bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Topic Discovery for Multi-Object Tracking
Authors: Luo Wenhan , Stenger Björn , Zhao Xiaowei , Kim Tae-Kyun
Published: June 2022

This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet Process Mixture Model (DPMM). The tracking problem is cast as a topic-discovery task where the video sequence is treated analogously to a document. This formulation addresses tracking issues such as object exclusivity constraints as well as cannot-link constraints which are integrated without the need for heuristic thresholds. The video is temporally segmented into epochs to model the dynamics of word (superpixel) co-occurrences and to model the temporal damping effect. In experiments on public data sets we demonstrate the effectiveness of the proposed algorithm.
Activity Planning for a Lunar Orbital Mission
Authors: Bresina John
Published: June 2022

This paper describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric Dust Environment Explorer). We present the approach taken to reduce the complexity of the activity planning task in order to effectively perform it within the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft’s orbit, given their dynamic nature due to the continually updated orbit determination solution.
Process Diagnosis System (PDS) — A 30 Year History
Authors: Thompson E. , Frolich E. , Bellows J. , Bassford B. , Skiko E. , Fox Mark
Published: June 2022

PDS (Process Diagnosis System) is an expert system shell developed in the early 1980's. It could handle thousands of sensor inputs and produce thousands of diagnostic messages with confidence factors based on complex logic designed to mimic the thinking of human experts. PDS went into commercial operation in 1985 to monitor seven power plant generators from a centralized diagnostic center at Westinghouse Power Generation headquarters. In the 1990’s the popularity of advanced technology gas turbines provided a renaissance in PDS utilization. The software has undergone rewrites and improvements since its inception, and the current PCPDS now supports the Siemens Power Diagnostics® Center with centralized rule based monitoring of over 1200 gas turbines, steam turbines, and generators.
Capturing Human Route Preferences from Track Information: New Results
Authors: Gohde Johnathan , Boddy Mark , Shackleton Hazel , Johnston Steve
Published: June 2022

In previous work, we described G2I2, a system that adjusts the cost function used by an off-road route planning system in order to more closely mimic the route choices made by humans. In this paper, we report on an extension to G2I2, called GUIDE, which adds significant new capabilities. GUIDE has the ability to induce a cost function starting with a set of historical tracks used as training input, with no requirement that these tracks be even close to cost-optimal. Given a cost function, either induced as above or provided from elsewhere, GUIDE can then compare planned routes with the actual tracks executed to adjust that cost function as either the environment or human preferences change over time. The features used by GUIDE in both the initial induction of the cost function and subsequent tuning include time-varying meta-data such as the temperature and precipitation at the time a given track was executed. We present results showing that, even when presented with tracks that are very far from cost-optimal, GUIDE can learn a set of preferences that closely mimics terrain choices made by humans.
The Winograd Schema Challenge: Evaluating Progress in Commonsense Reasoning
Authors: Morgenstern Leora , Ortiz Charles
Published: June 2022

This paper describes the Winograd Schema Challenge (WSC), which has been suggested as an alternative to the Turing Test and as a means of measuring progress in commonsense reasoning. A competition based on the WSC has been organized and announced to the AI research community. The WSC is of special interest to the AI applications community and we encourage its members to participate.
Topic Concentration in Query Focused Summarization Datasets
Authors: Baumel Tal , Cohen Raphael , Elhadad Michael
Published: June 2022

Query-Focused Summarization (QFS) summarizes a document cluster in response to a specific input query. QFS algorithms must combine query relevance assessment, central content identification, and redundancy avoidance. Frustratingly, state of the art algorithms designed for QFS do not significantly improve upon generic summarization methods, which ignore query relevance, when evaluated on traditional QFS datasets. We hypothesize this lack of success stems from the nature of the dataset. We define a task-based method to quantify topic concentration in datasets, i.e., the ratio of sentences within the dataset that are relevant to the query, and observe that the DUC 2005, 2006 and 2007 datasets suffer from very high topic concentration. We introduce TD-QFS, a new QFS dataset with controlled levels of topic concentration. We compare competitive baseline algorithms on TD-QFS and report strong improvement in ROUGE performance for algorithms that properly model query relevance as opposed to generic summarizers. We further present three new and simple QFS algorithms, RelSum, ThresholdSum, and TFIDF-KLSum that outperform state of the art QFS algorithms on the TD-QFS dataset by a large margin.
ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies
Authors: Espinosa-Anke Luis , Saggion Horacio , Ronzano Francesco , Navigli Roberto
Published: June 2022

We introduce ExTaSem!, a novel approach for the automatic learning of lexical taxonomies from domain terminologies. First, we exploit a very large semantic network to collect housands of in-domain textual definitions. Second, we extract (hyponym, hypernym) pairs from each definition with a CRF-based algorithm trained on manually-validated data. Finally, we introduce a graph induction procedure which constructs a full-fledged taxonomy where each edge is weighted according to its domain pertinence. ExTaSem! achieves state-of-the-art results in the following taxonomy evaluation experiments: (1) Hypernym discovery, (2) Reconstructing gold standard taxonomies, and (3) Taxonomy quality according to structural measures. We release weighted taxonomies for six domains for the use and scrutiny of the community.
A Generative Model of Words and Relationships from Multiple Sources
Authors: Hyland Stephanie , Karaletsos Theofanis , Rätsch Gunnar
Published: June 2022

Neural language models are a powerful tool to embed words into semantic vector spaces. However, learning such models generally relies on the availability of abundant and diverse training examples. In highly specialised domains this requirement may not be met due to difficulties in obtaining a large corpus, or the limited range of expression in average use. Such domains may encode prior knowledge about entities in a knowledge base or ontology. We propose a generative model which integrates evidence from diverse data sources, enabling the sharing of semantic information. We achieve this by generalising the concept of co-occurrence from distributional semantics to include other relationships between entities or words, which we model as affine transformations on the embedding space. We demonstrate the effectiveness of this approach by outperforming recent models on a link prediction task and demonstrating its ability to profit from partially or fully unobserved data training labels. We further demonstrate the usefulness of learning from different data sources with overlapping vocabularies.
Learning Statistical Scripts with LSTM Recurrent Neural Networks
Authors: Pichotta Karl , Mooney Raymond
Published: June 2022

Scripts encode knowledge of prototypical sequences of events. We describe a Recurrent Neural Network model for statistical script learning using Long Short-Term Memory, an architecture which has been demonstrated to work well on a range of Artificial Intelligence tasks. We evaluate our system on two tasks, inferring held-out events from text and inferring novel events from text, substantially outperforming prior approaches on both tasks.
Numerical Relation Extraction with Minimal Supervision
Authors: Madaan Aman , Mittal Ashish , Mausam . , Ramakrishnan Ganesh , Sarawagi Sunita
Published: June 2022

We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity ( e.g., atomic_number(Aluminium, 13), inflation_rate(India, 10.9%)). This task presents peculiar challenges not found in standard IE, such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.
To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation
Authors: Hadiwinoto Christian , Liu Yang , Ng Hwee Tou
Published: June 2022

Reordering poses a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present a novel reordering approach utilizing sparse features based on dependency word pairs. Each instance of these features captures whether two words, which are related by a dependency link in the source sentence dependency parse tree, follow the same order or are swapped in the translation output. Experiments on Chinese-to-English translation show a statistically significant improvement of 1.21 BLEU point using our approach, compared to a state-of-the-art statistical MT system that incorporates prior reordering approaches.
A Semi-Supervised Learning Approach to Why-Question Answering
Authors: Oh Jong-Hoon , Torisawa Kentaro , Hashimoto Chikara , Iida Ryu , Tanaka Masahiro , Kloetzer Julien
Published: June 2022

We propose a semi-supervised learning method for improving why-question answering (why-QA). The key of our method is to generate training data (question-answer pairs) from causal relations in texts such as "[Tsunamis are generated](effect) because [the ocean's water mass is displaced by an earthquake](cause)." A naive method for the generation would be to make a question-answer pair by simply converting the effect part of the causal relations into a why-question, like "Why are tsunamis generated?" from the above example, and using the source text of the causal relations as an answer. However, in our preliminary experiments, this naive method actually failed to improve the why-QA performance. The main reason was that the machine-generated questions were often incomprehensible like "Why does (it) happen?", and that the system suffered from overfitting to the results of our automatic causality recognizer. Hence, we developed a novel method that effectively filters out incomprehensible questions and retrieves from texts answers that are likely to be paraphrases of a given causal relation. Through a series of experiments, we showed that our approach significantly improved the precision of the top answer by 8% over the current state-of-the-art system for Japanese why-QA.
Exploring Multiple Feature Spaces for Novel Entity Discovery
Authors: Wu Zhaohui , Song Yang , Giles C.
Published: June 2022

Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity discovery as a submodule of entity linking systems, our model is more a generalized approach and can be applied as a pre-filtering step of novel entities for any entity linking systems. Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities.
A POMDP Formulation of Proactive Learning
Authors: Wray Kyle , Zilberstein Shlomo
Published: June 2022

We cast the Proactive Learning (PAL) problem—Active Learning (AL) with multiple reluctant, fallible, cost-varying oracles—as a Partially Observable Markov Decision Process (POMDP). The agent selects an oracle at each time step to label a data point, while it maintains a belief over the true underlying correctness of its current dataset’s labels. The goal is to minimize labeling costs while considering the value of obtaining correct labels, thus maximizing final resultant classifier accuracy. We prove three properties that show our particular formulation leads to a structured and bounded-size set of belief points, enabling strong performance of point-based methods to solve the POMDP. Our method is compared with the original three algorithms proposed by Donmez and Carbonell and a simple baseline. We demonstrate that our approach matches or improves upon the original approach within five different oracle scenarios, each on two datasets. Finally, our algorithm provides a general, well-defined mathematical foundation to build upon.
Computing Contingent Plans Using Online Replanning
Authors: Komarnitsky Radimir , Shani Guy
Published: June 2022

In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. For this class of problems the solution can be represented as a plan tree, branching on various possible observations. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. While this approach has been successful in many domains, the translation may become very large, encumbering the task of the non-deterministic planner. In this paper we suggest a different approach - using an online contingent solver repeatedly to construct a plan tree. We execute the plan returned by the online solver until the next observation action, and then branch on the possible observed values, and replan for every branch independently. In many cases a plan tree can be exponential in the number of state variables, but still, the tree has a structure that allows us to compactly represent it using a directed graph. We suggest a mechanism for tailoring such a graph that reduces both the computational effort and the storage space. Furthermore, unlike recent state of the art offline planners, our approach is not bounded to a specific class of contingent problems, such as limited problem width, or simple contingent problems. We present a set of experiments, showing our approach to scale better than state of the art offline planners.
Solving Goal Recognition Design Using ASP
Authors: Son Tran , Sabuncu Orkunt , Schulz-Hanke Christian , Schaub Torsten , Yeoh William
Published: June 2022

Goal Recognition Design involves identifying the best ways to modify an underlying environment that agents operate in, typically by making asubset of feasible actions infeasible, so that agents are forced to reveal their goals as early as possible. Thus far, existing work has focused exclusively on imperative classical planning. In this paper, we address the same problem with a different paradigm, namely, declarative approaches based on Answer Set Programming (ASP). Our experimental results show that one of our ASP encodings is more scalable and is significantly faster by up to three orders of magnitude than thecurrent state of the art.
General Error Bounds in Heuristic Search Algorithms for Stochastic Shortest Path Problems
Authors: Hansen Eric , Abdoulahi Ibrahim
Published: June 2022

We consider recently-derived error bounds that can be used to bound the quality of solutions found by heuristic search algorithms for stochastic shortest path problems. In their original form, the bounds can only be used for problems with positive action costs. We show how to generalize the bounds so that they can be used in solving any stochastic shortest path problem, regardless of cost structure. In addition, we introduce a simple new heuristic search algorithm that performs as well or better than previous algorithms for this class of problems, while being easier to implement and analyze.
Learning Bayesian Networks with Bounded Tree-width via Guided Search
Authors: Nie Siqi , De Campos Cassio , Ji Qiang
Published: June 2022

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.
A Symbolic SAT-Based Algorithm for Almost-Sure Reachability with Small Strategies in POMDPs
Authors: Chatterjee Krishnendu , Chmelík Martin , Davies Jessica
Published: June 2022

POMDPs are standard models for probabilistic planning problems, where an agent interacts with an uncertain environment. We study the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a policy to ensure that the target set is reached with probability 1 (almost-surely). While in general the problem is EXPTIME-complete, in many practical cases policies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. In this work, we first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. We report experimental results demonstrating the scalability of our symbolic (SAT-based) approach.
Online Spatio-Temporal Matching in Stochastic and Dynamic Domains
Authors: Lowalekar Meghna , Varakantham Pradeep , Jaillet Patrick
Published: June 2022

Spatio-temporal matching of services to customers online is a problem that arises on a large scale in many domains associated with shared transportation (ex: taxis, ride sharing, super shuttles, etc.) and delivery services (ex: food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that matching of services to customers in one round has a direct impact on the matching of services to customers in the next round. For instance, in the case of taxis, in the second round taxis can only pick up customers closer to the drop off point of the customer from the first round of matching. Traditionally, greedy myopic approaches have been adopted to address such large scale online matching problems. While they provide solutions in a scalable manner, due to their myopic nature the quality of matching obtained can be improved significantly (demonstrated in our experimental results). In this paper, we present a two stage stochastic optimization formulation to consider expected future demand. We then provide multiple enhancements to solve large scale problems more effectively and efficiently. Finally, we demonstrate the significant improvement provided by our techniques over myopic approaches on two real world taxi data sets.
The Meta-Problem for Conservative Mal'tsev Constraints
Authors: Carbonnel Clement
Published: June 2022

In the algebraic approach to CSP (Constraint Satisfaction Problem), the complexity of constraint languages is studied using closure operations called polymorphisms. Many of these operations are known to induce tractability of any language they preserve. We focus on the meta-problem: given a language G, decide if G has a polymorphism with nice properties. We design an algorithm that decides in polynomial-time if a constraint language has a conservative Mal'tsev polymorphism, and outputs one if one exists. As a corollary we obtain that the class of conservative Mal'tsev constraints is uniformly tractable, and we conjecture that this result remains true in the non-conservative case.
Face Video Retrieval via Deep Learning of Binary Hash Representations
Authors: Dong Zhen , Jia Su , Wu Tianfu , Pei Mingtao
Published: June 2022

Retrieving faces from large mess of videos is an attractive research topic with wide range of applications. Its challenging problems are large intra-class variations, and tremendous time and space complexity. In this paper, we develop a new deep convolutional neural network (deep CNN) to learn discriminative and compact binary representations of faces for face video retrieval. The network integrates feature extraction and hash learning into a unified optimization framework for the optimal compatibility of feature extractor and hash functions. In order to better initialize the network, the low-rank discriminative binary hashing is proposed to pre-learn hash functions during the training procedure. Our method achieves excellent performances on two challenging TV-Series datasets.
Discrete Image Hashing Using Large Weakly Annotated Photo Collections
Authors: Zhang Hanwang , Zhao Na , Shang Xindi , Luan Huanbo , Chua Tat-seng
Published: June 2022

We address the problem of image hashing by learning binary codes from large and weakly supervised photo collections. Due to the explosive growth of user generated media on the Web, this problem is becoming critical for large-scale visual applications like image retrieval. While most existing hashing methods fail to address this challenge well, our method shows promising improvement due to the following two key advantages.First, we formulate a novel hashing objective that can effectively mine implicit weak supervision by collaborative filtering. Second, we propose a discrete hashing algorithm, offered with efficient optimization, to overcome the inferior optimizations in obtaining binary codes from real-valued solutions. In this way, our method can be considered as a weakly-supervised discrete hashing framework which jointly learns image semantics and their corresponding binary codes. Through training on one million weakly annotated images, our experimental results demonstrate that image retrieval using the proposed hashing method outperforms the other state-of-the-art ones on image and video benchmarks.
Pose-Guided Human Parsing by an AND/OR Graph Using Pose-Context Features
Authors: Xia Fangting , Zhu Jun , Wang Peng , Yuille Alan
Published: June 2022

Parsing human into semantic parts is crucial to human-centric analysis. In this paper, we propose a human parsing pipeline that uses pose cues, e.g., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. These segment proposals are ranked using standard appearance cues, deep-learned semantic feature, and a novel pose feature called pose-context. Then these proposals are selected and assembled using an And-Or graph to output a parse of the person. The And-Or graph is able to deal with large human appearance variability due to pose, choice of clothing, etc. We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, showing that it significantly outperforms the state of the art, and perform diagnostics to demonstrate the effectiveness of different stages of our pipeline.
Articulated Pose Estimation Using Hierarchical Exemplar-Based Models
Authors: Liu Jiongxin , Li Yinxiao , Allen Peter , Belhumeur Peter
Published: June 2022

Exemplar-based models have achieved great success on localizing the parts of semi-rigid objects. However, their efficacy on highly articulated objects such as humans is yet to be explored. Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical exemplar-based models and DCNNs in the spatial terms. Specifically, we obtain more expressive spatial models by assuming independence between exemplars at different levels in the hierarchy; we also obtain stronger spatial constraints by inferring the spatial relations between parts at the same level. As our method strikes a good balance between expressiveness and strength of spatial models, it is both effective and generalizable, achieving state-of-the-art results on different benchmarks: Leeds Sports Dataset and CUB-200-2011.
Decentralized Robust Subspace Clustering
Authors: Liu Bo , Yuan Xiao-Tong , Yu Yang , Liu Qingshan , Metaxas Dimitris
Published: June 2022

We consider the problem of subspace clustering using the SSC (Sparse Subspace Clustering) approach, which has several desirable theoretical properties and has been shown to be effective in various computer vision applications.We develop a large scale distributed framework for the computation of SSC via an alternating direction method of multiplier (ADMM) algorithm. The proposed framework solves SSC in column blocks and only involves parallel multivariate Lasso regression subproblems and sample-wise operations. This appealing property allows us to allocate multiple cores/machines for the processing of individual column blocks.We evaluate our algorithm on a shared-memory architecture. Experimental results on real-world datasets confirm that the proposed block-wise ADMM framework is substantially more efficient than its matrix counterpart used by SSC,without sacrificing accuracy. Moreover, our approach is directly applicable to decentralized neighborhood selection for Gaussian graphical models structure estimation.
Dynamic Concept Composition for Zero-Example Event Detection
Authors: Chang Xiaojun , Yang Yi , Long Guodong , Zhang Chengqi , Hauptmann Alexander
Published: June 2022

In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. birthday party) can be described by multiple mid-level semantic concepts (e.g. ``blowing candle'', ``birthday cake''). Towards this goal, we first pre-train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w.r.t. the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classifiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with free-form text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach.
Benders Decomposition for Large-Scale Prescriptive Evacuations
Authors: Romanski Julia , Van Hentenryck Pascal
Published: June 2022

This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.
Energy- and Cost-Efficient Pumping Station Control
Authors: Kanters Timon , Oliehoek Frans , Kaisers Michael , Van den Bosch Stan , Grispen Joep , Hermans Jeroen
Published: June 2022

With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.
An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings
Authors: Zenonos Alexandros , Stein Sebastian , Jennings Nicholas
Published: June 2022

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.
Two-Stream Contextualized CNN for Fine-Grained Image Classification
Authors: Liu Jiang , Gao Chenqiang , Meng Deyu , Zuo Wangmeng
Published: June 2022

Human's cognition system prompts that context information provides potentially powerful clue while recognizing objects. However, for fine-grained image classification, the contribution of context may vary over different images, and sometimes the context even confuses the classification result. To alleviate this problem, in our work, we develop a novel approach, two-stream contextualized Convolutional Neural Network, which provides a simple but efficient context-content joint classification model under deep learning framework. The network merely requires the raw image and a coarse segmentation as input to extract both content and context features without need of human interaction. Moreover, our network adopts a weighted fusion scheme to combine the content and the context classifiers, while a subnetwork is introduced to adaptively determine the weight for each image. According to our experiments on public datasets, our approach achieves considerable high recognition accuracy without any tedious human's involvements, as compared with the state-of-the-art approaches.
A Word Embedding and a Josa Vector for Korean Unsupervised Semantic Role Induction
Authors: Nam Kyeong-Min , Kim Yu-Seop
Published: June 2022

We propose an unsupervised semantic role labeling method for Korean language, one of the agglutinative languages which have complicated suffix structures telling much of syntactic. First, we construct an argument embedding and then develop a indicator vector of the suffix such as a Josa. And, we construct an argument tuple by concatenating above two vectors. The role induction is performed by clustering the argument tuples.These method which achieves up to a 70.16% of F1-score and 75.85% of accuracy.
Counter-Transitivity in Argument Ranking Semantics
Authors: Pu Fuan , Luo Jian , Luo Guiming
Published: June 2022

The principle of counter-transitivity plays a vital role in argumentation. It states that an argument is strong when its attackers are weak, and is weak when its attackers are strong. In this work, we develop a formal theory about the argument ranking semantics based on this principle. Three approaches, quantity-based, quality-based and the unity of them, are defined to implement the principle. Then, we show an iterative refinement algorithm for capturing the ranking on arguments based on the recursive nature of the principle.
A CP-Based Approach for Popular Matching
Authors: Chisca Danuta , Siala Mohamed , Simonin Gilles , O'Sullivan Barry
Published: June 2022

We propose a constraint programming approach to the popular matching problem. We show that one can use the Global Cardinality Constraint to encode the problem even in cases that involve ties in the ordinal preferences of the applicants.
User-Centric Affective Computing of Image Emotion Perceptions
Authors: Zhao Sicheng , Yao Hongxun , Xie Wenlong , Jiang Xiaolei
Published: June 2022

We propose to predict the personalized emotion perceptions of images for each viewer. Different factors that may influence emotion perceptions, including visual content, social context, temporal evolution, and location influence are jointly investigated via the presented rolling multi-task hypergraph learning. For evaluation, we set up a large scale image emotion dataset from Flickr, named Image-Emotion-Social-Net, with over 1 million images and about 8,000 users. Experiments conducted on this dataset demonstrate the superiority of the proposed method, as compared to state-of-the-art.
Bayesian Markov Games with Explicit Finite-Level Types
Authors: Chandrasekaran Muthukumaran , Chen Yingke , Doshi Prashant
Published: June 2022

In impromptu or ad hoc settings, participating players are precluded from precoordination. Subsequently, each player's own model is private and includes some uncertainty about the others' types or behaviors. Harsanyi's formulation of a Bayesian game lays emphasis on this uncertainty while the players each play exactly one turn. We propose a new game-theoretic framework where Bayesian players engage in a Markov game and each has private but imperfect information regarding other players' types. Consequently, we construct player types whose structure is explicit and includes a finite level belief hierarchy instead of utilizing Harsanyi's abstract types and a common prior distribution. We formalize this new framework and demonstrate its effectiveness on two standard ad hoc teamwork domains involving two or more ad hoc players.
Multivariate Conditional Outlier Detection and Its Clinical Application
Authors: Hong Charmgil , Hauskrecht Milos
Published: June 2022

This paper overviews and discusses our recent work on a multivariate conditional outlier detection framework for clinical applications.
Intrinsic and Extrinsic Evaluations of Word Embeddings
Authors: Zhai Michael , Tan Johnny , Choi Jinho
Published: June 2022

In this paper, we first analyze the semantic composition of word embeddings by cross-referencing their clusters with the manual lexical database, WordNet. We then evaluate a variety of word embedding approaches by comparing their contributions to two NLP tasks. Our experiments show that the word embedding clusters give high correlations to the synonym and hyponym sets in WordNet, and give 0.88% and 0.17% absolute improvements in accuracy to named entity recognition and part-of-speech tagging, respectively.
Abstracting Complex Domains Using Modular Object-Oriented Markov Decision Processes
Authors: Squire Shawn , DesJardins Marie
Published: June 2022

We present an initial proposal for modular object-oriented MDPs, an extension of OO-MDPs that abstracts complex domains that are partially observable and stochastic with multiple goals. Modes reduce the curse of dimensionality by reducing the number of attributes, objects, and actions into only the features relevant for each goal. These modes may also be used as an abstracted domain to be transferred to other modes or to another domain.
A Hidden Markov Model Approach to Infer Timescales for High-Resolution Climate Archives
Authors: Winstrup Mai
Published: June 2022

We present a Hidden Markov Model-based algorithm for constructing timescales for paleoclimate records by annual layer counting. This objective, statistics-based approach has a number of major advantages over the current manual approach, beginning with speed. Manual layer counting of a single core (up to 3km in length) can require multiple person-years of time; the StratiCounter algorithm can count up to 100 layers/min, corresponding to a full-length timescale constructed in a few days. Moreover, the algorithm gives rigorous uncertainty estimates for the resulting timescale, which are far smaller than those produced manually. We demonstrate the utility of StratiCounter by applying it to ice-core data from two cores from Greenland and Antarctica. Performance of the algorithm is comparable to a manual approach. When using all available data, false-discovery rates and miss rates are 1-1.2% and 1.2-1.6%, respectively, for the two cores. For one core, even better agreement is found when using only the chemistry series primarily employed by human experts in the manual approach.
Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing
Authors: Thomas Philip , Theocharous Georgios , Ghavamzadeh Mohammad , Durugkar Ishan , Brunskill Emma
Published: June 2022

In this paper we consider the problem of evaluating one digital marketing policy (or more generally, a policy for an MDP with unknown transition and reward functions) using data collected from the execution of a different policy. We call this problem off-policy policy evaluation. Existing methods for off-policy policy evaluation assume that the transition and reward functions of the MDP are stationary — an assumption that is typically false, particularly for digital marketing applications. This means that existing off-policy policy evaluation methods are reactive to nonstationarity, in that they slowly correct for changes after they occur. We argue that off-policy policy evaluation for nonstationary MDPs can be phrased as a time series prediction problem, which results in predictive methods that can anticipate changes before they happen. We therefore propose a synthesis of existing off-policy policy evaluation methods with existing time series prediction methods, which we show results in a drastic reduction of mean squared error when evaluating policies using real digital marketing data set.
Postur Kerja Pekerja Pengelolaan Limbah Radioaktif Padat di Instalasi Nuklir X : Potensi Risiko dan Rekomendasi Pengendalian
Authors: Ramadania Puspitasari
Published: June 2022

Pengelolaan limbah radioaktif padat termasuk kegiatan yang melibatkan aktivitas manual yang memiliki risiko ergonomi dan dapat menimbulkan gangguan kesehatan pada pekerjaannya. Penelitian ini bertujuan untuk menganalisa kesalahan postur kerja pekerja radiasi saat pengelolaan limbah radioaktif padat di instalasi nuklir X dan merekomendasikan pengendalian untuk mengurangi risiko terjadinya MSDs. Penelitian ini merupakan penelitian deskriptif kuantitatif. Instrumen yang digunakan untuk mengukur risiko ergonomi adalah Quick Exposure Check sedangkan keluhan MSDs menggunakan Nordic Body Map. Metode analisis yang digunakan analisis univariat dan bivariat dengan uji statistik Chi-Square dan rekomendasi postur kerja digambarkan dengan software CATIA. Hasil penelitian menunjukkan bahwa sebagian besar pekerja mengalami keluhan MSDs sebesar 90% dengan keluhan tertinggi pada pinggang (28,57%), lengan kiri atas (17,86%), leher bawah, bahu kanan dan kiri, punggung, lutut kanan dan kiri (14,29%) dan tangan kanan (10,71%). Tingkat risiko ergonomi dengan kategori tinggi sebanyak 6 orang (60%) dan kategori sangat tinggi sebanyak 4 orang (40%). Potensi risiko ergonomi memiliki hubungan signifikan terhadap keluhan MSDs (P-value = 0,002) sedangkan jenis kelamin (P-value = 0,197), usia (P-value = 0,240), masa kerja (P-value = 0,240) dan indeks massa tubuh (P-value = 0,778) tidak memiliki hubungan signifikan dengan keluhan MSDs. Rekomendasi pengendalian dengan perbaikan postur kerja, perbaikan stasiun kerja dan penambahan alat bantu kerja.
A non-dispersive infrared sensor for real-time detection of cyanogen chloride
Authors: Ma B , Ren L , Liu G , Li J , Xiao Y , Li D , Gao Y , Kuang P
Published: June 2022

Cyanogen chloride, as a systemic toxic agent, can cause death rapidly. In this paper, a non-dispersive infrared sensor was designed for the infrared absorption detection of cyanogen chloride at 800 cm-1. The roughness of the internal coating material was analyzed by experiments, and the gold-plated gas chamber was selected. The light path propagation of different cross-section gas chambers was simulated, and the circular section gas chamber was selected to increase the infrared detector signal. The effect of flow rate on voltage was studied. The standard curve between voltage and concentration was obtained under the optimal condition of 0.4 L min-1. The maximum response time was 19 s, and RSD was less than 2%. The interference experiment results showed that common gases entering the gas chamber do not cause interference. The non-dispersive infrared sensor for cyanogen chloride has good stability and detects cyanogen chloride in real-time.
A radiographic analysis of Mandibular Symphysis dimension in black South African adult patients with differing skeletal patterns
Authors: Khan Mohamed Imran , Gininda Dikeledi Maureen
Published: June 2022

Orthodontic treatment often involves planned tooth movement within the confined spaces of the alveolar bone trough. Tooth movement within the alveolar trough may be limited by thin labial and lingual cortical plates. Moving lower incisors beyond the mandibular symphysis dimensions may result in damage to roots and alveolar bone.4 Aim and objective The aim of the study was to evaluate limitation of treatment in different skeletal patterns due to mandibular symphysis dimension in order to evaluate limitations of tooth movement within the confines of the mandibular alveolar trough.The objective was to determine the mandibular symphysis dimensions in subjects with differing skeletal patterns Design The design was a retrospective, cross-sectional study. Methods A sample of 180 pre-treatment lateral cephalometric radiographs of black South African subjects were stratified into three groups based on their skeletal classification. Each Class was further divided into equal numbers of males and females. Descriptive statistics, Student’s t-test, ANOVA test and Pearson correlation coefficient were used to analyse the data and p-values of <0.05 were considered statistically significant. Results Subjects with skeletal Class I pattern had a greater LA compared to subjects with skeletal Class II pattern. Subjects with skeletal Class I pattern had a greater LH and LA in females than in males. Subjects with skeletal Class III pattern had greater LH in males than in females.
The perceived business management knowledge and skills of dentists in private practice in South Africa
Authors: Daya-Roopa Variza , Owen CP
Published: June 2022

This study evaluated the perceived business (practice) management knowledge and skills of dentists involved in the management of a private practice in South Africa by means of a self-administered web-based questionnaire. Invited participants were members of the South African Dental Association (numbering 2,462) as well as dentists in their second and third year of practice after graduation (numbering 199). There were 533 respondents but not all ofthese were involved in management and 63 of these failed to complete the survey, leaving a total of 367 respondents. Results Overall, the respondents reported that their undergraduate training had not prepared them adequately for the nonclinical aspects of practice management; 56% had not had a formal undergraduate course. However, for those who did, 71% reported that the course was only slightly or not at all useful. Only 29% attended any form of postgraduate course, but only three of these were considered to be effective. Management knowledge and skills were also obtained from accountants as well as financial advisors, friends, family and lawyers. Conclusion In line with the opinion expressed by the majority ofrespondents (86%), it is recommended that appropriate dental practice management courses be introduced throughout the curriculum, preferably in association with a Business School, and that postgraduate courses should be made available
Criteria that must be considered in order to optimise the success of computer aided designed and computer aided manufactured (CAD/CAM) restorations
Authors: Van Outshoorn Nic Van Reede , Bradfield Charles , Sykes Leanne
Published: June 2022

Several factors influence the success or failure of ceramic dental restorations, and need to be considered and understood prior to embarking on these restorations. It is incumbent on the clinician to have an in-depth knowledge on the science of ceramic materials, current bonding agents and techniques, understanding and working proficiency of computer aided design and computer aided manufacture (CAD/CAM) computer hardware and software, an appreciation of oral biology, the role of occlusion and occlusal schemes, as well as recognition and management of patients with parafunctional habits. This paper will cover the principles of cavity preparation and tooth preparation designs, maintenance and / or achievement of inter-arch stability, preservation of marginal integrity, provision of occlusal stability, and digital impression techniques required to optimise accuracy. Provision of a chairside manufactured CAD/CAM dental restoration requires dentists to perform both the clinical and the laboratory aspects of the procedure. The responsibility for the quality of the final restoration thus rests in their hands alone and it is their duty to ensure they are adequately trained and skilled in all aspects