OpenRaiser/Intern-Atlas huggingface dataset:
Anaphoric Binding: an integrated overview
Venue: arXiv cs.CL 2021
Year: 2021

The interpretation of anaphors depends on their antecedents as the semantic value that an anaphor eventually conveys is co-specified by the value of its antecedent. Interestingly, when occurring in a given syntactic position, different anaphors may have different sets of admissible antecedents. Such differences are the basis for the categorization of anaphoric expressions according to their anaphoric capacity, being important to determine what are the sets of admissible antecedents and how to represent and process this anaphoric capacity for each type of anaphor. From an empirical perspective, these constraints stem from what appears as quite cogent generalisations and exhibit a universal character, given their cross linguistic validity. From a conceptual point of view, in turn, the relations among binding constraints involve non-trivial cross symmetry, which lends them a modular nature and provides further strength to the plausibility of their universal character. This kind of anaphoric binding constraints appears thus as a most significant subset of natural language knowledge, usually referred to as binding theory. This paper provides an integrated overview of these constraints holding on the pairing of nominal anaphors with their admissible antecedents that are based on grammatical relations and structure. Along with the increasing interest on neuro-symbolic approaches to natural language, this paper seeks to contribute to revive the interest on this most intriguing research topic.
Imagined-Trailing-Whitespace-Agnostic Levenshtein Distance For Plaintext Table Detection
Venue: arXiv cs.DS 2021
Year: 2021

The standard algorithm for Levenshtein distance, treats trailing whitespace the same as any other letter or symbol. However, when humans compare 2 strings, we implicitly assume that both strings are padded by infinite trailing whitespace. This informs our expectations for what the costs for insertion, deletion and replacement, should be. This violation of our expectations results in non-intuitive edit distance values. To account for this specific human intuition, a naive approach which considers "all possible" substrings of trailing whitespace would yield an $O(n^3)$ algorithm. In this work, we provide an efficient $O(n^2)$ algorithm to compute the same. Keywords: Imagined Infinite Trailing Whitespace, Human Friendly, Intuitive Edit Distance, Table Detection, Table Alignment
Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter
Venue: arXiv cs.CL 2021
Year: 2021

The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from elite politicians in the U.S., we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.
Federated Functional Gradient Boosting
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we initiate a study of functional minimization in Federated Learning. First, in the semi-heterogeneous setting, when the marginal distributions of the feature vectors on client machines are identical, we develop the federated functional gradient boosting (FFGB) method that provably converges to the global minimum. Subsequently, we extend our results to the fully-heterogeneous setting (where marginal distributions of feature vectors may differ) by designing an efficient variant of FFGB called FFGB.C, with provable convergence to a neighborhood of the global minimum within a radius that depends on the total variation distances between the client feature distributions. For the special case of square loss, but still in the fully heterogeneous setting, we design the FFGB.L method that also enjoys provable convergence to a neighborhood of the global minimum but within a radius depending on the much tighter Wasserstein-1 distances. For both FFGB.C and FFGB.L, the radii of convergence shrink to zero as the feature distributions become more homogeneous. Finally, we conduct proof-of-concept experiments to demonstrate the benefits of our approach against natural baselines.
Document Towers: A MATLAB software implementing a three-dimensional architectural paradigm for the visual exploration of digital documents and libraries
Venue: arXiv cs.DL 2021
Year: 2021

This article introduces the generic Document Towers paradigm, visualization, and software for visualizing the structure of paginated documents, based on the metaphor of documents-as-architecture. The Document Towers visualizations resemble three-dimensional building models and represent the physical boundaries of logical (e.g., titles, images), semantic (e.g., topics, named entities), graphical (e.g., typefaces, colors), and other types of information with spatial extent as a stack of rooms and floors. The software takes as input user-supplied JSON-formatted coordinates and labels of document entities, or extracts them itself from ALTO and InDesign IDML files. The Document Towers paradigm and visualization enable information systems to support information behaviors other than goal-oriented searches. Visualization encourages exploration by generating panoramic overviews and fostering serendipitous insights, while the use of metaphors assists with comprehension of the representations through the application of a familiar cognitive model. Document Towers visualizations also provide access to types of information other than textual content, specifically by means of their physical structure, which corresponds to the material, logical, semantic, and contextual aspects of documents. Visualization renders documents transparent, making the invisible visible and facilitating analysis at a glance and without the need for physical manipulation. Keyword searches and other language-based interactions with documents must be clearly expressed and will return only answers to questions asked; by contrast, visual observation is well suited to fuzzy goals and uncovering unexpected aspects of the data.
Robust Lower Bounds for Graph Problems in the Blackboard Model of Communication
Venue: arXiv cs.DS 2021
Year: 2021

We give lower bounds on the communication complexity of graph problems in the multi-party blackboard model. In this model, the edges of an $n$-vertex input graph are partitioned among $k$ parties, who communicate solely by writing messages on a shared blackboard that is visible to every party. We show that any non-trivial graph problem on $n$-vertex graphs has blackboard communication complexity $Ω(n)$ bits, even if the edges of the input graph are randomly assigned to the $k$ parties. We say that a graph problem is non-trivial if the output cannot be computed in a model where every party holds at most one edge and no communication is allowed. Our lower bound thus holds for essentially all key graph problems relevant to distributed computing, including Maximal Independent Set (MIS), Maximal Matching, ($Δ+1$)-coloring, and Dominating Set. In many cases, e.g., MIS, Maximal Matching, and $(Δ+1)$-coloring, our lower bounds are optimal, up to poly-logarithmic factors.
Improving Authorship Verification using Linguistic Divergence
Venue: arXiv cs.CL 2021
Year: 2021

We propose an unsupervised solution to the Authorship Verification task that utilizes pre-trained deep language models to compute a new metric called DV-Distance. The proposed metric is a measure of the difference between the two authors comparing against pre-trained language models. Our design addresses the problem of non-comparability in authorship verification, frequently encountered in small or cross-domain corpora. To the best of our knowledge, this paper is the first one to introduce a method designed with non-comparability in mind from the ground up, rather than indirectly. It is also one of the first to use Deep Language Models in this setting. The approach is intuitive, and it is easy to understand and interpret through visualization. Experiments on four datasets show our methods matching or surpassing current state-of-the-art and strong baselines in most tasks.
Wireframe-Based UI Design Search Through Image Autoencoder
Venue: arXiv cs.SE 2021
Year: 2021

UI design is an integral part of software development. For many developers who do not have much UI design experience, exposing them to a large database of real-application UI designs can help them quickly build up a realistic understanding of the design space for a software feature and get design inspirations from existing applications. However, existing keyword-based, image-similarity-based, and component-matching-based methods cannot reliably find relevant high-fidelity UI designs in a large database alike to the UI wireframe that the developers sketch, in face of the great variations in UI designs. In this article, we propose a deep-learning-based UI design search engine to fill in the gap. The key innovation of our search engine is to train a wireframe image autoencoder using a large database of real-application UI designs, without the need for labeling relevant UI designs. We implement our approach for Android UI design search, and conduct extensive experiments with artificially created relevant UI designs and human evaluation of UI design search results. Our experiments confirm the superior performance of our search engine over existing image-similarity or component-matching-based methods and demonstrate the usefulness of our search engine in real-world UI design tasks.
Enabling Smart Reflection in Integrated Air-Ground Wireless Network: IRS Meets UAV
Venue: arXiv cs.IT 2021
Year: 2021

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as two promising technologies to boost the performance of wireless communication networks, by proactively altering the wireless communication channels via smart signal reflection and maneuver control, respectively. However, they face different limitations in practice, which restrain their future applications. In this article, we propose new methods to jointly apply IRS and UAV in integrated air-ground wireless networks by exploiting their complementary advantages. Specifically, terrestrial IRS is used to enhance the UAV-ground communication performance, while UAV-mounted IRS is employed to assist in the terrestrial communication. We present their promising application scenarios, new communication design issues as well as potential solutions. In particular, we show that it is practically beneficial to deploy both the terrestrial and aerial IRSs in future wireless networks to reap the benefits of smart reflections in three-dimensional (3D) space.
A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts
Venue: arXiv cs.SE 2021
Year: 2021

Code comment has been an important part of computer programs, greatly facilitating the understanding and maintenance of source code. However, high-quality code comments are often unavailable in smart contracts, the increasingly popular programs that run on the blockchain. In this paper, we propose a Multi-Modal Transformer-based (MMTrans) code summarization approach for smart contracts. Specifically, the MMTrans learns the representation of source code from the two heterogeneous modalities of the Abstract Syntax Tree (AST), i.e., Structure-based Traversal (SBT) sequences and graphs. The SBT sequence provides the global semantic information of AST, while the graph convolution focuses on the local details. The MMTrans uses two encoders to extract both global and local semantic information from the two modalities respectively, and then uses a joint decoder to generate code comments. Both the encoders and the decoder employ the multi-head attention structure of the Transformer to enhance the ability to capture the long-range dependencies between code tokens. We build a dataset with over 300K <method, comment> pairs of smart contracts, and evaluate the MMTrans on it. The experimental results demonstrate that the MMTrans outperforms the state-of-the-art baselines in terms of four evaluation metrics by a substantial margin, and can generate higher quality comments.
Phrase-Verified Voting: Verifiable Low-Tech Remote Boardroom Voting
Venue: arXiv cs.CR 2021
Year: 2021

We present Phrase-Verified Voting, a voter-verifiable remote voting system assembled from commercial off-the-shelf software for small private elections. The system is transparent and enables each voter to verify that the tally includes their ballot selection without requiring any understanding of cryptography. This paper describes the system and its use in fall 2020, to vote remotely in promotion committees in a university. Each voter fills out a form in the cloud with their vote V (YES, NO, ABSTAIN) and a passphrase P-two words entered by the voter. The system generates a verification prompt of the (P,V) pairs and a tally of the votes, organized to help visualize how the votes add up. After the polls close, each voter verifies that this table lists their (P,V) pair and that the tally is computed correctly. The system is especially appropriate for any small group making sensitive decisions. Because the system would not prevent a coercer from demanding that their victim use a specified passphrase, it is not designed for applications where such malfeasance would be likely or go undetected. Results from 43 voters show that the system was well-accepted, performed effectively for its intended purpose, and introduced users to the concept of voter-verified elections. Compared to the commonly-used alternatives of paper ballots or voting by email, voters found the system easier to use, and that it provided greater privacy and outcome integrity.
Urban Surface Reconstruction in SAR Tomography by Graph-Cuts
Venue: arXiv cs.CV 2021
Year: 2021

SAR (Synthetic Aperture Radar) tomography reconstructs 3-D volumes from stacks of SAR images. High-resolution satellites such as TerraSAR-X provide images that can be combined to produce 3-D models. In urban areas, sparsity priors are generally enforced during the tomographic inversion process in order to retrieve the location of scatterers seen within a given radar resolution cell. However, such priors often miss parts of the urban surfaces. Those missing parts are typically regions of flat areas such as ground or rooftops. This paper introduces a surface segmentation algorithm based on the computation of the optimal cut in a flow network. This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces. Illustrations on a TerraSAR-X tomographic dataset demonstrate the potential of the approach to produce a 3-D model of urban surfaces such as ground, façades and rooftops.
Determining the Impacts of Social Media on Mood, Time Management and Academic Activities of Students and the Relationship with their Academic Performance
Venue: arXiv cs.CY 2021
Year: 2021

The number of social media sites have increased exponentially with new ones cashing in on the weaknesses of older ones and others going beyond community guidelines by offering uncensored content. The vendors of these platforms in order to have a wider reach do not place restrictions on viewing age, promises young people with fame, and other such attractive offers that make the youths addicted to the site. The possibility of hacking into accounts of users and using same for fraud is another rave among Nigerian youths with desire for quick riches. The crash in prices of data, smart phones, and related digital devices have increased availability and access thereby closing digital divide and widening its adverse effects on the youths morals and academic pursuits. It is important that the Nigerian government understand factors that contribute to the dwindling performance level of students in government owned institutions to put in place policies and infrastructure that would help combat the challenges. This study investigated the effects of social media on students academic activities, mood and time management abilities. The result indicated that association between social media and academic activities is statistically significant. However, a negative association exists between them which implies that the high the level of social media activity, the lower academic activities participation. Similar association was observed on the effects of social media on students time management ability.
Medical data wrangling with sequential variational autoencoders
Venue: arXiv cs.LG 2021
Year: 2021

Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some time or data collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE, which extends the capabilities of VAEs to sequential streams of data with missing observations. We compare our model against state-of-the-art solutions in an intensive care unit database (ICU) and a dataset of passive human monitoring. Furthermore, we find that standard error metrics such as RMSE are not conclusive enough to assess temporal models and include in our analysis the cross-correlation between the ground truth and the imputed signal. We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model, which is the state-of-the-art method for medical records.
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications. In this paper, we study verification problems for Binarized Neural Networks (BNNs), the 1-bit quantization of general real-numbered neural networks. Our approach is to encode BNNs into Binary Decision Diagrams (BDDs), which is done by exploiting the internal structure of the BNNs. In particular, we translate the input-output relation of blocks in BNNs to cardinality constraints which are then encoded by BDDs. Based on the encoding, we develop a quantitative verification framework for BNNs where precise and comprehensive analysis of BNNs can be performed. We demonstrate the application of our framework by providing quantitative robustness analysis and interpretability for BNNs. We implement a prototype tool BDD4BNN and carry out extensive experiments which confirm the effectiveness and efficiency of our approach.
Experiences and insights from using Github Classroom to support Project-Based Courses
Venue: arXiv cs.SE 2021
Year: 2021

This work presents an approach for using GitHub classroom as a shared, structured, and persistent repository to support project-based courses at the Software Engineering Undergraduate program at PUC Minas, in Brazil. We discuss the needs of the different stakeholders that guided the development of the approach. Results on the perceptions of professors and students show that the approach brings benefits. Besides the lessons learned, we present insights on improving the education of the next generation of software engineers by employing metrics to monitor skill development, verifying student work portfolios, and employing tooling support in project-based courses.
Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case
Venue: arXiv cs.LG 2021
Year: 2021

6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account to apply the algorithms. While machine learning offers significant advantages for 6G, AI models' security is ignored. Since it has many applications in the real world, security is a vital part of the algorithms. This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction use case. We have also presented the adversarial learning mitigation method's performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model and undefended model are very close.