OpenRaiser/Intern-Atlas huggingface dataset:
OSDG -- Open-Source Approach to Classify Text Data by UN Sustainable Development Goals (SDGs)
Venue: arXiv cs.DL 2020
Year: 2020

Sustainable Development Goals (SDGs) bring together the diverse development community and provide a clear set of development targets for 2030. Given a large number of actors and initiatives related to these goals, there is a need to have a way to accurately and reliably assign text to different input: scientific research, research projects, technological output or documents to specific SDGs. In this paper we present Open Source SDG (OSDG) project and tool which does so by integrating existing research and previous classification into a robust and coherent framework. This integration is based on linking the features from the variety of previous approaches, like ontology items, keywords or features from machine-learning models, to the topics in Microsoft Academic Graph.
Fixed-size Objects Encoding for Visual Relationship Detection
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a fixed-size object encoding method (FOE-VRD) to improve performance of visual relationship detection tasks. Comparing with previous methods, FOE-VRD has an important feature, i.e., it uses one fixed-size vector to encoding all objects in each input image to assist the process of relationship detection. Firstly, we use a regular convolution neural network as a feature extractor to generate high-level features of input images. Then, for each relationship triplet in input images, i.e., $<$subject-predicate-object$>$, we apply ROI-pooling to get feature vectors of two regions on the feature maps that corresponding to bounding boxes of the subject and object. Besides the subject and object, our analysis implies that the results of predicate classification may also related to the rest objects in input images (we call them background objects). Due to the variable number of background objects in different images and computational costs, we cannot generate feature vectors for them one-by-one by using ROI pooling technique. Instead, we propose a novel method to encode all background objects in each image by using one fixed-size vector (i.e., FBE vector). By concatenating the 3 vectors we generate above, we successfully encode the objects using one fixed-size vector. The generated feature vector is then feed into a fully connected neural network to get predicate classification results. Experimental results on VRD database (entire set and zero-shot tests) show that the proposed method works well on both predicate classification and relationship detection.
Dynamic Merge Point Prediction
Venue: arXiv cs.AR 2020
Year: 2020

Despite decades of research, conditional branch mispredictions still pose a significant problem for performance. Moreover, limit studies on infinite size predictors show that many of the remaining branches are impossible to predict by current strategies. Our work focuses on mitigating performance loss in the face of impossible to predict branches. This paper presents a dynamic merge point predictor, which uses instructions fetched on the wrong path of the branch to dynamically detect the merge point. Our predictor locates the merge point with an accuracy of 95%, even when faced with branches whose direction is impossible to predict. Furthermore, we introduce a novel confidence-cost system, which identifies costly hard-to-predict branches. Our complete system replaces 58% of all branch mispredictions with a correct merge point prediction, effectively reducing MPKI by 43%. This result demonstrates the potential for dynamic merge point prediction to significantly improve performance.
Data-Driven Convergence Prediction of Astrobots Swarms
Venue: arXiv cs.RO 2020
Year: 2020

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination are so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of a coordination before its execution. However, a formal verification method does not exist for this purpose. In this paper, we instead use machine learning to predict the convergence of astrobots swarm. In particular, we propose a weighted $k$-NN-based algorithm which requires the initial status of a swarm as well as its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy.
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
Venue: arXiv cs.CL 2020
Year: 2020

Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data. This structured survey provides an overview of the evolving research area by categorising reported weaknesses in models and datasets and the methods proposed to reveal and alleviate those weaknesses for the English language. We summarise and discuss the findings and conclude with a set of recommendations for possible future research directions. We hope it will be a useful resource for researchers who propose new datasets, to have a set of tools to assess the suitability and quality of their data to evaluate various phenomena of interest, as well as those who develop novel architectures, to further understand the implications of their improvements with respect to their model's acquired capabilities.
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Venue: arXiv cs.IR 2020
Year: 2020

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.
Multi-modal Transfer Learning for Grasping Transparent and Specular Objects
Venue: arXiv cs.RO 2020
Year: 2020

State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To improve grasping performance on such objects, we introduce a method for learning a multi-modal perception model by bootstrapping from an existing uni-modal model. This transfer learning approach requires only a pre-existing uni-modal grasping model and paired multi-modal image data for training, foregoing the need for ground-truth grasp success labels nor real grasp attempts. Our experiments demonstrate that our approach is able to reliably grasp transparent and reflective objects. Video and supplementary material are available at https://sites.google.com/view/transparent-specular-grasping.
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models
Venue: arXiv cs.CL 2020
Year: 2020

Lexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of popular neural language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet, applied to the task of lexical substitution. We show that already competitive results achieved by SOTA LMs/MLMs can be further improved if information about the target word is injected properly, and compare several target injection methods. In addition, we provide analysis of the types of semantic relations between the target and substitutes generated by different models providing insights into what kind of words are really generated or given by annotators as substitutes.
Biclique Graphs of $K_3$-free Graphs and Bipartite Graphs
Venue: arXiv cs.DM 2020
Year: 2020

A biclique of a graph is a maximal complete bipartite subgraph. The biclique graph of a graph $G$, $KB(G)$, defined as the intersection graph of the bicliques of $G$, was introduced and characterized in 2010. However, this characterization does not lead to polynomial time recognition algorithms. The time complexity of its recognition problem remains open. There are some works on this problem when restricted to some classes. In this work we give a characterization of the biclique graph of a $K_3$-free graph $G$. We prove that $KB(G)$ is the square graph of a particular graph which we call Mutually Included Biclique Graph of $G$ ($KB_m(G)$). Although it does not lead to a polynomial time recognition algorithm, it gives a new tool to prove properties of biclique graphs (restricted to $K_3$-free graphs) using known properties of square graphs. For instance we generalize a property about induced ${P_3}'$s in biclique graphs to a property about stars and proved a conjecture posted by Groshaus and Montero, when restricted to $K_3$-free graphs. Also we characterize the class of biclique graphs of bipartite graphs. We prove that $KB($bipartite$) = ($IIC-comparability$)^2$, where IIC-comparability is a subclass of comparability graphs that we call Interval Intersection Closed Comparability.
Structural characterization of some problems on circle and interval graphs
Venue: arXiv cs.DM 2020
Year: 2020

A graph is circle if there is a family of chords in a circle such that two vertices are adjacent if the corresponding chords cross each other. There are diverse characterizations of circle graphs, many of them using the notions of local complementation or split decomposition. However, there are no known structural characterization by minimal forbidden induced subgraphs for circle graphs. In this thesis, we give a characterization by forbidden induced subgraphs of circle graphs within split graphs. A $(0,1)$-matrix has the consecutive-ones property (C1P) for the rows if there is a permutation of its columns such that the $1$'s in each row appear consecutively. In this thesis, we develop characterizations by forbidden subconfigurations of $(0,1)$-matrices with the C1P for which the rows are $2$-colorable under a certain adjacency relationship, and we characterize structurally some auxiliary circle graph subclasses that arise from these special matrices. Given a graph class $Π$, a $Π$-completion of a graph $G = (V,E)$ is a graph $H = (V, E \cup F)$ such that $H$ belongs to $Π$. A $Π$-completion $H$ of $G$ is minimal if $H'= (V, E \cup F')$ does not belong to $Π$ for every proper subset $F'$ of $F$. A $Π$-completion $H$ of $G$ is minimum if for every $Π$-completion $H' = (V, E \cup F')$ of $G$, the cardinal of $F$ is less than or equal to the cardinal of $F'$. In this thesis, we study the problem of completing minimally to obtain a proper interval graph when the input is an interval graph. We find necessary conditions that characterize a minimal completion in this particular case, and we leave some conjectures for the future.
Deep Fusion Siamese Network for Automatic Kinship Verification
Venue: arXiv cs.CV 2020
Year: 2020

Automatic kinship verification aims to determine whether some individuals belong to the same family. It is of great research significance to help missing persons reunite with their families. In this work, the challenging problem is progressively addressed in two respects. First, we propose a deep siamese network to quantify the relative similarity between two individuals. When given two input face images, the deep siamese network extracts the features from them and fuses these features by combining and concatenating. Then, the fused features are fed into a fully-connected network to obtain the similarity score between two faces, which is used to verify the kinship. To improve the performance, a jury system is also employed for multi-model fusion. Second, two deep siamese networks are integrated into a deep triplet network for tri-subject (i.e., father, mother and child) kinship verification, which is intended to decide whether a child is related to a pair of parents or not. Specifically, the obtained similarity scores of father-child and mother-child are weighted to generate the parent-child similarity score for kinship verification. Recognizing Families In the Wild (RFIW) is a challenging kinship recognition task with multiple tracks, which is based on Families in the Wild (FIW), a large-scale and comprehensive image database for automatic kinship recognition. The Kinship Verification (track I) and Tri-Subject Verification (track II) are supported during the ongoing RFIW2020 Challenge. Our team (ustc-nelslip) ranked 1st in track II, and 3rd in track I. The code is available at https://github.com/gniknoil/FG2020-kinship.
Joint Person Objectness and Repulsion for Person Search
Venue: arXiv cs.CV 2020
Year: 2020

Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its \emph{neighbors}, the gallery images having a higher similarity with the \emph{neighbors of probe} should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.
BrePartition: Optimized High-Dimensional kNN Search with Bregman Distances
Venue: arXiv cs.DB 2020
Year: 2020

Bregman distances (also known as Bregman divergences) are widely used in machine learning, speech recognition and signal processing, and kNN searches with Bregman distances have become increasingly important with the rapid advances of multimedia applications. Data in multimedia applications such as images and videos are commonly transformed into space of hundreds of dimensions. Such high-dimensional space has posed significant challenges for existing kNN search algorithms with Bregman distances, which could only handle data of medium dimensionality (typically less than 100). This paper addresses the urgent problem of high-dimensional kNN search with Bregman distances. We propose a novel partition-filter-refinement framework. Specifically, we propose an optimized dimensionality partitioning scheme to solve several non-trivial issues. First, an effective bound from each partitioned subspace to obtain exact kNN results is derived. Second, we conduct an in-depth analysis of the optimized number of partitions and devise an effective strategy for partitioning. Third, we design an efficient integrated index structure for all the subspaces together to accelerate the search processing. Moreover, we extend our exact solution to an approximate version by a trade-off between the accuracy and efficiency. Experimental results on four real-world datasets and two synthetic datasets show the clear advantage of our method in comparison to state-of-the-art algorithms.
Integrating global spatial features in CNN based Hyperspectral/SAR imagery classification
Venue: arXiv cs.LG 2020
Year: 2020

The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dual-branch convolutional neural network (CNN) classification method is designed in combination with the global information to mine the pixel features of the image. Then, the features of the two neural networks are fused with another fully neural network to realize the classification of remote sensing images. Finally, two remote sensing images are used to verify the effectiveness of our method, including hyperspectral imaging (HSI) and polarimetric synthetic aperture radar (PolSAR) imagery. The result of the proposed method is superior to the traditional single-channel convolutional neural network.
Measuring and Visualizing Place-Based Space-Time Job Accessibility
Venue: arXiv cs.CY 2020
Year: 2020

Place-based accessibility measures, such as the gravity-based model, are widely applied to study the spatial accessibility of workers to job opportunities in cities. However, gravity-based measures often suffer from three main limitations: (1) they are sensitive to the spatial configuration and scale of the units of analysis, which are not specifically designed for capturing job accessibility patterns and are often too coarse; (2) they omit the temporal dynamics of job opportunities and workers in the calculation, instead assuming that they remain stable over time; and (3) they do not lend themselves to dynamic geovisualization techniques. In this paper, a new methodological framework for measuring and visualizing place-based job accessibility in space and time is presented that overcomes these three limitations. First, discretization and dasymetric mapping approaches are used to disaggregate counts of jobs and workers over specific time intervals to a fine-scale grid. Second, Shen (1998) gravity-based accessibility measure is modified to account for temporal fluctuations in the spatial distributions of the supply of jobs and the demand of workers and is used to estimate hourly job accessibility at each cell. Third, a four-dimensional volumetric rendering approach is employed to integrate the hourly job access estimates into a space-time cube environment, which enables the users to interactively visualize the space-time job accessibility patterns. The integrated framework is demonstrated in the context of a case study of the Tampa Bay region of Florida. The findings demonstrate the value of the proposed methodology in job accessibility analysis and the policy-making process.
Motivating Good Practices for the Creation of Contiguous Area Cartograms
Venue: arXiv cs.HC 2020
Year: 2020

Cartograms are maps in which the areas of regions (e.g., countries or provinces) are proportional to a thematic mapping variable (e.g., population or gross domestic product). A cartogram is called contiguous if it keeps geographically adjacent regions connected. Over the past few years, several web tools have been developed for the creation of contiguous cartograms. However, most of these tools do not advise how to use cartograms correctly. To mitigate these shortcomings, we attempt to establish good practices through our recently developed web application go-cart.io: (1) use cartograms to show numeric data that add up to an interpretable total, (2) present a cartogram alongside a conventional map that uses the same color scheme, (3) indicate whether the data for a region are missing, (4) include a legend so that readers can infer the magnitude of the mapping variable, (5) if a cartogram is presented electronically, assist readers with interactive graphics.
Finite-Support Capacity-Approaching Distributions for AWGN Channels
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, the Dynamic-Assignment Blahut-Arimoto (DAB) algorithm identifies finite-support probability mass functions (PMFs) with small cardinality that achieve capacity for amplitude-constrained (AC) Additive White Gaussian Noise (AWGN) Channels, or approach capacity to within less than 1% for power-constrained (PC) AWGN Channels. While a continuous Gaussian PDF is well-known to be a theoretical capacity-achieving distribution for the PC-AWGN channel, DAB identifies PMFs with small-cardinality that are, for practical purposes, indistinguishable in performance. We extend the results of Ozarow and Wyner that require a constellation cardinality of $2^{C+1}$ to approach capacity C to within the shaping loss. PMF's found by DAB approach capacity with essentially no shaping loss with constellation cardinality of $2^{C+1.2}$. For AC-AWGN channels, DAB characterizes the evolution of minimum-cardinality finite-support capacity-achieving PMFs.
Thinging-Oriented Modeling of Unmanned Aerial Vehicles
Venue: arXiv cs.SE 2020
Year: 2020

In recent years, there has been a dramatic increase in both practical and research applications of unmanned aerial vehicles (UAVs). According to the literature, there is a need in this area to develop a more refined model of UAV system architecture, in other words, a conceptual model that defines the system s structure and behavior. The existing models mostly are fractional and do not account for the entire important dynamic attributes. Progress in this area could reduce ambiguity and increase reliability in the design of such systems. This paper aims to advance the modeling of UAV system architecture by adopting a conceptual model called a thinging (abstract) machine in which all of the UAV s software and hardware components are viewed in terms of the flow of things and five generic operations. We apply this model to a real case study of a drone. The results, an integrated conceptual representation of the drone, support the viability of this approach.