Reviewerly/exHarmony huggingface dataset:
Strategic Classification with a Light Touch: Learning Classifiers that Incentivize Constructive Adaptation

Machine learning systems are often deployed in settings where individuals are able to adapt their features to obtain a specific predicted outcome. This kind of strategic behavior leads to a sharp loss in model performance in deployment. In this work, we aim to address this problem by learning classifiers that incentivize their decision subjects to change their features in a way that benefits all parties. We frame the dynamics of prediction and adaptation as a two-stage game and characterize equilibrium strategies for the model owner and its decision subjects. We benchmark our method on simulated and real-world datasets to demonstrate how it can be used to incentivize improvement or discourage adversarial manipulation. Our empirical results show that our method outperforms existing approaches, even when our assumptions may be misspecified.
Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Abstract Purpose Automatic keyphrase extraction (AKE) is an important task for grasping the main points of the text. In this paper, we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research. Design/methodology/approach We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT, which was released by Google in 2018. We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain, which contains 100,000 abstracts as training set, 6,000 abstracts as development set and 3,094 abstracts as test set. We use unsupervised keyphrase extraction methods including term frequency (TF), TF-IDF, TextRank and supervised machine learning methods including Conditional Random Field (CRF), Bidirectional Long Short Term Memory Network (BiLSTM), and BiLSTM-CRF as baselines. Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models. Findings Compared with character-level BiLSTM-CRF, the best baseline model with F1 score of 50.16%, our character-level sequence labeling model based on BERT obtains F1 score of 59.80%, getting 9.64% absolute improvement. Research limitations We just consider automatic keyphrase extraction task rather than keyphrase generation task, so only keyphrases that are occurred in the given text can be extracted. In addition, our proposed dataset is not suitable for dealing with nested keyphrases. Practical implications We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts (CAKE) publicly available for the benefits of research community, which is available at: https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction . Originality/value By designing comparative experiments, our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models. And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.
Nomograms for Predicting Axillary Lymph Node Status Reconciled With Preoperative Breast Ultrasound Images

The axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status.A total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models.The AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set.The ALNFM could effectively predict ALN status based on US images especially for different US tumor size.
COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform

The Coronavirus Disease 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multiview comparisons, including confirmed cases per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease spreading dynamics in China and showing how the epidemic is taken under control in China. This demo will spur further disease spreading modeling for researchers, support decision-maker, and enrich the public awareness of the spreading situations of COVID-19 worldwide. This demo offers decision-makers with accurate data-driven representations in an easy to understand format that informs them the pandemic dynamics and enables them to make more timely and cost-effective preparation and response plans.
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications

Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements.However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications.In order to comprehensively verify the robustness and generalization of MRC models, we introduce a realworld Chinese dataset -DuReader robust .It is designed to evaluate the MRC models from three aspects: over-sensitivity, over-stability and generalization.Comparing to previous work, the instances in DuReader robust are natural texts, rather than the altered unnatural texts.It presents the challenges when applying MRC models to real-world applications.The experimental results show that MRC models do not perform well on the challenge test set.Moreover, we analyze the behavior of existing models on the challenge test set, which may provide suggestions for future model development.The dataset and codes are publicly available at
Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example

The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation.
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction

Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document.Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation.However, these methods either ignore the diversity among keyphrases or only weakly capture the relation across tasks implicitly.In this paper, we propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases.In UniKeyphrase, stacked relation layer and bagof-words constraint are proposed to fully exploit the latent semantic relation between extraction and generation in the view of model structure and training process, respectively.Experiments on KP benchmarks demonstrate that our joint approach outperforms mainstream methods by a large margin. 1 * Equal contribution. 1 Our code is available on https://github.com/thinkwee/UniKeyphraseDocument: On selecting an optimal wavelet for detecting singularities in traffic and vehicular data.…… applications of wavelet transform s ( wts ) in traffic engineering have been introduced however , …… , second order difference , oblique cumulative curve , and short time fourier transform ) . it then mathematically describes wts ability to detect singularities in traffic data .…… , it is shown that selecting a suitable wavelet largely depends on the specific research topic , and that the mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data .
Tripartite Collaborative Filtering with Observability and Selection for Debiasing Rating Estimation on Missing-Not-at-Random Data

Most collaborative filtering (CF) models estimate missing ratings with an implicit assumption that the ratings are missing-at-random, which may cause the biased rating estimation and degraded performance since recent deep exploration shows that ratings may likely be missing-not-at-random (MNAR). To debias MNAR rating estimation, we introduce item observability and user selection to depict the generation of MNAR ratings and propose a tripartite CF (TCF) framework to jointly model the triple aspects of rating generation: item observability, user selection, and ratings, and to estimate the MNAR ratings. An item observability variable is introduced to a complete observability model to infer whether an item is observable to a user. TCF also conducts a complete rating model for rating generation and utilizes a user selection model dependent on the item observability and rating values to model user selection of the observable items. We further elaborately instantiate TCF as a Tripartite Probabilistic Matrix Factorization model (TPMF) by leveraging the probabilistic matrix factorization. Besides, TPMF introduces multifaceted dependency between user selection and ratings to model the influence of user selection on ratings. Extensive experiments on synthetic and real-world datasets show that modeling item observability and user selection effectively debias MNAR rating estimation, and TPMF outperforms the state-of-the-art methods in estimating the MNAR ratings.
Video monitoring of Landslide based on background subtraction with Gaussian mixture model algorithm

Landslides represent a major threat to human life, properties, and natural environments. Therefore, accurate monitoring landslide occurrence is very important tasks to reduce the damages and losses. Although a variety of monitoring methods have been developed, the problem of remote monitoring of suspected landslide areas that cannot be reached by human beings still cannot be solved. Moving object detection is an application of intelligent video surveillance. As a remote monitoring method, it has the ability to remotely identify moving objects. As a moving target detection algorithm, background subtraction can identify moving areas from video sequences in real time, and Gaussian mixture model algorithm is the most widely used. This paper explores the feasibility of using Gaussian mixture model algorithm to process monitoring video and monitor landslide in real time. Some landslide video is used for experiments and the results prove the efficiency of the proposed method.
Emerging Online Science Teaching Practices: Insights from High School Physics Teaching Cases in South Korea during COVID-19 Pandemic

Abstract By analyzing cases in South Korea, this study examined high school physics teachers’ online teaching practices during the first semester of school lockdown due to COVID -19. A total of eight physics teachers participated in this study. Data sources included observations of online classes, interviews with the teachers, and lesson materials. Data analysis was informed by social practice theory and literature on online learning. As a result, teachers’ practices were distinguished based on teaching emphases including content explanation, participatory learning, and independent first-hand experience. Through the application of various technological tools, teaching practices varied in terms of the degree of interactions and student self-directedness. Three essential practices of physics teaching, defined as those preserved from on-site teaching, were identified. Those features reflected the nature of schooling and the subject matter. The teachers were found to have professional development opportunities through online teaching experiences. Implications for professional development and further research topics are suggested.
The Design of an RS Decoder Based on the mCS-RiBM Algorithm for 100 Gb/s Optical Communication Systems

In this brief, we propose the modified compensated simplified reformulated inversionless Berlekamp-Massey (mCS-RiBM) algorithm, which is proved to successfully remove one redundant processing element and eliminate one iteration in comparison to the original CS-RiBM algorithm. Given the requirements of high speed and low complexity, we design a 16-channel two-parallel RS-FEC architecture based on the mCS-RiBM algorithm for 100 Gb/s optical communication systems. To further increase the throughput, the technique of pipelining is adopted in the three blocks of the RS decoder. The proposed architecture has been designed and synthesized with TSMC 90-nm CMOS technology library. Synthesis results illustrate that the proposed architecture can operate up to 625 MHz, and achieve a throughput rate of 162 Gb/s with a total gate count of 255 400.
Weakly Supervised Object Detection with Segmentation Collaboration

Weakly supervised object detection aims at learning precise object detectors, given image category labels. In recent prevailing works, this problem is generally formulated as a multiple instance learning module guided by an image classification loss. The object bounding box is assumed to be the one contributing most to the classification among all proposals. However, the region contributing most is also likely to be a crucial part or the supporting context of an object. To obtain a more accurate detector, in this work we propose a novel end-to-end weakly supervised detection approach, where a newly introduced generative adversarial segmentation module interacts with the conventional detection module in a collaborative loop. The collaboration mechanism takes full advantages of the complementary interpretations of the weakly supervised localization task, namely detection and segmentation tasks, forming a more comprehensive solution. Consequently, our method obtains more precise object bounding boxes, rather than parts or irrelevant surroundings. Expectedly, the proposed method achieves an accuracy of 51.0% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-arts and demonstrating its superiority for weakly supervised object detection.
Understanding the Security Threats of Esoteric Subdomain Takeover and Prevention Scheme

With the impetuous improvement of cyber intelligence and networking technology, cybersecurity becomes an important area of research. Domain name system (DNS) has been an essential aspect of cyber security and a crucial part of internet services. The nameservers are responsible for the functionality and safety of their corresponding domain names. But due to the laggings of proper security and DNS misconfiguration, there is a chance to takeover subdomain from the assigned external services e.g., cloud platform, e-commerce or content delivery service, which can lead to several high severity risks. Due to trackless and easier exploitation effort, subdomain hijacking has become an alluring attack vector among the hackers, which raise serious concern on cyber security. This paper focuses on comprehensive analysis on subdomain takeover and figures out the security vulnerability reason and attack scenarios. Element for subdomain enumeration, subdomain takeover process and finally, a proposed inclusive prevention model of subdomain takeover have been discussed throughout the paper.
Intelligent networks assisted by cognitive computing and machine learning

This Special Issue (SI) brings together academic and industrial researchers to discuss technical challenges and recent results related to intelligent networks in order to meet the demanding requirements for user experience, efficiency, and performance in a complex network environment, novel design, configuration, and optimization for communications and networking. In response to the Call for Papers, we received over 60 paper submissions. After a careful review process, 12 outstanding articles have been selected for this SI. The articles in this SI are classified into three categories: (1) Intelligent network resources management, optimization, and sharing at the edge; (2) Cognitive computing and AI assisting the Internet of Things; and (3) AI-assisted network security and applications. The articles are briefly summarized.
Ray-Space Projection Model for Light Field Camera

Light field essentially represents the collection of rays in space. The rays captured by multiple light field cameras form subsets of full rays in 3D space and can be transformed to each other. However, most previous approaches model the projection from an arbitrary point in 3D space to corresponding pixel on the sensor. There are few models on describing the ray sampling and transformation among multiple light field cameras. In the paper, we propose a novel ray-space projection model to transform sets of rays captured by multiple light field cameras in term of the Pl\ucker coordinates. We first derive a $6\times6$ ray-space intrinsic matrix based on multi-projection-center (MPC) model. A homogeneous ray-space projection matrix and a fundamental matrix are then proposed to establish ray-ray correspondences among multiple light fields. Finally, based on the ray-space projection matrix, a novel camera calibration method is proposed to verify the proposed model. A linear constraint and a ray-ray cost function are established for linear initial solution and non-linear optimization respectively. Experimental results on both synthetic and real light field data have verified the effectiveness and robustness of the proposed model.
Weighted visual secret sharing with multiple decryptions and lossless recovery

Traditional visual secret sharing (VSS) encodes the original secret image into n shares, and each share is of equal importance. However, in some scenarios, we need to make a difference between the participants according to the levels of their importance. Therefore, the capability of each share to recover the original secret image will be different. In this paper, we proposed a weighted (k,n)-threshold random grid VSS(RG-VSS) with multiple decrytions and lossless recovery. When we get k or more shares for decryption, we will recover different levels of the original image because of the different weights of the shares. More importantly, the secret information can be recovered by OR and XOR operations in our scheme. When we get all the n shares and using the XOR operation to recover the image, we can recover the secret information losslessly. The experimental results and analyses show that our scheme outperforms the related schemes.
A laser source driver with adjustable amplitude and pulse-width in 130-nm CMOS technology for quantum key distribution experiments

We present a laser source driver using a 130-nm complementary metal oxide semiconductor technology, named quantum laser source driver 2018 (QLSD2018). QLSD2018 drives the optical source with a current pulse signal, and the output of QLSD2018 has an adjustable pulse-width from 300 ps to 3.8 ns and an adjustable amplitude up to 70 mA. The data rate is up to 625 Mb/s, and the extinction ratio of the optical source (the 1550-nm distributed feedback laser or the 850-nm vertical-cavity surface-emitting laser) driven by QLSD2018 can reach 26 dB. The test results indicate that QLSD2018 can be used in quantum key distribution experiments. Using QLSD2018 on the transmitter side can significantly simplify the peripheral circuit of the optical source.
Building Hybrid Interfaces to Increase Interaction with Young Children and Children with Special Needs

Young children as well as children with special educational needs learn from their environment with social, emotional and physical stimuli. In this context, educational resources and teaching strategies play a main role for them in order to understand the new information. This paper describes the experience of building hybrid interfaces that combine technology with traditional educational resources. A total of 60 teachers divided in two groups completed some tasks which consisted of generating new educative resources with tecnology. Through Design Thinking methodology, teachers designed three hybrid interfaces: 1. Interactive books, combining traditional fairy tales books with mobile devices, where QR codes and NFC tags give life to the stories; 2. Educational Board Games, where augmented reality markers give an extra information to the players; 3. Tangible educational resources, which integrate Makey-Makey device and Scratch with fruit, clay, aluminum foil or water to build laboratory.