bluuebunny/crossref_metadata_2025 huggingface dataset:
Semantic Representation Using Explicit Concept Space Models
Authors: Shalaby Walid , Zadrozny Wlodek
Published: June 2022

Explicit concept space models have proven efficacy for text representation in many natural language and text mining applications. The idea is to embed textual structures into a semantic space of concepts which captures the main topics of these structures. Despite their wide applicability, existing models have many shortcomings such as sparsity and being restricted to Wikipedia as the main knowledge source from which concepts are extracted. In this paper we highlight some of these limitations. We also describe Mined Semantic Analysis (MSA); a novel concept space model which employs unsupervised learning in order to uncover implicit relations between concepts. MSA leverages the discovered concept-concept associations to enrich the semantic representations. We evaluate MSA’s performance on benchmark data sets for measuring lexical semantic relatedness. Empirical results show superior performance of MSA compared to prior state-of-the-art methods.
Kernelized Evolutionary Distance Metric Learning for Semi-Supervised Clustering
Authors: Kalintha Wasin , Ono Satoshi , Numao Masayuki , Fukui Ken-ichi
Published: June 2022

Many research studies on distance metric learning (DML) reiterate that the definition of distance between two data points substantially affects clustering tasks. Recently, variety of DML methods have been proposed to improve the accuracy of clustering by learning a distance metric; however, most of them only perform a linear transformation, which yields insignificant to non-linear separable data. This study proposes a DML method which provides an integration of kernelization technique with Mahalanobis-based DML. Thus, non-linear transformation of the distance metric can be performed. Moreover, a cluster validity index is optimized by an evolutionary algorithm. The empirical results on semi-supervised clustering suggest the promising result on both synthetic and real-world data set.
Learning Options in Multiobjective Reinforcement Learning
Authors: Bonini Rodrigo , Silva Felipe , Costa Anna
Published: June 2022

Reinforcement Learning (RL) is a successful technique to train autonomous agents. However, the classical RL methods take a long time to learn how to solve tasks. Option-based solutions can be used to accelerate learning and transfer learned behaviors across tasks by encapsulating a partial policy into an action. However, the literature report only single-agent and single-objective option-based methods, but many RL tasks, especially real-world problems, are better described through multiple objectives. We here propose a method to learn options in Multiobjective Reinforcement Learning domains in order to accelerate learning and reuse knowledge across tasks. Our initial experiments in the Goldmine Domain show that our proposal learn useful options that accelerate learning in multiobjective domains. Our next steps are to use the learned options to transfer knowledge across tasks and evaluate this method with stochastic policies.
Frame-Based Ontology Alignment
Authors: Asprino Luigi , Presutti Valentina , Gangemi Aldo , Ciancarini Paolo
Published: June 2022

The need of handling semantic heterogeneity of resources is a key problem of the Semantic Web. State of the art techniques for ontology matching are the key technology for addressing this issue. However, they only partially exploit the natural lan- guage descriptions of ontology entities and they are mostly unable to find correspondences between entities having dif- ferent logical types (e.g. mapping properties to classes). We introduce a novel approach aimed at finding correspondences between ontology entities according to the intensional mean- ing of their models, hence abstracting from their logical types. Lexical linked open data and frame semantics play a crucial role in this proposal. We argue that this approach may lead to a step ahead in the state of the art of ontology matching, and positively affect related applications such as question an- swering and knowledge reconciliation.
PAG2ADMG: A Novel Methodology to Enumerate Causal Graph Structures
Authors: Subramani Nishant , Downey Doug
Published: June 2022

Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent causal relationships among variables in a model. Methods exist for learning DAGs and PAGs from data and for converting DAGs to PAGs. However, these methods only output a single causal graph consistent with the independencies/dependencies (the Markov equivalence class M) estimated from the data. However, many distinct graphs may be consistent with M, and a data modeler may wish to select among these using domain knowledge. In this paper, we present a method that makes this possible. We introduce PAG2ADMG, the first method for enumerating all causal graphs consistent with M, under certain assumptions. PAG2ADMG converts a given PAG into a set of acyclic directed mixed graphs (ADMGs). We prove the correctness of the approach and demonstrate its efficiency relative to brute-force enumeration.
Grounded Action Transformation for Robot Learning in Simulation
Authors: Hanna Josiah , Stone Peter
Published: June 2022

Robot learning in simulation is a promising alternative to the prohibitive sample cost of learning in the physical world. Unfortunately, policies learned in simulation often perform worse than hand-coded policies when applied on the physical robot. This paper proposes a new algorithm for learning in simulation — Grounded Action Transformation — and applies it to learning of humanoid bipedal locomotion. Our approach results in a 43.27% improvement in forward walk velocity compared to a state-of-the art hand-coded walk.
A BTP-Based Family of Variable Elimination Rules for Binary CSPs
Authors: El Mouelhi Achref
Published: June 2022

The study of broken-triangles is becoming increasingly ambitious, by both solving constraint satisfaction problems (CSPs) in polynomial time and reducing search space size through value merging or variable elimination. Considerable progress has been made in extending this important concept, such as dual broken-triangle and weakly broken-triangle, in order to maximize the number of captured tractable CSP instances and/or the number of merged values. Specifically, m-wBTP allows to merge more values than BTP. k-BTP, WBTP and m-BTP permit to capture more tractable instances than BTP. Here, we introduce a new weaker form of BTP, which will be called m-fBTP for flexible broken-triangle property. m-fBTP allows on the one hand to eliminate more variables than BTP while preserving satisfiability and on the other to define new bigger tractable class for which arc consistency is a decision procedure. Likewise, m-fBTP permits to merge more values than BTP but less than m-wBTP.
Extending Compact-Table to Negative and Short Tables
Authors: Verhaeghe Hélène , Lecoutre Christophe , Schaus Pierre
Published: June 2022

Table constraints are very useful for modeling combinatorial constrained problems, and thus play an important role in Constraint Programming (CP). During the last decade, many algorithms have been proposed for enforcing the property known as Generalized Arc Consistency (GAC) on such constraints. A state-of-the art GAC algorithm called Compact-Table (CT), which has been recently proposed, significantly outperforms all previously proposed algorithms. In this paper, we extend this algorithm in order to deal with both short supports and negative tables, i.e., tables that contain universal values and conflicts. Our experimental results show the interest of using this fast general algorithm.
Rigging Nearly Acyclic Tournaments Is Fixed-Parameter Tractable
Authors: Ramanujan M. , Szeider Stefan
Published: June 2022

Single-elimination tournaments (or knockout tournaments) are a popular format in sports competitions that is also widely used for decision making and elections. In this paper we study the algorithmic problem of manipulating the outcome of a tournament. More specifically, we study the problem of finding a seeding of the players such that a certain player wins the resulting tournament. The problem is known to be NP-hard in general. In this paper we present an algorithm for this problem that exploits structural restrictions on the tournament. More specifically, we establish that the problem is fixed-parameter tractable when parameterized by the size of a smallest feedback arc set of the tournament (interpreting the tournament as an oriented complete graph). This is a natural parameter because most problems on tournaments (including this one) are either trivial or easily solvable on acyclic tournaments, leading to the question — what about nearly acyclic tournaments or tournaments with a small feedback arc set? Our result significantly improves upon a recent algorithm by Aziz et al. (2014) whose running time is bounded by an exponential function where the size of a smallest feedback arc set appears in the exponent and the base is the number of players.
Why Teaching Ethics to AI Practitioners Is Important
Authors: Goldsmith Judy , Burton Emanuelle
Published: June 2022

We argue that it is crucial to the future of AI that our students be trained in multiple complementary modes of ethical reasoning, so that they may make ethical design and implementation choices, ethical career decisions, and that their software will be programmed to take into account the complexities of acting ethically in the world.
Explaining Ourselves: Human-Aware Constraint Reasoning
Authors: Freuder Eugene
Published: June 2022

Human-aware AI is increasingly important as AI becomes more powerful and ubiquitous. A good foundation for human-awareness should enable ourselves and our "AIs" to "explain ourselves" naturally to each other. Constraint reasoning offers particular opportunities and challenges in this regard. This paper takes note of the history of work in this area and encourages increased attention, laying out a rough research agenda.
A Selected Summary of AI for Computational Sustainability
Authors: Fisher Douglas
Published: June 2022

This paper and summary talk broadly survey computational sustainability research. Rather than a detailed treatment of the research projects in the area, which is beyond the scope of the paper and talk, the paper includes a meta-survey, pointing to edited collections and overviews in the literature for the interested reader. Computational sustainability research has been broadly characterized by AI methods employed, sustainability areas addressed, and contributions made to (typically, human) decision-making. The paper addresses these characterizations as well, which will facilitate a deeper synthesis later, to include the potential for developing sophisticated and holistic AI decision-making and advisory agents.
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Authors: Speer Robyn , Chin Joshua , Havasi Catherine
Published: June 2022

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.
Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data
Authors: Ben Taieb Souhaib , Yu Jiafan , Barreto Mateus , Rajagopal Ram
Published: June 2022

Accurate electricity demand forecast plays a key role in sustainable power systems. It enables better decision making in the planning of electricity generation and distribution for many use cases. The electricity demand data can often be represented in a hierarchical structure. For example, the electricity consumption of a whole country could be disaggregated by states, cities, and households. Hierarchical forecasts require not only good prediction accuracy at each level of the hierarchy, but also the consistency between different levels. State-of-the-art hierarchical forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. However, the high-dimensionality of the unpenalized regression problem and the estimation errors in the high-dimensional error covariance matrix can lead to increased variability in the revised forecasts with poor prediction performance. In order to provide more robustness to estimation errors in the adjustments, we present a new hierarchical forecasting algorithm that computes sparse adjustments while still preserving the aggregation constraints. We formulate the problem as a high-dimensional penalized regression, which can be efficiently solved using cyclical coordinate descent methods. We also conduct experiments using a large-scale hierarchical electricity demand data. The results confirm the effectiveness of our approach compared to state-of-the-art hierarchical forecasting methods, in both the sparsity of the adjustments and the prediction accuracy. The proposed approach to hierarchical forecasting could be useful for energy generation including solar and wind energy, as well as numerous other applications.
Three New Algorithms to Solve N-POMDPs
Authors: Dujardin Yann , Dietterich Tom , Chadès Iadine
Published: June 2022

In many fields in computational sustainability, applications of POMDPs are inhibited by the complexity of the optimal solution. One way of delivering simple solutions is to represent the policy with a small number of alpha-vectors. We would like to find the best possible policy that can be expressed using a fixed number N of alpha-vectors. We call this the N-POMDP problem. The existing solver alpha-min approximately solves finite-horizon POMDPs with a controllable number of alpha-vectors. However alpha-min is a greedy algorithm without performance guarantees, and it is rather slow. This paper proposes three new algorithms, based on a general approach that we call alpha-min-2. These three algorithms are able to approximately solve N-POMDPs. Alpha-min-2-fast (heuristic) and alpha-min-2-p (with performance guarantees) are designed to complement an existing POMDP solver, while alpha-min-2-solve (heuristic) is a solver itself. Complexity results are provided for each of the algorithms, and they are tested on well-known benchmarks. These new algorithms will help users to interpret solutions to POMDP problems in computational sustainability.
Extracting Urban Microclimates from Electricity Bills
Authors: Vu Thuy , Parker D. Stott
Published: June 2022

Sustainable energy policies are of growing importance in all urban centers.Climate — and climate change — will play increasingly important roles in these policies.Climate zones defined by the California Energy Commissionhave long been influential in energy management.For example, recently a two-zone division of Los Angeles(defined by historical temperature averages) was introduced for electricity rate restructuring.The importance of climate zones has been enormous,and climate change could make them still more important. AI can provide improvements on the ways climate zones are derived and managed.This paper reports on analysis of aggregate household electricity consumption (EC) data from local utilities in Los Angeles,seeking possible improvements in energy management. In this analysis we noticed that EC data permits identificationof interesting geographical zones  — regions having EC patterns that are characteristically different from surrounding regions.We believe these zones could be useful in a variety of urban models.
Robust Optimization for Tree-Structured Stochastic Network Design
Authors: Wu Xiaojian , Kumar Akshat , Sheldon Daniel , Zilberstein Shlomo
Published: June 2022

Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning approach that computes the policies with either high robust ratio or low regret. Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates.
Counting-Based Reliability Estimation for Power-Transmission Grids
Authors: Duenas-Osorio Leonardo , Meel Kuldeep , Paredes Roger , Vardi Moshe
Published: June 2022

Modern society is increasingly reliant on the functionality of infrastructure facilities and utility services. Consequently, there has been surge of interest in the problem of quantification of system reliability, which is known to be #P-complete. Reliability also contributes to the resilience of systems, so as to effectively make them bounce back after contingencies. Despite diverse progress, most techniques to estimate system reliability and resilience remain computationally expensive. In this paper, we investigate how recent advances in hashing-based approaches to counting can be exploited to improve computational techniques for system reliability.The primary contribution of this paper is a novel framework, RelNet, that reduces the problem of computing reliability for a given network to counting the number of satisfying assignments of a Σ11 formula, which is amenable to recent hashing-based techniques developed for counting satisfying assignments of SAT formula. We then apply RelNet to ten real world power-transmission grids across different cities in the U.S. and are able to obtain, to the best of our knowledge, the first theoretically sound a priori estimates of reliability between several pairs of nodes of interest. Such estimates will help managing uncertainty and support rational decision making for community resilience.
Building Task-Oriented Dialogue Systems for Online Shopping
Authors: Yan Zhao , Duan Nan , Chen Peng , Zhou Ming , Zhou Jianshe , Li Zhoujun
Published: June 2022

We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner. As a pioneering work, we show what & how existing NLP techniques, data resources, and crowdsourcing can be leveraged to build such task-oriented dialogue systems for E-commerce usage. To demonstrate its effectiveness, we integrate our system into a mobile online shopping app. To the best of our knowledge, this is the first time that an AI bot in Chinese is practically used in online shopping scenario with millions of real consumers. Interesting and insightful observations are shown in the experimental part, based on the analysis of human-bot conversation log. Several current challenges are also pointed out as our future directions.
Learning to Predict Intent from Gaze During Robotic Hand-Eye Coordination
Authors: Razin Yosef , Feigh Karen
Published: June 2022

Effective human-aware robots should anticipate their user’s intentions. During hand-eye coordination tasks, gaze often precedes hand motion and can serve as a powerful predictor for intent. However, cooperative tasks where a semi-autonomous robot serves as an extension of the human hand have rarely been studied in the context of hand-eye coordination. We hypothesize that accounting for anticipatory eye movements in addition to the movements of the robot will improve intent estimation. This research compares the application of various machine learning methods to intent prediction from gaze tracking data during robotic hand-eye coordination tasks. We found that with proper feature selection, accuracies exceeding 94% and AUC greater than 91% are achievable with several classification algorithms but that anticipatory gaze data did not improve intent prediction.
Mixed Discrete-Continuous Planning with Convex Optimization
Authors: Fernandez-Gonzalez Enrique , Karpas Erez , Williams Brian
Published: June 2022

Robots operating in the real world must be able to handle both discrete and continuous change. Many robot behaviors can be controlled through numeric parameters (called control variables), which affect the rate of the continuous change. Previous approaches capable of reasoning efficiently with control variables impose severe restrictions that limit the expressivity of the problems that can be solved. A broad class of robotic applications require, for example, convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. However, extensions to prior approaches are not straightforward, since these characteristics are non-linear and hard to scale. We introduce cqScotty, a heuristic forward search planner that solves these problems efficiently. While naive formulations of consistency checks are not convex and do not scale, cqScotty uses an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We demonstrate the scalability of our approach on three new realistic domains.
Efficient Object Instance Search Using Fuzzy Objects Matching
Authors: Yu Tan , Wu Yuwei , Bhattacharjee Sreyasee , Yuan Junsong
Published: June 2022

Recently, global features aggregated from local convolutional features of the convolutional neural network have shown to be much more effective in comparison with hand-crafted features for image retrieval. However, the global feature might not effectively capture the relevance between the query object and reference images in the object instance search task, especially when the query object is relatively small and there exist multiple types of objects in reference images. Moreover, the object instance search requires to localize the object in the reference image, which may not be achieved through global representations. In this paper, we propose a Fuzzy Objects Matching (FOM) framework to effectively and efficiently capture the relevance between the query object and reference images in the dataset. In the proposed FOM scheme, object proposals are utilized to detect the potential regions of the query object in reference images. To achieve high search efficiency, we factorize the feature matrix of all the object proposals from one reference image into the product of a set of fuzzy objects and sparse codes. In addition, we refine the feature of the generated fuzzy objects according to its neighborhood in the feature space to generate more robust representation. The experimental results demonstrate that the proposed FOM framework significantly outperforms the state-of-the-art methods in precision with less memory and computational cost on three public datasets.
TextBoxes: A Fast Text Detector with a Single Deep Neural Network
Authors: Liao Minghui , Shi Baoguang , Bai Xiang , Wang Xinggang , Liu Wenyu
Published: June 2022

This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.
Multi-Path Feedback Recurrent Neural Networks for Scene Parsing
Authors: Jin Xiaojie , Chen Yunpeng , Jie Zequn , Feng Jiashi , Yan Shuicheng
Published: June 2022

In this paper, we consider the scene parsing problem and propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene images. MPF-RNN can enhance the capability of RNNs in modeling long-range context information at multiple levels and better distinguish pixels that are easy to confuse. Different from feedforward CNNs and RNNs with only single feedback, MPF-RNN propagates the contextual features learned at top layer through multiple weighted recurrent connections to learn bottom features. For better training MPF-RNN, we propose a new strategy that considers accumulative loss at multiple recurrent steps to improve performance of the MPF-RNN on parsing small objects. With these two novel components, MPF-RNN has achieved significant improvement over strong baselines (VGG16 and Res101) on five challenging scene parsing benchmarks, including traditional SiftFlow, Barcelona, CamVid, Stanford Background as well as the recently released large-scale ADE20K.
A Multi-Task Deep Network for Person Re-Identification
Authors: Chen Weihua , Chen Xiaotang , Zhang Jianguo , Huang Kaiqi
Published: June 2022

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID. We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.
Quantifying and Detecting Collective Motion by Manifold Learning
Authors: Wang Qi , Chen Mulin , Li Xuelong
Published: June 2022

The analysis of collective motion has attracted many researchers in artificial intelligence. Though plenty of works have been done on this topic, the achieved performance isstill unsatisfying due to the complex nature of collective motions. By investigating the similarity of individuals, this paper proposes a novel framework for both quantifying and detecting collective motions. Our main contributions are threefold: (1) the time-varying dynamics of individuals are deeply investigated to better characterize the individual motion; (2) a structure-based collectiveness measurement is designed toprecisely quantify both individual-level and scene-level properties of collective motions; (3) a multi-stage clustering strategy is presented to discover a more comprehensive understanding of the crowd scenes, containing both local and global collective motions. Extensive experimental results on realworld data sets show that our method is capable of handling crowd scenes with complicated structures and various dynamics, and demonstrate its superior performance against state-of-the-art competitors.
Nonnegative Orthogonal Graph Matching
Authors: Jiang Bo , Tang Jin , Ding Chris , Luo Bin
Published: June 2022

Graph matching problem that incorporates pair-wise constraints can be formulated as Quadratic Assignment Problem(QAP). The optimal solution of QAP is discrete and combinational, which makes QAP problem NP-hard. Thus, many algorithms have been proposed to find approximate solutions. In this paper, we propose a new algorithm, called Nonnegative Orthogonal Graph Matching (NOGM), for QAP matching problem. NOGM is motivated by our new observation that the discrete mapping constraint of QAP can be equivalently encoded by a nonnegative orthogonal constraint which is much easier to implement computationally. Based on this observation, we develop an effective multiplicative update algorithm to solve NOGM and thus can find an effective approximate solution for QAP problem. Comparing with many traditional continuous methods which usually obtain continuous solutions and should be further discretized, NOGM can obtain a sparse solution and thus incorporates the desirable discrete constraint naturally in its optimization. Promising experimental results demonstrate benefits of NOGM algorithm.
Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval
Authors: Dai Guoxian , Xie Jin , Zhu Fan , Fang Yi
Published: June 2022

The explosive growth of 3D models has led to the pressing demand for an efficient searching system. Traditional model-based search is usually not convenient, since people don't always have 3D model available by side. The sketch-based 3D shape retrieval is a promising candidate due to its simpleness and efficiency. The main challenge for sketch-based 3D shape retrieval is the discrepancy across different domains. In the paper, we propose a novel deep correlated metric learning (DCML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCML trains two distinct deep neural networks (one for each domain) jointly with one loss, which learns two deep nonlinear transformations to map features from both domains into a nonlinear feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the transfered space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features at least a predefined margin within each domain, while the correlation loss focuses on minimizing the distribution discrepancy across different domains. Our proposed method is evaluated on SHREC 2013 and 2014 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.
DECK: Discovering Event Composition Knowledge from Web Images for Zero-Shot Event Detection and Recounting in Videos
Authors: Gan Chuang , Sun Chen , Nevatia Ram
Published: June 2022

We address the problem of zero-shot event recognition in consumer videos. An event usually consists of multiple human-human and human-object interactions over a relative long period of time. A common approach proceeds by representing videos with banks of object and action concepts, but requires additional user inputs to specify the desired concepts per event. In this paper, we provide a fully automatic algorithm to select representative and reliable concepts for event queries. This is achieved by discovering event composition knowledge (DECK) from web images. To evaluate our proposed method, we use the standard zero-shot event detection protocol (ZeroMED), but also introduce a novel zero-shot event recounting (ZeroMER) problem to select supporting evidence of the events. Our ZeroMER formulation aims to select video snippets that are relevant and diverse. Evaluation on the challenging TRECVID MED dataset show that our proposed method achieves promising results on both tasks.
Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification
Authors: He Xiangteng , Peng Yuxin
Published: June 2022

Fine-grained image classification is challenging due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Since two different sub-categories is distinguished only by the subtle differences in some specific parts, semantic part localization is crucial for fine-grained image classification. Most previous works improve the accuracy by looking for the semantic parts, but rely heavily upon the use of the object or part annotations of images whose labeling are costly. Recently, some researchers begin to focus on recognizing sub-categories via weakly supervised part detection instead of using the expensive annotations. However, these works ignore the spatial relationship between the object and its parts as well as the interaction of the parts, both of them are helpful to promote part selection. Therefore, this paper proposes a weakly supervised part selection method with spatial constraints for fine-grained image classification, which is free of using any bounding box or part annotations. We first learn a whole-object detector automatically to localize the object through jointly using saliency extraction and co-segmentation. Then two spatial constraints are proposed to select the distinguished parts. The first spatial constraint, called box constraint, defines the relationship between the object and its parts, and aims to ensure that the selected parts are definitely located in the object region, and have the largest overlap with the object region. The second spatial constraint, called parts constraint, defines the relationship of the object's parts, is to reduce the parts' overlap with each other to avoid the information redundancy and ensure the selected parts are the most distinguishing parts from other categories. Combining two spatial constraints promotes parts selection significantly as well as achieves a notable improvement on fine-grained image classification. Experimental results on CUB-200-2011 dataset demonstrate the superiority of our method even compared with those methods using expensive annotations.
Differentiating Between Posed and Spontaneous Expressions with Latent Regression Bayesian Network
Authors: Gan Quan , Nie Siqi , Wang Shangfei , Ji Qiang
Published: June 2022

Spatial patterns embedded in human faces are crucial for differentiating posed expressions from spontaneous ones, yet they have not been thoroughly exploited in the literature. To tackle this problem, we present a generative model, i.e., Latent Regression Bayesian Network (LRBN), to effectively capture the spatial patterns embedded in facial landmark points to differentiate between posed and spontaneous facial expressions. The LRBN is a directed graphical model consisting of one latent layer and one visible layer. Due to the “explaining away“ effect in Bayesian networks, LRBN is able to capture both the dependencies among the latent variables given the observation and the dependencies among visible variables. We believe that such dependencies are crucial for faithful data representation. Specifically, during training, we construct two LRBNs to capture spatial patterns inherent in displacements of landmark points from spontaneous facial expressions and posed facial expressions respectively. During testing, the samples are classified into posed or spontaneous expressions according to their likelihoods on two models. Efficient learning and inference algorithms are proposed. Experimental results on two benchmark databases demonstrate the advantages of the proposed approach in modeling spatial patterns as well as its superior performance to the existing methods in differentiating between posed and spontaneous expressions.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Authors: Szegedy Christian , Ioffe Sergey , Vanhoucke Vincent , Alemi Alexander
Published: June 2022

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.
Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition
Authors: Dong Zhen , Jia Su , Zhang Chi , Pei Mingtao , Wu Yuwei
Published: June 2022

In this paper, we aim to construct a deep neural network which embeds high dimensional symmetric positive definite (SPD) matrices into a more discriminative low dimensional SPD manifold. To this end, we develop two types of basic layers: a 2D fully connected layer which reduces the dimensionality of the SPD matrices, and a symmetrically clean layer which achieves non-linear mapping. Specifically, we extend the classical fully connected layer such that it is suitable for SPD matrices, and we further show that SPD matrices with symmetric pair elements setting zero operations are still symmetric positive definite. Finally, we complete the construction of the deep neural network for SPD manifold learning by stacking the two layers. Experiments on several face datasets demonstrate the effectiveness of the proposed method.
Title Learning Latent Subevents in Activity Videos Using Temporal Attention Filters
Authors: Piergiovanni A. , Fan Chenyou , Ryoo Michael
Published: June 2022

In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos. Many high-level activities are often composed of multiple temporal parts (e.g., sub-events) with different duration/speed, and our objective is to make the model explicitly learn such temporal structure using multiple attention filters and benefit from them. Our temporal filters are designed to be fully differentiable, allowing end-of-end training of the temporal filters together with the underlying frame-based or segment-based convolutional neural network architectures. This paper presents an approach of learning a set of optimal static temporal attention filters to be shared across different videos, and extends this approach to dynamically adjust attention filters per testing video using recurrent long short-term memory networks (LSTMs). This allows our temporal attention filters to learn latent sub-events specific to each activity. We experimentally confirm that the proposed concept of temporal attention filters benefits the activity recognition, and we visualize the learned latent sub-events.
On Validation and Predictability of Digital Badges’ Influence on Individual Users
Authors: Kuśmierczyk Tomasz , Nørvåg Kjetil
Published: June 2022

Badges are a common, and sometimes the only, method of incentivizing users to perform certain actions on on- line sites. However, due to many competing factors influencing user temporal dynamics, it is difficult to determine whether the badge had (or will have) the intended effect or not. In this paper, we introduce two complementary approaches for determining badge influence on users. In the first one, we cluster users’ temporal traces (represented with Poisson processes) and apply covariates (user features) to regularize results. In the second approach, we first classify users’ temporal traces with a novel statistical framework, and then we refine the classification results with a semi-supervised clustering of covariates. Outcomes obtained from an evaluation on synthetic datasets and experiments on two badges from a pop- ular Q&A platform confirm that it is possible to validate, characterize and to some extent predict users affected by the badge.
Exploring Implicit Feedback for Open Domain Conversation Generation
Authors: Zhang Wei-Nan , Li Lingzhi , Cao Dongyan , Liu Ting
Published: June 2022

User feedback can be an effective indicator to the success of the human-robot conversation. However, to avoid to interrupt the online real-time conversation process, explicit feedback is usually gained at the end of a conversation. Alternatively, users' responses usually contain their implicit feedback, such as stance, sentiment, emotion, etc., towards the conversation content or the interlocutors. Therefore, exploring the implicit feedback is a natural way to optimize the conversation generation process. In this paper, we propose a novel reward function which explores the implicit feedback to optimize the future reward of a reinforcement learning based neural conversation model. A simulation strategy is applied to explore the state-action space in training and test. Experimental results show that the proposed approach outperforms the Seq2Seq model and the state-of-the-art reinforcement learning model for conversation generation on automatic and human evaluations on the OpenSubtitles and Twitter datasets.
Discovering and Distinguishing Multiple Visual Senses for Polysemous Words
Authors: Yao Yazhou , Zhang Jian , Shen Fumin , Yang Wankou , Huang Pu , Tang Zhenmin
Published: June 2022

To reduce the dependence on labeled data, there have been increasing research efforts on learning visual classifiers by exploiting web images. One issue that limits their performance is the problem of polysemy. To solve this problem, in this work, we present a novel framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses to retrieve sense-specific images. Then we merge visual similar semantic senses and prune noises by using the retrieved images. Finally, we train a visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and re-ranking search results demonstrate the superiority of our proposed approach.
Representation Learning for Scale-Free Networks
Authors: Feng Rui , Yang Yang , Hu Wenjie , Wu Fei , Zhang Yueting
Published: June 2022

Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored. Scale-free property depicts the fact that vertex degrees follow a heavy-tailed distribution (i.e., only a few vertexes have high degrees) and is a critical property of real-world networks, such as social networks. In this paper, we study the problem of learning representations for scale-free networks. We first theoretically analyze the difficulty of embedding and reconstructing a scale-free network in the Euclidean space, by converting our problem to the sphere packing problem. Then, we propose the "degree penalty" principle for designing scale-free property preserving network embedding algorithm: punishing the proximity between high-degree vertexes. We introduce two implementations of our principle by utilizing the spectral techniques and a skip-gram model respectively. Extensive experiments on six datasets show that our algorithms are able to not only reconstruct heavy-tailed distributed degree distribution, but also outperform state-of-the-art embedding models in various network mining tasks, such as vertex classification and link prediction.
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
Authors: Yang Zhiyong , Xu Qianqian , Cao Xiaochun , Huang Qingming
Published: June 2022

Visual attributes, which refer to human-labeled semantic annotations, have gained increasing popularity in a wide range of real world applications. Generally, the existing attribute learning methods fall into two categories: one focuses on learning user-specific labels separately for different attributes, while the other one focuses on learning crowd-sourced global labels jointly for multiple attributes. However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes. To overcome this challenge, we propose a novel model to learn user-specific predictors across multiple attributes. In our proposed model, the diversity of personalized opinions and the intrinsic relationship among multiple attributes are unified in a common-to-special manner. To this end, we adopt a three-component decomposition. Specifically, our model integrates a common cognition factor, an attribute-specific bias factor and a user-specific bias factor. Meanwhile Lasso and group Lasso penalties are adopted to leverage efficient feature selection. Furthermore, theoretical analysis is conducted to show that our proposed method could reach reasonable performance. Eventually, the empirical study carried out in this paper demonstrates the effectiveness of our proposed method.
Location-Sensitive User Profiling Using Crowdsourced Labels
Authors: Niu Wei , Caverlee James , Lu Haokai
Published: June 2022

In this paper, we investigate the impact of spatial variation on the construction of location-sensitive user profiles. We demonstrate evidence of spatial variation over a collection of Twitter Lists, wherein we find that crowdsourced labels are constrained by distance. For example, that energy in San Francisco is more associated with the green movement, whereas in Houston it is more associated with oil and gas. We propose a three-step framework for location-sensitive user profiling: first, it constructs a crowdsourced label similarity graph, where each labeler and labelee are annotated with a geographic coordinate; second, it transforms this similarity graph into a directed weighted tree that imposes a hierarchical structure over these labels; third, it embeds this location-sensitive folksonomy into a user profile ranking algorithm that outputs a ranked list of candidate labels for a partially observed user profile. Through extensive experiments over a Twitter list dataset, we demonstrate the effectiveness of this location-sensitive user profiling.