bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint
Authors: Zhao Li , Huang Minlie , Yao Ziyu , Su Rongwei , Jiang Yingying , Zhu Xiaoyan
Published: June 2022

Multinomial Naive Bayes with Expectation Maximization (MNB-EM) is a standard semi-supervised learning method to augment Multinomial Naive Bayes (MNB) for text classification. Despite its success, MNB-EM is not stable, and may succeed or fail to improve MNB. We believe that this is because MNB-EM lacks the ability to preserve the class distribution on words. In this paper, we propose a novel method to augment MNB-EM by leveraging the word-level statistical constraint to preserve the class distribution on words. The word-level statistical constraints are further converted to constraints on document posteriors generated by MNB-EM. Experiments demonstrate that our method can consistently improve MNB-EM, and outperforms state-of-art baselines remarkably.
Siamese Recurrent Architectures for Learning Sentence Similarity
Authors: Mueller Jonas , Thyagarajan Aditya
Published: June 2022

We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-embedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding.
Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning
Authors: Mitra Arindam , Baral Chitta
Published: June 2022

A group of researchers from Facebook has recently proposed a set of 20 question-answering tasks (Facebook's bAbl dataset) as a challenge for the natural language understanding ability of an intelligent agent. These tasks are designed to measure various skills of an agent, such as: fact based question-answering, simple induction, the ability to find paths, co-reference resolution and many more. Their goal is to aid in the development of systems that can learn to solve such tasks and to allow a proper evaluation of such systems. They show existing systems cannot fully solve many of those toy tasks. In this work, we present a system that excels at all the tasks except one. The proposed model of the agent uses the Answer Set Programming (ASP) language as the primary knowledge representation and reasoning language along with the standard statistical Natural Language Processing (NLP) models. Given a training dataset containing a set of narrations, questions and their answers, the agent jointly uses a translation system, an Inductive Logic Programming algorithm and Statistical NLP methods to learn the knowledge needed to answer similar questions. Our results demonstrate that the introduction of a reasoning module significantly improves the performance of an intelligent agent.
Modeling Evolving Relationships Between Characters in Literary Novels
Authors: Chaturvedi Snigdha , Srivastava Shashank , Daume III Hal , Dyer Chris
Published: June 2022

Studying characters plays a vital role in computationally representing and interpreting narratives. Unlike previous work, which has focused on inferring character roles, we focus on the problem of modeling their relationships. Rather than assuming a fixed relationship for a character pair, we hypothesize that relationships temporally evolve with the progress of the narrative, and formulate the problem of relationship modeling as a structured prediction problem. We propose a semi-supervised framework to learn relationship sequences from fully as well as partially labeled data. We present a Markovian model capable of accumulating historical beliefs about the relationship and status changes. We use a set of rich linguistic and semantically motivated features that incorporate world knowledge to investigate the textual content of narrative. We empirically demonstrate that such a framework outperforms competitive baselines.
Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching
Authors: Wick Michael , Kanani Pallika , Pocock Adam
Published: June 2022

We present a method that consumes a large corpus of multilingual text and produces a single, unified word embedding in which the word vectors generalize across languages. In contrast to current approaches that require language identification, our method is agnostic about the languages with which the documents in the corpus are expressed, and does not rely on parallel corpora to constrain the spaces. Instead we utilize a small set of human provided word translations---which are often freely and readily available. We can encode such word translations as hard constraints in the model's objective functions; however, we find that we can more naturally constrain the space by allowing words in one language to borrow distributional statistics from context words in another language. We achieve this via a process we term artificial code-switching. As the name suggests, we induce code-switching so that words across multiple languages appear in contexts together. Not only do embedding models trained on code-switched data learn common cross-lingual structure, the common structure allows an NLP model trained in a source language to generalize to multiple target languages (achieving up to 80% of the accuracy of models trained with target-language data).
Character-Aware Neural Language Models
Authors: Kim Yoon , Jernite Yacine , Sontag David , Rush Alexander
Published: June 2022

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway net work over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.
Distant IE by Bootstrapping Using Lists and Document Structure
Authors: Bing Lidong , Ling Mingyang , Wang Richard , Cohen William
Published: June 2022

Distant labeling for information extraction (IE) suffers from noisy training data. We describe a way of reducing the noise associated with distant IE by identifying coupling constraints between potential instance labels. As one example of coupling,items in a list are likely to have the same label.A second example of coupling comes from analysis of document structure: in some corpora,sections can be identified such that items in the same section are likely to have the same label. Such sections do not exist in all corpora, but we show that augmenting a large corpus with coupling constraints from even a small, well-structured corpus can improve performance substantially, doubling F1 on one task.
A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification
Authors: Liu Shulin , Liu Kang , He Shizhu , Zhao Jun
Published: June 2022

Global information such as event-event association, and latent local information such as fine-grained entity types, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-of-speech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as event-event association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set.
Global Distant Supervision for Relation Extraction
Authors: Han Xianpei , Sun Le
Published: June 2022

Machine learning approaches to relation extraction are typically supervised and require expensive labeled data. To break the bottleneck of labeled data, a promising approach is to exploit easily obtained indirect supervision knowledge – which we usually refer to as distant supervision (DS). However, traditional DS methods mostly only exploit one specific kind of indirect supervision knowledge – the relations/facts in a given knowledge base, thus often suffer from the problem of lack of supervision. In this paper, we propose a global distant supervision model for relation extraction, which can: 1) compensate the lack of supervision with a wide variety of indirect supervision knowledge; and 2) reduce the uncertainty in DS by performing joint inference across relation instances. Experimental results show that, by exploiting the consistency between relation labels, the consistency between relations and arguments, and the consistency between neighbor instances using Markov logic, our method significantly outperforms traditional DS approaches.
Gated Neural Networks for Targeted Sentiment Analysis
Authors: Zhang Meishan , Zhang Yue , Vo Duy-Tin
Published: June 2022

Targeted sentiment analysis classifies the sentiment polarity towards each target entity mention in given text documents. Seminal methods extract manual discrete features from automatic syntactic parse trees in order to capture semantic information of the enclosing sentence with respect to a target entity mention. Recently, it has been shown that competitive accuracies can be achieved without using syntactic parsers, which can be highly inaccurate on noisy text such as tweets. This is achieved by applying distributed word representations and rich neural pooling functions over a simple and intuitive segmentation of tweets according to target entity mentions. In this paper, we extend this idea by proposing a sentence-level neural model to address the limitation of pooling functions, which do not explicitly model tweet-level semantics. First, a bi-directional gated neural network is used to connect the words in a tweet so that pooling functions can be applied over the hidden layer instead of words for better representing the target and its contexts. Second, a three-way gated neural network structure is used to model the interaction between the target mention and its surrounding contexts. Experiments show that our proposed model gives significantly higher accuracies compared to the current best method for targeted sentiment analysis.
A Joint Model for Question Answering over Multiple Knowledge Bases
Authors: Zhang Yuanzhe , He Shizhu , Liu Kang , Zhao Jun
Published: June 2022

As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.
Argument Mining from Speech: Detecting Claims in Political Debates
Authors: Lippi Marco , Torroni Paolo
Published: June 2022

The automatic extraction of arguments from text, also known as argument mining, has recently become a hot topic in artificial intelligence. Current research has only focused on linguistic analysis. However, in many domains where communication may be also vocal or visual, paralinguistic features too may contribute to the transmission of the message that arguments intend to convey. For example, in political debates a crucial role is played by speech. The research question we address in this work is whether in such domains one can improve claim detection for argument mining, by employing features from text and speech in combination. To explore this hypothesis, we develop a machine learning classifier and train it on an original dataset based on the 2015 UK political elections debate.
A Joint Model for Entity Set Expansion and Attribute Extraction from Web Search Queries
Authors: Zhang Zhenzhong , Sun Le , Han Xianpei
Published: June 2022

Entity Set Expansion (ESE) and Attribute Extraction (AE) are usually treated as two separate tasks in Information Extraction (IE). However, the two tasks are tightly coupled, and each task can benefit significantly from the other by leveraging the inherent relationship between entities and attributes. That is, 1) an attribute is important if it is shared by many typical entities of a class; 2) an entity is typical if it owns many important attributes of a class. Based on this observation, we propose a joint model for ESE and AE, which models the inherent relationship between entities and attributes as a graph. Then a graph reinforcement algorithm is proposed to jointly mine entities and attributes of a specific class. Experimental results demonstrate the superiority of our method for discovering both new entities and new attributes.
Identifying Search Keywords for Finding Relevant Social Media Posts
Authors: Wang Shuai , Chen Zhiyuan , Liu Bing , Emery Sherry
Published: June 2022

In almost any application of social media analysis, the user is interested in studying a particular topic or research question. Collecting posts or messages relevant to the topic from a social media source is a necessary step. Due to the huge size of social media sources (e.g., Twitter and Facebook), one has to use some topic keywords to search for possibly relevant posts. However, gathering a good set of keywords is a very tedious and time-consuming task. It often involves a lengthy iterative process of searching and manual reading. In this paper, we propose a novel technique to help the user identify topical search keywords. Our experiments are carried out on identifying such keywords for five (5) real-life application topics to be used for searching relevant tweets from the Twitter API. The results show that the proposed method is highly effective.
Topical Analysis of Interactions Between News and Social Media
Authors: Hua Ting , Ning Yue , Chen Feng , Lu Chang-Tien , Ramakrishnan Naren
Published: June 2022

The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends.Researchers have explored such interactions by examining volume changes or information diffusions,however, most of them ignore the semantical and topical relationships between news and social media data.Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge.We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions.We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets.By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.
Microsummarization of Online Reviews: An Experimental Study
Authors: Mason Rebecca , Gaska Benjamin , Van Durme Benjamin , Choudhury Pallavi , Hart Ted , Dolan Bill , Toutanova Kristina , Mitchell Margaret
Published: June 2022

Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and find we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we find our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.
Approximation Algorithms for Route Planning with Nonlinear Objectives
Authors: Yang Ger , Nikolova Evdokia
Published: June 2022

We consider optimal route planning when the objective function is a general nonlinear and non-monotonic function. Such an objective models user behavior more accurately, for example, when a user is risk-averse, or the utility function needs to capture a penalty for early arrival. It is known that as non-linearity arises, the problem can become NP-hard and little is known on computing optimal solutions when in addition there is no monotonicity guarantee. We show that an approximately optimal non-simple path can be efficiently computed under some natural constraints. In particular, we provide a fully polynomial approximation scheme under hop constraints. Our approximation algorithm can extend to run in pseudo-polynomial time under an additional linear constraint that sometimes is useful. As a by-product, we show that our algorithm can be applied to the problem of finding a path that is most likely to be on time for a given deadline.
Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies
Authors: Cimatti Alessandro , Micheli Andrea , Roveri Marco
Published: June 2022

The Temporal Network with Uncertainty (TNU) modeling framework is used to represent temporal knowledge in presence of qualitative temporal uncertainty. Dynamic Controllability (DC) is the problem of deciding the existence of a strategy for scheduling the controllable time points of the network observing past happenings only. In this paper, we address the DC problem for a very general class of TNU, namely Disjunctive Temporal Network with Uncertainty. We make the following contributions. First, we define strategies in the form of an executable language; second, we propose the first decision procedure to check whether a given strategy is a solution for the DC problem; third we present an efficient algorithm for strategy synthesis based on techniques derived from Timed Games and Satisfiability Modulo Theory. The experimental evaluation shows that the approach is superior to the state-of-the-art.
Solving Risk-Sensitive POMDPs With and Without Cost Observations
Authors: Hou Ping , Yeoh William , Varakantham Pradeep
Published: June 2022

Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in Risk-Sensitive POMDPs (RS-POMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new search-based algorithm to solve RS-POMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated with real-world data.
Goal Recognition Design with Non-Observable Actions
Authors: Keren Sarah , Gal Avigdor , Karpas Erez
Published: June 2022

Goal recognition design involves the offline analysis of goal recognition models by formulating measures that assess the ability to perform goal recognition within a model and finding efficient ways to compute and optimize them. In this work we relax the full observability assumption of earlier work by offering a new generalized model for goal recognition design with non-observable actions. A model with partial observability is relevant to goal recognition applications such as assisted cognition and security, which suffer from reduced observability due to sensor malfunction or lack of sufficient budget. In particular we define a worst case distinctiveness (wcd) measure that represents the maximal number of steps an agent can take in a system before the observed portion of his trajectory reveals his objective. We present a method for calculating wcd based on a novel compilation to classical planning and propose a method to improve the design using sensor placement. Our empirical evaluation shows that the proposed solutions effectively compute and improve wcd.
Efficient Spatio-Temporal Tactile Object Recognition with Randomized Tiling Convolutional Networks in a Hierarchical Fusion Strategy
Authors: Cao Lele , Kotagiri Ramamohanarao , Sun Fuchun , Li Hongbo , Huang Wenbing , Aye Zay Maung Maung
Published: June 2022

Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data into spatial and temporal threads, and incorporates the strength of randomized tiling convolutional networks. Experimental evaluations show that it outperforms some state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance; we also achieve an order-of-magnitude speedup over equivalent networks with pretraining and finetuning. Practical suggestions and hints are summarized in the end for effectively handling the tactile data.
Selectively Reactive Coordination for a Team of Robot Soccer Champions
Authors: Mendoza Juan Pablo , Biswas Joydeep , Cooksey Philip , Wang Richard , Klee Steven , Zhu Danny , Veloso Manuela
Published: June 2022

CMDragons 2015 is the champion of the RoboCup Small Size League of autonomous robot soccer. The team won all of its six games, scoring a total of 48 goals and conceding 0. This unprecedented dominant performance is the result of various features, but we particularly credit our novel offense multi-robot coordination. This paper thus presents our Selectively Reactive Coordination (SRC) algorithm, consisting of two layers: A coordinated opponent-agnostic layer enables the team to create its own plans, setting the pace of the game in offense. An individual opponent-reactive action selection layer enables the robots to maintain reactivity to different opponents. We demonstrate the effectiveness of our coordination through results from RoboCup 2015, and through controlled experiments using a physics-based simulator and an automated referee.
On Learning Causal Models from Relational Data
Authors: Lee Sanghack , Honavar Vasant
Published: June 2022

Many applications call for learning causal models from relational data. We investigate Relational Causal Models (RCM) under relational counterparts of adjacency-faithfulness and orientation-faithfulness, yielding a simple approach to identifying a subset of relational d-separation queries needed for determining the structure of an RCM using d-separation against an unrolled DAG representation of the RCM. We provide original theoretical analysis that offers the basis of a sound and efficient algorithm for learning the structure of an RCM from relational data. We describe RCD-Light, a sound and efficient constraint-based algorithm that is guaranteed to yield a correct partially-directed RCM structure with at least as many edges oriented as in that produced by RCD, the only other existing algorithm for learning RCM. We show that unlike RCD, which requires exponential time and space, RCD-Light requires only polynomial time and space to orient the dependencies of a sparse RCM.
Closed-Form Gibbs Sampling for Graphical Models with Algebraic Constraints
Authors: Mohasel Afshar Hadi , Sanner Scott , Webers Christfried
Published: June 2022

Probabilistic inference in many real-world problems requires graphical models with deterministic algebraic constraints between random variables (e.g., Newtonian mechanics, Pascal’s law, Ohm’s law) that are known to be problematic for many inference methods such as Monte Carlo sampling. Fortunately, when such constraintsare invertible, the model can be collapsed and the constraints eliminated through the well-known Jacobian-based change of variables. As our first contributionin this work, we show that a much broader classof algebraic constraints can be collapsed by leveraging the properties of a Dirac delta model of deterministic constraints. Unfortunately, the collapsing processcan lead to highly piecewise densities that pose challenges for existing probabilistic inference tools. Thus,our second contribution to address these challenges is to present a variation of Gibbs sampling that efficiently samples from these piecewise densities. The key insight to achieve this is to introduce a class of functions that (1) is sufficiently rich to approximate arbitrary models up to arbitrary precision, (2) is closed under dimension reduction (collapsing) for models with (non)linear algebraic constraints and (3) always permits one analytical integral sufficient to automatically derive closed-form conditionals for Gibbs sampling. Experiments demonstrate the proposed sampler converges at least an order of magnitude faster than existing Monte Carlo samplers.
RAO*: An Algorithm for Chance-Constrained POMDP's
Authors: Rodrigues Quemel e Assis Santana Pedro , Thiébaux Sylvie , Williams Brian
Published: June 2022

Autonomous agents operating in partially observable stochastic environments often face the problem of optimizing expected performance while bounding the risk of violating safety constraints. Such problems can be modeled as chance-constrained POMDP's (CC-POMDP's). Our first contribution is a systematic derivation of execution risk in POMDP domains, which improves upon how chance constraints are handled in the constrained POMDP literature. Second, we present RAO*, a heuristic forward search algorithm producing optimal, deterministic, finite-horizon policies for CC-POMDP's. In addition to the utility heuristic, RAO* leverages an admissible execution risk heuristic to quickly detect and prune overly-risky policy branches. Third, we demonstrate the usefulness of RAO* in two challenging domains of practical interest: power supply restoration and autonomous science agents.
Learning Ensembles of Cutset Networks
Authors: Rahman Tahrima , Gogate Vibhav
Published: June 2022

Cutset networks — OR (decision) trees that have Bayesian networks whose treewidth is bounded by one at each leaf — are a new class of tractable probabilistic models that admit fast, polynomial-time inference and learning algorithms. This is unlike other state-of-the-art tractable models such as thin junction trees, arithmetic circuits and sum-product networks in which inference is fast and efficient but learning can be notoriously slow. In this paper, we take advantage of this unique property to develop fast algorithms for learning ensembles of cutset networks. Specifically, we consider generalized additive mixtures of cutset networks and develop sequential boosting-based and parallel bagging-based approaches for learning them from data. We demonstrate, via a thorough experimental evaluation, that our new algorithms are superior to competing approaches in terms of test-set log-likelihood score and learning time.
On Parameter Tying by Quantization
Authors: Chou Li , Sarkhel Somdeb , Ruozzi Nicholas , Gogate Vibhav
Published: June 2022

The maximum likelihood estimator (MLE) is generally asymptotically consistent but is susceptible to over-fitting. To combat this problem, regularization methods which reduce the variance at the cost of (slightly) increasing the bias are often employed in practice. In this paper, we present an alternative variance reduction (regularization) technique that quantizes the MLE estimates as a post processing step, yielding a smoother model having several tied parameters. We provide and prove error bounds for our new technique and demonstrate experimentally that it often yields models having higher test-set log-likelihood than the ones learned using the MLE. We also propose a new importance sampling algorithm for fast approximate inference in models having several tied parameters. Our experiments show that our new inference algorithm is superior to existing approaches such as Gibbs sampling and MC-SAT on models having tied parameters, learned using our quantization-based approach.
Learning to Answer Questions from Image Using Convolutional Neural Network
Authors: Ma Lin , Lu Zhengdong , Li Hang
Published: June 2022

In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA) task. Our proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer. More specifically, our model consists of three CNNs: one image CNN to encode the image content, one sentence CNN to compose the words of the question, and one multimodal convolution layer to learn their joint representation for the classification in the space of candidate answer words. We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA datasets, which are two benchmark datasets for image QA, with the performances significantly outperforming the state-of-the-art.
Learning Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval
Authors: Zhu Fan , Xie Jin , Fang Yi
Published: June 2022

Sketch-based 3D shape retrieval, which returns a set of relevant 3D shapes based on users' input sketch queries, has been receiving increasing attentions in both graphics community and vision community. In this work, we address the sketch-based 3D shape retrieval problem with a novel Cross-Domain Neural Networks (CDNN) approach, which is further extended to Pyramid Cross-Domain Neural Networks (PCDNN) by cooperating with a hierarchical structure. In order to alleviate the discrepancies between sketch features and 3D shape features, a neural network pair that forces identical representations at the target layer for instances of the same class is trained for sketches and 3D shapes respectively. By constructing cross-domain neural networks at multiple pyramid levels, a many-to-one relationship is established between a 3D shape feature and sketch features extracted from different scales. We evaluate the effectiveness of both CDNN and PCDNN approach on the extended large-scale SHREC 2014 benchmark and compare with some other well established methods. Experimental results suggest that both CDNN and PCDNN can outperform state-of-the-art performance, where PCDNN can further improve CDNN when employing a hierarchical structure.
Transductive Zero-Shot Recognition via Shared Model Space Learning
Authors: Guo Yuchen , Ding Guiguang , Jin Xiaoming , Wang Jianmin
Published: June 2022

Zero-shot Recognition (ZSR) is to learn recognition models for novel classes without labeled data. It is a challenging task and has drawn considerable attention in recent years. The basic idea is to transfer knowledge from seen classes via the shared attributes. This paper focus on the transductive ZSR, i.e., we have unlabeled data for novel classes. Instead of learning models for seen and novel classes separately as in existing works, we put forward a novel joint learning approach which learns the shared model space (SMS) for models such that the knowledge can be effectively transferred between classes using the attributes. An effective algorithm is proposed for optimization. We conduct comprehensive experiments on three benchmark datasets for ZSR. The results demonstrates that the proposed SMS can significantly outperform the state-of-the-art related approaches which validates its efficacy for the ZSR task.
Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair
Authors: Xiong Hao , Liu Tongliang , Tao Dacheng
Published: June 2022

Videos can be conserved on different media. However, storing on media such as films and hard disks can suffer from unexpected data loss, for instance from physical damage. Repair of missing or damaged pixels is essential for video maintenance and preservation. Most methods seek to fill in missing holes by synthesizing similar textures from local or global frames. However, this can introduce incorrect contexts, especially when the missing hole or number of damaged frames is large. Furthermore, simple texture synthesis can introduce artifacts in undamaged and recovered areas. To address aforementioned problems, we propose the diversified dynamical Gaussian process latent variable model (D2GPLVM) for considering the variety in existing videos and thus introducing a diversity encouraging prior to inducing points. The aim is to ensure that the trained inducing points, which are a smaller set of all observed undamaged frames, are more diverse and resistant for context-aware and artifacts-free based video repair. The defined objective function in our proposed model is initially not analytically tractable and must be solved by variational inference. Finally, experimental testing illustrates the robustness and effectiveness of our method for damaged video repair.
Multi-View 3D Human Tracking in Crowded Scenes
Authors: Liu Xiaobai
Published: June 2022

This paper presents a robust multi-view method for tracking people in 3D scene. Our method distinguishes itself from previous works in two aspects. Firstly, we define a set of binary spatial relationships for individual subjects or pairs of subjects that appear at the same time, e.g. being left or right, being closer or further to the camera, etc. These binary relationships directly reflect relative positions of subjects in 3D scene and thus should be persisted during inference. Secondly, we introduce an unified probabilistic framework to exploit binary spatial constraints for simultaneous 3D localization and cross-view human tracking. We develop a cluster Markov Chain Monte Carlo method to search the optimal solution. We evaluate our method on both public video benchmarks and newly built multi-view video dataset. Results with comparisons showed that our method could achieve state-of-the-art tracking results and meter-level 3D localization on challenging videos.
Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
Authors: Bagherinezhad Hessam , Hajishirzi Hannaneh , Choi Yejin , Farhadi Ali
Published: June 2022

Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.
Intelligent Habitat Restoration Under Uncertainty
Authors: Urli Tommaso , Brotánková Jana , Kilby Philip , Van Hentenryck Pascal
Published: June 2022

Conservation is an ethic of sustainable use of natural resources which focuses on the preservation of biodiversity, i.e., the degree of variation of life. Conservation planning seeks to reach this goal by means of deliberate actions, aimed at the protection (or restoration) of biodiversity features. In this paper we present an intelligent system to assist conservation managers in planning habitat restoration actions, with focus on the activities to be carried out in the islands of the Great Barrier Reef (QLD) and the Pilbara (WA) regions of Australia. In particular, we propose a constrained optimisation formulation of the habitat restoration planning (HRP) problem, capturing aspects such as population dynamics and uncertainty. We show that the HRP is NP-hard, and develop a constraint programming (CP) model and a large neighbourhood search (LNS) procedure to generate activity plans under budgeting constraints.
Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations
Authors: Anantharam Pramod , Thirunarayan Krishnaprasad , Marupudi Surendra , Sheth Amit , Banerjee Tanvi
Published: June 2022

Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
Authors: Xie Michael , Jean Neal , Burke Marshall , Lobell David , Ermon Stefano
Published: June 2022

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.
Shortest Path Based Decision Making Using Probabilistic Inference
Authors: Kumar Akshat
Published: June 2022

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of roads post some disaster within a fixed budget such that the connectivity between a set of nodes is optimized. We show that our likelihood maximization approach using the EM algorithm scales well for this problem taking the form of message-passing among nodes of the graph, and provides significantly better quality solutions than a standard mixed-integer programming solver.
Adaptable Regression Method for Ensemble Consensus Forecasting
Authors: Williams John , Neilley Peter , Koval Joseph , McDonald Jeff
Published: June 2022

Accurate weather forecasts enhance sustainability by facilitating decision making across a broad range of endeavors including public safety, transportation, energy generation and management, retail logistics, emergency preparedness, and many others. This paper presents a method for combining multiple scalar forecasts to obtain deterministic predictions that are generally more accurate than any of the constituents. Exponentially-weighted forecast bias estimates and error covariance matrices are formed at observation sites, aggregated spatially and temporally, and used to formulate a constrained, regularized least squares regression problem that may be solved using quadratic programming. The model is re-trained when new observations arrive, updating the forecast bias estimates and consensus combination weights to adapt to weather regime and input forecast model changes. The algorithm is illustrated for 0-72 hour temperature forecasts at over 1200 sites in the contiguous U.S. based on a 22-member forecast ensemble, and its performance over multiple seasons is compared to a state-of-the-art ensemble-based forecasting system. In addition to weather forecasts, this approach to consensus may be useful for ensemble predictions of climate, wind energy, solar power, energy demand, and numerous other quantities.
A Framework for Resolving Open-World Referential Expressions in Distributed Heterogeneous Knowledge Bases
Authors: Williams Tom , Scheutz Matthias
Published: June 2022

We present a domain-independent approach to reference resolution that allows a robotic or virtual agent to resolve references to entities (e.g., objects and locations) found in open worlds when the information needed to resolve such references is distributed among multiple heterogeneous knowledge bases in its architecture. An agent using this approach can combine information from multiple sources without the computational bottleneck associated with centralized knowledge bases. The proposed approach also facilitates “lazy constraint evaluation”, i.e., verifying properties of the referent through different modalities only when the information is needed. After specifying the interfaces by which a reference resolution algorithm can request information from distributed knowledge bases, we present an algorithm for performing open-world reference resolution within that framework, analyze the algorithm’s performance, and demonstrate its behavior on a simulated robot.
MicroScholar: Mining Scholarly Information from Chinese Microblogs
Authors: Yu Yang , Wan Xiaojun
Published: June 2022

For many researchers, one of the biggest issues is the lack of an efficient method to obtain latest academic progresses in related research fields. We notice that many researchers tend to share their research progresses or recommend scholarly information they have known on their microblogs. In order to exploit microblogging to benefit scientific research, we build a system called MicroScholar to automatically collecting and mining scholarly information from Chinese microblogs. In this paper, we briefly introduce the system framework and focus on the component of scholarly microblog categorization. Several kinds of features have been used in the component and experimental results demonstrate their usefulness.
ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
Authors: Santus Enrico , Lenci Alessandro , Chiu Tin-Shing , Lu Qin , Huang Chu-Ren
Published: June 2022

In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. The system relies on a Random Forest algorithm and 13 unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT13 achieves an F1 score of 88.3%, against a baseline of 57.6% (vector cosine). When the classification is binary, ROOT13 achieves the following results: hypernyms-co-hyponyms (93.4% vs. 60.2%), hypernyms-random (92.3% vs. 65.5%) and co-hyponyms-random (97.3% vs. 81.5%). Our results are competitive with state-of-the-art models.
Iterative Project Quasi-Newton Algorithm for Training RBM
Authors: Mi Shuai , Zhao Xiaozhao , Hou Yuexian , Zhang Peng , Li Wenjie , Song Dawei
Published: June 2022

The restricted Boltzmann machine (RBM) has been used as building blocks for many successful deep learning models, e.g., deep belief networks (DBN) and deep Boltzmann machine (DBM) etc. The training of RBM can be extremely slow in pathological regions. The second order optimization methods, such as quasi-Newton methods, were proposed to deal with this problem. However, the non-convexity results in many obstructions for training RBM, including the infeasibility of applying second order optimization methods. In order to overcome this obstruction, we introduce an em-like iterative project quasi-Newton (IPQN) algorithm. Specifically, we iteratively perform the sampling procedure where it is not necessary to update parameters, and the sub-training procedure that is convex. In sub-training procedures, we apply quasi-Newton methods to deal with the pathological problem. We further show that Newton's method turns out to be a good approximation of the natural gradient (NG) method in RBM training. We evaluate IPQN in a series of density estimation experiments on the artificial dataset and the MNIST digit dataset. Experimental results indicate that IPQN achieves an improved convergent performance over the traditional CD method.