bluuebunny/crossref_metadata_2025 huggingface dataset:
New l1-Norm Relaxations and Optimizations for Graph Clustering
Authors: Nie Feiping , Wang Hua , Deng Cheng , Gao Xinbo , Li Xuelong , Huang Heng
Published: June 2022

In recent data mining research, the graph clustering methods, such as normalized cut and ratio cut, have been well studied and applied to solve many unsupervised learning applications. The original graph clustering methods are NP-hard problems. Traditional approaches used spectral relaxation to solve the graph clustering problems. The main disadvantage of these approaches is that the obtained spectral solutions could severely deviate from the true solution. To solve this problem, in this paper, we propose a new relaxation mechanism for graph clustering methods. Instead of minimizing the squared distances of clustering results, we use the l1-norm distance. More important, considering the normalized consistency, we also use the l1-norm for the normalized terms in the new graph clustering relaxations. Due to the sparse result from the l1-norm minimization, the solutions of our new relaxed graph clustering methods get discrete values with many zeros, which are close to the ideal solutions. Our new objectives are difficult to be optimized, because the minimization problem involves the ratio of nonsmooth terms. The existing sparse learning optimization algorithms cannot be applied to solve this problem. In this paper, we propose a new optimization algorithm to solve this difficult non-smooth ratio minimization problem. The extensive experiments have been performed on three two-way clustering and eight multi-way clustering benchmark data sets. All empirical results show that our new relaxation methods consistently enhance the normalized cut and ratio cut clustering results.
Metric Learning for Ordinal Data
Authors: Shi Yuan , Li Wenzhe , Sha Fei
Published: June 2022

A large amount of ordinal-valued data exist in many domains, including medical and health science, social science, economics, political science, etc. Unlike image and speech datasets of real-valued data, learning with ordinal variables (i.e., features) presents unique challenges. In particular, the nominal differences between those feature values, which are just ranks, do not necessarily correspond to the real distances between the corresponding categories. Given their wide existence, it is imperative to develop machine learning algorithms that specifically address the need to model and infer with such data. In this paper, we present a novel metric learning algorithm that takes into consideration the nature of ordinal data. Our approach treats ordinal values as latent variables in intervals. Our algorithm then learns what those intervals are as well as distance metrics to measure distances between latent variables in those intervals. We derive the corresponding optimization algorithm and demonstrate how that can be solved effectively. Experimental results show that the proposed approach significantly improves baselines that do not explicitly model ordinal features.
Towards Safe Semi-Supervised Learning for Multivariate Performance Measures
Authors: Li Yu-Feng , Kwok James , Zhou Zhi-Hua
Published: June 2022

Semi-supervised learning (SSL) is an important research problem in machine learning. While it is usually expected that the use of unlabeled data can improve performance, in many cases SSL is outperformed by supervised learning using only labeled data. To this end, the construction of a performance-safe SSL method has become a key issue of SSL study. To alleviate this problem, we propose in this paper the UMVP (safe semi-sUpervised learning for MultiVariate Performance measure) method, because of the need of various performance measures in practical tasks. The proposed method integrates multiple semi-supervised learners, and maximizes the worst-case performance gain to derive the final prediction. The overall problem is formulated as a maximin optimization. In oder to solve the resultant difficult maximin optimization, this paper shows that when the performance measure is the Top-k Precision, Fβ score or AUC, a minimax convex relaxation of the maximin optimization can be solved efficiently. Experimental results show that the proposed method can effectively improve the safeness of SSL under multiple multivariate performance measures.
Deep Learning with S-Shaped Rectified Linear Activation Units
Authors: Jin Xiaojie , Xu Chunyan , Feng Jiashi , Wei Yunchao , Xiong Junjun , Yan Shuicheng
Published: June 2022

Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectifiedlinear activation unit (SReLU) to learn both convexand non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a “freezing” method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.
Scaling Simultaneous Optimistic Optimization for High-Dimensional Non-Convex Functions with Low Effective Dimensions
Authors: Qian Hong , Yu Yang
Published: June 2022

Simultaneous optimistic optimization (SOO) is a recently proposed global optimization method with a strong theoretical foundation. Previous studies have shown that SOO has a good performance in low-dimensional optimization problems, however, its performance is unsatisfactory when the dimensionality is high. This paper adapts random embedding to scaling SOO, resulting in the RESOO algorithm. We prove that the simple regret of RESOO depends only on the effective dimension of the problem, while that of SOO depends on the dimension of the solution space. Empirically, on some high-dimensional non-convex testing functions as well as hyper-parameter tuning tasks for multi-class support vector machines, RESOO shows significantly improved performance from SOO.
Text Classification with Heterogeneous Information Network Kernels
Authors: Wang Chenguang , Song Yangqiu , Li Haoran , Zhang Ming , Han Jiawei
Published: June 2022

Text classification is an important problem with many applications. Traditional approaches represent text as a bag-of-words and build classifiers based on this representation. Rather than words, entity phrases, the relations between the entities, as well as the types of the entities and relations carry much more information to represent the texts. This paper presents a novel text as network classification framework, which introduces 1) a structured and typed heterogeneous information networks (HINs) representation of texts, and 2) a meta-path based approach to link texts. We show that with the new representation and links of texts, the structured and typed information of entities and relations can be incorporated into kernels. Particularly, we develop both simple linear kernel and indefinite kernel based on meta-paths in the HIN representation of texts, where we call them HIN-kernels. Using Freebase, a well-known world knowledge base, to construct HIN for texts, our experiments on two benchmark datasets show that the indefinite HIN kernel based on weighted meta-paths outperforms the state-of-the-art methods and other HIN-kernels.
Return of Frustratingly Easy Domain Adaptation
Authors: Sun Baochen , Feng Jiashi , Saenko Kate
Published: June 2022

Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being ``frustratingly easy'' to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple--it can be implemented in four lines of Matlab code--CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.
Uncorrelated Group LASSO
Authors: Kong Deguang , Liu Ji , Liu Bo , Bao Xuan
Published: June 2022

l2,1-norm is an effective regularization to enforce a simple group sparsity for feature learning. To capture some subtle structures among feature groups, we propose a new regularization called exclusive group l2,1-norm. It enforces the sparsity at the intra-group level by using l2,1-norm, while encourages the selected features to distribute in different groups by using l2 norm at the inter-group level. The proposed exclusivegroup l2,1-norm is capable of eliminating the feature correlationsin the context of feature selection, if highly correlated features are collected in the same groups. To solve the generic exclusive group l2,1-norm regularized problems, we propose an efficient iterative re-weighting algorithm and provide a rigorous convergence analysis. Experiment results on real world datasets demonstrate the effectiveness of the proposed new regularization and algorithm.
An Efficient Time Series Subsequence Pattern Mining and Prediction Framework with an Application to Respiratory Motion Prediction
Authors: Wang Shouyi , Kam Kinming , Xiao Cao , Bowen Stephen , Chaovalitwongse Wanpracha
Published: June 2022

Traditional time series analysis methods are limited on some complex real-world time series data. Respiratory motion prediction is one of such challenging problems. The memory-based nearest neighbor approaches haveshown potentials in predicting complex nonlinear time series compared to many traditional parametric prediction models. However, the massive time series subsequences representation, the similarity distance measures, the number of nearest neighbors, and the ensemble functions create challenges as well as limit the performance of nearest neighbor approaches in complex time series prediction. To address these problems, we propose a flexible time series pattern representation and selection framework, called the orthogonalpolynomial-based variant-nearest-neighbor (OPVNN) approach. For the respiratory motion prediction problem, the proposed approach achieved the highest and most robust prediction performance compared to the state-of-the-art time series prediction methods. With a solid mathematical and theoretical foundation in orthogonal polynomials, the proposed time series representation, subsequence pattern mining and prediction framework has a great potential to benefit those industry and medical applications that need to handle highly nonlinear and complex time series data streams, such as quasi-periodic ones.
Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms
Authors: Joulani Pooria , Gyorgy Andras , Szepesvari Csaba
Published: June 2022

We present a unified, black-box-style method for developing and analyzing online convex optimization (OCO) algorithms for full-information online learning in delayed-feedback environments. Our new, simplified analysis enables us to substantially improve upon previous work and to solve a number of open problems from the literature. Specifically, we develop and analyze asynchronous AdaGrad-style algorithms from the Follow-the-Regularized-Leader (FTRL) and Mirror-Descent family that, unlike previous works, can handle projections and adapt both to the gradients and the delays, without relying on either strong convexity or smoothness of the objective function, or data sparsity. Our unified framework builds on a natural reduction from delayed-feedback to standard (non-delayed) online learning. This reduction, together with recent unification results for OCO algorithms, allows us to analyze the regret of generic FTRL and Mirror-Descent algorithms in the delayed-feedback setting in a unified manner using standard proof techniques. In addition, the reduction is exact and can be used to obtain both upper and lower bounds on the regret in the delayed-feedback setting.
Ethical Dilemmas for Adaptive Persuasion Systems
Authors: Stock Oliviero , Guerini Marco , Pianesi Fabio
Published: June 2022

A key acceptability criterion for artificial agents will be the possible moral implications of their actions. In particular, intelligent persuasive systems (systems designed to influence humans via communication) constitute a highly sensitive topic because of their intrinsically social nature. Still, ethical studies in this area are rare and tend to focus on the output of the required action; instead, this work focuses on the acceptability of persuasive acts themselves.Building systems able to persuade while being ethically acceptable requires that they be capable of intervening flexibly and of taking decisions about which specific persuasive strategy to use. We show how, exploiting a behavioral approach, based on human assessment of moral dilemmas, we obtain results that will lead to more ethically appropriate systems. Experiments we have conducted address the type of persuader, the strategies adopted and the circumstances. Dimensions surfaced that can characterize the interpersonal differences concerning moral acceptability of machine performed persuasion, usable for strategy adaptation. We also show that the prevailing preconceived negative attitude toward persuasion by a machine is not predictive of actual moral acceptability judgement when subjects are confronted with specific cases.
Writing Stories with Help from Recurrent Neural Networks
Authors: Roemmele Melissa
Published: June 2022

This thesis explores the use of a recurrent neural network model for a novel story generation task. In this task, the model analyzes an ongoing story and generates a sentence that continues the story.
Privacy Management in Agent-Based Social Networks
Authors: Kokciyan Nadin
Published: June 2022

In online social networks (OSNs), users are allowed to create and share content about themselves and others. When multiple entities start distributing content, information can reach unintended individuals and inference can reveal more information about the user. Existing applications do not focus on detecting privacy violations before they occur in the system. This thesis proposes an agent-based representation of a social network, where the agents manage users' privacy requirements and create privacy agreements with agents. The privacy context, such as the relations among users, various content types in the system, and so on are represented with a formal language. By reasoning with this formal language, an agent checks the current state of the system to resolve privacy violations before they occur. We argue that commonsense reasoning could be useful to solve some of privacy examples reported in the literature. We will develop new methods to automatically identify private information using commonsense reasoning, which has never been applied to privacy context. Moreover, agents may have conflicting privacy requirements. We will study how to use agreement technologies in privacy settings for agents to resolve conflicts automatically.
Unsupervised Learning of HTNs in Complex Adversarial Domains
Authors: Leece Michael
Published: June 2022

While Hierarchical Task Networks are frequently cited as flexible and powerful planning models, they are often ignored due to the intensive labor cost for experts/programmers, due to the need to create and refine the model by hand. While recent work has begun to address this issue by working towards learning aspects of an HTN model from demonstration, or even the whole framework, the focus so far has been on simple domains, which lack many of the challenges faced in the real world such as imperfect information and real-time environments. I plan to extend this work using the domain of real-time strategy (RTS) games, which have gained recent popularity as a challenging and complex domain for AI research.
SAPE: A System for Situation-Aware Public Security Evaluation
Authors: Wu Shu , Liu Qiang , Bai Ping , Wang Liang , Tan Tieniu
Published: June 2022

Public security events are occurring all over the world, bringing threat to personal and property safety, and homeland security. It is vital to construct an effective model to evaluate and predict the public security. In this work, we establish a Situation-Aware Public Security Evaluation (SAPE) platform. Based on conventional Recurrent Neural Networks (RNN), we develop a new variant of RNN to handle temporal contexts in public security event datasets. The proposed model can achieve better performance than the compared state-of-the-art methods. On SAPE, There are two parts of demonstrations, i.e., global public security evaluation and China public security evaluation. In the global part, based on Global Terrorism Database from UMD, for each country, SAPE can predict risk level and top-n potential terrorist organizations which might attack the country. The users can also view the actual attacking organizations and predicted results. For each province in China, SAPE can predict the risk level and the probability scores of different types of events in the next month. The users can also view the actual numbers of events and predicted risk levels of the past one year.
Artificial Swarm Intelligence, a Human-in-the-Loop Approach to A.I.
Authors: Rosenberg Louis
Published: June 2022

Most research into Swarm Intelligence explores swarms of autonomous robots or simulated agents. Little work, however, has been done on swarms of networked humans. This paper introduces UNU, an online platform that enables networked users to assemble in real-time swarms and tackle problems as an Artificial Swarm Intelligence (ASI). Modeled after biological swarms, UNU enables large groups of networked users to work together in real-time synchrony, forging a unified dynamic system that can quickly answer questions and make decisions. Early testing suggests that human swarming has significant potential for harnessing the Collective Intelligence (CI) of online groups, often exceeding the natural abilities of individual participants.
Jikan to Kukan: A Hands-On Musical Experience in AI, Games and Art
Authors: Martins Georgia , Escarce Junior Mário , Soriano Marcolino Leandro
Published: June 2022

AI is typically applied in video games in the creation of artificial opponents, in order to make them strong, realistic or even fallible (for the game to be "enjoyable" by human players). We offer a different perspective: we present the concept of "Art Games", a view that opens up many possibilities for AI research and applications. Conference participants will play Jikan to Kukan, an art game where the player dynamically creates the soundtrack with the AI system, while developing her experience in the unconscious world of a character.
Markov Argumentation Random Fields
Authors: Tang Yuqing , Oren Nir , Sycara Katia
Published: June 2022

We demonstrate an implementation of Markov Argumentation Random Fields (MARFs), a novel formalism combining elements of formal argumentation theory and probabilistic graphical models. In doing so MARFs provide a principled technique for the merger of probabilistic graphical models and non-monotonic reasoning, supporting human reasoning in ``messy’’ domains where the knowledge about conflicts should be applied. Our implementation takes the form of a graphical tool which supports users in interpreting complex information. We have evaluated our implementation in the domain of intelligence analysis, where analysts must reason and determine likelihoods of events using information obtained from conflicting sources.
Design of an Online Course on Knowledge-Based AI
Authors: Goel Ashok , Joyner David
Published: June 2022

In Fall 2014 we offered an online course on Knowledge-Based Artificial Intelligence (KBAI) to about 200 students as part of the Georgia Tech Online MS in CS program. By now we have offered the course to more than 1000 students. We describe the design, development and delivery of the online KBAI class in Fall 2014.
Competition of Distributed and Multiagent Planners (CoDMAP)
Authors: Štolba Michal , Komenda Antonín , Kovacs Daniel
Published: June 2022

As a part of the workshop on Distributed and Multiagent Planning (DMAP) at the International Conference on Automated Planning and Scheduling (ICAPS) 2015, we have organized a competition in distributed and multiagent planning. The main aims of the competition were to consolidate the planners in terms of input format; to promote development of multiagent planners both inside and outside of the multiagent research community; and to provide a proof-of-concept of a potential future multiagent planning track of the International Planning Competition (IPC). In this paper we summarize course and highlights of the competition.
What’s Hot in the Answer Set Programming Competition
Authors: Gebser Martin , Maratea Marco , Ricca Francesco
Published: June 2022

Answer Set Programming (ASP) is a declarative programming paradigm with roots in logic programming, knowledge representation, and non-monotonic reasoning. The ASP competition series aims at assessing and promoting the evolution of ASP systems and applications. Its growing range of challenging application-oriented benchmarks inspires and showcases continuous advancements of the state of the art in ASP.
Strategic Behaviour When Allocating Indivisible Goods
Authors: Walsh Toby
Published: June 2022

We survey some recent research regarding strategic behaviour in resource allocation problems, focusing on the fair division of indivisible goods. We consider a number of computational questions like how a single strategic agent misreports their preferences to ensure a particular outcome, and how agents compute a Nash equilibrium when they all act strategically. We also identify a number of future directions like dealing with non-additive utilities, and partial or probabilistic information about the preferences of other agents.
Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior
Authors: Mishra Abhijit , Kanojia Diptesh , Bhattacharyya Pushpak
Published: June 2022

Sarcasm understandability or the ability to understand textual sarcasm depends upon readers' language proficiency, social knowledge, mental state and attentiveness. We introduce a novel method to predict the sarcasm understandability of a reader. Presence of incongruity in textual sarcasm often elicits distinctive eye-movement behavior by human readers. By recording and analyzing the eye-gaze data, we show that eye-movement patterns vary when sarcasm is understood vis-à-vis when it is not. Motivated by our observations, we propose a system for sarcasm understandability prediction using supervised machine learning. Our system relies on readers' eye-movement parameters and a few textual features, thence, is able to predict sarcasm understandability with an F-score of 93%, which demonstrates its efficacy. The availability of inexpensive embedded-eye-trackers on mobile devices creates avenues for applying such research which benefits web-content creators, review writers and social media analysts alike.
MIDCA: A Metacognitive, Integrated Dual-Cycle Architecture for Self-Regulated Autonomy
Authors: Cox Michael , Alavi Zohreh , Dannenhauer Dustin , Eyorokon Vahid , Munoz-Avila Hector , Perlis Don
Published: June 2022

We present a metacognitive, integrated, dual-cycle architecture whose function is to provide agents with a greater capacity for acting robustly in a dynamic environment and managing unexpected events. We present MIDCA 1.3, an implementation of this architecture which explores a novel approach to goal generation, planning and execution given surprising situations. We formally define the mechanism and report empirical results from this goal generation algorithm. Finally, we describe the similarity between its choices at the cognitive level with those at the metacognitive.
QART: A System for Real-Time Holistic Quality Assurance for Contact Center Dialogues
Authors: Roy Shourya , Mariappan Ragunathan , Dandapat Sandipan , Srivastava Saurabh , Galhotra Sainyam , Peddamuthu Balaji
Published: June 2022

Quality assurance (QA) and customer satisfaction (C-Sat) analysis are two commonly used practices to measure goodness of dialogues between agents and customers in contact centers. The practices however have a few shortcomings. QA puts sole emphasis on agents’ organizational compliance aspect whereas C-Sat attempts to measure customers’ satisfaction only based on post dialogue surveys. As a result, outcome of independent QA and C-Sat analysis may not always be in correspondence. Secondly, both processes are retrospective in nature and hence, evidences of bad past dialogues (and consequently bad customer experiences) can only be found after hours or days or weeks depending on their periodicity. Finally, human intensive nature of these practices lead to time and cost overhead while being able to analyze only a small fraction of dialogues. In this paper, we introduce an automatic real-time quality assurance system for contact centers — QART (pronounced cart). QART performs multi-faceted analysis on dialogue utterances, as they happen, using sophisticated statistical and rule-based natural language processing (NLP) techniques. It covers various aspects inspired by today’s QA and C-Sat practices as well as introduces novel incremental dialogue summarization capability. QART front-end is an interactive dashboard providing views of ongoing dialogues at different granularity enabling agents’ supervisors to monitor and take corrective actions as needed. We demonstrate effectiveness of different back-end modules as well as the overall system by experimental results on a real-life contact center chat dataset.
Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model
Authors: Nakahashi Ryo , Baker Chris , Tenenbaum Joshua
Published: June 2022

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our ac- tion planning and execution, but when we observe others, the latent structure of their actions is typ- ically unobservable, and must be inferred in order to learn new skills by demonstration, or to as- sist others in completing their tasks. For example, an assistant who has learned the subgoal struc- ture of a colleague’s task can more rapidly rec- ognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approxi- mately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed diﬀerent se- ries of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high ac- curacy, and signiﬁcantly better than several al- ternative models and straightforward heuristics. Motivated by this result, we simulate how learn- ing and inference of subgoals can improve perfor- mance in an artiﬁcial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.
Top-N Recommender System via Matrix Completion
Authors: Kang Zhao , Peng Chong , Cheng Qiang
Published: June 2022

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.
Community-Based Question Answering via Heterogeneous Social Network Learning
Authors: Fang Hanyin , Wu Fei , Zhao Zhou , Duan Xinyu , Zhuang Yueting , Ester Martin
Published: June 2022

Community-based question answering (cQA) sites have accumulated vast amount of questions and corresponding crowdsourced answers over time. How to efficiently share the underlying information and knowledge from reliable (usually highly-reputable) answerers has become an increasingly popular research topic. A major challenge in cQA tasks is the accurate matching of high-quality answers w.r.t given questions. Many of traditional approaches likely recommend corresponding answers merely depending on the content similarity between questions and answers, therefore suffer from the sparsity bottleneck of cQA data. In this paper, we propose a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks. More specifically, our framework collaboratively utilizes the rich interaction among questions, answers and answerers to learn the relative quality rank of different answers w.r.t a same question. Moreover, the information in heterogeneous social networks is comprehensively employed to enhance the quality of question-answering (QA) matching by our deep random walk learning framework. Extensive experiments on a large-scale dataset from a real world cQA site show that leveraging the heterogeneous social information indeed achieves better performance than other state-of-the-art cQA methods.
Predicting Online Protest Participation of Social Media Users
Authors: Ranganath Suhas , Morstatter Fred , Hu Xia , Tang Jiliang , Wang Suhang , Liu Huan
Published: June 2022

Social media has emerged to be a popular platform for people to express their viewpoints on political protests like the Arab Spring. Millions of people use social media to communicate and mobilize their viewpoints on protests. Hence, it is a valuable tool for organizing social movements. However, the mechanisms by which protest affects the population is not known, making it difficult to estimate the number of protestors. In this paper, we are inspired by sociological theories of protest participation and propose a framework to predict from the user's past status messages and interactions whether the next post of the user will be a declaration of protest. Drawing concepts from these theories, we model the interplay between the user's status messages and messages interacting with him over time and predict whether the next post of the user will be a declaration of protest. We evaluate the framework using data from the social media platform Twitter on protests during the recent Nigerian elections and demonstrate that it can effectively predict whether the next post of a user is a declaration of protest.
Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags
Authors: Tandon Niket , Hariman Charles , Urbani Jacopo , Rohrbach Anna , Rohrbach Marcus , Weikum Gerhard
Published: June 2022

Commonsense knowledge about part-whole relations (e.g., screen partOf notebook) is important for interpreting user input in web search and question answering, or for object detection in images. Prior work on knowledge base construction has compiled part-whole assertions, but with substantial limitations: i) semantically different kinds of part-whole relations are conflated into a single generic relation, ii) the arguments of a part-whole assertion are merely words with ambiguous meaning, iii) the assertions lack additional attributes like visibility (e.g., a nose is visible but a kidney is not) and cardinality information (e.g., a bird has two legs while a spider eight), iv) limited coverage of only tens of thousands of assertions. This paper presents a new method for automatically acquiring part-whole commonsense from Web contents and image tags at an unprecedented scale, yielding many millions of assertions, while specifically addressing the four shortcomings of prior work. Our method combines pattern-based information extraction methods with logical reasoning. We carefully distinguish different relations: physicalPartOf, memberOf, substanceOf. We consistently map the arguments of all assertions onto WordNet senses, eliminating the ambiguity of word-level assertions. We identify whether the parts can be visually perceived, and infer cardinalities for the assertions. The resulting commonsense knowledge base has very high quality and high coverage, with an accuracy of 89% determined by extensive sampling, and is publicly available.
Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns
Authors: He Jing , Li Xin , Liao Lejian , Song Dandan , Cheung William
Published: June 2022

In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task in location-based social networks (LBSNs), but not well studied yet. With the conjecture that, under different contextual scenario, human exhibits distinct mobility patterns, we attempt here to jointly model the next POI recommendation under the influence of user's latent behavior pattern. We propose to adopt a third-rank tensor to model the successive check-in behaviors. By incorporating softmax function to fuse the personalized Markov chain with latent pattern, we furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly. Expectation Maximization (EM) is then used to estimate the model parameters. Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods.
Predicting ICU Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements
Authors: Luo Yuan , Xin Yu , Joshi Rohit , Celi Leo , Szolovits Peter
Published: June 2022

ICU mortality risk prediction may help clinicians take effective interventions to improve patient outcome. Existing machine learning approaches often face challenges in integrating a comprehensive panel of physiologic variables and presenting to clinicians interpretable models. We aim to improve both accuracy and interpretability of prediction models by introducing Subgraph Augmented Non-negative Matrix Factorization (SANMF) on ICU physiologic time series. SANMF converts time series into a graph representation and applies frequent subgraph mining to automatically extract temporal trends. We then apply non-negative matrix factorization to group trends in a way that approximates patient pathophysiologic states. Trend groups are then used as features in training a logistic regression model for mortality risk prediction, and are also ranked according to their contribution to mortality risk. We evaluated SANMF against four empirical models on the task of predicting mortality or survival 30 days after discharge from ICU using the observed physiologic measurements between 12 and 24 hours after admission. SANMF outperforms all comparison models, and in particular, demonstrates an improvement in AUC (0.848 vs. 0.827, p<0.002) compared to a state-of-the-art machine learning method that uses manual feature engineering. Feature analysis was performed to illuminate insights and benefits of subgraph groups in mortality risk prediction.
Multiple Source Detection without Knowing the Underlying Propagation Model
Authors: Wang Zheng , Wang Chaokun , Pei Jisheng , Ye Xiaojun
Published: June 2022

Information source detection, which is the reverse problem of information diffusion, has attracted considerable research effort recently. Most existing approaches assume that the underlying propagation model is fixed and given as input, which may limit their application range. In this paper, we study the multiple source detection problem when the underlying propagation model is unknown. Our basic idea is source prominence, namely the nodes surrounded by larger proportions of infected nodes are more likely to be infection sources. As such, we propose a multiple source detection method called Label Propagation based Source Identification (LPSI). Our method lets infection status iteratively propagate in the network as labels, and finally uses local peaks of the label propagation result as source nodes. In addition, both the convergent and iterative versions of LPSI are given. Extensive experiments are conducted on several real-world datasets to demonstrate the effectiveness of the proposed method.
CLARE: A Joint Approach to Label Classification and Tag Recommendation
Authors: Wang Yilin , Wang Suhang , Tang Jiliang , Qi Guojun , Liu Huan , Li Baoxin
Published: June 2022

Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.
Web-Based Semantic Fragment Discovery for On-Line Lingual-Visual Similarity
Authors: Sun Xiaoshuai , Cao Jiewei , Li Chao , Zhu Lei , Shen Heng Tao
Published: June 2022

In this paper, we present an automatic approach for on-line discovery of visual-lingual semantic fragments from weakly labeled Internet images. Instead of learning region-entity correspondences from well-labeled image-sentence pairs, our approach directly collects and enhances the weakly labeled visual contents from the Web and constructs an adaptive visual representation which automatically links generic lingual phrases to their related visual contents. To ensure reliable and efficient semantic discovery, we adopt non-parametric density estimation to re-rank the related visual instances and proposed a fast self-similarity-based quality assessment method to identify the high-quality semantic fragments. The discovered semantic fragments provide an adaptive joint representation for texts and images, based on which lingual-visual similarity can be defined for further co-analysis of heterogeneous multimedia data. Experimental results on semantic fragment quality assessment, sentence-based image retrieval, automatic multimedia insertion and ordering demonstrated the effectiveness of the proposed framework.The experiments show that the proposed methods can make effective use of the Web knowledge, and are able to generate competitive results compared to state-of-the-art approaches in various tasks.
Multi-Task Deep Learning for User Intention Understanding in Speech Interaction Systems
Authors: Ning Yishuang , Jia Jia , Wu Zhiyong , Li Runnan , An Yongsheng , Wang Yanfeng , Meng Helen
Published: June 2022

Speech interaction systems have been gaining popularity in recent years. The main purpose of these systems is to generate more satisfactory responses according to users' speech utterances, in which the most critical problem is to analyze user intention. Researches show that user intention conveyed through speech is not only expressed by content, but also closely related with users' speaking manners (e.g. with or without acoustic emphasis). How to incorporate these heterogeneous attributes to infer user intention remains an open problem. In this paper, we define Intention Prominence (IP) as the semantic combination of focus by text and emphasis by speech, and propose a multi-task deep learning framework to predict IP. Specifically, we first use long short-term memory (LSTM) which is capable of modeling long short-term contextual dependencies to detect focus and emphasis, and incorporate the tasks for focus and emphasis detection with multi-task learning (MTL) to reinforce the performance of each other. We then employ Bayesian network (BN) to incorporate multimodal features (focus, emphasis, and location reflecting users' dialect conventions) to predict IP based on feature correlations. Experiments on a data set of 135,566 utterances collected from real-world Sogou Voice Assistant illustrate that our method can outperform the comparison methods over 6.9-24.5% in terms of F1-measure. Moreover, a real practice in the Sogou Voice Assistant indicates that our method can improve the performance on user intention understanding by 7%.
Taming the Matthew Effect in Online Markets with Social Influence
Authors: Berbeglia Franco , Van Hentenryck Pascal
Published: June 2022

Social influence has been shown to create a Matthew effect in online markets, increasing inequalities and leading to “winner-take-all” phenomena. Matthew effects have been observed for numerous market policies, including when the products are presented to consumers by popularity or quality. This paper studies how to reduce Matthew effects, while keeping markets efficient and predictable when social influence is used. It presents a market strategy based on randomization and segmentation, that ensures that the best products, if they are close in quality, will have reasonably close market shares. The benefits of this market strategy is justified both theoretically and empirically and the loss in market efficiency is shown to be acceptable.
Structured Prediction in Time Series Data
Authors: Li Jia
Published: June 2022

Time series data is common in a wide range of disciplines including finance, biology, sociology, and computer science. Analyzing and modeling time series data is fundamental for studying various problems in those fields. For instance, studying time series physiological data can be used to discriminate patients’ abnormal recovery trajectories and normal ones (Hripcsak, Albers, and Perotte 2015). GPS data are useful for studying collective decision making of groupliving animals (Strandburg-Peshkin et al. 2015). There are different methods for studying time series data such as clustering, regression, and anomaly detection. In this proposal, we are interested in structured prediction problems in time series data. Structured prediction focuses on prediction task where the outputs are structured and interdependent, contrary to the non-structured prediction which assumes that the outputs are independent of other predicted outputs. Structured prediction is an important problem as there are structures inherently existing in time series data. One difficulty for structured prediction is that the number of possible outputs can be exponential which makes modeling all the potential outputs intractable.
Transfer of Knowledge through Collective Learning
Authors: Rostami Mohammad
Published: June 2022

Learning fast and efficiently using minimal data has been consistently a challenge in machine learning. In my thesis, I explore this problem for knowledge transfer for multi-agent multi-task learning in a life-long learning paradigm. My goal is to demonstrate that by sharing knowledge between agents and similar tasks, efficient algorithms can be designed that can increase the speed of learning as well as improve performance. Moreover, this would allow for handling hard tasks through collective learning of multiple agents that share knowledge. As an initial step, I study the problem of incorporating task descriptors into lifelong learning of related tasks to perform zero-shot knowledge transfer. Zero-shot learning is highly desirable because it leads to considerable speedup in handling similar sequential tasks. Then I focus on a multi-agent learning setting, where related tasks are learned collectively and/or address privacy concerns.
Human-Like Spatial Reasoning Formalisms
Authors: Walega Przemyslaw
Published: June 2022

My work on the PhD thesis concerns human-like reasoning about relations between spatial objects and the way they change in time. In particular, my research is focused on logic-based reasoning systems that model human spatial reasoning methods and may enable better understanding of humans reasoning mechanisms in future. Importantly, such formalisms are also interested from the practical point of view – they have a number of potential applications, e.g., in robotics, architecture design, databases, among others.
Sarcasm Suite: A Browser-Based Engine for Sarcasm Detection and Generation
Authors: Joshi Aditya , Kanojia Diptesh , Bhattacharyya Pushpak , Carman Mark
Published: June 2022

Sarcasm Suite is a browser-based engine that deploys five of our past papers in sarcasm detection and generation. The sarcasm detection modules use four kinds of incongruity: sentiment incongruity, semantic incongruity, historical context incongruity and conversational context incongruity. The sarcasm generation module is a chatbot that responds sarcastically to user input. With a visually appealing interface that indicates predictions using `faces' of our co-authors from our past papers, Sarcasm Suite is our first demonstration of our work in computational sarcasm.
Arnold: An Autonomous Agent to Play FPS Games
Authors: Chaplot Devendra Singh , Lample Guillaume
Published: June 2022

Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present Arnold, a completely autonomous agent to play First-Person Shooter Games using only screen pixel data and demonstrate its effectiveness on Doom, a classical first-person shooter game. Arnold is trained with deep reinforcement learning using a recent Action-Navigation architecture, which uses separate deep neural networks for exploring the map and fighting enemies. Furthermore, it utilizes a lot of techniques such as augmenting high-level game features, reward shaping and sequential updates for efficient training and effective performance. Arnold outperforms average humans as well as in-built game bots on different variations of the deathmatch. It also obtained the highest kill-to-death ratio in both the tracks of the Visual Doom AI Competition and placed second in terms of the number of frags.
Integrating Verbal and Nonvebval Input into a Dynamic Response Spoken Dialogue System
Authors: Hu Ting-Yao , Raman Chirag , Maza Salvador Medina , Gui Liangke , Baltrusaitis Tadas , Frederking Robert , Morency Louis-Philippe , Black Alan , Eskenazi Maxine
Published: June 2022

In this work, we present a dynamic response spoken dialogue system (DRSDS). It is capable of understanding the verbal and nonverbal language of users and making instant, situation-aware response. Incorporating with two external systems, MultiSense and email summarization, we built an email reading agent on mobile device to show the functionality of DRSDS.