Reviewerly/exHarmony huggingface dataset:
Image Inpainting with Learnable Feature Imputation

A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts in the inpainted image. Several studies address this issue with feature re-normalization on the output of the convolution. However, these models use a significant amount of learnable parameters for feature re-normalization [41, 48], or assume a binary representation of the certainty of an output [11, 26]. We propose (layer-wise) feature imputation of the missing input values to a convolution. In contrast to learned feature re-normalization [41, 48], our method is efficient and introduces a minimal number of parameters. Furthermore, we propose a revised gradient penalty for image inpainting, and a novel GAN architecture trained exclusively on adversarial loss. Our quantitative evaluation on the FDF dataset reflects that our revised gradient penalty and alternative convolution improves generated image quality significantly. We present comparisons on CelebA-HQ and Places2 to current state-of-the-art to validate our model. (Code is available at: github.com/hukkelas/DeepPrivacy . Supplementary material can be downloaded from: folk.ntnu.no/haakohu/GCPR_supplementary.pdf )
Stance detection using diverse feature sets based on machine learning techniques

Sentiment analysis is the field that analyzes sentiments, and opinions of people about entities such as products, businesses, and events. As opinions influence the people’s behaviors, it has numerous applications in real life such as marketing, politics, social media etc. Stance detection is the sub-field of sentiment analysis. The stance classification aims to automatically identify from the source text, whether the source is in favor, neutral, or opposed to the target. This research study proposed a framework to explore the performance of the conventional (NB, DT, SVM), ensemble learning (RF, AdaBoost) and deep learning-based (DBN, CNN-LSTM, and RNN) machine learning techniques. The proposed method is feature centric and extracted the (sentiment, content, tweet specific and part-of-speech) features from both datasets of SemEval2016 and SemEval2017. The proposed study has also explored the role of deep features such as GloVe and Word2Vec for stance classification which has not received attention yet for stance detection. Some base line features such as Bag of words, N-gram, TF-IDF are also extracted from both datasets to compare the proposed features along with deep features. The proposed features are ranked using feature ranking methods such as (information gain, gain ration and relief-f). Further, the results are evaluated using standard performance evaluation measures for stance classification with existing studies. The calculated results show that the proposed feature sets including sentiment, (part-of-speech, content, and tweet specific) are helpful for stance classification when applied with SVM and GloVe a deep feature has given the best results when applied with deep learning method RNN.
HITS-based attentional neural model for abstractive summarization

Automatic abstractive summary generation is still an open problem in natural language processing field. Conventional encoder–decoder model based abstractive summarization methods often suffer from repetition and semantic irrelevance. Recent studies apply traditional attention or graph-based attention on the encoder–decoder model to tackle the problem, under the assumption that all the sentences in the original document are indistinguishable from each other. But in a document, the same words in different sentences are not equally important, i.e., the words in a trivial sentence are less important than the words in a salient sentence. Based on it, we develop a HITS-based attention mechanism in this paper, which fully leverages sentence-level and word-level information by considering sentences and words in the original document as authorities and hubs. Based on it, we present a novel abstractive summarization method, with Kullback–Leibler (KL) divergence to refine the attention value, meanwhile we propose a comparison mechanism in summary generation to further improve the summarization performance. When evaluated on the CNN/Daily Mail and NYT datasets, the experimental results demonstrate the improvement of summarization performance and show the performance of our proposed method is comparable with that of the other summarization methods. Besides, we also conduct experiments on CORD-19 dataset (COVID-19 Open Research Dataset) which is a biomedical domain dataset, and the experimental results show great performance of our proposed method compared with that of the other state-of-the-art summarization methods.
Relation Extraction for Massive News Texts

With the development of information technology including Internet technologies, the amount of textual information that people need to process daily is increasing. In order to automatically obtain valuable and user-informed information from massive amounts of textual data, many researchers have conducted in-depth research in the area of entity relation extraction. Based on the existing research of word vector and the method of entity relation extraction, this paper designs and implements an method based on support vector machine (SVM) for extracting English entity relationships from massive news texts. The method converts sentences in natural language into a form of numerical matrix that can be understood and processed by computers through word embedding and position embedding. Then the key features are extracted, and feature vectors are constructed and sent to the SVM classifiers for relation classification. In the process of feature extraction, we had two different models to finish the job, one by Principal Component Analysis (PCA) and the other by Convolutional Neural Networks (CNN). We designed experiments to evaluate the algorithm.
Robust online rain removal for surveillance videos with dynamic rains

The current rain removal techniques proposed for surveillance videos mainly assume consistent rains with invariant extents and types, and are implemented in a batch-mode learning manner. Such assumption deviates from the continuously varying insights of practical rains, and the batch mode further makes the techniques infeasible for real long-lasting videos. To alleviate these issues, this study proposes a novel online rain removal approach to represent practical dynamic rains embedded in surveillance videos. Particularly, we model the rain streaks scattered in each video frame as a patch-wise mixture of Gaussians (P-MoG) distribution, and update its parameters frame by frame. Such a P-MoG modeling manner finely reflects the non-i.i.d. dynamic variations of rains along time. In specific, the P-MoG rain model in each frame is regularized by the learned rain knowledge in previous frames, making the online model adaptable to not-identically-distributed rains in each frame while regularized by not-independently-distributed rains in previous frames. The proposed model is formulated as a concise probabilistic MAP model, which can be readily solved by EM algorithm. We further embed an affine transformation operator into the proposed model, making it adaptable to a wider range of videos with camera jitters. The superiority of the proposed method is substantiated by extensive experiments implemented on synthetic and real videos containing static and dynamic rains as compared with state-of-the-arts in both accuracy and efficiency.
Physical Computing: Unifying Real Number Computation to Enable Energy Efficient Computing

Physical computing unifies real value computing including analog, neuromorphic, optical, and quantum computing. Many real-valued techniques show improvements in energy efficiency, enable smaller area per computation, and potentially improve algorithm scaling. These physical computing techniques suffer from not having a strong computational theory to guide application development in contrast to digital computation’s deep theoretical grounding in application development. We consider the possibility of a real-valued Turing machine model, the potential computational and algorithmic opportunities of these techniques, the implications for implementation applications, and the computational complexity space arising from this model. These techniques have shown promise in increasing energy efficiency, enabling smaller area per computation, and potentially improving algorithm scaling.
Multi-Modal Supplementary-Complementary Summarization using Multi-Objective Optimization

Large amounts of multi-modal information online make it difficult for users to obtain proper insights. In this paper, we introduce and formally define the concepts of supplementary and complementary multi-modal summaries in the context of the overlap of information covered by different modalities in the summary output. A new problem statement of combined complementary and supplementary multi-modal summarization (CCS-MMS) is formulated. The problem is then solved in several steps by utilizing the concepts of multi-objective optimization by devising a novel unsupervised framework. An existing multi-modal summarization data set is further extended by adding outputs in different modalities to establish the efficacy of the proposed technique. The results obtained by the proposed approach are compared with several strong baselines; ablation experiments are also conducted to empirically justify the proposed techniques. Furthermore, the proposed model is evaluated separately for different modalities quantitatively and qualitatively, demonstrating the superiority of our approach.
Modeling patient-related workload in the emergency department using electronic health record data

Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload).One month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies.Linear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R2 > 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour).The main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification.
Deep matrix factorization with knowledge transfer for lifelong clustering and semi-supervised clustering

Clustering analysis aims to group unlabeled data in an unsupervised learning manner. However, most existing methods are tailored for single-task data and do not work for a sequence of tasks. In this paper, we propose a Deep Matrix factorization method with Knowledge transfer (DMK) to address clustering problem in a lifelong setting, where DMK approaches a sequence of tasks; after each task is learned, its knowledge will be retained and later used to help future clustering task. To this end, we delve into deep matrix factorization and graph co-clustering, where (1) the former learns a basis feature library across all arrived tasks and a specific representation for each target task to deal with lifelong clustering and (2) the latter builds a consistent feature embedding library to transfer knowledge between each pair of tasks. An iterative optimization algorithm is then proposed to alternatively update the two libraries. In addition, we extend our DMK into a semi-supervised version and propose a Semi-supervised Deep Matrix factorization method with Knowledge transfer (SDMK) by exploiting a few of prior label information for lifelong semi-supervised clustering. Experimental results using four datasets with sequential tasks demonstrate that the proposed methods outperform state-of-the-art baseline methods markedly.
Research on the Teaching Mode of College English Based on Artificial Intelligence

Abstract In recent years, the breakthroughs of AI have recently occurred in the fields of natural language processing, image recognition and so on. How to introduce AI technology into college English teaching and efficiently integrate with college English “teaching” and “learning” has not yet been fully studied. The AI technologies, especially in NLP, provide the possibilities of enhancing college English teaching qualities and build the new college English teaching mode. Studies on the efficient connection of teaching and learning concerning the applying of AI are scarcely found. This thesis analyses the integrate on of AI and college English teaching, especially the feedback of self-regulated learning evaluation and try to probe new teaching modes against the AI backdrop. Related researches have demonstrated that it will provide new ideas and entry points for the reform of the current college English teaching mode, and is of great significance for achieving the goals of college English teaching.
Research on optimization of deep learning algorithm based on convolutional neural network

Abstract Convolutional neural network is a representative one of deep learning algorithms. It plays an important role in improving the learning level of artificial intelligence. Therefore, this article discusses the optimization of convolutional neural networks and deep learning algorithms. The optimization of deep learning algorithms and its application in actual image recognition are discussed for reference.
RETRACTED: Exploration and Practice of College English Blended Teaching Model from the Perspective of “Internet+”

Abstract In the context of the rapid development of Internet technology, the use of network information technology to improve and innovate the college English teaching model will help improve the efficiency and quality of college English teaching. This is also the main trend in the innovation and development of college English teaching. The effective use of the mixed teaching model can meet the requirements of teaching reform and improve the overall level of university English teaching. However, there are still some problems in the development of the college English blended teaching model under the “Internet +” perspective. Especially the mixed teaching methods and teaching policies are immature and imperfect. Therefore, colleges and universities need to strengthen the research work on the blended teaching mode of college English under the background of “Internet +”, and understand the problems existing in the application of blended teaching mode. At the same time, colleges and universities need to propose effective practical strategies to improve the level of Chinese college English teaching reform.
A Generative Variational Model for Inverse Problems in Imaging

This paper is concerned with the development, analysis, and numerical realization of a novel variational model for the regularization of inverse problems in imaging. The proposed model is inspired by the architecture of generative convolutional neural networks; it aims to generate the unknown from variables in a latent space via multilayer convolutions and nonlinear penalties, and penalizes an associated cost. In contrast to conventional neural-network-based approaches, however, the convolution kernels are learned directly from the measured data such that no training is required. The present work provides a mathematical analysis of the proposed model in a function space setting, including proofs for regularity and existence/stability of solutions, and convergence for vanishing noise. Moreover, in a discretized setting, a numerical algorithm for solving various types of inverse problems with the proposed model is derived. Numerical results are provided for applications in inpainting, denoising, deblurring under noise, superresolution, and JPEG decompression with multiple test images.
Pose-aware Multi-feature Fusion Network for Driver Distraction Recognition

Traffic accidents caused by distracted driving have gradually increased in recent years. In this work, we propose a novel multi-feature fusion network based on pose estimation, for image based distracted driving detection. Since hand is the most important part of driver to infer the distracted actions, our proposed method firstly detects hands using the human body posture information. In addition to the features extracted from the whole image, our network also include the important information of hand and human body posture. The global feature, hand and pose features are finally fused by weighted combination of probability vectors and concatenation of feature maps. The experimental results show that our method achieves state-of-the-art performance on our own SZ Bus Driver dataset and the public AUC Distracted Driver dataset.
A Hybrid Framework for Session Context Modeling

Understanding user intent is essential for various retrieval tasks. By leveraging contextual information within sessions, e.g., query history and user click behaviors, search systems can capture user intent more accurately and thus perform better. However, most existing systems only consider intra-session contexts and may suffer from the problem of lacking contextual information, because short search sessions account for a large proportion in practical scenarios. We believe that in these scenarios, considering more contexts, e.g., cross-session dependencies, may help alleviate the problem and contribute to better performance. Therefore, we propose a novel Hybrid framework for Session Context Modeling (HSCM), which realizes session-level multi-task learning based on the self-attention mechanism. To alleviate the problem of lacking contextual information within current sessions, HSCM exploits the cross-session contexts by sampling user interactions under similar search intents in the historical sessions and further aggregating them into the local contexts. Besides, application of the self-attention mechanism rather than RNN-based frameworks in modeling session-level sequences also helps (1) better capture interactions within sessions, (2) represent the session contexts in parallelization. Experimental results on two practical search datasets show that HSCM not only outperforms strong baseline solutions such as HiNT, CARS, and BERTserini in document ranking, but also performs significantly better than most existing query suggestion methods. According to the results in an additional experiment, we have also found that HSCM is superior to most ranking models in click prediction.
Classification of Indian Dance Forms using Pre-Trained Model-VGG

Indian classical dance often referred to as ICD acts as a mirror of the rich cultural heritage of India. Every dance comes from a different state in the country. The national level academy for performing arts in India called The Sangeet Natak Academy identifies eight different dance forms namely- `Bharatanatyam', `Kathak', `Kathakali', `Kuchipudi', `Manipuri', `Mohiniyattam', `Odissi' and `Sattriya'. In this paper, we have used transfer learning to solve this multiclass problem. Pre-trained models such as VGG16 and VGG19 were used to classify the images of the 8 dance forms known in India. This was completed using the ICD data set from Kaggle consisting of 8 different classes and multiple images in each class.
LEDet: A Single-Shot Real-Time Object Detector Based on Low-Light Image Enhancement

Abstract Recently, significant breakthroughs have been achieved in the field of object detection. However, existing methods mostly focus on the generic object detection task. Performance degradation can be unavoidable when applying the existing methods to some specific situations directly, e.g. a low-light environment. To address this issue, we propose a single-shot real-time object Detector based on Low-light image Enhancement, namely LEDet. LEDet adapts itself to the low-light detection task in three aspects. First, a low-light enhancement module is introduced as the image preprocessor, producing the augmented inputs from the low-light images. Second, two modules, i.e. low-light and enhanced features fusion module and the scale-aware channel attention dilated convolution module are designed. These two modules aim at learning robust and discriminative features from objects of various sizes hidden in the darkness. In experiments, we validate the effectiveness of each part of our LEDet model via several ablation studies. We also compare LEDet with various methods on the Exclusively Dark dataset, showing that our model achieves the state-of-the-art performance on the balance between speed and accuracy.
PRF-Ped: Multi-scale Pedestrian Detector with Prior-based Receptive Field

Multi-scale feature representation is a common strategy to handle the scale variation in pedestrian detection. Existing methods simply utilize the convolutional pyramidal features for multi-scale representation. However, they rarely pay attention to the differences among different feature scales and extract multi-scale features from a single feature map, which may make the detectors sensitive to scale-variance in multi-scale pedestrian detection. In this paper, we introduce a bidirectional feature enhancement module (BFEM) to augment the semantic information of low-level features and the localization information of high-level features. In addition, we propose a prior-based receptive field block (PRFB) for multi-scale pedestrian feature extraction, where the receptive field is closer to the aspect ratio of the pedestrian target. Consequently, it is less affected by the surrounding background when extracting features. Experimental results indicate that the proposed method outperforms the state-of-the-art methods on the CityPersons and Caltech datasets.
Prevalence of spelling errors affects reading behavior across languages.

This cross-linguistic study investigated the impact of spelling errors on reading behavior in five languages (Chinese, English, Finnish, Greek, and Hebrew). Learning theories predict that correct and incorrect spelling alternatives (e.g., "tomorrow" and "tommorrow") provide competing cues to the sound and meaning of a word: The closer the alternatives are to each other in their frequency of occurrence, the more uncertain the reader is regarding the spelling of that word. An information-theoretic measure of entropy was used as an index of uncertainty. Based on theories of learning, we predicted that higher entropy would lead to slower recognition of words even when they are spelled correctly. This prediction was confirmed in eye-tracking sentence-reading experiments in five languages widely variable in their writing systems' phonology and morphology. Moreover, in each language, we observed a characteristic Entropy × Frequency interaction; arguably, its functional shape varied as a function of the orthographic transparency of a given written language. (PsycInfo Database Record (c) 2021 APA, all rights reserved).