bluuebunny/crossref_metadata_2025 huggingface dataset:
Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization
Authors: Zhang Dongqing , Li Wu-Jun
Published: June 2022

Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing~(SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization~(SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.
Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence
Authors: Brys Tim , Nowé Ann , Kudenko Daniel , Taylor Matthew
Published: June 2022

Multi-objective problems with correlated objectives are a class of problems that deserve specific attention. In contrast to typical multi-objective problems, they do not require the identification of trade-offs between the objectives, as (near-) optimal solutions for any objective are (near-) optimal for every objective. Intelligently combining the feedback from these objectives, instead of only looking at a single one, can improve optimization. This class of problems is very relevant in reinforcement learning, as any single-objective reinforcement learning problem can be framed as such a multi-objective problem using multiple reward shaping functions. After discussing this problem class, we propose a solution technique for such reinforcement learning problems, called adaptive objective selection. This technique makes a temporal difference learner estimate the Q-function for each objective in parallel, and introduces a way of measuring confidence in these estimates. This confidence metric is then used to choose which objective's estimates to use for action selection. We show significant improvements in performance over other plausible techniques on two problem domains. Finally, we provide an intuitive analysis of the technique's decisions, yielding insights into the nature of the problems being solved.
Constructing Symbolic Representations for High-Level Planning
Authors: Konidaris George , Kaelbling Leslie , Lozano-Perez Tomas
Published: June 2022

We consider the problem of constructing a symbolic description of a continuous, low-level environment for use in planning. We show that symbols that can represent the preconditions and effects of an agent's actions are both necessary and sufficient for high-level planning. This eliminates the symbol design problem when a representation must be constructed in advance, and in principle enables an agent to autonomously learn its own symbolic representations. The resulting representation can be converted into PDDL, a canonical high-level planning representation that enables very fast planning.
Non-Convex Feature Learning via Lp,inf Operator
Authors: Kong Deguang , Ding Chris
Published: June 2022

We present a feature selection method for solving sparse regularization problem, which hasa composite regularization of $\ell_p$ norm and $\ell_{\infty}$ norm.We use proximal gradient method to solve this \L1inf operator problem, where a simple but efficient algorithm is designed to minimize a relatively simple objective function, which contains a vector of $\ell_2$ norm and $\ell_\infty$ norm. Proposed method brings some insight for solving sparsity-favoring norm, andextensive experiments are conducted to characterize the effect of varying $p$ and to compare with other approaches on real world multi-class and multi-label datasets.
Active Learning with Model Selection
Authors: Ali Alnur , Caruana Rich , Kapoor Ashish
Published: June 2022

Most active learning methods avoid model selection by training models of one type (SVMs, boosted trees, etc.) using one pre-defined set of model hyperparameters. We propose an algorithm that actively samples data to simultaneously train a set of candidate models (different model types and/or different hyperparameters) and also select the best model from this set. The algorithm actively samples points for training that are most likely to improve the accuracy of the more promising candidate models, and also samples points for model selection---all samples count against the same labeling budget. This exposes a natural trade-off between the focused active sampling that is most effective for training models, and the unbiased sampling that is better for model selection. We empirically demonstrate on six test problems that this algorithm is nearly as effective as an active learning oracle that knows the optimal model in advance.
Learning Instance Concepts from Multiple-Instance Data with Bags as Distributions
Authors: Doran Gary , Ray Soumya
Published: June 2022

We analyze and evaluate a generative process for multiple-instance learning (MIL) in which bags are distributions over instances. We show that our generative process contains as special cases generative models explored in prior work, while excluding scenarios known to be hard for MIL. Further, under the mild assumption that every negative instance is observed with nonzero probability in some negative bag, we show that it is possible to learn concepts that accurately label instances from MI data in this setting. Finally, we show that standard supervised approaches can learn concepts with low area-under-ROC error from MI data in this setting. We validate this surprising result with experiments using several synthetic and real-world MI datasets that have been annotated with instance labels.
Coactive Learning for Locally Optimal Problem Solving
Authors: Goetschalckx Robby , Fern Alan , Tadepalli Prasad
Published: June 2022

Coactive learning is an online problem solving setting where the solutions provided by a solver are interactively improved by a domain expert, which in turn drives learning. In this paper we extend the study of coactive learning to problems where obtaining a globally optimal or near-optimal solution may be intractable or where an expert can only be expected to make small, local improvements to a candidate solution. The goal of learning in this new setting is to minimize the cost as measured by the expert effort over time. We first establish theoretical bounds on the average cost of the existing coactive Perceptron algorithm. In addition, we consider new online algorithms that use cost-sensitive and Passive-Aggressive (PA) updates, showing similar or improved theoretical bounds. We provide an empirical evaluation of the learners in various domains, which show that the Perceptron based algorithms are quite effective and that unlike the case for online classification, the PA algorithms do not yield significant performance gains.
Online Multi-Task Learning via Sparse Dictionary Optimization
Authors: Ruvolo Paul , Eaton Eric
Published: June 2022

This paper develops an efficient online algorithm for learning multiple consecutive tasks based on the K-SVD algorithm for sparse dictionary optimization. We first derive a batch multi-task learning method that builds upon K-SVD, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current lifelong learning algorithms while maintaining nearly identical model performance. Additionally, the proposed method offers an alternate formulation for lifelong learning that supports both task and feature similarity matrices.
A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures
Authors: Sadeghipour Amir , Kopp Stefan
Published: June 2022

In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition. We analyze a dataset of iconic gestures and show how the proposed Feature-based Stochastic Context-Free Grammar (FSCFG) can generalize over both structural and feature-based variations among different gesture performances.
Symbolic Domain Predictive Control
Authors: Löhr Johannes , Wehrle Martin , Fox Maria , Nebel Bernhard
Published: June 2022

Planning-based methods to guide switched hybrid systems from an initial state into a desired goal region opens an interesting field for control. The idea of the Domain Predictive Control (DPC) approach is to generate input signals affecting both the numerical states and the modes of the system by stringing together atomic actions to a logically consistent plan. However, the existing DPC approach is restricted in the sense that a discrete and pre-defined input signal is required for each action. In this paper, we extend the approach to deal with symbolic states. This allows for the propagation of reachable regions of the state space emerging from actions with inputs that can be arbitrarily chosen within specified input bounds. This symbolic extension enables the applicability of DPC to systems with bounded inputs sets and increases its robustness due to the implicitly reduced search space. Moreover, precise numeric goal states instead of goal regions become reachable.
Optimal Decoupling in Linear Constraint Systems
Authors: Witteveen Cees , Wilson Michel , Klos Tomas
Published: June 2022

Decomposition is a technique to obtain complete solutions by assembling independently obtained partial solutions. In particular, constraint decomposition plays an important role in distributed databases, distributed scheduling and violation detection: It enables conflict-free local decision making, while avoiding communication overloading. One of the main issues in decomposition is the loss of flexibility due to decomposition. Here, flexibility roughly refers to the freedom in choosing suitable values for the variables in order to satisfy the constraints. In this paper, we concentrate on linear constraint systems and efficient decomposition techniques for them. Using a generalization of a flexibility metric developed for Simple Temporal Networks, we show how an efficient decomposition technique for linear constraint systems can be derived that minimizes the loss of flexibility. As a by-product of this decomposition technique, we propose an intuitively attractive flexibility metric for linear constraint systems where decomposition does not incur any loss of flexibility.
Using Timed Game Automata to Synthesize Execution Strategies for Simple Temporal Networks with Uncertainty
Authors: Cimatti Alessandro , Hunsberger Luke , Micheli Andrea , Roveri Marco
Published: June 2022

A Simple Temporal Network with Uncertainty (STNU) is a structure for representing and reasoning about temporal constraints in domains where some temporal durations are not controlled by the executor. The most important property of an STNU is whether it is dynamically controllable (DC) whether there exists a strategy for executing the controllable time-points that guarantees that all constraints will be satisfied no matter how the uncontrollable durations turn out. This paper provides a novel mapping from STNUs to Timed Game Automata (TGAs) that: (1) explicates the deep theoretical relationships between STNUs and TGAs; and (2) enables the memoryless strategies generated from the TGA to be transformed into equivalent STNU execution strategies that reduce the real-time computational burden for the executor. The paper formally proves that the STNU-to-TGA encoding properly captures the execution semantics of STNUs.
Cost-Based Query Optimization via AI Planning
Authors: Robinson Nathan , McIlraith Sheila , Toman David
Published: June 2022

In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques. Our efforts focus on the specific problem of cost-based join-order optimization for conjunctive relational queries, a critical component of production-quality query optimizers. We characterize the general query-planning problem as a delete-free planning problem, and query plan optimization as a context-sensitive cost-optimal planning problem. We propose algorithms that generate high-quality query plans, guaranteeing optimality under certain conditions. Our approach is general, supporting the use of a broad suite of domain-independent and domain-specific optimization criteria. Experimental results demonstrate the effectiveness of AI planning techniques for query plan generation and optimization.
Flexible and Scalable Partially Observable Planning with Linear Translations
Authors: Bonet Blai , Geffner Hector
Published: June 2022

The problem of on-line planning in partially observable settings involves two problems: keeping track of beliefs about the environment and selecting actions for achieving goals. While the two problems are computationally intractable in the worst case, significant progress has been achieved in recent years through the use of suitable reductions. In particular, the state-of-the-art CLG planner is based on a translation that maps deterministic partially observable problems into fully observable non-deterministic ones. The translation, which is quadratic in the number of problem fluents and gets rid of the belief tracking problem, is adequate for most benchmarks, and it is in fact complete for problems that have width 1. The more recent K-replanner uses translations that are linear, one for keeping track of beliefs and the other for selecting actions using off-the-shelf classical planners. As a result, the K-replanner scales up better but it is not as general. In this work, we combine the benefits of the two approaches - the scope of the CLG planner and the efficiency of the Kreplanner. The new planner, called LW1, is based on a translation that is linear but complete for width-1 problems. The scope and scalability of the new planner is evaluated experimentally by considering the existing benchmarks and new problems.
Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects
Authors: Boularias Abdeslam , Bagnell James , Stentz Anthony
Published: June 2022

Manipulating natural objects of irregular shapes, such as rocks, is an essential capability of robots operating in outdoor environments. Physics-based simulators are commonly used to plan stable grasps for man-made objects. However, planning is an expensive process that is based on simulating hand and object trajectories in different configurations, and evaluating the outcome of each trajectory. This problem is particularly concerning when the objects are irregular or cluttered, because the space of feasible grasps is significantly smaller, and more configurations need to be evaluated before finding a good one. In this paper, we first present a learning technique for fast detection of an initial set of potentially stable grasps in a cluttered scene. The best detected grasps are further optimized by fine-tuning the configuration of the hand in simulation. To reduce the computational burden of this last operation, we model the outcomes of the grasps as a Gaussian Process, and use an entropy-search method in order to focus the optimization on regions where the best grasp is most likely to be. This approach is tested on the task of clearing piles of real, unknown, rock debris with an autonomous robot. Empirical results show a clear advantage of the proposed approach when the time window for decision is short.
Qualitative Planning with Quantitative Constraints for Online Learning of Robotic Behaviours
Authors: Wiley Timothy , Sammut Claude , Bratko Ivan
Published: June 2022

This paper resolves previous problems in the Multi-Strategy architecture for online learning of robotic behaviours. The hybrid method includes a symbolic qualitative planner that constructs an approximate solution to a control problem. The approximate solution provides constraints for a numerical optimisation algorithm, which is used to refine the qualitative plan into an operational policy. Introducing quantitative constraints into the planner gives previously unachievable domain independent reasoning. The method is demonstrated on a multi-tracked robot intended for urban search and rescue.
State Aggregation in Monte Carlo Tree Search
Authors: Hostetler Jesse , Fern Alan , Dietterich Tom
Published: June 2022

Monte Carlo tree search (MCTS) algorithms are a popular approach to online decision-making in Markov decision processes (MDPs). These algorithms can, however, perform poorly in MDPs with high stochastic branching factors. In this paper, we study state aggregation as a way of reducing stochastic branching in tree search. Prior work has studied formal properties of MDP state aggregation in the context of dynamic programming and reinforcement learning, but little attention has been paid to state aggregation in MCTS. Our main result is a performance loss bound for a class of value function-based state aggregation criteria in expectimax search trees. We also consider how to construct MCTS algorithms that operate in the abstract state space but require a simulator of the ground dynamics only. We find that trajectory sampling algorithms like UCT can be adapted easily, but that sparse sampling algorithms present difficulties. As a proof of concept, we experimentally confirm that state aggregation can improve the finite-sample performance of UCT.
Decentralized Stochastic Planning with Anonymity in Interactions
Authors: Varakantham Pradeep , Adulyasak Yossiri , Jaillet Patrick
Published: June 2022

In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only do we introduce a general model model called D-SPAIT to capture anonymity in interactions, but also provide optimization based optimal and local-optimal solutions for generalizable sub-categories of D-SPAIT.
Relational One-Class Classification: A Non-Parametric Approach
Authors: Khot Tushar , Natarajan Sriraam , Shavlik Jude
Published: June 2022

One-class classification approaches have been proposed in the literature to learn classifiers from examples of only one class. But these approaches are not directly applicable to relational domains due to their reliance on a feature vector or a distance measure. We propose a non-parametric relational one-class classification approach based on first-order trees. We learn a tree-based distance measure that iteratively introduces new relational features to differentiate relational examples. We update the distance measure so as to maximize the one-class classification performance of our model. We also relate our model definition to existing work on probabilistic combination functions and density estimation. We experimentally show that our approach can discover relevant features and outperform three baseline approaches.
Predicting the Hardness of Learning Bayesian Networks
Authors: Malone Brandon , Kangas Kustaa , Jarvisalo Matti , Koivisto Mikko , Myllymaki Petri
Published: June 2022

There are various algorithms for finding a Bayesian networkstructure (BNS) that is optimal with respect to a given scoring function. No single algorithm dominates the others in speed, and, given a problem instance, it is a priori unclear which algorithm will perform best and how fast it will solve the problem. Estimating the runtimes directly is extremely difficult as they are complicated functions of the instance. The main contribution of this paper is characterization of the empirical hardness of an instance for a given algorithm based on a novel collection of non-trivial, yet efficiently computable features. Our empirical results, based on the largest evaluation of state-of-the-art BNS learning algorithms to date, demonstrate that we can predict the runtimes to a reasonable degree of accuracy, and effectively select algorithms that perform well on a particular instance. Moreover, we also show how the results can be utilized in building a portfolio algorithm that combines several individual algorithms in an almost optimal manner.
Monte-Carlo Simulation Adjusting
Authors: Araki Nobuo , Muramatsu Masakazu , Kunihito Hoki , Takahashi Satoshi
Published: June 2022

In this paper, we propose a new learning method sim- ulation adjusting that adjusts simulation policy to im- prove the move decisions of the Monte Carlo method. We demonstrated simulation adjusting for 4 × 4 board Go problems. We observed that the rate of correct an- swers moderately increased.
Coordination of Multiple Teams of Robots for an Optimal Global Plan
Authors: Saribatur Zeynep , Erdem Esra , Patoglu Volkan
Published: June 2022

We consider multiple teams of heterogeneous robots, where each team is given a feasible task to complete in its workspace on its own, and where teams are allowed to transfer robots between each other. We study the problem of finding a coordination of robot transfers between teams to ensure an optimal global plan (with minimum makespan) so that all tasks can be completed as soon as possible by helping each other. We propose to solve this problem using answer set programming.
Crowdsourced Explanations for Humorous Internet Memes
Authors: Lin Chi-Chin , Hsu Jane Yung-jen
Published: June 2022

Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because of the subculture among the community is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system leveraging crowdsourcing technique to generate explanations for meme images. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes through reading the explanations. Our template-based explanation can illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by going through 2 designed humor tasks. In our pilot study, acceptable explanations for 5 unique memes are generated. For further study, generating explanations for more general text jokes are possible.
Online Search Algorithm Configuration
Authors: Fitzgerald Tadhg , O'Sullivan Barry , Malitsky Yuri , Tierney Kevin
Published: June 2022

This paper outlines an online approach for algorithm configuration which uses the power of modern multicore system to evaluate multiple parameters configurations in parallel.
Adaptive Singleton-Based Consistencies
Authors: Balafrej Amine , Bessiere Christian , Bouyakhf El Houssine , Trombettoni Gilles
Published: June 2022

Singleton-based consistencies have been shown to dramatically improve the performance of constraint solvers on some difficult instances. However, they are in general too expensive to be applied exhaustively during the whole search. In this paper, we focus on partition-one-AC, a singleton-based consistency which, as opposed to singleton arc consistency, is able to prune values on all variables when it performs singleton tests on one of them. We propose adaptive variants of partition-one-AC that do not necessarily run until having proved the fixpoint. The pruning can be weaker than the full version but the computational effort can be significantly reduced. Our experiments show that adaptive Partition-one-AC can obtain significant speed-ups over arc consistency and over the full version of partition-one-AC.
Maximum Satisfiability Using Core-Guided MaxSAT Resolution
Authors: Narodytska Nina , Bacchus Fahiem
Published: June 2022

Core-guided approaches to solving MAXSAT have proved to be effective on industrial problems. These approaches solve a MAXSAT formula by building a sequence of SAT formulas, where in each formula a greater weight of soft clauses can be relaxed. The soft clauses are relaxed via the addition of blocking variables, and the total weight of soft clauses that can be relaxed is limited by placing constraints on the blocking variables. In this work we propose an alternative approach. Our approach also builds a sequence of new SAT formulas. However, these formulas are constructed using MAXSAT resolution, a sound rule of inference for MAXSAT. MAXSAT resolution can in the worst case cause a quadratic blowup in the formula, so we propose a new compressed version of MAXSAT resolution. Using compressed MAXSAT resolution our new core-guided solver improves the state-of-theart, solving significantly more problems than other state-ofthe-art solvers on the industrial benchmarks used in the 2013 MAXSAT Solver Evaluation.
Diagnosing Analogue Linear Systems Using Dynamic Topological Reconfiguration
Authors: Feldman Alexander , Provan Gregory
Published: June 2022

Fault diagnosis of analogue linear systems poses many challenges, such as the size of the search space that must be explored and the possibility of simulation instabilities introduced by particular fault classes. We study a novel algorithm that addresses both problems. This algorithm dynamically modifies the simulation model during diagnosis by pruning parametrized components that cause discontinuity in the model. We provide a theoretical framework for predicting the speedups, which depends on the topology of the model. We empirically validate the theoretical predictions through extensive experimentation on a benchmark of circuits.
Learning Low-Rank Representations with Classwise Block-Diagonal Structure for Robust Face Recognition
Authors: Li Yong , Liu Jing , Li Zechao , Zhang Yangmuzi , Lu Hanqing , Ma Songde
Published: June 2022

Face recognition has been widely studied due to its importance in various applications. However, the case that both training images and testing images are corrupted is not well addressed. Motivated by the success of low-rank matrix recovery, we propose a novel semi-supervised low-rank matrix recovery algorithm for robust face recognition. The proposed method can learn robust discriminative representations for both training images and testing images simultaneously by exploiting the classwise block-diagonal structure. Specifically, low-rank matrix approximation can handle the possible contamination of data. Moreover, the classwise block-diagonal structure is exploited to promote discrimination of representations for robust recognition. The above issues are formulated into a unified objective function and we design an efficient optimization procedure based on augmented Lagrange multiplier method to solve it. Extensive experiments on three public databases are performed to validate the effectiveness of our approach. The strong identification capability of representations with block-diagonal structure is verified.
Sub-Selective Quantization for Large-Scale Image Search
Authors: Li Yeqing , Chen Chen , Liu Wei , Huang Junzhou
Published: June 2022

Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping highdimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to two popular quantization techniques PCA Quantization (PCAQ) and Iterative Quantization (ITQ). Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.
Semantic Graph Construction for Weakly-Supervised Image Parsing
Authors: Xie Wenxuan , Peng Yuxin , Xiao Jianguo
Published: June 2022

We investigate weakly-supervised image parsing, i.e., assigning class labels to image regions by using image-level labels only. Existing studies pay main attention to the formulation of the weakly-supervised learning problem, i.e., how to propagate class labels from images to regions given an affinity graph of regions. Notably, however, the affinity graph of regions, which is generally constructed in relatively simpler settings in existing methods, is of crucial importance to the parsing performance due to the fact that the weakly-supervised parsing problem cannot be solved within a single image, and that the affinity graph enables label propagation among multiple images. In order to embed more semantics into the affinity graph, we propose novel criteria by exploiting the weak supervision information carefully, and develop two graphs: L1 semantic graph and k-NN semantic graph. Experimental results demonstrate that the proposed semantic graphs not only capture more semantic relevance, but also perform significantly better than conventional graphs in image parsing.
Grounding Acoustic Echoes in Single View Geometry Estimation
Authors: Hussain Muhammad Wajahat , Civera Javier , Montano Luis
Published: June 2022

Extracting the 3D geometry plays an important part in scene understanding. Recently, robust visual descriptors are proposed for extracting the indoor scene layout from a passive agent’s perspective, specifically from a single image. Their robustness is mainly due to modelling the physical interaction of the underlying room geometry with the objects and the humans present in the room. In this work we add the physical constraints coming from acoustic echoes, generated by an audio source, to this visual model. Our audio-visual 3D geometry descriptor improves over the state of the art in passive perception models as we show in our experiments.
Engineering Works Scheduling for Hong Kong’s Rail Network
Authors: Chun Andy , Suen Ted
Published: June 2022

This paper describes how AI is used to plan, schedule, and optimise nightly engineering works for both the commuter and rapid transit lines in Hoag Kong. The MTR Corporation Limited operates and manages all the rail lines in Hong Kong. Its Engineering Works and Traffic Information Management System (ETMS) is a mission critical systemthat manages all information related to engineering works and their related track possessions and engineering train movements. The Al Engine described in this paper is a component of this ETMS. In Hong Kong, the maintenance, inspection, repair, or installation works along the rail lines are done during the very short non-traffic hours (NTH) of roughly 4 to 5 hours each night. These engineering works can be along the running tracks, track-side, tunnel, freightyards, sub-depots, depot maintenance tracks, etc. The proper scheduling of necessary engineering works is crucial to maintaining a reliable and safe train service during normal hours. The Al Engine optimizes resource allocation to maximize the number of engineering works that can be performed, while ensuring all safety, environment, and operational rules and constraints are met. The work described is part of a project to redesign and replace the existing ETMS, deployed in 2004 with an updated technology platform and modern IT architecture, to provide a more robust and scalable system that potentially can be deployed to other cities around the world.
THink: Inferring Cognitive Status from Subtle Behaviors
Authors: Davis Randall , Libon David , Rhoda Rhoda , Pitman David , Penney Dana
Published: June 2022

The Digital Clock Drawing Test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We also set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.
A Speech-Driven Second Screen Application for TV Program Discovery
Authors: Yeh Peter , Douglas Ben , Jarrold William , Ratnaparkhi Adwait , Ramachandran Deepak , Patel-Schneider Peter , Laverty Stephen , Tikku Nirvana , Brown Sean , Mendel Jeremy
Published: June 2022

In this paper, we present a speech-driven second screen application for TV program discovery. We give an overview of the application and its architecture. We also present a user study along with a failure analysis. The results from the study are encouraging, and demonstrate our application’s effectiveness in the target domain. We conclude with a discussion of follow-on efforts to further enhance our application.
Kernel Density Estimation for Text-Based Geolocation
Authors: Hulden Mans , Silfverberg Miikka , Francom Jerid
Published: June 2022

Text-based geolocation classifiers often operate with a grid-based view of the world. Predicting document location of origin based on text content on a geodesic grid is computationally attractive since many standard methods for supervised document classification carry over unchanged to geolocation in the form of predicting a most probable grid cell for a document. However, the grid-based approach suffers from sparse data problems if one wants to improve classification accuracy by moving to smaller cell sizes. In this paper we investigate an enhancement of common methods for determining the geographic point of origin of a text document by kernel density estimation. For geolocation of tweets we obtain a improvements upon non-kernel methods on datasets of U.S. and global Twitter content.
Collaborative Topic Ranking: Leveraging Item Meta-Data for Sparsity Reduction
Authors: Yao Weilong , He Jing , Wang Hua , Zhang Yanchun , Cao Jie
Published: June 2022

Pair-wise ranking methods have been widely used in recommender systems to deal with implicit feedback. They attempt to discriminate between a handful of observed items and the large set of unobserved items. In these approaches, however, user preferences and item characteristics cannot be estimated reliably due to overfitting given highly sparse data. To alleviate this problem, in this paper, we propose a novel hierarchical Bayesian framework which incorporates ``bag-of-words'' type meta-data on items into pair-wise ranking models for one-class collaborative filtering. The main idea of our method lies in extending the pair-wise ranking with a probabilistic topic modeling. Instead of regularizing item factors through a zero-mean Gaussian prior, our method introduces item-specific topic proportions as priors for item factors. As a by-product, interpretable latent factors for users and items may help explain recommendations in some applications. We conduct an experimental study on a real and publicly available dataset, and the results show that our algorithm is effective in providing accurate recommendation and interpreting user factors and item factors.
Incorporating Implicit Link Preference Into Overlapping Community Detection
Authors: Zhang Hongyi , King Irwin , Lyu Michael
Published: June 2022

Community detection is an important technique to understand structures and patterns in complex networks. Recently, overlapping community detection becomes a trend due to the ubiquity of overlapping and nested communities in real world. However, existing approaches have ignored the use of implicit link preference information, i.e., links can reflect a node's preference on the targets of connections it wants to build. This information has strong impact on community detection since a node prefers to build links with nodes inside its community than those outside its community. In this paper, we propose a preference-based nonnegative matrix factorization (PNMF) model to incorporate implicit link preference information. Unlike conventional matrix factorization approaches, which simply approximate the original adjacency matrix in value, our model maximizes the likelihood of the preference order for each node by following the intuition that a node prefers its neighbors than other nodes. Our model overcomes the indiscriminate penalty problem in which non-linked pairs inside one community are equally penalized in objective functions as those across two communities. We propose a learning algorithm which can learn a node-community membership matrix via stochastic gradient descent with bootstrap sampling. We evaluate our PNMF model on several real-world networks. Experimental results show that our model outperforms state-of-the-art approaches and can be applied to large datasets.
Causal Inference via Sparse Additive Models with Application to Online Advertising
Authors: Sun Wei , Wang Pengyuan , Yin Dawei , Yang Jian , Chang Yi
Published: June 2022

Advertising effectiveness measurement is a fundamental problem in online advertising. Various causal inference methods have been employed to measure the causal effects of ad treatments. However, existing methods mainly focus on linear logistic regression for univariate and binary treatments and are not well suited for complex ad treatments of multi-dimensions, where each dimension could be discrete or continuous. In this paper we propose a novel two-stage causal inference framework for assessing the impact of complex ad treatments. In the first stage, we estimate the propensity parameter via a sparse additive model; in the second stage, a propensity-adjusted regression model is applied for measuring the treatment effect. Our approach is shown to provide an unbiased estimation of the ad effectiveness under regularity conditions. To demonstrate the efficacy of our approach, we apply it to a real online advertising campaign to evaluate the impact of three ad treatments: ad frequency, ad channel, and ad size. We show that the ad frequency usually has a treatment effect cap when ads are showing on mobile device. In addition, the strategies for choosing best ad size are completely different for mobile ads and online ads.
Perceiving Group Themes from Collective Social and Behavioral Information
Authors: Cui Peng , Zhang Tianyang , Wang Fei , He Peng
Published: June 2022

Collective social and behavioral information commonly exists in nature. There is a widespread intuitive sense that the characteristics of these social and behavioral information are to some extend related to the themes (or semantics) of the activities or targets. In this paper, we explicitly validate the interplay of collective social behavioral information and group themes using a large scale real dataset of online groups, and demonstrate the possibility of perceiving group themes from collective social and behavioral information. We propose a REgularized miXEd Regression (REXER) model based on matrix factorization to infer hierarchical semantics (including both group category and group labels) from collective social and behavioral information of group members. We extensively evaluate the proposed method in a large scale real online group dataset. For the prediction of group themes, the proposed REXER achieves satisfactory performances in various criterions. More specifically, we can predict the category of a group (among 6 categories) purely based on the collective social and behavioral information of the group with the Precision@1 to be 55.16% , without any assistance from group labels or conversation contents. We also show, perhaps counterintuitively, that the collective social and behavioral information is more reliable than the titles and labels of groups for inferring the group categories.
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
Authors: You Quanzeng , Luo Jiebo , Jin Hailin , Yang Jianchao
Published: June 2022

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.
A Tri-Role Topic Model for Domain-Specific Question Answering
Authors: Ma Zongyang , Sun Aixin , Yuan Quan , Cong Gao
Published: June 2022

Stack Overflow and MedHelp are examples of domain-specific community-based question answering (CQA) systems. Different from CQA systems for general topics (e.g., Yahoo! Answers, Baidu Knows), questions and answers in domain-specific CQA systems are mostly in the same topical domain, enabling more comprehensive interaction between users on fine-grained topics. In such systems, users are more likely to ask questions on unfamiliar topics and to answer questions matching their expertise. Users can also vote answers based on their judgements. In this paper, we propose a Tri-Role Topic Model (TRTM) to model the tri-roles of users (i.e., as askers, answerers, and voters, respectively) and the activities of each role including composing question, selecting question to answer, contributing and voting answers. The proposed model can be used to enhance CQA systems from many perspectives. As a case study, we conducted experiments on ranking answers for questions on Stack Overflow, a CQA system for professional and enthusiast programmers. Experimental results show that TRTM is effective in facilitating users getting ideal rankings of answers, particularly for new and less popular questions. Evaluated on nDCG, TRTM outperforms state-of-the-art methods.
Probabilistic Graphical Models for Boosting Cardinal and Ordinal Peer Grading in MOOCs
Authors: Mi Fei , Yeung Dit-Yan
Published: June 2022

With the enormous scale of massive open online courses (MOOCs), peer grading is vital for addressing the assessment challenge for open-ended assignments or exams while at the same time providing students with an effective learning experience through involvement in the grading process. Most existing MOOC platforms use simple schemes for aggregating peer grades, e.g., taking the median or mean. To enhance these schemes, some recent research attempts have developed machine learning methods under either the cardinal setting (for absolute judgment) or the ordinal setting (for relative judgment). In this paper, we seek to study both cardinal and ordinal aspects of peer grading within a common framework. First, we propose novel extensions to some existing probabilistic graphical models for cardi- nal peer grading. Not only do these extensions give su- perior performance in cardinal evaluation, but they also outperform conventional ordinal models in ordinal eval- uation. Next, we combine cardinal and ordinal models by augmenting ordinal models with cardinal predictions as prior. Such combination can achieve further performance boosts in both cardinal and ordinal evaluations, suggesting a new research direction to pursue for peer grading on MOOCs. Extensive experiments have been conducted using real peer grading data from a course called “Science, Technology, and Society in China I” offered by HKUST on the Coursera platform.
Inference Graphs: Combining Natural Deduction and Subsumption Inference in a Concurrent Reasoner
Authors: Schlegel Daniel , Shapiro Stuart
Published: June 2022

There are very few reasoners which combine natural deduction and subsumption reasoning, and there are none which do so while supporting concurrency. Inference Graphs are a graph-based inference mechanism using an expressive first-order logic, capable of subsumption and natural deduction reasoning using concurrency. Evaluation of concurrency characteristics on a combination natural deduction and subsumption reasoning problem has shown linear speedup with the number of processors.
A Nonparametric Online Model for Air Quality Prediction
Authors: Guizilini Vitor , Ramos Fabio
Published: June 2022

We introduce a novel method for the continuous online prediction of particulate matter in the air (more specifically, PM10 and PM2.5) given sparse sensor information. A nonparametric model is developed using Gaussian Processes, which eschews the need for an explicit formulation of internal -- and usually very complex -- dependencies between meteorological variables. Instead, it uses historical data to extrapolate pollutant values both spatially (in areas with no sensor information) and temporally (the near future). Each prediction also contains a respective variance, indicating its uncertainty level and thus allowing a probabilistic treatment of results. A novel training methodology (Structural Cross-Validation) is presented, which preserves the spatio-temporal structure of available data during the hyperparameter optimization process. Tests were conducted using a real-time feed from a sensor network in an area of roughly 50x80 km, alongside comparisons with other techniques for air pollution prediction. The promising results motivated the development of a smartphone applicative and a website, currently in use to increase the efficiency of air quality monitoring and control in the area.
Predisaster Preparation of Transportation Networks
Authors: Schichl Hermann , Sellmann Meinolf
Published: June 2022

We develop a new approach for a pre-disaster planning problem which consists in computing an optimal investment plan to strengthen a transportation network, given that a future disaster probabilistically destroys links in the network. We show how the problem can be formulated as a non-linear integer program and devise an AI algorithm to solve it. In particular, we introduce a new type of extreme resource constraint and develop a practically efficient propagation algorithm for it. Experiments show several orders of magnitude improvements over existing approaches, allowing us to close an existing real-world benchmark and to solve to optimality other, more challenging benchmarks.