bluuebunny/crossref_metadata_2025 huggingface dataset:
Neural Machine Translation with Reconstruction
Authors: Tu Zhaopeng , Liu Yang , Shang Lifeng , Liu Xiaohua , Li Hang
Published: June 2022

Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words. To alleviate this problem, we propose a novel encoder-decoder-reconstructor framework for NMT. The reconstructor, incorporated into the NMT model, manages to reconstruct the input source sentence from the hidden layer of the output target sentence, to ensure that the information in the source side is transformed to the target side as much as possible. Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-the-art NMT and statistical MT systems.
Incorporating Knowledge Graph Embeddings into Topic Modeling
Authors: Yao Liang , Zhang Yin , Wei Baogang , Jin Zhe , Zhang Rui , Zhang Yangyang , Chen Qinfei
Published: June 2022

Probabilistic topic models could be used to extract low-dimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not process fact-oriented triple knowledge in knowledge graphs. Knowledge graph embeddings, on the other hand, automatically capture relations between entities in knowledge graphs. In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling. By combining latent Dirichlet allocation, a widely used topic model with knowledge encoded by entity vectors, we improve the semantic coherence significantly and capture a better representation of a document in the topic space. Our evaluation results will demonstrate the effectiveness of our method.
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
Authors: Ji Guoliang , Liu Kang , He Shizhu , Zhao Jun
Published: June 2022

Distant supervision for relation extraction is an efficient method to scale relation extraction to very large corpora which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and lack of background knowledge about the entities. In this paper, we propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases. And we extract entity descriptions from Freebase and Wikipedia pages to supplement background knowledge for our task. The background knowledge not only provides more information for predicting relations, but also brings better entity representations for the attention module. We conduct three experiments on a widely used dataset and the experimental results show that our approach outperforms all the baseline systems significantly.
Neural Bag-of-Ngrams
Authors: Li Bofang , Liu Tao , Zhao Zhe , Wang Puwei , Du Xiaoyong
Published: June 2022

Bag-of-ngrams (BoN) models are commonly used for representing text. One of the main drawbacks of traditional BoN is the ignorance of n-gram's semantics. In this paper, we introduce the concept of Neural Bag-of-ngrams (Neural-BoN), which replaces sparse one-hot n-gram representation in traditional BoN with dense and rich-semantic n-gram representations. We first propose context guided n-gram representation by adding n-grams to word embeddings model. However, the context guided learning strategy of word embeddings is likely to miss some semantics for text-level tasks. Text guided n-gram representation and label guided n-gram representation are proposed to capture more semantics like topic or sentiment tendencies. Neural-BoN with the latter two n-gram representations achieve state-of-the-art results on 4 document-level classification datasets and 6 semantic relatedness categories. They are also on par with some sophisticated DNNs on 3 sentence-level classification datasets. Similar to traditional BoN, Neural-BoN is efficient, robust and easy to implement. We expect it to be a strong baseline and be used in more real-world applications.
Unit Dependency Graph and Its Application to Arithmetic Word Problem Solving
Authors: Roy Subhro , Roth Dan
Published: June 2022

Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper. Units associated with the quantities often provide information that is essential to support this reasoning. This paper proposes a principled way to capture and reason about units and shows how it can benefit an arithmetic word problem solver. This paper presents the concept of Unit Dependency Graphs (UDGs), which provides a compact representation of the dependencies between units of numbers mentioned in a given problem. Inducing the UDG alleviates the brittleness of the unit extraction system and allows for a natural way to leverage domain knowledge about unit compatibility, for word problem solving. We introduce a decomposed model for inducing UDGs with minimal additional annotations, and use it to augment the expressions used in the arithmetic word problem solver of (Roy and Roth 2015) via a constrained inference framework. We show that introduction of UDGs reduces the error of the solver by over 10 %, surpassing all existing systems for solving arithmetic word problems. In addition, it also makes the system more robust to adaptation to new vocabulary and equation forms .
Distinguish Polarity in Bag-of-Words Visualization
Authors: Xie Yusheng , Chen Zhengzhang , Agrawal Ankit , Choudhary Alok
Published: June 2022

Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities. However, such models are insensitive to word polarity. We show that, coupled with simple information such as word spellings, word-embedding vectors can preserve both semantic regularity and conceptual polarity without supervision. We then describe a nontrivial modification to the t-distributed stochastic neighbor embedding (t-SNE) algorithm that visualizes these semantic- and polarity-preserving vectors in reduced dimensions. On a real Facebook corpus, our experiments show significant improvement in t-SNE visualization as a result of the proposed modification.
Condensed Memory Networks for Clinical Diagnostic Inferencing
Authors: Prakash Aaditya , Zhao Siyuan , Hasan Sadid , Datla Vivek , Lee Kathy , Qadir Ashequl , Liu Joey , Farri Oladimeji
Published: June 2022

Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms
Authors: Wang Wenya , Pan Sinno Jialin , Dahlmeier Daniel , Xiao Xiaokui
Published: June 2022

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multi-layer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves state-of-the-art performances compared with several baselines.
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Authors: Serban Iulian , Sordoni Alessandro , Lowe Ryan , Charlin Laurent , Pineau Joelle , Courville Aaron , Bengio Yoshua
Published: June 2022

Sequential data often possesses hierarchical structures with complex dependencies between sub-sequences, such as found between the utterances in a dialogue. To model these dependencies in a generative framework, we propose a neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with other recent neural-network architectures. We evaluate the model performance through a human evaluation study. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate both the generation of meaningful, long and diverse responses and maintaining dialogue state.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Authors: Serban Iulian , Klinger Tim , Tesauro Gerald , Talamadupula Kartik , Zhou Bowen , Bengio Yoshua , Courville Aaron
Published: June 2022

We introduce a new class of models called multiresolution recurrent neural networks, which explicitly model natural language generation at multiple levels of abstraction. The models extend the sequence-to-sequence framework to generate two parallel stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language words (e.g. sentences). The coarse sequences follow a latent stochastic process with a factorial representation, which helps the models generalize to new examples. The coarse sequences can also incorporate task-specific knowledge, when available. In our experiments, the coarse sequences are extracted using automatic procedures, which are designed to capture compositional structure and semantics. These procedures enable training the multiresolution recurrent neural networks by maximizing the exact joint log-likelihood over both sequences. We apply the models to dialogue response generation in the technical support domain and compare them with several competing models. The multiresolution recurrent neural networks outperform competing models by a substantial margin, achieving state-of-the-art results according to both a human evaluation study and automatic evaluation metrics. Furthermore, experiments show the proposed models generate more fluent, relevant and goal-oriented responses.
Incorporating Expert Knowledge into Keyphrase Extraction
Authors: Gollapalli Sujatha Das , Li Xiao-li , Yang Peng
Published: June 2022

Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words.In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surface-form, and document-structure information through sequence labeling. We experimentally illustrate that using within document features alone, our tagger trained with ConditionalRandom Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.
Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction
Authors: Ding Ying , Yu Jianfei , Jiang Jing
Published: June 2022

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for different domains. When this hidden representation is used for opinion target extraction, we find that it can outperform a number of strong baselines with a large margin.
Computational Issues in Time-Inconsistent Planning
Authors: Tang Pingzhong , Teng Yifeng , Wang Zihe , Xiao Shenke , Xu Yichong
Published: June 2022

Time-inconsistency refers to a paradox in decision making where agents exhibit inconsistent behaviors over time. Examples are procrastination where agents tend to postpone easy tasks, and abandonments where agents start a plan and quit in the middle. To capture such behaviors and to quantify inefficiency caused by such behaviors, Kleinberg and Oren (2014) propose a graph model with a certain cost structure and initiate the study of several interesting computation problems: 1) cost ratio: the worst ratio between the actual cost of the agent and the optimal cost, over all the graph instances; 2) motivating subgraph: how to motivate the agent to reach the goal by deleting nodes and edges; 3) Intermediate rewards: how to incentivize agents to reach the goal by placing intermediate rewards. Kleinberg and Oren give partial answers to these questions, but the main problems are open. In this paper, we give answers to all three open problems. First, we show a tight upper bound of cost ratio for graphs, and confirm the conjecture by Kleinberg and Oren that Akerlof’s structure is indeed the worst case for cost ratio. Second, we prove that finding a motivating subgraph is NP-hard, showing that it is generally inefficient to motivate agents by deleting nodes and edges in the graph. Last but not least, we show that computing a strategy to place minimum amount of total reward is also NP-hard and we provide a 2n- approximation algorithm.
Landmark-Based Heuristics for Goal Recognition
Authors: Pereira Ramon , Oren Nir , Meneguzzi Felipe
Published: June 2022

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.
An Analysis of Monte Carlo Tree Search
Authors: James Steven , Konidaris George , Rosman Benjamin
Published: June 2022

Monte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. Despite the vast amount of research into MCTS, the effect of modifications on the algorithm, as well as the manner in which it performs in various domains, is still not yet fully known. In particular, the effect of using knowledge-heavy rollouts in MCTS still remains poorly understood, with surprising results demonstrating that better-informed rollouts often result in worse-performing agents. We present experimental evidence suggesting that, under certain smoothness conditions, uniformly random simulation policies preserve the ordering over action preferences. This explains the success of MCTS despite its common use of these rollouts to evaluate states. We further analyse non-uniformly random rollout policies and describe conditions under which they offer improved performance.
Multi-Agent Path Finding with Delay Probabilities
Authors: Ma Hang , Kumar T. K. Satish , Koenig Sven
Published: June 2022

Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.
Unsupervised Feature Learning for 3D Scene Reconstruction with Occupancy Maps
Authors: Guizilini Vitor , Ramos Fabio
Published: June 2022

This paper addresses the task of unsupervised feature learning for three-dimensional occupancy mapping, as a way to segment higher-level structures based on raw unorganized point cloud data. In particular, we focus on detecting planar surfaces, which are common in most structured or semi-structured environments. This segmentation is then used to minimize the amount of parameters necessary to properly create a 3D occupancy model of the surveyed space, thus increasing computational speed and decreasing memory requirements. As the 3D modeling tool, an extension to Hilbert Maps was selected, since it naturally uses a feature-based representation of the environment to achieve real-time performance. Experiments conducted in simulated and real large-scale datasets show a substantial gain in performance, while decreasing the amount of stored information by orders of magnitude without sacrificing accuracy.
I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior
Authors: Schmitt Felix , Bieg Hans-Joachim , Herman Michael , Rothkopf Constantin
Published: June 2022

Human motor behavior is naturally guided by sensing the environment. To predict such sensori-motor behavior, it is necessary to model what is sensed and how actions are chosen based on the obtained sensory measurements. Although several models of human sensing haven been proposed, rarely data of the assumed sensory measurements is available. This makes statistical estimation of sensor models problematic. To overcome this issue, we propose an abstract structural estimation approach building on the ideas of Herman et al.'s Simultaneous Estimation of Rewards and Dynamics (SERD). Assuming optimal fusion of sensory information and rational choice of actions the proposed method allows to infer sensor models even in absence of data of the sensory measurements. To the best of our knowledge, this work presents the first general approach for joint inference of sensor and policy models. Furthermore, we consider its concrete implementation in the important class of sensor scheduling linear quadratic Gaussian problems. Finally, the effectiveness of the approach is demonstrated for prediction of the behavior of automobile drivers. Specifically, we model the glance and steering behavior of driving in the presence of visually demanding secondary tasks. The results show, that prediction benefits from the inference of sensor models. This is the case, especially, if also information is considered, that is contained in gaze switching behavior.
Authorship Attribution with Topic Drift Model
Authors: Yang Min , Zhu Dingju , Tang Yong , Wang Jingxuan
Published: June 2022

Authorship attribution is an active research direction due to its legal and financial importance. The goal is to identify the authorship of anonymous texts. In this paper, we propose a Topic Drift Model (TDM), monitoring the dynamicity of authors’ writing style and latent topics of interest. Our model is sensitive to the temporal information and the ordering of words, thus it extracts more information from texts.
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Authors: Killian Taylor , Konidaris George , Doshi-Velez Finale
Published: June 2022

An intriguing application of transfer learning emerges when tasks arise with similar, but not identical, dynamics. Hidden Parameter Markov Decision Processes (HiP-MDP) embed these tasks into a low-dimensional space; given the embedding parameters one can identify the MDP for a particular task. However, the original formulation of HiP-MDP had a critical flaw: the embedding uncertainty was modeled independently of the agent's state uncertainty, requiring an arduous training procedure. In this work, we apply a Gaussian Process latent variable model to jointly model the dynamics and the embedding, leading to a more elegant formulation, one that allows for better uncertainty quantification and thus more robust transfer.
SReN: Shape Regression Network for Comic Storyboard Extraction
Authors: He Zheqi , Zhou Yafeng , Wang Yongtao , Tang Zhi
Published: June 2022

The goal of storyboard extraction is to decompose the comic image into several storyboards(or frames), which is the fundamental step of comic image understanding and producing digital comic documents suitable for mobile reading. Most of existing approaches are based on hand crafted low-level visual patters like edge segments and line segments, which do not capture high-level vision. To overcome shortcomings of the existing approaches, we propose a novel architecture based on deep convolutional neural network, namely Shape Regression Network(SReN), to detect storyboards within comic images. Firstly, we use Fast R-CNN to generate rectangle bounding boxes as storyboard proposals. Then we train a deep neural network to predict quadrangles for these propos- als. Unlike existing object detection methods which only output rectangle bounding boxes, SReN can produce more precise quadrangle bounding boxes. Experimental results, evaluating on 7382 comic pages, demonstrate that SReN outperforms the state-of-the-art methods by more than 10% in terms of F1-score and page correction rate.
Keyphrase Extraction with Sequential Pattern Mining
Authors: Wang Qingren , Sheng Victor , Wu Xindong
Published: June 2022

Existing studies show that extracting a complete keyphrase candidate set is the first and crucial step to extract high quality keyphrases from documents. Based on a common sense that words do not repeatedly appear in an effective keyphrase, we propose a novel algorithm named KCSP for document-specific keyphrase candidate search using sequential pattern mining with gap constraints, which only needs to scan a document once and automatically specifies appropriate gap constraints for words without users’ participation. The experimental results confirm that it helps improve the quality of keyphrase extraction.
ATSUM: Extracting Attractive Summaries for News Propagation on Microblogs
Authors: Liu Fang , Wan Xiaojun
Published: June 2022

In this paper, we investigate how to automatically extract attractive summaries for news propagation on microblogs and propose a novel system called ATSUM to achieve this goal via text attractiveness analysis. It first analyzes the sentences in a news article and automatically predict the attractiveness score of each sentence by using the support vector regression method. The predicted attractiveness scores are then incorporated into a summarization system. Experimental results on a manually labeled dataset verify the effectiveness of the proposed methods.
Semantic Interpretation of Social Network Communities
Authors: Maheshwari Tushar , Reganti Aishwarya , Kumar Upendra , Chakraborty Tanmoy , Das Amitava
Published: June 2022

A community in a social network is considered to be a group of nodes densely connected internally and sparsely connected externally.Although previous work intensely studied network topology within a community, its semantic interpretation is hardly understood. In this paper, we attempt to understand whether individuals in a community possess similar Personalities, Values and Ethical background. Finally, we show that Personality and Values models could be used as features to discover more accurate community structure compared to the one obtained from only network information.
Neuron Learning Machine for Representation Learning
Authors: Liu Jia , Gong Maoguo , Miao Qiguang
Published: June 2022

This paper presents a novel neuron learning machine (NLM) which can extract hierarchical features from data. We focus on the single-layer neural network architecture and propose to model the network based on the Hebbian learning rule. Hebbian learning rule describes how synaptic weight changes with the activations of presynaptic and postsynaptic neurons. We model the learning rule as the objective function by considering the simplicity of the network and stability of solutions. We make a hypothesis and introduce a correlation based constraint according to the hypothesis. We find that this biologically inspired model has the ability of learning useful features from the perspectives of retaining abstract information. NLM can also be stacked to learn hierarchical features and reformulated into convolutional version to extract features from 2-dimensional data.
A Deep Learning Approach for Arabic Caption Generation Using Roots-Words
Authors: Jindal Vasu
Published: June 2022

Automatic caption generation is a key research field in the machine learning community. However, most of the current research is performed on English caption generation ignoring other languages like Arabic and Persian. In this paper, we propose a novel technique leveraging the heavy influence of root words in Arabic to automatically generate captions in Arabic. Fragments of the images are associated with root words and deep belief network pre-trained using Restricted Boltzmann Machines are used to extract words associated with image. Finally, dependency tree relations are used to generate sentence-captions by using the dependency on root words. Our approach is robust and attains BLEU-1 score of 34.8.
SEAPoT-RL: Selective Exploration Algorithm for Policy Transfer in RL
Authors: Narayan Akshay , Li Zhuoru , Leong Tze-Yun
Published: June 2022

We propose a new method for transferring a policy from a source task to a target task in model-based reinforcement learning. Our work is motivated by scenarios where a robotic agent operates in similar but challenging environments, such as hospital wards, differentiated by structural arrangements or obstacles, such as furniture. We address problems that require fast responses adapted from incomplete, prior knowledge of the agent in new scenarios. We present an efficient selective exploration strategy that maximally reuses the source task policy. Reuse efficiency is effected through identifying sub-spaces that are different in the target environment, thus limiting the exploration needed in the target task. We empirically show that SEAPoT performs better in terms of jump starts and cumulative average rewards, as compared to existing state-of-the-art policy reuse methods.
Towards User Personality Profiling from Multiple Social Networks
Authors: Buraya Kseniya , Farseev Aleksandr , Filchenkov Andrey , Chua Tat-Seng
Published: June 2022

The exponential growth of online social networks has inspired us to tackle the problem of individual user attributes inference from the Big Data perspective. It is well known that various social media networks exhibit different aspects of user interactions, and thus represent users from diverse points of view. In this preliminary study, we make the first step towards solving the significant problem of personality profiling from multiple social networks. Specifically, we tackle the task of relationship prediction, which is closely related to our desired problem. Experimental results show that the incorporation of multi-source data helps to achieve better prediction performance as compared to single-source baselines.
Hybridizing Interval Temporal Logics: The First Step
Authors: Wałęga Przemysław
Published: June 2022

Temporal reasoning is one of the main topics investigated within the field of Artificial Intelligence. Formal methods for temporal reasoning arouse interest of researchers from both theoretical and practical point of view. Such methods enable modelling and studying human-like reasoning mechanisms, thus constituting a valuable tool in cognitive science, philosophy, and linguistics. On the other hand, temporal reasoning formalisms have a number of potential practical applications, e.g., in task scheduling, action planning, and temporal databases. Temporal reasoning methods may be divided into point-based and interval-based depending on the type of the considered primitive ontological objects. My work revolves around the latter type of methods which seem to be more human-like and more suitable for such applications as continuous process modelling. My main result is that the satisfiability problem in a hybridized fragment of Halpern-Shoham logic in which formulas are in a form of conjunction of Horn clauses and only box modal operators are allowed (diamond operators are disallowed) is NP-complete over reflexive, as well as over irreflexive and dense time frames. Before hybridization this fragment was P-complete over such time structures.
Coalition Structure Generation Utilizing Graphical Representation of Partition Function Games
Authors: Nomoto Kazuki , Sakurai Yuko , Yokoo Makoto
Published: June 2022

Forming effective coalition is a central research challenge in AI and multi-agent systems. The Coalition Structure Generation (CSG) problem is well-known as one of major research topics in coalitional games. The CSG problem is to partition a set of agents into coalitions so that the sum of utilities is maximized. This paper studies a CSG problem for partition function games (PFGs), where the value of a coalition differs depending on the formation of other coalitions generated by non-member agents. Traditionally, in PFGs, the input of a coalitional game is a black-box function called a partition function that maps an embedded coalition (a coalition and the coalition structure) to its value. Recently, a novel concise representation scheme called the Partition Decision Trees (PDTs) has been proposed. The PDTs is a graphical representation based on multiple rules. In this paper, we propose new algorithms that can solve a CSG problem by utilizing PDTs representation. More specifically, we modify PDTs representation to effectively handle negative value rules and apply the depth-first branch and bound algorithm. We experimentally show that our algorithm can solve a CSG problem well.
A Finite Memory Automaton for Two-Armed Bernoulli Bandit Problems
Authors: Rao Ariel
Published: June 2022

Existing approaches to the multi-armed bandit (MAB) primarily rely on perfect recall of past actions to generate estimates for arm payoff probabilities; it is further assumed that the decision maker knows whether arm payoff probabilities can change. To capture the computational limitations many decision making systems face, we explore performance under bounded resources in the form of imperfect recall of past information. We present a finite memory automaton (FMA) designed to solve static and dynamic MAB problems. The FMA demonstrates that an agent can learn a low regret strategy without knowing whether arm payoff probabilities are static or dynamic and without having perfect recall of past actions. Roughly speaking, the automaton works by maintaining a relative ranking of arms rather than estimating precise payoff probabilities.
Soft and Cost MDD Propagators
Authors: Perez Guillaume , Régin Jean-Charles
Published: June 2022

Recent developments of efficient propagators, operations and creation methods for MDDs allow us to directly build efficient MDD-based models, without the need for intermediate data structures. In this paper, we take another step in this direction by improving the propagators of cost MDDs. In addition, we introduce a soft MDD propagator in order to deal with unsatisfiable problems. This directly offers cost and soft versions for table constraints and any constraints which can be represented by an MDD (regular, slide, knapsack...).
Phase Transitions for Scale-Free SAT Formulas
Authors: Friedrich Tobias , Krohmer Anton , Rothenberger Ralf , Sutton Andrew
Published: June 2022

Recently, a number of non-uniform random satisfiability models have been proposed that are closer to practical satisfiability problems in some characteristics. In contrast to uniform random Boolean formulas, scale-free formulas have a variable occurrence distribution that follows a power law. It has been conjectured that such a distribution is a more accurate model for some industrial instances than the uniform random model. Though it seems that there is already an awareness of a threshold phenomenon in such models, there is still a complete picture lacking. In contrast to the uniform model, the critical density threshold does not lie at a single point, but instead exhibits a functional dependency on the power-law exponent. For scale-free formulas with clauses of length k=2, we give a lower bound on the phase transition threshold as a function of the scaling parameter. We also perform computational studies that suggest our bound is tight and investigate the critical density for formulas with higher clause lengths. Similar to the uniform model, on formulas with k>=3, we find that the phase transition regime corresponds to a set of formulas that are difficult to solve by backtracking search.
Between Subgraph Isomorphism and Maximum Common Subgraph
Authors: Hoffmann Ruth , McCreesh Ciaran , Reilly Craig
Published: June 2022

When a small pattern graph does not occur inside a larger target graph, we can ask how to find "as much of the pattern as possible" inside the target graph. In general, this is known as the maximum common subgraph problem, which is much more computationally challenging in practice than subgraph isomorphism. We introduce a restricted alternative, where we ask if all but k vertices from the pattern can be found in the target graph. This allows for the development of slightly weakened forms of certain invariants from subgraph isomorphism which are based upon degree and number of paths.  We show that when k is small, weakening the invariants still retains much of their effectiveness. We are then able to solve this problem on the standard problem instances used to benchmark subgraph isomorphism algorithms, despite these instances being too large for current maximum common subgraph algorithms to handle. Finally, by iteratively increasing k, we obtain an algorithm which is also competitive for the maximum common subgraph
Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge
Authors: Dai Wang-Zhou , Zhou Zhi-Hua
Published: June 2022

In many real tasks there are human knowledge expressed in logic formulae as well as data samples described by raw features (e.g., pixels, strings). It is popular to apply SRL or PILPtechniques to exploit human knowledge through learning of symbolic data, or statistical learning techniques to learn from the raw data samples; however, it is often desired to directly exploit these logic formulae on raw data processing, like human beings utilizing knowledge to guide perception. In this paper, we propose an approach, LASIN, which combines Logical Abduction and Statistical Induction. The LASIN approach generates candidate hypotheses based on the abduction of first-order formulae, and then, the hypotheses are exploited as constraints for statistical induction. We apply theLASIN approach to the learning of representation of written primitives, where a primitive is a basic component in human writing. Our results show that the discovered primitives are reasonable for human perception, and these primitives, if used in learning tasks such as classification and domain adaptation, lead to better performances than simply applying feature learning based on raw data only.
Towards Continuous Scientific Data Analysis and Hypothesis Evolution
Authors: Gil Yolanda , Garijo Daniel , Ratnakar Varun , Mayani Rajiv , Adusumilli Ravali , Boyce Hunter , Srivastava Arunima , Mallick Parag
Published: June 2022

Scientific data is continuously generated throughout the world. However, analyses of these data are typically performed exactly once and on a small fragment of recently generated data. Ideally, data analysis would be a continuous process that uses all the data available at the time, and would be automatically re-run and updated when new data appears. We present a framework for automated discovery from data repositories that tests user-provided hypotheses using expert-grade data analysis strategies, and reassesses hypotheses when more data becomes available. Novel contributions of this approach include a framework to trigger new analyses appropriate for the available data through lines of inquiry that support progressive hypothesis evolution, and a representation of hypothesis revisions with provenance records that can be used to inspect the results. We implemented our approach in the DISK framework, and evaluated it using two scenarios from cancer multi-omics: 1) data for new patients becomes available over time, 2) new types of data for the same patients are released. We show that in all scenarios DISK updates the confidence on the original hypotheses as it automatically analyzes new data.
Identifying Useful Inference Paths in Large Commonsense Knowledge Bases by Retrograde Analysis
Authors: Sharma Abhishek , Goolsbey Keith
Published: June 2022

Commonsense reasoning at scale is a critical problem for modern cognitive systems. Large theories have millions of axioms, but only a handful are relevant for answering a given goal query. Irrelevant axioms increase the search space, overwhelming unoptimized inference engines in large theories. Therefore, methods that help in identifying useful inference paths are an essential part of large cognitive systems. In this paper, we use retrograde analysis to build a database of proof paths that lead to at least one successful proof. This database helps the inference engine identify more productive parts of the search space. A heuristic based on this approach is used to order nodes during a search. We study the efficacy of this approach on hundreds of queries from the Cyc KB. Empirical results show that this approach leads to significant reduction in inference time.
Natural Language Acquisition and Grounding for Embodied Robotic Systems
Authors: Alomari Muhannad , Duckworth Paul , Hogg David , Cohn Anthony
Published: June 2022

We present a cognitively plausible novel framework capable of learning the grounding in visual semantics and the grammar of natural language commands given to a robot in a table top environment. The input to the system consists of video clips of a manually controlled robot arm, paired with natural language commands describing the action. No prior knowledge is assumed about the meaning of words, or the structure of the language, except that there are different classes of words (corresponding to observable actions, spatial relations, and objects and their observable properties). The learning process automatically clusters the continuous perceptual spaces into concepts corresponding to linguistic input. A novel relational graph representation is used to build connections between language and vision. As well as the grounding of language to perception, the system also induces a set of probabilistic grammar rules. The knowledge learned is used to parse new commands involving previously unseen objects.
Flexible Model Induction through Heuristic Process Discovery
Authors: Langley Pat , Arvay Adam
Published: June 2022

Inductive process modeling involves the construction of explanatory accounts for multivariate time series. As typically specified, background knowledge is available in the form of generic processes that serve as the building blocks for candidate model structures. In this paper, we present a more flexible approach that, when available processes are insufficient to construct an acceptable model, automatically produces new generic processes that let it complete the task. We describe FPM, a system that implements this idea by composing knowledge about algebraic rate expressions and about conceptual processes like predation and remineralization in ecology. We demonstrate empirically FPM's ability to construct new generic processes when necessary and to transfer them later to new modeling tasks. We also compare its failure-driven approach with a naive scheme that generates all possible processes at the outset. We conclude by discussing prior work on equation discovery and model construction, along with plans for additional research.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Authors: You Jiaxuan , Li Xiaocheng , Low Melvin , Lobell David , Ermon Stefano
Published: June 2022

Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts. A central challenge is yield estimation, i.e., predicting crop yields before harvest. We introduce a scalable, accurate, and inexpensive method to predict crop yields using publicly available remote sensing data. Our approach improves existing techniques in three ways. First, we forego hand-crafted features traditionally used in the remote sensing community and propose an approach based on modern representation learning ideas. We also introduce a novel dimensionality reduction technique that allows us to train a Convolutional Neural Network or Long-short Term Memory network and automatically learn useful features even when labeled training data are scarce. Finally, we incorporate a Gaussian Process component to explicitly model the spatio-temporal structure of the data and further improve accuracy. We evaluate our approach on county-level soybean yield prediction in the U.S. and show that it outperforms competing techniques.
Cross-View People Tracking by Scene-Centered Spatio-Temporal Parsing
Authors: Xu Yuanlu , Liu Xiaobai , Qin Lei , Zhu Song-Chun
Published: June 2022

In this paper, we propose a Spatio-temporal Attributed Parse Graph (ST-APG) to integrate semantic attributes with trajectories for cross-view people tracking. Given videos from multiple cameras with overlapping field of view (FOV), our goal is to parse the videos and organize the trajectories of all targets into a scene-centered representation. We leverage rich semantic attributes of human, e.g., facing directions, postures and actions, to enhance cross-view tracklet associations, besides frequently used appearance and geometry features in the literature.In particular, the facing direction of a human in 3D, once detected, often coincides with his/her moving direction or trajectory. Similarly, the actions of humans, once recognized, provide strong cues for distinguishing one subject from the others. The inference is solved by iteratively grouping tracklets with cluster sampling and estimating people semantic attributes by dynamic programming.In experiments, we validate our method on one public dataset and create another new dataset that records people's daily life in public, e.g., food court, office reception and plaza, each of which includes 3-4 cameras. We evaluate the proposed method on these challenging videos and achieve promising multi-view tracking results.
Weakly-Supervised Deep Nonnegative Low-Rank Model for Social Image Tag Refinement and Assignment
Authors: Li Zechao , Tang Jinhui
Published: June 2022

It has been well known that the user-provided tags of social images are imperfect, i.e., there exist noisy, irrelevant or incomplete tags. It heavily degrades the performance of many multimedia tasks. To alleviate this problem, we propose a Weakly-supervised Deep Nonnegative Low-rank model (WDNL) to improve the quality of tags by integrating the low-rank model with deep feature learning. A nonnegative low-rank model is introduced to uncover the intrinsic relationships between images and tags by simultaneously removing noisy or irrelevant tags and complementing missing tags. The deep architecture is leveraged to seamlessly connect the visual content and the semantic tag. That is, the proposed model can well handle the scalability by assigning tags to new images. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed method compared with some state-of-the-art methods.
Image Cosegmentation via Saliency-Guided Constrained Clustering with Cosine Similarity
Authors: Tao Zhiqiang , Liu Hongfu , Fu Huazhu , Fu Yun
Published: June 2022

Cosegmentation jointly segments the common objects from multiple images. In this paper, a novel clustering algorithm, called Saliency-Guided Constrained Clustering approach with Cosine similarity (SGC3), is proposed for the image cosegmentation task, where the common foregrounds are extracted via a one-step clustering process. In our method, the unsupervised saliency prior is utilized as a partition-level side information to guide the clustering process. To guarantee the robustness to noise and outlier in the given prior, the similarities of instance-level and partition-level are jointly computed for cosegmentation. Specifically, we employ cosine distance to calculate the feature similarity between data point and its cluster centroid, and introduce a cosine utility function to measure the similarity between clustering result and the side information. These two parts are both based on the cosine similarity, which is able to capture the intrinsic structure of data, especially for the non-spherical cluster structure. Finally, a K-means-like optimization is designed to solve our objective function in an efficient way. Experimental results on two widely-used datasets demonstrate our approach achieves competitive performance over the state-of-the-art cosegmentation methods.