Reviewerly/exHarmony huggingface dataset:
Single Image Very Deep Super Resolution (SIVDSR) Dehaze

Adverse climate conditions affect digital photography causing colour shifting, poor visibility, contrast reduction, and fainted appearance due to the scattering of atmospheric Particulate Matter (APM). To get an optimum transmission matrix is the key success of any single image dehazing technique. Deep Learning based Super Resolution technique with VDSR 20-weighted Layers ImageNet classifier improves any image resolution leading to noise suppression. High Residual Learning gradient clipping makes the algorithm converge fast with denoising and removal of artifacts by compression. This key observation has been exercised in improving resolution of the hazy images with an optical image formation model. In addition, benchmark established images are evaluated and their comparisons to the state-of-the-art methods show a consistent improvement in accurate scene transmission estimation resulting in clear, natural haze-free radiance. A good balance between execution speed and processing speed has been achieved.
Speckle Noise Reduction Using a New Weighted-Average Filter Based on Euclidean Distance

This paper addresses the problem of speckle noise in medical ultrasound imaging. This noise drastically affects the image quality as it is random in nature. For this reason, a new weighted-average filter-based image restoration technique is developed to suppress speckle noise from ultrasound images. The proposed filter structure is based on geometric Euclidean distances of the pixels in an image. The potency of the proposed filter is tested by applying it on a gray-level image and then real ultrasound images using several image quality metrics. A performance comparison with other traditional methods indicates the better performance of the proposed filter.
A Hybrid Approach for Extractive Summarization of Medical Documents

Text summarization helps us to obtain the most significant content from any document saving time and resources. Many researches of automatic summarization have been done with documents of general domain. In recent years, artificial intelligence and machine learning are being more and more integrated with medical field. As the field of medical requires efficiency more than any other field of science, proper summarization of medical documents is important. Some works and studies have been done in this topic but they have many limitations and restrictions. In this paper, we have presented a hybrid approach for extractive summarization of medical documents. In the combinational method, we have filtered neutral content of a document through sentiment analysis and with interconnection and content of sentences and presence of keyphrases, summarization has been done. After evaluation, the introduced method has shown promise with good scores.
Development of Practical Learning Materials for Manufacturing Site Improvement Utilizing IoT

As practical learning materials for the acquisition of improvement methods in manufacturing sites that utilize internet of things (IoT), we developed simple devices that simulate a manufacturing machine, an inspection device, etc. In addition, we built a virtual factory equipped with a machine line having the simulated manufacturing machines and a manual line that allow manual assembly. We also lectured university students using the developed materials. Through preparing devices that visualize operating time data toward process improvements, many students believed that their knowledge of improvement methods in manufacturing sites and ability to generally utilize IoT had improved. Thus, the developed materials showed potential in fostering personnel with the knowledge of manufacturing site improvements that utilizes IoT.
Stain-AgLr: Stain Agnostic Learning for Computational Histopathology Using Domain Consistency and Stain Regeneration Loss

Stain color variations between Whole Slide Images (WSIs) is a key challenge in the application of Computational Histopathology. Deep learning-based algorithms are susceptible to domain shift and degrade in performance on the WSIs captured from a different source than the training data due to stain color variations. We propose a training methodology Stain-AgLr, that achieves high invariance to stain color changes on unseen test data. In addition to task loss, Stain-AgLr training is supervised with a consistency regularization loss that enforces consistent predictions for training samples and their stain altered versions. An additional decoder is used to regenerate stain color from feature representation of the stain altered images. We compare the proposed approach to state-of-the-art strategies using two histopathology datasets and show significant improvement in model performance on unseen stain variations. We also visualize the feature space distribution of test samples from multiple diagnostic labs and show that Stain-AgLr achieves a significant overlap between the distributions.
Face-based Authentication Using Computational Secure Sketch

Biometric features are quite suitable for identity authentication due to its inherent properties – universality, uniqueness and persistence. In fact, biometric authentication has been widely used in our daily life, especially in mobile devices. However, biometric features are quite sensitive, and once a feature is leaked to an evil adversary, it cannot be used in authentication any more. This leads to a push on research of biometric privacy protection. In this paper, we propose a face-based authentication system with the help of a computational secure sketch. The computational secure sketch takes charge of error tolerance on the face samplings. Then the face features of the same user are used to extract an authentication key, which is in turn used to do the identity authentication for the user. The computational security of the computational secure sketch makes sure that the public information obtained by the adversary does not affect the pseudorandomness of the authentication key, hence the privacy of face features is guaranteed. Moreover, the privacy protection technique in our face-based authentication system can be extended to other biometric-based authentication.
Temporal Weighting Appearance-Aligned Network for Nighttime Video Retrieval

Video-based person re-identification (ReID) aims at re-identifying video sequences of a specified person from videos captured by disjoint cameras. Existing datasets and works on this task all focus on daytime scenarios and cannot adapt well to the nighttime scenarios, which is also of significant importance for practical applications. In this paper, we contribute a new dataset for nighttime video-based ReID, termed NIVIR, which contains 800 identities with over 228,000 images. NIVIR contains video shots under various lighting conditions, different weathers, and complex scenarios, which is consistent with the real nighttime outdoor surveillance. Furthermore, we propose a temporal weighting appearance-aligned network (TWAN) for nighttime video-based ReID, which is composed of a correlation-based appearance-aligned module (CAM) and a temporal weighting module (TWM). Specifically, CAM is proposed to reconstruct the adjacent feature maps to guarantee the appearance alignment between the central frame and its adjacent frames. TWM is designed to evaluate the frame quality of a tracklet and generate temporal weights to enhance the video representation. Extensive experiments conducted on our new NIVIR dataset demonstrate that the proposed TWAN outperforms the state-of-the-art methods. We believe that our NIVIR dataset and the comprehensive attempts for solving the nighttime ReID problem will push forward the development of the ReID research community.
HmcNet: A General Approach for Hierarchical Multi-Label Classification

Hierarchical multi-label classification (HMC) deals with the problem of assigning each entity to multiple classes with a taxonomic structure (e.g., tree). Within this structure, classes at different levels tend to have dependencies under the hierarchy constraints. However, most prior studies for HMC tasks tend to ignore the class dependencies within the hierarchy. Moreover, most existing methods generate incoherent predictions and do not satisfy the hierarchy constraint. To this end, based on previously developed HARNN, we propose a general framework, HmcNet, for introducing explicit and implicit class hierarchy constraints to generate coherent predictions. We develop an efficient Prune-based Coherent Prediction (PCP) strategy for the optimal paths selection, which produces coherent predictions in a principled way. HmcNet can be well explained from two perspectives. First, it develops the Hierarchical Attention-based Memory (HAM) unit with implicit class hierarchy constraints to capture class dependencies more intuitively; Second, it subsumes explicit class hierarchy constraints during training and inference phases and generates coherent predictions in a consistent manner. Finally, extensive experimental results on six real-world datasets demonstrate the effectiveness and interpretability of the HmcNet frameworks. To facilitate future research, our code has been made publicly available.
MalView: Interactive Visual Analytics for Comprehending Malware Behavior

Malicious applications are usually comprehended through two major techniques, namely static and dynamic analyses.Through static analysis, a given malicious program is parsed, and some representative artifacts (e.g., control-flow graphs) are produced without any execution; whereas, the given malicious application needs to be executed when conducting dynamic analysis.These two mainstream techniques for analyzing the given software are effective in detecting certain classes of malware.More specifically, through static analysis, the patterns and signature of the malware are exposed, helping in detecting any known malicious payload hidden in or injected into the code.On the other hand, behavioral and run-time execution patterns of software are explored through dynamic analysis.To ease the analysis process, a third analysis approach, known as the visual representation of the artifacts created by both static and dynamic analysis tools, would also be a supplementary asset for malware experts.This paper introduces "MalView", an interactive visualization platform, for malware analysis by which pattern matching techniques on both signature-based and behavioral analysis artifacts can be utilized to 1) classify malware, 2) identify the intention and location of the malicious payload in the artifacts, 3) analyze unknown malware (i.e., zero-day malware) by recognizing any unusual signature or behavior, and 4) explore the time dependencies and thus the system components affected or tampered by the underlying malware.The results of several case studies conducted in this work show that MalView offers more features and information compared to some other visualization tools, facilitating the malware analysis process.
Weakly-Supervised Video Object Grounding via Learning Uni-Modal Associations

Grounding objects described in natural language to visual regions in the video is a crucial capability needed in vision-and-language fields. In this paper, we deal with the weakly-supervised video object grounding (WSVOG) task, where only video-sentence pairs are provided for learning. The essence of this task is to learn the cross-modal associations between words in textual modality and regions in visual modality. Despite the recent progress, we find that most existing methods focus on the association learning for cross-modal samples, while the rich and complementary information within uni-modal samples has not been fully exploited. To this end, we propose to explicitly learn uni-modal associations on both textual and visual sides, so as to fully exploit the useful uni-modal information for accurate video object grounding. Specifically, (1) we learn textual prototypes by considering rich contextual information of the same object in different sentences, and (2) we estimate visual prototypes in an adaptive manner so as to overcome the uncertainties in selecting object-relevant visual regions. Besides, a cross-modal correspondence is learned which not only bridges the visual and textual modalities for WSVOG task, but also tightly cooperates with the uni-modal association learning process. We conduct extensive experiments on three popular datasets, and the favorable results demonstrate the effectiveness of our method.
Dynamic Topic Model for Short Texts about Hot Issues on Microblog Based on TFIDF

Short texts such as posts on Microblog contain much semantic information such as time, attitude, as well as topics, and how to extract topics in these texts has been widely concerned recently. However, on the one hand, most topic models are aimed at long texts, and cannot show a good performance on short texts because of its semantic sparsity. On the other hand, traditional topic models ignore the time factor of documents. Nevertheless, hot issues on the Internet have an opinion life cycle, topics around an issue tend to emerge, develop and vanish rapidly in a short period of time. As a result, ignoring time factor is not consistent with reality. Besides, traditional topic models are likely to extract topics with unnecessarily repeated keywords because pronouns about the same issue have a high frequency in different posts. To solve these problems, we propose a topic model for short texts about hot issues on Microblog based on Dynamic Topic Model(DTM). By introducing Term Frequency and Inverse Document Frequency(TF-IDF) to extract more characteristic words, TFIDF-DTM could reduce the input dimension for DTM and make the extracted topics more representative in the final. The model is tested on a self-crawled dataset, compared with standard DTM, The extraction process of TFIDF-DTM costs less time, and the result has a higher topic discrimination, as well as more explicit semantics.
Reranking by Voting Mechanism in Neural Machine Translation

Reranking technique has demonstrated the effectiveness of further improving the performance in Neural Machine Translation (NMT). Current reranking methods focus on fusing different neural machine translation models. Although even higher quality can be achieved by combining multiple models by techniques. Training multiple models may bring extra time and space consumption. In this paper, we propose a new method that concentrates on a single model's N-Best candidates voting for each other with features produced by BERT (Bidirectional Encoder Representation from Transformers) and n-gram. The experimental results show that our method performs well on WMT'20 Chinese-English, WMT'09 Hungarian-English and CCMT'21 Chinese-Uyghur.
Adversarial Rap Lyric Generation

In this paper, we design a novel text generation method called RapGAN for RLG (Rap Lyric Generation). RapGAN proposes two new schemes, PRO (Phrase Roll-Out) and AREGS (Attention Reward at Every Generation Step), to achieve efficient and effective RLG. The conventional text generation methods need to complete the entire-generated sentence in GAN (Generative Adversarial Network) training. RapGAN uses PRO as selective attention to reward only meaningful phrases in every generation step. Due to the varying length of phrases segmented by PRO, we employ an attention-based neural network that can capture the local feature of phrases and the semantics of global sentences as our discriminator. Therefore, we propose AREGS that reward the alignment weights of the attention-based discriminator with feature matching. Our method can provide different weights on different words to improve RLG’s diversity, originality and fluency. Experimental results s how that our RapGAN outperforms the state-of-the-art RLG methods on many essential metrics of text generation. We also show the human evaluation to illustrate the relations between human perception and typical NLP metrics in diversity, originality and fluency. Our proposed method can facilitate real-world applications in content open RLG dataset in this paper.
Few-Shot Learning for Vehicle Footprint Recognition: State-of-the-Art

Vehicle footprint recognition is an important means for obtaining clues in traffic accident control and criminal case solving. However, in practical applications, due to the limited number of available training samples, overfitting often occurs in the recognition task. Few-shot learning can effectively solve the problem of inaccurate recognition due to the scarcity of training data. This paper summarizes the state-of-the-art methods for few-shot vehicle footprint recognition and divides them into three categories including model fine-tuning-based, feature representation-based and feature distribution-based according to different learning mechanisms applied. In addition, experimental results are provided to compare the performance of representative methods in the three categories. Finally, future research directions in this field are discussed.
Design Framework of Smart Pension Platform Based on Big Data Technology

With the aggravation of the aging problem of China's population and the promotion of the "smart city", the smart pension characterized by intelligence and information technology has developed rapidly in recent years. Smart pension refers to the use of advanced information technology means to carry out connected, interconnected, and intelligent elderly care services for the elderly. The core advantage of the smart pension lies in the use of modern information technology to conveniently combine the elderly with the government, hospitals, communities, and other social resources. In this context, based on knowledge and practical experience, this study is carried out to put forward the design framework of the smart pension platform. We first analyzed the functional requirements of the smart pension platform based on big data, and then presented the system design framework of the big data pension platform, including physical architecture framework, dynamic capacity expansion design framework, database design framework, and big data platform design framework. The research results provide basic technical solutions for the development and maintenance of a smart pension platform based on big data and help to integrate the current scientific and technological resources and improve the lives of the elderly in their later years.
Unmasking the Stereotypes: Evaluating Social Biases in Chinese BERT

Pretrained language models like BERT have great performances in various NLP tasks. However, recent researches have shown that language model can learn social biases from corpus. We designed a new bias evaluation experiment, and made the first attempt to evaluate social bias in Chinese BERT. Instead of pursuing absolute fairness in numbers, we compared BERT’s bias performances after fine-tuning on different contexts to study whether the model encode social biases. After adopting Predicted Likelihood Probability (PLP) and All Unmasked Likelihood (AUL) for bias measurement, we found original Chinese BERT reflects the real-world stereotypes of gender, like IT jobs are more suitable for male. We found Chinese BERT demonstrate significant biases, as with the increase of proportion of male-dominated sentences in the training set, BERT is more favorable to males after fine-tuning. Our result suggests that social biases are also encoded in Chinese language models, and one of the effective ways to mitigate bias is fine-tuning model on a gender-balanced corpus, although this method may not be robust enough.
Design of Motor Skill Training Auxiliary Decision-Making Support System Based on Kinect

Aiming at the problem of insufficient stability of traditional motor skill training auxiliary decision-making system in dark or dark environment, this paper designs a motor skill training auxiliary decision-making support system based on Kinect. In terms of hardware design, Kinect is used as the main hardware and TOF technology is used to set the parameters of distance and reachable field of view to obtain motor skill training data. In terms of software design, Kinect is used to determine the bone node of human characteristics, track human data and smooth it, store the smoothed data in the designed data warehouse, and connect the scattered databases together as the support of auxiliary decision-making. On this basis, priority constraints are set for each module in the system to ensure the normal operation of the system. So far, the overall design of the system has been completed. The experimental results show that the motion training aided decision support system based on Kinect designed in this paper has more accurate recognition angle of human motion image and less joint position positioning error in the case of darkness or dark light, which proves that the system has good stability.
KG-SQL: Hybrid Knowledge-Guided Semantic Understanding for Text-to-SQL

Text-to-SQL translates natural language questions into SQL queries which can be executed on tables to obtain answers. The translation accuracy depends on the integrated understanding of questions and tables, between which there would be extensive semantic gap. However, previous methods specialize in investigating and utilizing simple and common relational information, rather than complex and professional relational information. In this work, we propose a novel hybrid knowledge-guided question and table understanding model (KG-SQL) to improve text-to-SQL translation by bridging the semantic gap between questions and tables. It depicts the semantic relationship between components of questions and tables as a hybrid graph resorting to language knowledge, world knowledge and task-specific knowledge, and integrates these kinds of knowledge in the hybrid graph dynamically. Experiments on WikiSQL and Spider show that, this strategy can effectively integrate different kinds of knowledge, and lead to significant improvement over strong baselines built upon large-scaled pre-trained language models.
Chinese Text Sentiment Classification Model Based on FastText and Multi-scale Deep Pyramid Convolutional Neural Network

In order to improve the efficiency of Chinese text sentiment classification, a text sentiment classification model based on FastText and multi-scale DPCNN are proposed. First, a text vector matrix is constructed using the FastText model. Then, multiple feature maps are extracted from the text vector matrix with a multi-scale filter. Finally, multiple feature maps are fused and input into the DPCNN model for sentiment classification. Experiments are carried out on the Chinese sentiment mining corpus (ChnSentiCorp) dataset, and the comparison results of multiple groups of experiments show that the proposed model can effectively improve the accuracy of text sentiment classification compared with other algorithms.
Deep Neural Network-based Chinese Natural Language Generation on Dynamic Auxiliary Words

Many tasks have been solved by deep neural networks in the field of natural language processing (NLP) and natural language generation (NLG). However, some critical issues in teaching Chinese as a foreign or second language for foreign students are still unsolved. The teaching and learning of dynamic auxiliary words (le, zhe and guo) in Chinese are seldom considered in deep learning-powered applications. Taking three typical dynamic auxiliary words in Chinese as inputs, three Chinese language generation models are designed and evaluated in our work. The designed models, including BERT, BERT-WWM and RoBERTa, are optimized by observing the characteristics of two typical Chinese corpora. The experimental results show that BERT-WWM achieves better accuracy than RoBERTa and BERT by 29.4% and 3.983% on average, respectively. We also summarized the major error patterns on dynamic auxiliary word zhe based on detailed observations on falsely generated samples.