OpenRaiser/Intern-Atlas huggingface dataset:
Tracking Public Opinion in China through Various Stages of the COVID-19 Pandemic
Venue: arXiv cs.SI 2020
Year: 2020

In recent months, COVID-19 has become a global pandemic and had a huge impact on the world. People under different conditions have very different attitudes toward the epidemic. Due to the real-time and large-scale nature of social media, we can continuously obtain a massive amount of public opinion information related to the epidemic from social media. In particular, researchers may ask questions such as "how is the public reacting to COVID-19 in China during different stages of the pandemic?", "what factors affect the public opinion orientation in China?", and so on. To answer such questions, we analyze the pandemic related public opinion information on Weibo, China's largest social media platform. Specifically, we have first collected a large amount of COVID-19-related public opinion microblogs. We then use a sentiment classifier to recognize and analyze different groups of users' opinions. In the collected sentiment orientated microblogs, we try to track the public opinion through different stages of the COVID-19 pandemic. Furthermore, we analyze more key factors that might have an impact on the public opinion of COVID-19 (e.g., users in different provinces or users with different education levels). Empirical results show that the public opinions vary along with the key factors of COVID-19. Furthermore, we analyze the public attitudes on different public-concerning topics, such as staying at home and quarantine.
Retrieval of Family Members Using Siamese Neural Network
Venue: arXiv cs.CV 2020
Year: 2020

Retrieval of family members in the wild aims at finding family members of the given subject in the dataset, which is useful in finding the lost children and analyzing the kinship. However, due to the diversity in age, gender, pose and illumination of the collected data, this task is always challenging. To solve this problem, we propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity computation and ranking. In training procedure, the Siamese network firstly takes two candidate images as input and produces two feature vectors. And then, the similarity between the two vectors is computed with several fully connected layers. While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors. After similarity computation, we use the ranking algorithm to merge the similarity scores with the same identity and output the ordered list according to their similarities. To gain further improvement, we try different combinations of backbones, training methods and similarity computing methods. Finally, we submit the best combination as our solution and our team(ustc-nelslip) obtains favorable result in the track3 of the RFIW2020 challenge with the first runner-up, which verifies the effectiveness of our method. Our code is available at: https://github.com/gniknoil/FG2020-kinship
An Efficient Planar Bundle Adjustment Algorithm
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents an efficient algorithm for the least-squares problem using the point-to-plane cost, which aims to jointly optimize depth sensor poses and plane parameters for 3D reconstruction. We call this least-squares problem \textbf{Planar Bundle Adjustment} (PBA), due to the similarity between this problem and the original Bundle Adjustment (BA) in visual reconstruction. As planes ubiquitously exist in the man-made environment, they are generally used as landmarks in SLAM algorithms for various depth sensors. PBA is important to reduce drift and improve the quality of the map. However, directly adopting the well-established BA framework in visual reconstruction will result in a very inefficient solution for PBA. This is because a 3D point only has one observation at a camera pose. In contrast, a depth sensor can record hundreds of points in a plane at a time, which results in a very large nonlinear least-squares problem even for a small-scale space. Fortunately, we find that there exist a special structure of the PBA problem. We introduce a reduced Jacobian matrix and a reduced residual vector, and prove that they can replace the original Jacobian matrix and residual vector in the generally adopted Levenberg-Marquardt (LM) algorithm. This significantly reduces the computational cost. Besides, when planes are combined with other features for 3D reconstruction, the reduced Jacobian matrix and residual vector can also replace the corresponding parts derived from planes. Our experimental results verify that our algorithm can significantly reduce the computational time compared to the solution using the traditional BA framework. Besides, our algorithm is faster, more accuracy, and more robust to initialization errors compared to the start-of-the-art solution using the plane-to-plane cost
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
Venue: arXiv cs.CL 2020
Year: 2020

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Venue: arXiv cs.CL 2020
Year: 2020

There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff's alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.
Web page classification with Google Image Search results
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we introduce a novel method that combines multiple neural network results to decide the class of the input. This is the first study which used the method for web pages classification. In our model, each element is represented by multiple descriptive images. After the training process of the neural network model, each element is classified by calculating its descriptive image results. We apply our idea to the web page classification problem using Google Image Search results as descriptive images. We obtained a classification rate of 94.90% on the WebScreenshots dataset that contains 20000 web sites in 4 classes. The method is easily applicable to similar problems.
Private Index Coding
Venue: arXiv cs.IT 2020
Year: 2020

We study the fundamental problem of index coding under an additional privacy constraint that requires each receiver to learn nothing more about the collection of messages beyond its demanded messages from the server and what is available to it as side information. To enable such private communication, we allow the use of a collection of independent secret keys, each of which is shared amongst a subset of users and is known to the server. The goal is to study properties of the key access structures which make the problem feasible and then design encoding and decoding schemes efficient in the size of the server transmission as well as the sizes of the secret keys. We call this the private index coding problem. We begin by characterizing the key access structures that make private index coding feasible. We also give conditions to check if a given linear scheme is a valid private index code. For up to three users, we characterize the rate region of feasible server transmission and key rates, and show that all feasible rates can be achieved using scalar linear coding and time sharing; we also show that scalar linear codes are sub-optimal for four receivers. The outer bounds used in the case of three users are extended to arbitrary number of users and seen as a generalized version of the well-known polymatroidal bounds for the standard non-private index coding. We also show that the presence of common randomness and private randomness does not change the rate region. Furthermore, we study the case where no keys are shared among the users and provide some necessary and sufficient conditions for feasibility in this setting under a weaker notion of privacy. If the server has the ability to multicast to any subset of users, we demonstrate how this flexibility can be used to provide privacy and characterize the minimum number of server multicasts required.
Impact of Coastal Hazards on Residents Spatial Accessibility to Health Services
Venue: arXiv cs.CY 2020
Year: 2020

The mobility of residents and their access to essential services can be highly affected by transportation network closures that occur during and after coastal hazard events. Few studies have used geographic information systems coupled with infrastructure vulnerability models to explore how spatial accessibility to goods and services shifts after a hurricane. Models that explore spatial accessibility to health services are particularly lacking. This study provides a framework to examine how the disruption of transportation networks during and after a hurricane can impact a residents ability to access health services over time. Two different bridge closure conditions, inundation and structural failure, along with roadway inundation are used to quantify post-hurricane accessibility at short- and long-term temporal scales. Inundation may close a bridge for hours or days, but a structural failure may close a route for weeks or months. Both forms of closure are incorporated using probabilistic vulnerability models coupled with GIS-based models to assess spatial accessibility in the aftermath of a coastal hazard. Harris County, an area in Southeastern Texas prone to coastal hazards, is used as a case study. The results indicate changes in the accessibility scores of specific areas depending on the temporal scale of interest and intensity of the hazard scenario. Sociodemographic indicators are also examined for the study region, revealing the populations most likely to suffer from lack of accessibility. Overall, the presented framework helps to understand how both short-term functionality loss and long-term damage affect access to critical services such as health care after a hazard. This information, in turn, can shape decisions about future mitigation and planning efforts, while the presented framework can be expanded to other hazard-prone areas.
Introducing Network Coding to RPL: The Chained Secure Mode (CSM)
Venue: arXiv cs.NI 2020
Year: 2020

The current standard of Routing Protocol for Low Power and Lossy Networks (RPL) incorporates three modes of security: the Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and the Authenticated Secure Mode (ASM). While the PSM and ASM are intended to protect against external routing attacks and some replay attacks (through an optional replay protection mechanism), recent research showed that RPL in PSM is still vulnerable to many routing attacks, both internal and external. In this paper, we propose a novel secure mode for RPL, the Chained Secure Mode (CSM), based on the concept of intraflow Network Coding. The main goal of CSM is to enhance RPL resilience against replay attacks, with the ability to mitigate some of them. The security and performance of a proof-of-concept prototype of CSM were evaluated and compared against RPL in UM and PSM (with and without the optional replay protection) in the presence of Neighbor attack as an example. It showed that CSM has better performance and more enhanced security compared to both the UM and PSM with the replay protection. On the other hand, it showed a need for a proper recovery mechanism for the case of losing a control message.
Improving Quality of a Post's Set of Answers in Stack Overflow
Venue: arXiv cs.SE 2020
Year: 2020

Community Question Answering platforms such as Stack Overflow help a wide range of users solve their challenges online. As the popularity of these communities has grown over the years, both the number of members and posts have escalated. Also, due to the diverse backgrounds, skills, expertise, and viewpoints of users, each question may obtain more than one answers. Therefore, the focus has changed toward producing posts that have a set of answers more valuable for the community as a whole, not just one accepted-answer aimed at satisfying only the question-asker. Same as every universal community, a large number of low-quality posts on Stack Overflow require improvement. We call these posts deficient and define them as posts with questions that either have no answer yet or can be improved by other ones. In this paper, we propose an approach to automate the identification process of such posts and boost their set of answers, utilizing the help of related experts. With the help of 60 participants, we trained a classification model to identify deficient posts by investigating the relationship between characteristics of 3075 questions posted on Stack Overflow and their need for better answers set. Then, we developed an Eclipse plugin named SOPI and integrated the prediction model in the plugin to link these deficient posts to related developers and help them improve the answer set. We evaluated both the functionality of our plugin and the impact of answers submitted to Stack Overflow with the help of 10 and 15 expert industrial developers, respectively. Our results indicate that decision trees, specifically J48, predicts a deficient question better than the other methods with 0.945 precision and 0.903 recall. We conclude that not only our plugin helps programmers contribute more easily to Stack Overflow, but also it improves the quality of answers.
Analyzing Student Strategies In Blended Courses Using Clickstream Data
Venue: arXiv cs.CY 2020
Year: 2020

Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Fine-grained clickstream data is collected through Diderot, a non-commercial educational support system that spans a wide range of functionalities. We find that interaction patterns differ considerably based on the assessment type students are preparing for, and many of the extracted features can be used for reliable performance prediction. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses given enough data granularity.
Pseudo-Representation Labeling Semi-Supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, semi-supervised learning (SSL) has shown tremendous success in leveraging unlabeled data to improve the performance of deep learning models, which significantly reduces the demand for large amounts of labeled data. Many SSL techniques have been proposed and have shown promising performance on famous datasets such as ImageNet and CIFAR-10. However, some exiting techniques (especially data augmentation based) are not suitable for industrial applications empirically. Therefore, this work proposes the pseudo-representation labeling, a simple and flexible framework that utilizes pseudo-labeling techniques to iteratively label a small amount of unlabeled data and use them as training data. In addition, our framework is integrated with self-supervised representation learning such that the classifier gains benefits from representation learning of both labeled and unlabeled data. This framework can be implemented without being limited at the specific model structure, but a general technique to improve the existing model. Compared with the existing approaches, the pseudo-representation labeling is more intuitive and can effectively solve practical problems in the real world. Empirically, it outperforms the current state-of-the-art semi-supervised learning methods in industrial types of classification problems such as the WM-811K wafer map and the MIT-BIH Arrhythmia dataset.
SANA : Sentiment Analysis on Newspapers comments in Algeria
Venue: arXiv cs.CL 2020
Year: 2020

It is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who is the purpose is finding the behind phenomenon masked in opinionated texts. We are interested in our work by comments in Algerian newspaper websites. For this end, two corpora were used SANA and OCA. SANA corpus is created by collection of comments from three Algerian newspapers, and annotated by two Algerian Arabic native speakers, while OCA is a freely available corpus for sentiment analysis. For the classification we adopt Supports vector machines, naive Bayes and knearest neighbors. Obtained results are very promising and show the different effects of stemming in such domain, also knearest neighbors give important improvement comparing to other classifiers unlike similar works where SVM is the most dominant. From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works.
End-to-End Change Detection for High Resolution Drone Images with GAN Architecture
Venue: arXiv cs.CV 2020
Year: 2020

Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection. We demonstrate this concept on solar panel installation. A deep learning, data-driven algorithm for identifying changes based on a change detection deep learning algorithm was proposed. We use the Conditional Adversarial Network approach to present a framework for change detection in images. The proposed network architecture is based on pix2pix GAN framework. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art change detection methods.
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
Venue: arXiv cs.NI 2020
Year: 2020

In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous. This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning, in which model training is executed at the edge of the network. In this article, we first introduce the principles and technologies of collaborative edge learning. Then, we establish that a successful, scalable implementation of edge learning requires the communication, caching, computation, and learning resources (3C-L) of end devices and edge servers to be leveraged jointly in an efficient manner. However, users may not consent to contribute their resources without receiving adequate compensation. In consideration of the heterogeneity of edge nodes, e.g., in terms of available computation resources, we discuss the challenges of incentive mechanism design to facilitate resource sharing for edge learning. Furthermore, we present a case study involving optimal auction design using Deep Learning to price fresh data contributed for edge learning. The performance evaluation shows the revenue maximizing properties of our proposed auction over the benchmark schemes.
Staffing for many-server systems facing non-standard arrival processes
Venue: arXiv cs.PF 2020
Year: 2020

Arrival processes to service systems often display (i) larger than anticipated fluctuations, (ii) a time-varying rate, and (iii) temporal correlation. Motivated by this, we introduce a specific non-homogeneous Poisson process that incorporates these three features. The resulting arrival process is fed into an infinite-server system, which is then used as a proxy for its many-server counterpart. This leads to a staffing rule based on the square-root staffing principle that acknowledges the three features. After a slight rearrangement of servers over the time slots, we succeed to stabilize system performance even under highly varying and strongly correlated conditions. We fit the arrival stream model to real data from an emergency department and demonstrate (by simulation) the performance of the novel staffing rule.
Transferring Inductive Biases through Knowledge Distillation
Venue: arXiv cs.LG 2020
Year: 2020

Having the right inductive biases can be crucial in many tasks or scenarios where data or computing resources are a limiting factor, or where training data is not perfectly representative of the conditions at test time. However, defining, designing and efficiently adapting inductive biases is not necessarily straightforward. In this paper, we explore the power of knowledge distillation for transferring the effect of inductive biases from one model to another. We consider families of models with different inductive biases, LSTMs vs. Transformers and CNNs vs. MLPs, in the context of tasks and scenarios where having the right inductive biases is critical. We study the effect of inductive biases on the solutions the models converge to and investigate how and to what extent the effect of inductive biases is transferred through knowledge distillation, in terms of not only performance but also different aspects of converged solutions.
Decoding Reed-Solomon Skew-Differential Codes
Venue: arXiv cs.IT 2020
Year: 2020

A large class of MDS linear codes is constructed. These codes are endowed with an efficient decoding algorithm. Both the definition of the codes and the design of their decoding algorithm only require from Linear Algebra methods, making them fully accesible for everyone. Thus, the first part of the paper develops a direct presentation of the codes by means of parity-check matrices, and the decoding algorithm rests upon matrix and linear maps manipulations. The somewhat more sophisticated mathematical context (non-commutative rings) needed for the proof of the correctness of the decoding algorithm is postponed to the second part. A final section locates the Reed-Solomon skew-differential codes introduced here within the general context of codes defined by means of skew polynomial rings.