bluuebunny/crossref_metadata_2025 huggingface dataset:
A Wiki with Multiagent Tracking, Modeling, and Coalition Formation
Authors: Khandaker Nobel , Soh Leen-Kiat
Published: June 2022

Wikis are being increasingly used as a tool for conducting colla-borative writing assignments in today’s classrooms. However, Wikis in general (1) do not provide group formation methods to more specifically facilitate collaborative learning of the students and (2) suffer from typical problems of collaborative learning like detection of free-riding (earning credit without contribution). To improve the state of the art of the use of Wikis as a collaborative writing tool, we have designed and implemented ClassroomWiki - a Web-based collaborative Wiki that utilizes a set of learner pedagogy theories to provide multiagent-based tracking, modeling, and group formation functionalities. For the students, ClassroomWiki provides a Web interface for writing and revising their group’s Wiki and a topic-based forum for discussing their ideas during collaboration. When the students collaborate, ClassroomWiki’s agents track all student activities to learn a model of the students and use a Bayesian Network to learn a probabilistic mapping that describes the ability of a group of students with a specific set of models to work together. For the teacher, Clas-sroomWiki provides a framework that uses the learned student models and the mapping to form student groups to improve the collaborative learning of students. ClassroomWiki was deployed in three university-level courses and the results suggest that ClassroomWiki can (1) form better student groups that improve stu-dent learning and collaboration and (2) alleviate free-riding and allow the instructor to provide scaffolding by its multiagent-based tracking and modeling.
Natural Language Aided Visual Query Building for Complex Data Access
Authors: Pan Shimei , Zhou Michelle , Houck Keith , Kissa Peter
Published: June 2022

Over the past decades, there have been significant efforts on developing robust and easy-to-use query interfaces to databases. So far, the typical query interfaces are GUI-based visual query interfaces. Visual query interfaces however, have limitations especially when they are used for accessing large and complex datasets. Therefore, we are developing a novel query interface where users can use natural language expressions to help author visual queries. Our work enhances the usability of a visual query interface by directly addressing the "knowledge gap" issue in visual query interfaces. We have applied our work in several real-world applications. Our preliminary evaluation demonstrates the effectiveness of our approach
Ad Hoc Teamwork in Variations of the Pursuit Domain
Authors: Barrett Samuel , Stone Peter
Published: June 2022

In multiagent team settings, the agents are often given a protocol for coordinating their actions. When such a protocol is not available, agents must engage in ad hoc teamwork to effectively cooperate with one another. A fully general ad hoc team agent needs to be capable of collaborating with a wide range of potential teammates on a varying set of joint tasks. This paper extends previous research in a new direction with the introduction of an efficient method for reasoning about the value of information.  Then, we show how previous theoretical results can aid ad hoc agents in a set of testbed pursuit domains.
Role-Based Ad Hoc Teamwork
Authors: Genter Katie , Agmon Noa , Stone Peter
Published: June 2022

An ad hoc team setting is one in which teammates must work together to obtain a common goal, but without any prior agreement regarding how to work together. In this abstract we present a role-based approach for ad hoc teamwork, in which each teammate is inferred to be following a specialized role that accomplishes a specific task or exhibits a particular behavior. In such cases, the role an ad hoc agent should select depends both on its own capabilities and on the roles currently selected by the other team members. We present methods for evaluating the influence of the ad hoc agent's role selection on the team's utility and we examine empirically how to select the best suited method for role assignment in a complex environment. Finally, we show that an appropriate assignment method can be determined from a limited amount of data and used successfully in similar new tasks that the team has not encountered before.
An Intelligent System for Prolonging Independent Living of Elderly
Authors: Pogorelc Bogdan
Published: June 2022

The number of elderly people is constantly increasing in the developed countries. Elderly tend to lead an isolated life away from their offspring; however, they may fear being unable to obtain help if they are injured or ill. During the last decades, this fear has generated research attempts to find assistive technologies for making living of elderly people at homes easier and independent, as is the aim of this research work. Research study proposes a generalized approach to an intelligent and ubiquitous care system to recognize a few of the most common and important health problems of the elderly, which can be detected by analyzing their movement. In the event that the system was to recognize a health problem, it would automatically notify a physician with an included explanation of the automatic diagnosis. It is two-step approach; in the first step it classifies person's activities into five activities: fall, unconscious fall, walking, standing/sitting, lying down/lying. In the second step, it classifies walking patterns into five different health states; one healthy and four unhealthy: hemiplegia (usually the result of stroke), Parkinson’s disease, leg pain and back pain. Moreover, since elderly having these health problems are less stable and more prone to falls, recognizing them leads not only to detection but indirectly also to prevention of falls of elderly people. In the initial approach movement of the user is captured with the motion capture system, which consists of the tags attached to the body, whose coordinates are acquired by the sensors situated in the apartment. In the current approach wearable inertial sensors are used, allowing monitoring inside or outside of the buildings. Output time-series of coordinates are modeled with the proposed data mining approach to recognize the specific health problem.
Towards Practical ABox Abduction in Large OWL DL Ontologies
Authors: Du Jianfeng , Qi Guilin , Shen Yi-Dong , Pan Jeff
Published: June 2022

ABox abduction is an important aspect for abductive reasoning in Description Logics (DLs). It finds all minimal sets of ABox axioms that should be added to a background ontology to enforce entailment of a specified set of ABox axioms. As far as we know, by now there is only one ABox abduction method in expressive DLs computing abductive solutions with certain minimality. However, the method targets an ABox abduction problem that may have infinitely many abductive solutions and may not output an abductive solution in finite time. Hence, in this paper we propose a new ABox abduction problem which has only finitely many abductive solutions and also propose a novel method to solve it. The method reduces the original problem to an abduction problem in logic programming and solves it with Prolog engines. Experimental results show that the method is able to compute abductive solutions in benchmark OWL DL ontologies with large ABoxes.
Detecting Multilingual and Multi-Regional Query Intent in Web Search
Authors: Chang Yi , Zhang Ruiqiang , Reddy Srihari , Liu Yan
Published: June 2022

With rapid growth of commercial search engines, detecting multilingual and multi-regional intent underlying search queries becomes a critical challenge to serve international users with diverse language and region requirements. We introduce a query intent probabilistic model, whose input is the number of clicks on documents from different regions and in different language, while the output of this model is a smoothed probabilistic distribution of multilingual and multi-regional query intent. Based on an editorial test to evaluate the accuracy of the intent classifier, our probabilistic model could improve the accuracy of multilingual intent detection for 15%, and improve multi-regional intent detection for 18%. To improve web search quality, we propose a set of new ranking features to combine multilingual and multi-regional query intent with document language/region attributes, and apply different approaches in integrating intent information to directly affect ranking. The experiments show that the novel features could provide 2.31% NDCG@1 improvement and 1.81% NDCG@5 improvement.
Analyzing and Predicting Not-Answered Questions in Community-based Question Answering Services
Authors: Yang Lichun , Bao Shenghua , Lin Qingliang , Wu Xian , Han Dingyi , Su Zhong , Yu Yong
Published: June 2022

This paper focuses on analyzing and predicting not-answered questions in Community based Question Answering (CQA) services, such as Yahoo! Answers. In CQA services, users express their information needs by submitting natural language questions and await answers from other human users. Comparing to receiving results from web search engines using keyword queries, CQA users are likely to get more specific answers, because human answerers may catch the main point of the question. However, one of the key problems of this pattern is that sometimes no one helps to give answers, while web search engines hardly fail to response. In this paper, we analyze the not-answered questions and give a first try of predicting whether questions will receive answers. More specifically, we first analyze the questions of Yahoo Answers based on the features selected from different perspectives. Then, we formalize the prediction problem as supervised learning – binary classification problem and leverage the proposed features to make predictions. Extensive experiments are made on 76,251 questions collected from Yahoo! Answers. We analyze the specific characteristics of not-answered questions and try to suggest possible reasons for why a question is not likely to be answered. As for prediction, the experimental results show that classification based on the proposed features outperforms the simple word-based approach significantly.
Heterogeneous Transfer Learning for Image Classification
Authors: Zhu Yin , Chen Yuqiang , Lu Zhongqi , Pan Sinno , Xue Gui-Rong , Yu Yong , Yang Qiang
Published: June 2022

Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset.
Identifying Missing Node Information in Social Networks
Authors: Eyal Ron , Kraus Sarit , Rosenfeld Avi
Published: June 2022

In recent years, social networks have surged in popularity as one of the main applications of the Internet. This has generated great interest in researching these networks by various fields in the scientific community. One key aspect of social network research is identifying important missing information which is not explicitly represented in the network, or is not visible to all. To date, this line of research typically focused on what connections were missing between nodes,or what is termed the "Missing Link Problem." This paper introduces a new Missing Nodes Identification problem where missing members in the social network structure must be identified. Towards solving this problem, we present an approach based on clustering algorithms combined with measures from missing link research. We show that this approach has beneficial results in the missing nodes identification process and we measure its performance in several different scenarios.
Artificial Intelligence for Artificial Artificial Intelligence
Authors: Dai Peng , Mausam . , Weld Daniel
Published: June 2022

Crowdsourcing platforms such as Amazon Mechanical Turk have become popular for a wide variety of human intelligence tasks; however, quality control continues to be a significant challenge. Recently, we propose TurKontrol, a theoretical model based on POMDPs to optimize iterative, crowd-sourced workflows. However, they neither describe how to learn the model parameters, nor show its effectiveness in a real crowd-sourced setting. Learning is challenging due to the scale of the model and noisy data: there are hundreds of thousands of workers with high-variance abilities. This paper presents an end-to-end system that first learns TurKontrol's POMDP parameters from real Mechanical Turk data, and then applies the model to dynamically optimize live tasks. We validate the model and use it to control a successive-improvement process on Mechanical Turk. By modeling worker accuracy and voting patterns, our system produces significantly superior artifacts compared to those generated through nonadaptive workflows using the same amount of money.
Testing Cyber Security with Simulated Humans
Authors: Blythe Jim , Botello Aaron , Sutton Joseph , Mazzocco David , Lin Jerry , Spraragen Marc , Zyda Mike
Published: June 2022

Human error is one of the most common causes of vul- nerability in a secure system. However it is often overlooked when these systems are tested, partly because human tests are costly and very hard to repeat. We have developed a community of agents that test secure systems by running standard windows software while performing collaborative group tasks, mimicking more realistic patterns of communication and traffic, as well as human fatigue and errors. This system is being deployed on a large cyber testing range. One key attribute of humans is flexibility of response in order to achieve their goals when unexpected events occur. Our agents use reactive planning within a BDI architecture to flexibly replan if needed. Since the agents are goal-oriented, we are able to measure the impact of cyber attacks on mission accomplishment, a more salient measure of protection than raw penetration. We show experimentally how the agent teams can be resilient under attacks that are partly successful, and also how an organizational structure can lead to emergent properties of the traffic in the network.
Detecting Falls with Location Sensors and Accelerometers
Authors: Luštrek Mitja , Gjoreski Hristijan , Kozina Simon , Cvetkovic Božidara , Mirchevska Violeta , Gams Matjaž
Published: June 2022

Due to the rapid aging of the population, many technical solutions for the care of the elderly are being developed, often involving fall detection with accelerometers. We present a novel approach to fall detection with location sensors. In our application, a user wears up to four tags on the body whose locations are detected with radio sensors. This makes it possible to recognize the user’s activity, including falling any lying afterwards, and the context in terms of the location in the apartment. We compared fall detection using location sensors, accelerometers and accelerometers combined with the context. A scenario consisting of events difficult to recognize as falls or non- falls was used for the comparison. The accuracy of the methods that utilized the context was almost 40 percentage points higher compared to the methods without the context. The accuracy of pure location-based methods was around 10 percentage points higher than the accuracy of accelerometers combined with the context.
A Methodology for Deploying the Max-Sum Algorithm and a Case Study on Unmanned Aerial Vehicles
Authors: Delle Fave F. M. , Farinelli A. , Rogers A. , Jennings N. R.
Published: June 2022

We present a methodology for the deployment of the maxsum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world.
Creating Model-Based Adaptive Environments Using Game-Specific and Game-Independent Analytics
Authors: Harrison Brent
Published: June 2022

My research involves creating and evaluating adaptive game environments using player models created with data-driven techniques and algorithms. I hypothesize that I will be able to change parts of a game to elicit certain behaviors from players,and that these changes will also result in an increase of engagement and/or intrinsic motivation. Initial results in my testbed game, Scrabblesque, indicate that data-driven models and techniques can be used to influence player behavior and that these changes in play behavior manifest themselves as an increase in engagement and intrinsic motivation.
AAAI-13 Preface
Authors: Des Jardins Marie , Littman Michael
Published: June 2022

Welcome to the Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI-13! As can be seen in these proceedings, AI’s scope and influence continue to grow. This year, we received 827 submissions across a variety of tracks, allowing us to put together a diverse and exciting technical program featuring the field’s top research.
Hybrid Model-Based Diagnosis of Web Service Compositions
Authors: Jia Zhichun , Chen Rong
Published: June 2022

Fault diagnosis of web services composition at run time is appealing in creating a consolidated distributed application. For this purpose, we propose a hybrid model-based diagnosis method which exploits service process description or historical execution information to enhance service composition model, and localize faults by comparing the exceptional execution and the correct execution with the maximum likelihood. Experiments are conducted to evaluate the effectiveness of our method in web service composition fault diagnosis.
On a Noun-Driven Syntactic Paradigm
Authors: Stuart Lauren
Published: June 2022

In addressing a verb bias in syntactic analysis we present the beginnings of a noun-driven analysis paradigm. Such a paradigm may complement and compensate for some weaknesses in the existing verb-driven paradigm (in which the noun is subordinated) as applied to information sources or contexts in which the data is structured in objects and more than in events.
Empirical Comparison of Multi-Label Classification Algorithms
Authors: Tawiah Clifford , Sheng Victor
Published: June 2022

Multi-label classifications exist in many real world applications. This paper empirically studies the performance of a variety of multi-label classification algorithms. Some of them are developed based on problem transformation. Some of them are developed based on adaption. Our experimental results show that the adaptive Multi-Label K-Nearest Neighbor performs the best, followed by Random k-Label Set, followed by Classifier Chain and Binary Relevance. Adaboost.MH performs the worst, followed by Pruned Problem Transformation. Our experimental results also provide us the confidence of the correlations among multi-labels. These insights shed light for future research directions on multi-label classifications.
Trading Space for Time in Grid-Based Path Finding
Authors: Lee William , Lawrence Ramon
Published: June 2022

Grid-based path finding is required in many games to move agents. We present an algorithm called DBA* that uses a database of pre-computed paths to reduce the time to solve search problems. When evaluated using benchmark maps from Dragon Age™, DBA* requires less time for search and produces less suboptimal paths than the PRA* implementation used in Dragon Age™.
Imbalanced Multiple Noisy Labeling for Supervised Learning
Authors: Zhang Jing , Wu Xindong , Sheng Victor
Published: June 2022

When labeling objects via Internet-based outsourcing systems, the labelers may have bias, because they lack expertise, dedication and personal preference. These reasons cause Imbalanced Multiple Noisy Labeling. To deal with the imbalance labeling issue, we propose an agnostic algorithm PLAT (Positive LAbel frequency Threshold) which does not need any information about quality of labelers and underlying class distribution. Simulations on eight real-world datasets with different underlying class distributions demonstrate that PLAT not only effectively deals with the imbalanced multiple noisy labeling problem that off-the-shelf agnostic methods cannot cope with, but also performs nearly the same as majority voting under the circumstances that labelers have no bias.
Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers
Authors: Geschwender Daniel , Karakashian Shant , Woodward Robert , Choueiry Berthe , Scott Stephen
Published: June 2022

Computing the minimal network of a Constraint Satisfaction Problem (CSP) is a useful and difficult task. Two algorithms, PerTuple and AllSol, were proposed to this end. The performances of these algorithms vary with the problem instance. We use Machine Learning techniques to build a classifier that predicts which of the two algorithms is likely to be more effective.
Personalized Recommendation Based on Co-Ranking and Query-Based Collaborative Diffusion
Authors: Yang Xiao , Zhang Zhaoxin , Wang Qiang
Published: June 2022

In this paper, we present an adaptive graph-based personalized recommendation method based on co-ranking and query-based collaborative diffusion. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships and analyzing the representation of user’s preference in the graph. The experiments show that this algorithm can outperform the traditional CF methods and achieve competitive performance compared with many model-based and graph-based recommendation methods, and have better scalability and flexibility.
A Maximum K-Min Approach for Classification
Authors: Dong Mingzhi , Yin Liang
Published: June 2022

In this paper, a general Maximum K-Min approach for classification is proposed, which focuses on maximizing the gain obtained by the K worst-classified instances while ignoring the remaining ones. To make the original optimization problem with combinational constraints computationally tractable,  the optimization techniques are adopted and a general compact representation lemma is summarized. Based on the lemma, a Nonlinear Maximum K-Min (NMKM) classifier is presented and the experiment results demonstrate the superior performance of the Maximum K-Min Approach.
A First-Order Logic Based Framework for Verifying Simulations
Authors: Nyew Hui Meen , Onder Nilufer , Onder Soner , Wang Zhenlin
Published: June 2022

Modern science relies on simulation techniques for understanding phenomenon, exploring design options, or evaluating models. Assuring the correctness of simulators is a key problem where a multitude of solutions ranging from manual inspection to formal verification are applicable. Formal verification incorporates the rigor necessary but not all simulators are generated from formal specifications. Manual inspection is readily available but lacks the rigor and is prone to errors. In this paper, we describe an automated verification system (AVS) where the constraints that the system must adhere to are specified by the user in general purpose first-order logic. AVS translates these constraints into a verification program that scans the simulator traceand verifies that no constraints are violated. Computer microarchitecture simulations were successfully used to demonstrate the proposed approach. This paper describes the preliminary results and discusses how artificial intelligence techniques can be used to facilitate effective run-time verification of simulators.
Hypothesis Exploration for Malware Detection Using Planning
Authors: Sohrabi Shirin , Udrea Octavian , Riabov Anton
Published: June 2022

In this paper we apply AI planning to address the hypothesis exploration problem and provide assistance to network administrators in detecting malware based on unreliable observations derived from network traffic.Building on the already established characterization and use of AI planning for similar problems, we propose a formulation of the hypothesis generation problem for malware detection as an AI planning problem with temporally extended goals and actions costs. Furthermore, we propose a notion of hypothesis ``plausibility'' under unreliable observations, which we model as plan quality. We then show that in the presence of unreliable observations, simply finding one most ``plausible'' hypothesis, although challenging, is not sufficient for effective malware detection. To that end, we propose a method for applying a state-of-the-art planner within a principled exploration process, to generate multiple distinct high-quality plans. We experimentally evaluate this approach by generating random problems of varying hardness both with respect to the number of observations, as well as the degree of unreliability. Based on these experiments, we argue that our approach presents a significant improvement over prior work that are focused on finding a single optimal plan, and that our hypothesis exploration application can motivate the development of new planners capable of generating the top high-quality plans.
Gradient Networks: Explicit Shape Matching Without Extracting Edges
Authors: Hsiao Edward , Hebert Martial
Published: June 2022

We present a novel framework for shape-based template matching in images. While previous approaches required brittle contour extraction, considered only local information, or used coarse statistics, we propose to match the shape explicitly on low-level gradients by formulating the problem as traversing paths in a gradient network. We evaluate our algorithm on a challenging dataset of objects in cluttered environments and demonstrate significant improvement over state-of-the-art methods for shape matching and object detection.
The Cascade Auction — A Mechanism for Deterring Collusion in Auctions
Authors: Feige Uriel , Kalai Gil , Tennenholz Moshe
Published: June 2022

We introduce a sealed bid auction of a single item in which the winner is chosen at random among the highest k bidders according to a fixed probability distribution, and the price for the chosen winner is the Vickrey-Clarke-Groves price. We call such an auction a cascade auction. Our analysis suggests that this type of auction may give higher revenues compared to second price auction in cases of collusion.
Grounding Natural Language References to Unvisited and Hypothetical Locations
Authors: Williams Thomas , Cantrell Rehj , Briggs Gordon , Schermerhorn Paul , Scheutz Matthias
Published: June 2022

While much research exists on resolving spatial natural language references to known locations, little work deals with handling references to unknown locations. In this paper we introduce and evaluate algorithms integrated into a cognitive architecture which allow an agent to learn about its environ-ment while resolving references to both known and unknown locations. We also describe how multiple components in the architecture jointly facilitate these capabilities.
Convex Subspace Representation Learning from Multi-View Data
Authors: Guo Yuhong
Published: June 2022

Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We ﬁrst formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual norms, we then show a convex min-max dual formulation with a sparsity inducing trace norm can be obtained. We develop a proximal bundle optimization algorithm to globally solve the min-max optimization problem. Our empirical study shows the proposed subspace representation learning method can effectively facilitate multi-view clustering and induce superior clustering results than alternative multi-view clustering methods.
Dynamic Social Choice with Evolving Preferences
Authors: Parkes David , Procaccia Ariel
Published: June 2022

Social choice theory provides insights into a variety of collective decision making settings, but nowadays some of its tenets are challenged by internet environments, which call for dynamic decision making under constantly changing preferences. In this paper we model the problem via Markov decision processes (MDP), where the states of the MDP coincide with preference profiles and a (deterministic, stationary) policy corresponds to a social choice function. We can therefore employ the axioms studied in the social choice literature as guidelines in the design of socially desirable policies. We present tractable algorithms that compute optimal policies under different prominent social choice constraints. Our machinery relies on techniques for exploiting symmetries and isomorphisms between MDPs.
Fast Equilibrium Computation for Infinitely Repeated Games
Authors: Andersen Garrett , Conitzer Vincent
Published: June 2022

It is known that an equilibrium of an infinitely repeated two-player game (with limit average payoffs) can be computed in polynomial time, as follows: according to the folk theorem, we compute minimax strategies for both players to calculate the punishment values, and subsequently find a mixture over outcomes that exceeds these punishment values. However, for very large games, even computing minimax strategies can be prohibitive. In this paper, we propose an algorithmic framework for computing equilibria of repeated games that does not require linear programming and that does not necessarily need to inspect all payoffs of the game. This algorithm necessarily sometimes fails to compute an equilibrium, but we mathematically demonstrate that most of the time it succeeds quickly on uniformly random games, and experimentally demonstrate this for other classes of games. This also holds for games with more than two players, for which no efficient general algorithms are known.
Salient Object Detection via Low-Rank and Structured Sparse Matrix Decomposition
Authors: Peng Houwen , Li Bing , Ji Rongrong , Hu Weiming , Xiong Weihua , Lang Congyan
Published: June 2022

Salient object detection provides an alternative solution to various image semantic understanding tasks such as object recognition, adaptive compression and image retrieval. Recently, low-rank matrix recovery (LR) theory has been introduced into saliency detection, and achieves impressed results. However, the existing LR-based models neglect the underlying structure of images, and inevitably degrade the associated performance. In this paper, we propose a Low-rank and Structured sparse Matrix Decomposition (LSMD) model for salient object detection. In the model, a tree-structured sparsity-inducing norm regularization is firstly introduced to provide a hierarchical description of the image structure to ensure the completeness of the extracted salient object. The similarity of saliency values within the salient object is then guaranteed by the $\ell _\infty$-norm. Finally, high-level priors are integrated to guide the matrix decomposition and enhance the saliency detection. Experimental results on the largest public benchmark database show that our model outperforms existing LR-based approaches and other state-of-the-art methods, which verifies the effectiveness and robustness of the structure cues in our model.
Supervised Coupled Dictionary Learning with Group Structures for Multi-modal Retrieval
Authors: Zhuang Yue , Wang Yan , Wu Fei , Zhang Yin , Lu Wei
Published: June 2022

A better similarity mapping function across heterogeneous high-dimensional features is very desirable for many applications involving multi-modal data. In this paper, we introduce coupled dictionary learning (DL) into supervised sparse coding for multi-modal (cross-media) retrieval. We call this Supervised coupled dictionary learning with group structures for Multi-Modal retrieval (SliM2). SliM2 formulates the multi-modal mapping as a constrained dictionary learning problem. By utilizing the intrinsic power of DL to deal with the heterogeneous features, SliM2 extends unimodal DL to multi-modal DL. Moreover, the label information is employed in SliM2 to discover the shared structure inside intra-modality within the same class by a mixed norm (i.e., `l1/l2`-norm). As a result, the multimodal retrieval is conducted via a set of jointly learned mapping functions across multi-modal data. The experimental results show the effectiveness of our proposed model when applied to cross-media retrieval.
On the Subexponential Time Complexity of CSP
Authors: Kanj Iyad , Szeider Stefan
Published: June 2022

A Constraint Satisfaction Problem (CSP) with n variables ranging over a domain of d values can be solved by brute-force in d^n steps (omitting a polynomial factor). With a more careful approach, this trivial upper bound can be improved for certain natural restrictions of the CSP. In this paper we establish theoretical limits to such improvements, and draw a detailed landscape of the subexponential-time complexity of CSP. We first establish relations between the subexponential-time complexity of CSP and that of other problems, including CNF-Sat. We exploit this connection to provide tight characterizations of the subexponential-time complexity of CSP under common assumptions in complexity theory. For several natural CSP parameters, we obtain threshold functions that precisely dictate the subexponential-time complexity of CSP with respect to the parameters under consideration. Our analysis provides fundamental results indicating whether and when one can significantly improve on the brute-force search approach for solving CSP.
Goal-Oriented Euclidean Heuristics with Manifold Learning
Authors: Chen Wenlin , Chen Yixin , Weinberger Kilian , Lu Qiang , Chen Xiaoping
Published: June 2022

Recently, a Euclidean heuristic (EH) has been proposed for A* search. EH exploits manifold learning methods to construct an embedding of the state space graph, and derives an admissible heuristic distance between two states from the Euclidean distance between their respective embedded points. EH has shown good performance and memory efficiency in comparison to other existing heuristics such as differential heuristics. However, its potential has not been fully explored. In this paper, we propose a number of techniques that can significantly improve the quality of EH. We propose a goal-oriented manifold learning scheme that optimizes the Euclidean distance to goals in the embedding while maintaining admissibility and consistency. We also propose a state heuristic enhancement technique to reduce the gap between heuristic and true distances. The enhanced heuristic is admissible but no longer consistent. We then employ a modified search algorithm, known as B' algorithm, that achieves optimality with inconsistent heuristics using consistency check and propagation. We demonstrate the effectiveness of the above techniques and report un-matched reduction in search costs across several non-trivial benchmark search problems.
Teaching Classification Boundaries to Humans
Authors: Basu Sumit , Christensen Janara
Published: June 2022

Given a classification task, what is the best way to teach the resulting boundary to a human? While machine learning techniques can provide excellent methods for finding the boundary, including the selection of examples in an online setting, they tell us little about how we would teach a human the same task. We propose to investigate the problem of example selection and presentation in the context of teaching humans, and explore a variety of mechanisms in the interests of finding what may work best. In particular, we begin with the baseline of random presentation and then examine combinations of several mechanisms: the indication of an example’s relative difficulty, the use of the shaping heuristic from the cognitive science literature (moving from easier examples to harder ones), and a novel kernel-based “coverage model” of the subject’s mastery of the task. From our experiments on 54 human subjects learning and performing a pair of synthetic classification tasks via our teaching system, we found that we can achieve the greatest gains with a combination of shaping and the coverage model.
Ranking Scientific Articles by Exploiting Citations, Authors, Journals, and Time Information
Authors: Wang Yujing , Tong Yunhai , Zeng Ming
Published: June 2022

Ranking scientific articles is an important but challenging task, partly due to the dynamic nature of the evolving publication network. In this paper, we mainly focus on two problems: (1) how to rank articles in the heterogeneous network; and (2) how to use time information in the dynamic network in order to obtain a better ranking result. To tackle the problems, we propose a graph based ranking method, which utilizes citations, authors, journals/conferences and the publication time information collaboratively. The experiments were carried out on two public datasets. The result shows that our approach is practical and ranks scientific articles more accurately than the state-of-art methods.
Interactive Information Extraction and Navigation to Enable Effective Link Analysis and Visualization of Unstructured Text
Authors: Budlong Emily , Pine Carrie , Zappavigna Mark , Homer James , Proefrock Charles , Gucwa John , Crystal Michael , Weischedel Ralph
Published: June 2022

This paper describes the Advanced Text Exploitation Assistant (ATEA), a system developed to enable intelligence analysts to perform link analysis and visualization (A&V) from information in large volumes of unstructured text. One of the key design challenges that had to be addressed was that of imperfect Information Extraction (IE) technology. While IE seems like a promising candidate for exploiting information in unstructured text, it makes mistakes. As a result, analysts do not trust its results. In this paper, we discuss how ATEA overcomes the obstacle of imperfect IE by incorporating a human-in-the-loop for review and correction of extraction results. We also discuss how coupling consolidated extraction results (corpus-level information objects) with an intuitive user interface facilitates interactive navigation of the resulting information. With these key features, ATEA enables effective link analysis and visualization of information in unstructured text.
Policies to Optimize Work Performance and Thermal Safety in Exercising Humans
Authors: Buller Mark , Sodomka Eric , Tharion William , Clements Cynthia , Hoyt Reed , Jenkins Odest
Published: June 2022

Emergency workers engaged in strenuous work in hot environments risk overheating and mission failure. We describe a real-time application that would reduce these risks in terms of a real-time thermal-work strain index (SI) estimator; and a Markov Decision Process (MDP) to compute optimal work rate policies. We examined the thermo-physiological responses of 14 experienced U.S. Army Ranger students (26±4 years 1.77±0.04 m; 78.3±7.3 kg) who participated in a strenuous 8 mile time-restricted pass/fail road march conducted under thermally stressful conditions. A thermoregulatory model was used to derive SI state transition probabilities and model the students’ observed and policy driven movement rates. We found that policy end-state SI was significantly lower than SI when modeled using the student’s own movement rates (3.94±0.88 vs. 5.62±1.20, P<0.001). We also found an inverse relationship between our policy impact and maximum SI (r=0.64 P<0.05). These results suggest that modeling real world missions as an MDP can provide optimal work rate policies that improve thermal safety and allow students to finish in a “fresher” state. Ultimately, SI state estimation and MDP models incorporated into wearable physiological monitoring systems could provide real-time work rate guidance, thus minimizing thermal work-strain while maximizing the likelihood of accomplishing mission tasks.
Balancing the Traveling Tournament Problem for Weekday and Weekend Games
Authors: Hoshino Richard , Kawarabayashi Ken-ichi
Published: June 2022

The Traveling Tournament Problem (TTP) is a well-known NP-complete problem in sports scheduling that was inspired by the application of optimizing schedules for Major League Baseball to reduce total team travel. The techniques and heuristics from the n-team TTP can be extended to optimize the scheduling of other sports leagues, such as the Nippon Professional Baseball (NPB) league in Japan. In this paper, we describe the additional scheduling constraints required by the NPB league, such as the requirement that each team play the same number of weekend home games, weekday home games, weekend road games, and weekday road games. We fully solve this TTP-variant for the case n = 6, and conclude the paper by presenting the official 2013 NPB Central League Schedule, where we helped this Japanese baseball league reduce total team travel by over six thousand kilometres.
Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models
Authors: Meng Zhao , Mou Lili , Jin Zhi
Published: June 2022

In this paper, we address the problem of speaker classification in multi-party conversation, and collect massive data to facilitate research in this direction. We further investigate temporal-based and content-based models of speakers, and propose several hybrids of them. Experiments show that speaker classification is feasible, and that hybrid models outperform each single component.
Learning Feature Representations for Keyphrase Extraction
Authors: Florescu Corina , Jin Wei
Published: June 2022

In supervised approaches for keyphrase extraction, a candidate phrase is encoded with a set of hand-crafted features and machine learning algorithms are trained to discriminate keyphrases from non-keyphrases. Although the manually-designed features have shown to work well in practice, feature engineering is a difficult process that requires expert knowledge and normally does not generalize well. In this paper, we present SurfKE, a feature learning framework that exploits the text itself to automatically discover patterns that keyphrases exhibit. Our model represents the document as a graph and automatically learns feature representation of phrases. The proposed model obtains remarkable improvements in performance over strong baselines.
Deep Embedding for Determining the Number of Clusters
Authors: Wang Yiqi , Shi Zhan , Guo Xifeng , Liu Xinwang , Zhu En , Yin Jianping
Published: June 2022

Determining the number of clusters is important but challenging, especially for data of high dimension. In this paper, we propose Deep Embedding Determination (DED), a method that can solve jointly for the unknown number of clusters and feature extraction. DED first combines the virtues of the convolutional autoencoder and the t-SNE technique to extract low dimensional embedded features. Then it determines the number of clusters using an improved density-based clustering algorithm. Our experimental evaluation on image datasets shows significant improvement over state-of-the-art methods and robustness with respect to hyperparameter settings.
Different Cycle, Different Assignment: Diversity in Assignment Problems With Multiple Cycles
Authors: Spieker Helge , Gotlieb Arnaud , Mossige Morten
Published: June 2022

We present approaches to handle diverse assignments in multi-cycle assignment problems. The goal is to assign a task to different agents in each cycle, such that all possible combinations are made over time. Our method combines the original profit value, that is to be optimized by the assignment problem with an additional assignment preference. By merging both, we steer the optimization towards diverse assignments without large trade-offs in the original profits.
Efficient Support Vector Machine Training Algorithm on GPUs
Authors: Shi Jiashuai , Wen Zeyi , He Bingsheng , Chen Jian
Published: June 2022

Support Vector Machines (SVMs) are popular for many machine learning tasks. With rapid growth of dataset size, the high cost of training limits the wide use of SVMs. Several SVM implementations on GPUs have been proposed to accelerate SVMs. However, they support only classification (SVC) or regression (SVR). In this work, we propose a simple and effective SVM training algorithm on GPUs which can be used for SVC, SVR and one-class SVM. Initial experiments show that our implementation outperforms existing ones. We are in the process of encapsulating our algorithm into an easy-to-use library which has Python, R and MATLAB interfaces.
The interaction between monosulfoaluminate and calcium chloride aqueous solution
Authors: Wang Yang , Ge Yong , Fang Haibo , Pan Yupo , Chen Xin , Li Qinfei
Published: June 2022

Scanning transmission X-ray microscopy (STXM) and calcium L2,3-edge near-edge X-ray absorption fine structure (Nexafs) spectra were used to investigate the morphology and chemical information of cement-based materials. The interaction between calcium chloride (CaCl2) and monosulfoaluminate (Ms) was investigated and the intermediate phases were identified. With an increase in the concentration of the calcium chloride solution, calcium Ms was converted into ettringite, Kuzel's salt (Ks) and Friedel's salt (Fs) to varying degrees, while SO42− ions in the ettringite and Ks were continuously replaced by Cl− ions in the solution, eventually forming Fs and gypsum. Based on the calcium L2,3-edge Nexafs absorption spectra, ettringite, Ms, Ks and Ms were greatly distinguished by the number and shape of the leading peaks in the spectra due to the difference in SO42− content among the interlayer structures of Ms, Ks and Fs. The present work is based on the references for the STXM study of the calcium (sulfo)aluminates, for demonstrating a more complete understanding of the phase evolution under chloride environments.