifiers have been utilized to train our model. Stochastic Gradient Descent (SGD) vector machine accomplishes a higher accuracy of 63.89% among the others. This work will assist the specialists with building up organized and viable code review culture among worldwide programmers or software engineers by categorizing code review comments.Arafat, Yeasir Sumbul, Syeda Shamma, Hossain
Evaluation of Intensity Prediction Equations (IPEs) for Small-Magnitude Earthquakes

Abstract This study assesses existing intensity prediction equations (IPEs) for small unspecified magnitude (M ≤3.5) earthquakes at short hypocentral distances (Dh) and explores such earthquakes’ contribution to the felt shaking hazard. In particular, we consider IPEs by Atkinson and Wald (2007) and Atkinson et al. (2014), and evaluate their performance based on “Did You Feel It” (DYFI) reports and recorded peak ground velocities (PGVs) in the central United States. Both IPEs were developed based on DYFI reports in the central and eastern United States with moment magnitudes above Mw 3.0. DYFI reports are often used as the ground truth when evaluating and developing IPEs, but they could be less reliable when there are limited responses for small-magnitude earthquakes. We first compare the DYFI reports with intensities interpolated from recorded PGVs. Results suggest a minimal discrepancy between the two when the intensity is large enough to be felt (i.e., M &amp;gt;2 and Dh&amp;lt;15 km). We then compare intensities from 31,617 DYFI reports of 3049 earthquakes with the two IPEs. Results suggest that both the IPEs match well with observed intensities for 2.0&amp;lt; M &amp;lt;3.0 and Dh&amp;lt;10 km, but the IPE by Atkinson et al. (2014) matches better for larger distances. We also observe that intensities from DYFI reports attenuate faster compared with the two IPEs, especially for distances greater than 10 km. We then group DYFI reports by inferred VS30 as a proxy for site amplification effects. We observe that intensities at sites with VS30 around 300 m/s are consistently higher than at sites with VS30 around 700 m/s and are also closer to the two IPEs. Finally, we conduct hazard disaggregation for earthquakes at close distances (Dh=7.5 km) using the observed records. Results suggest that earthquakes with magnitudes below M 3.0 contribute more than 40% to the occurrence of felt shaking.
Multi-Modal Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation

Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task under unstable weather and illumination changes (e.g., foggy weather), which results in poor outdoor scene visibility. Such visibility limitations lead to non-optimal performance of generalised deep convolutional neural network-based semantic scene segmentation. In this paper, we propose an efficient end-to-end automotive semantic scene understanding approach that is robust to foggy weather conditions. As an end-to-end pipeline, our proposed approach provides: (1) the transformation of imagery from foggy to clear weather conditions using a domain transfer approach (correcting for poor visibility) and (2) semantically segmenting the scene using a competitive encoder-decoder architecture with low computational complexity (en-abling real-time performance). Our approach incorporates RGB colour, depth and luminance images via distinct encoders with dense connectivity and features fusion to effectively exploit information from different inputs, which contributes to an optimal feature representation within the overall model. Using this architectural formulation with dense skip connections, our model achieves comparable performance to contemporary approaches at a fraction of the overall model complexity.
A Cross-Cultural Adaptation of Chatbot Usability Questionnaire (CUQ): Indonesian Version

Chatbots are a highly developed technology, especially in the current COVID-19 pandemic to apply contactless activities. Unfortunately, most of companies that have been implemented chatbot has failed gained benefit from the chatbot. This shows companies need to evaluate usability of the chatbot. One of specific questionnaire to evaluate usability of chatbot is Chatbot Usability Questionnaire (CUQ). Currently CUQ is only available in English and there is no research that provides CUQ in the Indonesian version. This research focuses on translating CUQ and producing a reliable Indonesian version of CUQ. CUQ needs to be adapted naturally into Indonesian so it can be understood by all types of users. Cross-cultural adaptation approach is used to translate CUQ to Indonesia. The reliability test was run with 100 respondents and resulted in Cronbach's alpha value of 0.749. This value indicated that CUQ is reliable and can be used in chatbot research and evaluation.
The Macroeconomic Impact of Recent Political Conflicts in Africa: Generalized Synthetic Counterfactual Evidence

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A Group Blind Signature Scheme for Privacy Protection of Power Big Data in Smart Grid

With the continuous expansion of the scale of smart grid, a large amount of data is generated during the operation of smart grid. These massive electricity data can be analyzed in detail by using big data technology to promote the construction and development of smart grid. In addition to the massive data processing generated in smart grid, one of the key factors restricting the development of smart grid is the issue of privacy leakage. In order to solve the problem of user identity information and user electricity data privacy in smart grid, we propose a conditional anonymous group blind signature scheme in smart grid, and use homomorphic tag mechanism to verify the integrity of electricity data. From the safety analysis, the results show that our scheme is safe and effective.
Recognition of Isolated Gestures for Indian Sign Language Using Transfer Learning

Sign language is a visual and complete natural language used by Deaf to communicate with each other and hearing people. The Deaf community finds it very difficult to express their feelings to the hearing people, because of which human interpreters are needed to help them in emergency situations. But always human interpreting service is not available when needed, because in India only around 300 certified sign language interpreters are available. In order to fill this gap, an automated system can be designed that would facilitate recognition of Indian Sign Language (ISL) gestures. This paper describes such an approach that takes ISL image as an input and gives corresponding class as an output. We have shown that use of simple image manipulation techniques and transfer learning give promising result in ISL hand gesture recognition. For 11 categories of ISL words, we have achieved 99.07% accuracy that outperforms accuracy reported by existing approach.
Assessing universal competencies of students of non-linguistic specialties

The rapidly developing labor reality requires a modern specialist to be ready for rapid adaptation in the professional space, mobility within the labor market and constant self-improvement. The training of such specialists at the university aims at the formation of both highly professional and flexible (universal) competencies that provide the ability to think systematically, critically assess problem situations and search for a variety of resources for solving professional problems. Universal competencies are represented by eight groups which suppose that University training in its basic part should be identical at all university levels of education and for all areas and specialties. And it is grounded on the principles of universalism. The purpose of the paper is to present the concept of universal competencies in a meaningful and structural way and to determine the possibilities of the case-testing method in their evaluation. The research methodology is based on the principles of flexibility and variability applied to research process organization, practice-oriented principles of the results of research procedures. It is focused on a holistic, reflexive-analytical and methodological-instructive presentation of the results of diagnostics that reflect the development of universal competencies. The following research methods are used: theoretical analysis and content analysis of normative documents that describe the structure of universal competence and determine the contribution of the discipline Foreign Language to the development of this type of competence among students of non-linguistic specialties; generalization of Russian and foreign experience in assessing competencies to identify the best practices for diagnosing their development; analysis of current and interim control test-tasks designed to evaluate universal competencies that can be found in the OmSPU evaluation funds; interviewing teachers of the Department of Foreign Languages (interfaculty chair) of OmSPU that aims at showing the advantages and disadvantages of case-testing in assessing the universal competencies of students of non-linguistic specialties. As a result of the research, a model evaluation case is going to be designed and the recommendations for University teachers how to use it in their work will be given. The scientific novelty of the research includes the ideas developing a universal approach to the University training as well as the ideas enriching a competence-based approach how to organize and evaluate universal competencies. The practical significance is seen in the possibility of using the materials of the paper to improve the funds of evaluation tools for the discipline Foreign Language; developing an educational module for advanced training courses.