bluuebunny/crossref_metadata_2025 huggingface dataset:
Instructable Intelligent Personal Agent
Authors: Azaria Amos , Krishnamurthy Jayant , Mitchell Tom
Published: June 2022

Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is likely to grow. We introduce our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. LIA uses a CCG semantic parser to ground the semantics of each command in terms of primitive executable procedures defining sensors and effectors of the agent. Given a natural language command that LIA does not understand, it prompts the user to explain how to achieve the command through a sequence of steps, also specified in natural language. A novel lexicon induction algorithm enables LIA to generalize across taught commands, e.g., having been taught how to "forward an email to Alice," LIA can correctly interpret the command "forward this email to Bob." A user study involving email tasks demonstrates that users voluntarily teach LIA new commands, and that these taught commands significantly reduce task completion time. These results demonstrate the potential of natural language instruction as a significant, under-explored paradigm for machine learning.
Morphological Segmentation with Window LSTM Neural Networks
Authors: Wang Linlin , Cao Zhu , Xia Yu , De Melo Gerard
Published: June 2022

Morphological segmentation, which aims to break words into meaning-bearing morphemes, is an important task in natural language processing. Most previous work relies heavily on linguistic preprocessing. In this paper, we instead propose novel neural network architectures that learn the structure of input sequences directly from raw input words and are subsequently able to predict morphological boundaries. Our architectures rely on Long Short Term Memory (LSTM) units to accomplish this, but exploit windows of characters to capture more contextual information. Experiments on multiple languages confirm the effectiveness of our models on this task.
Joint Word Segmentation, POS-Tagging and Syntactic Chunking
Authors: Lyu Chen , Zhang Yue , Ji Donghong
Published: June 2022

Chinese chunking has traditionally been solved by assuming gold standard word segmentation.We find that the accuracies drop drastically when automatic segmentation is used.Inspired by the fact that chunking knowledge can potentially improve segmentation, we explore a joint model that performs segmentation, POS-tagging and chunking simultaneously.In addition, to address the sparsity of full chunk features, we employ a semi-supervised method to derive chunk cluster features from large-scale automatically-chunked data.Results show the effectiveness of the joint model with semi-supervised features.
Age of Exposure: A Model of Word Learning
Authors: Dascalu Mihai , McNamara Danielle , Crossley Scott , Trausan-Matu Stefan
Published: June 2022

Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners’ understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a large-scale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.
Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect Associations
Authors: Liu Qian , Liu Bing , Zhang Yuanlin , Kim Doo Soon , Gao Zhiqiang
Published: June 2022

Aspect extraction is a key task of fine-grained opinion mining. Although it has been studied by many researchers, it remains to be highly challenging. This paper proposes a novel unsupervised approach to make a major improvement. The approach is based on the framework of lifelong learning and is implemented with two forms of recommendations that are based on semantic similarity and aspect associations respectively. Experimental results using eight review datasets show the effectiveness of the proposed approach.
Aggregating Inter-Sentence Information to Enhance Relation Extraction
Authors: Zheng Hao , Li Zhoujun , Wang Senzhang , Yan Zhao , Zhou Jianshe
Published: June 2022

Previous work for relation extraction from free text is mainly based on intra-sentence information. As relations might be mentioned across sentences, inter-sentence information can be leveraged to improve distantly supervised relation extraction. To effectively exploit inter-sentence information, we propose a ranking based approach, which first learns a scoring function based on a listwise learning-to-rank model and then uses it for multi-label relation extraction. Experimental results verify the effectiveness of our method for aggregating information across sentences. Additionally, to further improve the ranking of high-quality extractions, we propose an effective method to rank relations from different entity pairs. This method can be easily integrated into our overall relation extraction framework, and boosts the precision significantly.
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs
Authors: Jin Zhiwei , Cao Juan , Zhang Yongdong , Luo Jiebo
Published: June 2022

Fake news spreading in social media severely jeopardizes the veracity of online content. Fortunately, with the interactive and open features of microblogs, skeptical and opposing voices against fake news always arise along with it. The conflicting information, ignored by existing studies, is crucial for news verification. In this paper, we take advantage of this "wisdom of crowds" information to improve news verification by mining conflicting viewpoints in microblogs. First, we discover conflicting viewpoints in news tweets with a topic model method. Based on identified tweets' viewpoints, we then build a credibility propagation network of tweets linked with supporting or opposing relations. Finally, with iterative deduction, the credibility propagation on the network generates the final evaluation result for news. Experiments conducted on a real-world data set show that the news verification performance of our approach significantly outperforms those of the baseline approaches.
Joint Inference over a Lightly Supervised Information Extraction Pipeline: Towards Event Coreference Resolution for Resource-Scarce Languages
Authors: Chen Chen , Ng Vincent
Published: June 2022

We address two key challenges in end-to-end event coreference resolution research: (1) the error propagation problem, where an event coreference resolver has to assume as input the noisy outputs produced by its upstream components in the standard information extraction (IE) pipeline; and (2) the data annotation bottleneck, where manually annotating data for all the components in the IE pipeline is prohibitively expensive. This is the case in the vast majority of the world's natural languages, where such annotated resources are not readily available. To address these problems, we propose to perform joint inference over a lightly supervised IE pipeline, where all the models are trained using either active learning or unsupervised learning. Using our approach, only 25% of the training sentences in the Chinese portion of the ACE 2005 corpus need to be annotated with entity and event mentions in order for our event coreference resolver to surpass its fully supervised counterpart in performance.
Discovering User Attribute Stylistic Differences via Paraphrasing
Authors: Preotiuc-Pietro Daniel , Xu Wei , Ungar Lyle
Published: June 2022

User attribute prediction from social media text has proven successful and useful for downstream tasks. In previous studies, differences in user trait language use have been limited primarily to the presence or absence of words that indicate topical preferences. In this study, we aim to find linguistic style distinctions across three different user attributes: gender, age and occupational class. By combining paraphrases with a simple yet effective method, we capture a wide set of stylistic differences that are exempt from topic bias. We show their predictive power in user profiling, conformity with human perception and psycholinguistic hypotheses, and potential use in generating natural language tailored to specific user traits.
Solving Transition-Independent Multi-Agent MDPs with Sparse Interactions
Authors: Scharpff Joris , Roijers Diederik , Oliehoek Frans , Spaan Matthijs , De Weerdt Mathijs
Published: June 2022

In cooperative multi-agent sequential decision making under uncertainty, agents must coordinate to find an optimal joint policy that maximises joint value. Typical algorithms exploit additive structure in the value function, but in the fully-observable multi-agent MDP (MMDP) setting such structure is not present. We propose a new optimal solver for transition-independent MMDPs, in which agents can only affect their own state but their reward depends on joint transitions. We represent these dependencies compactly in conditional return graphs (CRGs). Using CRGs the value of a joint policy and the bounds on partially specified joint policies can be efficiently computed. We propose CoRe, a novel branch-and-bound policy search algorithm building on CRGs. CoRe typically requires less runtime than available alternatives and finds solutions to previously unsolvable problems.
Multi-Agent Path Finding with Payload Transfers and the Package-Exchange Robot-Routing Problem
Authors: Ma Hang , Tovey Craig , Sharon Guni , Kumar T. K. , Koenig Sven
Published: June 2022

We study transportation problems where robots have to deliver packages and can transfer the packages among each other. Specifically, we study the package-exchange robot-routing problem (PERR), where each robot carries one package, any two robots in adjacent locations can exchange their packages, and each package needs to be delivered to a given destination. We prove that exchange operations make all PERR instances solvable. Yet, we also show that PERR is NP-hard to approximate within any factor less than 4/3 for makespan minimization and is NP-hard to solve for flowtime minimization, even when there are only two types of packages. Our proof techniques also generate new insights into other transportation problems, for example, into the hardness of approximating optimal solutions to the standard multi-agent path-finding problem (MAPF). Finally, we present optimal and suboptimal PERR solvers that are inspired by MAPF solvers, namely a flow-based ILP formulation and an adaptation of conflict-based search. Our empirical results demonstrate that these solvers scale well and that PERR instances often have smaller makespans and flowtimes than the corresponding MAPF instances.
Continual Planning in Golog
Authors: Hofmann Till , Niemueller Tim , Claßen Jens , Lakemeyer Gerhard
Published: June 2022

To solve ever more complex and longer tasks, mobile robots need to generate more elaborate plans and must handle dynamic environments and incomplete knowledge. We address this challenge by integrating two seemingly different approaches — PDDL-based planning for efficient plan generation and Golog for highly expressive behavior specification — in a coherent framework that supports continual planning. The latter allows to interleave plan generation and execution through assertions, which are placeholder actions that are dynamically expanded into conditional sub-plans (using classical planners) once a replanning condition is satisfied. We formalize and implement continual planning in Golog which was so far only supported in PDDL-based systems. This enables combining the execution of generated plans with regular Golog programs and execution monitoring. Experiments on autonomous mobile robots show that the approach supports expressive behavior specification combined with efficient sub-plan generation to handle dynamic environments and incomplete knowledge in a unified way.
Structured Features in Naive Bayes Classification
Authors: Choi Arthur , Tavabi Nazgol , Darwiche Adnan
Published: June 2022

We propose the structured naive Bayes (SNB) classifier, which augments the ubiquitous naive Bayes classifier with structured features. SNB classifiers facilitate the use of complex features, such as combinatorial objects (e.g., graphs, paths and orders) in a general but systematic way. Underlying the SNB classifier is the recently proposed Probabilistic Sentential Decision Diagram (PSDD), which is a tractable representation of probability distributions over structured spaces. We illustrate the utility and generality of the SNB classifier via case studies. First, we show how we can distinguish players of simple games in terms of play style and skill level based purely on observing the games they play. Second, we show how we can detect anomalous paths taken on graphs based purely on observing the paths themselves.
Alternative Filtering for the Weighted Circuit Constraint: Comparing Lower Bounds for the TSP and Solving TSPTW
Authors: Ducomman Sylvain , Cambazard Hadrien , Penz Bernard
Published: June 2022

Many problems, and in particular routing problems, require to find one or many circuits in a weighted graph. The weights often express the distance or the travel time between vertices. We propose in this paper various filtering algorithms for the weighted circuit constraint which maintain a circuit in a weighted graph. The filtering algorithms are typical cost based filtering algorithms relying on relaxations of the Traveling Salesman Problem. We investigate three bounds and show that they are incomparable. In particular we design a filtering algorithm based on a lower bound introduced in 1981 by Christophides et al.. This bound can provide stronger filtering than the classical Held and Karp’s approach when additional information, such as the possible positions of the clients in the tour, is available. This is particularly suited for problems with side constraints such as time windows.
Steiner Tree Problems with Side Constraints Using Constraint Programming
Authors: De Uña Diego , Gange Graeme , Schachte Peter , Stuckey Peter
Published: June 2022

The Steiner Tree Problem is a well know NP-complete problem that is well studied and for which fast algorithms are already available. Nonetheless, in the real world the Steiner Tree Problem is almost always accompanied by side constraints which means these approaches cannot be applied. For many problems with side constraints, only approximation algorithms are known. We introduce here a propagator for the tree constraint with explanations, as well as lower bounding techniques and a novel constraint programming approach for the Steiner Tree Problem and two of its variants. We find our propagators with explanations are highly advantageous when it comes to solving variants of this problem.
Bidirectional Search That Is Guaranteed to Meet in the Middle
Authors: Holte Robert , Felner Ariel , Sharon Guni , Sturtevant Nathan
Published: June 2022

We present MM, the first bidirectional heuristic search algorithm whose forward and backward searches are guaranteed to ''meet in the middle'', i.e. never expand a node beyond the solution midpoint. We also present a novel framework for comparing MM, A*, and brute-force search, and identify conditions favoring each algorithm. Finally, we present experimental results that support our theoretical analysis.
Face Model Compression by Distilling Knowledge from Neurons
Authors: Luo Ping , Zhu Zhenyao , Liu Ziwei , Wang Xiaogang , Tang Xiaoou
Published: June 2022

The recent advanced face recognition systems werebuilt on large Deep Neural Networks (DNNs) or theirensembles, which have millions of parameters. However, the expensive computation of DNNs make theirdeployment difficult on mobile and embedded devices. This work addresses model compression for face recognition,where the learned knowledge of a large teachernetwork or its ensemble is utilized as supervisionto train a compact student network. Unlike previousworks that represent the knowledge by the soften labelprobabilities, which are difficult to fit, we represent theknowledge by using the neurons at the higher hiddenlayer, which preserve as much information as the label probabilities, but are more compact. By leveragingthe essential characteristics (domain knowledge) of thelearned face representation, a neuron selection methodis proposed to choose neurons that are most relevant toface recognition. Using the selected neurons as supervisionto mimic the single networks of DeepID2+ andDeepID3, which are the state-of-the-art face recognition systems, a compact student with simple network structure achieves better verification accuracy on LFW than its teachers, respectively. When using an ensemble of DeepID2+ as teacher, a mimicked student is able to outperform it and achieves 51.6 times compression ratio and 90 times speed-up in inference, making this cumbersome model applicable on portable devices.
Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks
Authors: Zhu Wentao , Lan Cuiling , Xing Junliang , Zeng Wenjun , Li Yanghao , Shen Li , Xie Xiaohui
Published: June 2022

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.
Video Semantic Clustering with Sparse and Incomplete Tags
Authors: Wang Jingya , Zhu Xiatian , Gong Shaogang
Published: June 2022

Clustering tagged videos into semantic groups is importantbut challenging due to the need for jointly learning correlations between heterogeneous visual and tag data. The taskis made more difficult by inherently sparse and incompletetag labels. In this work, we develop a method for accuratelyclustering tagged videos based on a novel Hierarchical-MultiLabel Random Forest model capable of correlating structured visual and tag information. Specifically, our model exploits hierarchically structured tags of different abstractnessof semantics and multiple tag statistical correlations, thus discovers more accurate semantic correlations among differentvideo data, even with highly sparse/incomplete tags.
DARI: Distance Metric and Representation Integration for Person Veriﬁcation
Authors: Wang Guangrun , Lin Liang , Ding Shengyong , Li Ya , Wang Qing
Published: June 2022

The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i.e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches.
Reading Scene Text in Deep Convolutional Sequences
Authors: He Pan , Huang Weilin , Qiao Yu , Loy Chen , Tang Xiaoou
Published: June 2022

We develop a Deep-Text Recurrent Network (DTRN)that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered highlevel sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. It achieves impressive results on several benchmarks, advancing the-state-of-the-art substantially.
Exploiting View-Specific Appearance Similarities Across Classes for Zero-Shot Pose Prediction: A Metric Learning Approach
Authors: Kuznetsova Alina , Hwang Sung Ju , Rosenhahn Bodo , Sigal Leonid
Published: June 2022

Viewpoint estimation, especially in case of multiple object classes, remains an important and challenging problem. First, objects under different views undergo extreme appearance variations, often making within-class variance larger than between-class variance. Second, obtaining precise ground truth for real-world images, necessary for training supervised viewpoint estimation models, is extremely difficult and time consuming. As a result, annotated data is often available only for a limited number of classes. Hence it is desirable to share viewpoint information across classes. Additional complexity arises from unaligned pose labels between classes, i.e. a side view of a car might look more like a frontal view of a toaster, than its side view. To address these problems, we propose a metric learning approach for joint class prediction and pose estimation. Our approach allows to circumvent the problem of viewpoint alignment across multiple classes, and does not require dense viewpoint labels. Moreover, we show, that the learned metric generalizes to new classes, for which the pose labels are not available, and therefore makes it possible to use only partially annotated training sets, relying on the intrinsic similarities in the viewpoint manifolds. We evaluate our approach on two challenging multi-class datasets, 3DObjects and PASCAL3D+.
An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid
Authors: Bandyopadhyay Sambaran , Narayanam Ramasuri , Kumar Pratyush , Ramchurn Sarvapali , Arya Vijay , Petra Iskandarbin
Published: June 2022

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.
Multi-Instance Multi-Label Class Discovery: A Computational Approach for Assessing Bird Biodiversity
Authors: Briggs Forrest , Fern Xiaoli , Raich Raviv , Betts Matthew
Published: June 2022

We study the problem of analyzing a large volume ofbioacoustic data collected in-situ with the goal of assessingthe biodiversity of bird species at the data collectionsite. We are interested in the class discoveryproblem for this setting. Specifically, given a large collectionof audio recordings containing bird and othersounds, we aim to automatically select a fixed size subsetof the recordings for human expert labeling suchthat the maximum number of species/classes is discovered.We employ a multi-instance multi-label representationto address multiple simultaneously vocalizingbirds with sounds that overlap in time, and proposenew algorithms for species/class discovery using thisrepresentation. In a comparative study, we show that theproposed methods discover more species/classes thancurrent state-of-the-art in a real world datasetof 92,095 ten-second recordings collected in field conditions.
Achieving Stable and Fair Profit Allocation with Minimum Subsidy in Collaborative Logistics
Authors: Agussurja Lucas , Lau Hoong Chuin , Cheng Shih-Fen
Published: June 2022

With the advent of e-commerce, logistics providers are faced with the challenge of handling fluctuating and sparsely distributed demand, which raises their operational costs significantly. As a result, horizontal cooperation are gaining momentum around the world. One of the major impediments, however, is the lack of stable and fair profit sharing mechanism. In this paper, we address this problem using the framework of computational cooperative games. We first present cooperative vehicle routing game as a model for collaborative logistics operations. Using the axioms of Shapley value as the conditions for fairness, we show that a stable, fair and budget balanced allocation does not exist in many instances of the game. By relaxing budget balance, we then propose an allocation scheme based on the normalized Shapley value. We show that this scheme maintains stability and fairness while requiring minimum subsidy. Finally, using numerical experiments we demonstrate the feasibility of the scheme under various settings.
Affective Personalization of a Social Robot Tutor for Children’s Second Language Skills
Authors: Gordon Goren , Spaulding Samuel , Kory Westlund Jacqueline , Lee Jin Joo , Plummer Luke , Martinez Marayna , Das Madhurima , Breazeal Cynthia
Published: June 2022

Though substantial research has been dedicated towards using technology to improve education, no current methods are as effective as one-on-one tutoring. A critical, though relatively understudied, aspect of effective tutoring is modulating the student's affective state throughout the tutoring session in order to maximize long-term learning gains. We developed an integrated experimental paradigm in which children play a second-language learning game on a tablet, in collaboration with a fully autonomous social robotic learning companion. As part of the system, we measured children's valence and engagement via an automatic facial expression analysis system. These signals were combined into a reward signal that fed into the robot's affective reinforcement learning algorithm. Over several sessions, the robot played the game and personalized its motivational strategies (using verbal and non-verbal actions) to each student. We evaluated this system with 34 children in preschool classrooms for a duration of two months. We saw that (1) children learned new words from the repeated tutoring sessions, (2) the affective policy personalized to students over the duration of the study, and (3) students who interacted with a robot that personalized its affective feedback strategy showed a significant increase in valence, as compared to students who interacted with a non-personalizing robot. This integrated system of tablet-based educational content, affective sensing, affective policy learning, and an autonomous social robot holds great promise for a more comprehensive approach to personalized tutoring.
Mobility Sequence Extraction and Labeling Using Sparse Cell Phone Data
Authors: Yang Yingxiang , Widhalm Peter , Athavale Shounak , Gonzalez Marta
Published: June 2022

Human mobility modeling for either transportation system development or individual location based services has a tangible impact on people's everyday experience. In recent years cell phone data has received a lot of attention as a promising data source because of the wide coverage, long observation period, and low cost. The challenge in utilizing such data is how to robustly extract people's trip sequences from sparse and noisy cell phone data and endow the extracted trips with semantic meaning, i.e., trip purposes.In this study we reconstruct trip sequences from sparse cell phone records. Next we propose a Bayesian trip purpose classification method and compare it to a Markov random field based trip purpose clustering method, representing scenarios with and without labeled training data respectively. This procedure shows how the cell phone data, despite their coarse granularity and sparsity, can be turned into a low cost, long term, and ubiquitous sensor network for mobility related services.
Connecting the Dots Using Contextual Information Hidden in Text and Images
Authors: Kader Md Abdul , Naim Sheikh , Boedihardjo Arnold , Hossain M. Shahriar
Published: June 2022

Creation of summaries of events of interest from multitude of unstructured data is a challenging task commonly faced by intelligence analysts while seeking increased situational awareness. This paper proposes a framework called Storyboarding that leverages unstructured text and images to explain events as sets of sub-events. The framework first generates a textual context for each human face detected from images and then builds a chain of coherent documents where two consecutive documents of the chain contain a common theme as well as a context. Storyboarding helps analysts quickly narrow down large number of possibilities to a few significant ones for further investigation. Empirical studies on Wikipedia documents, images and news articles show that Storyboarding is able to provide deeper insights on events of interests.
Unsupervised Measure of Word Similarity: How to Outperform Co-Occurrence and Vector Cosine in VSMs
Authors: Santus Enrico , Lenci Alessandro , Chiu Tin-Shing , Lu Qin , Huang Chu-Ren
Published: June 2022

In this paper, we claim that vector cosine – which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models – can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words. To prove it, we describe and evaluate APSyn, a variant of the Average Precision that, without any optimization, outperforms the vector cosine and the co-occurrence on the standard ESL test set, with an improvement ranging between +9.00% and +17.98%, depending on the number of chosen top contexts.
Towards Structural Tractability in Hedonic Games
Authors: Peters Dominik
Published: June 2022

Hedonic games are a well-studied model of coalition formation, in which selfish agents are partitioned into disjoint sets, and agents care about the make-up of the coalition they end up in. The computational problem of finding a stable outcome tends to be computationally intractable, even after severely restricting the types of preferences that agents are allowed to report. We investigate a structural way of achieving tractability, by requiring that agents' preferences interact in a well-behaved manner. Precisely, we show that stable outcomes can be found in linear time for hedonic games that satisfy a notion of bounded treewidth and bounded degree.
BRBA: A Blocking-Based Association Rule Hiding Method
Authors: Cheng Peng , Lee Ivan , Li Li , Tseng Kuo-Kun , Pan Jeng-Shyang
Published: June 2022

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.
Heuristic Planning for Hybrid Systems
Authors: Piotrowski Wiktor , Fox Maria , Long Derek , Magazzeni Daniele , Mercorio Fabio
Published: June 2022

Planning in hybrid systems has been gaining research interest in the Artificial Intelligence community in recent years. Hybrid systems allow for a more accurate representation of real world problems, though solving them is very challenging due to complex system dynamics and a large model feature set. We developed DiNo, a new planner designed to tackle problems set in hybrid domains.DiNo is based on the discretise and validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic.
Decision Sum-Product-Max Networks
Authors: Melibari Mazen , Poupart Pascal , Doshi Prashant
Published: June 2022

Sum-Product Networks (SPNs) were recently proposed as a new class of probabilistic graphical models that guarantee tractable inference, even on models with high-treewidth. In this paper, we propose a new extension to SPNs, called Decision Sum-Product-Max Networks (Decision-SPMNs), that makes SPNs suitable for discrete multi-stage decision problems. We present an algorithm that solves Decision-SPMNs in a time that is linear in the size of the network. We also present algorithms to learn the parameters of the network from data.
Document Type Classification in Online Digital Libraries
Authors: Caragea Cornelia , Wu Jian , Gollapalli Sujatha , Giles C.
Published: June 2022

Online digital libraries make it easier for researchers to search for scientific information. They have been proven as powerful resources in many data mining, machine learning and information retrieval applications that require high-quality data. The quality of the data highly depends on the accuracy of classifiers that identify the types of documents that are crawled from the Web, e.g., as research papers, slides, books, etc., for appropriate indexing. These classifiers in turn depend on the choice of the feature representation. We propose novel features that result in high-accuracy classifiers for document type classification. Experimental results on several datasets show that our classifiers outperform models that are employed in current systems.
MetaSeer.STEM: Towards Automating Meta-Analyses
Authors: Neppalli Kishore , Caragea Cornelia , Mayes Robin , Nimon Kim , Oswald Fred
Published: June 2022

Meta-analysis is a principled statistical approach for summarizing quantitative information reported across studies within a research domain of interest. Although the results of metaanalyses can be highly informative, the process of collecting and coding the data for a meta-analysis is often a laborintensive effort fraught with the potential for human error and idiosyncrasy. This is due to the fact that researchers typically spend weeks poring over published journal articles, technical reports, book chapters and other materials in order to retrieve key data elements that are then manually coded for subsequent analyses (e.g., descriptive statistics, effect sizes, reliability estimates, demographics, and study conditions). In this paper, we propose a machine learning based system developed to support automated extraction of data pertinent to STEM education meta-analyses, including educational and human resource initiatives aimed at improving achievement, literacy and interest in the fields of science, technology, engineering, and mathematics.
Large-Scale Occupational Skills Normalization for Online Recruitment
Authors: Javed Faizan , Hoang Phuong , Mahoney Thomas , McNair Matt
Published: June 2022

Job openings often go unfulfilled despite a surfeit of unemployed or underemployed workers. One of the main reasons for this is a mismatch between the skills required by employers and the skills that workers possess. This mismatch, also known as the skills gap, can pose socio-economic challenges for an economy. A first step in alleviating the skills gap is to accurately detect skills in human capital data such as resumes and job ads. Comprehensive and accurate detection of skills facilitates analysis of labor market dynamics. It also helps bridge the divide between supply and demand of labor by facilitating reskilling and workforce training programs. In this paper, we describe SKILL, a Named Entity Normalization (NEN) system for occupational skills. SKILL is composed of 1) A skills tagger which uses properties of semantic word vectors to recognize and normalize relevant skills, and 2) A skill entity sense disambiguation component which infers the correct meaning of an identified skill by leveraging Markov Chain Monte Carlo (MCMC) algorithms. Datadriven evaluation using end-user surveys demonstrates that SKILL achieves 90% precision and 73% recall for skills tagging. SKILL is currently used by various internal teams at CareerBuilder for big data workforce analytics, semantic search, job matching, and recommendations.ch.
A Machine Learning Approach for Semantic Structuring of Scientific Charts in Scholarly Documents
Authors: Al-Zaidy Rabah , Giles C.
Published: June 2022

Large scholarly repositories are designed to provide scientists and researchers with a wealth of information that is retrieved from data present in a variety of formats. A typical scholarly document contains information in a combined layout of texts and graphic images. Common types of graphics found in these documents are scientific charts that are used to represent data values in a visual format. Experimental results are rarely described without the aid of one form of a chart or another, whether it is 2D plot, bar chart, pie chart, etc. Metadata of these graphics is usually the only content that is made available for search by user queries. By processing the image content and extracting the data represented in the graphics, search engines will be able to handle more specific queries related to the data itself. In this paper we describe a machine learning based system that extracts and recognizes the various data fields present in a bar chart for semantic labeling. Our approach comprises of a graphics and text separation and extraction phase, followed by a component role classification for both text and graphic components that are in turn used for semantic analysis and representation of the chart. The proposed system is tested on a set of over 200 bar charts extracted from over 1,000 scientific articles in PDF format.
ParkUs: A Novel Vehicle Parking Detection System
Authors: Carnelli Pietro , Yeh Joy , Sooriyabandara Mahesh , Khan Aftab
Published: June 2022

Finding on-street parking in congested urban areas is a challenging chore that most drivers worldwide dislike. Previous vehicle traffic studies have estimated that around thirty percent of vehicles travelling in inner city areas are made up of drivers searching for a vacant parking space. While there are hardware sensor based solutions to monitor on-street parking occupancy in real-time, instrumenting and maintaining such a city wide system is a substantial investment. In this paper, a novel vehicle parking activity detection method, called ParkUs, is introduced and tested with the aim to eventually reduce vacant car parking space search times. The system utilises accelerometer and magnetometer sensors found in all smartphones in order to detect parking activity within a city environment. Moreover, it uses a novel sensor fusion feature called the Orthogonality Error Estimate (OEE). We show that the OEE is an excellent indicator as it’s capable of detecting parking activities with high accuracy and low energy consumption. One of the envisioned applications of the ParkUs system will be to provide all drivers with guidelines on where they are most likely to find vacant parking spaces within a city. Therefore, reducing the time required to find a vacant parking space and subsequently vehicle congestion and emissions within the city.
Crowdsensing Air Quality with Camera-Enabled Mobile Devices
Authors: Pan Zhengxiang , Yu Han , Miao Chunyan , Leung Cyril
Published: June 2022

Crowdsensing of air quality is a useful way to improve public awareness and supplement local air quality monitoring data. However, current air quality monitoring approaches are either too sophisticated, costly or bulky to be used effectively by the mass. In this paper, we describe AirTick, a mobile app that can turn any camera enabled smart mobile device into an air quality sensor, thereby enabling crowdsensing of air quality. AirTick leverages image analytics and deep learning techniques to produce accurate estimates of air quality following the Pollutant Standards Index (PSI). We report the results of an initial experimental and empirical evaluations of AirTick. The AirTick tool has been shown to achieve, on average, 87% accuracy in day time operation and 75% accuracy in night time operation. Feedbacks from 100 test users indicate that they perceive AirTick to be highly useful and easy to use. Our results provide a strong positive case for the benefits of applying artificial intelligence techniques for convenient and scalable crowdsensing of air quality.
A Logic Based Approach to Answering Questions about Alternatives in DIY Domains
Authors: Wang Yi , Lee Joohyung , Kim Doo
Published: June 2022

Many question answering systems have primarily focused on factoid questions. These systems require the answers to be explicitly stored in a knowledge base (KB) but due to this requirement, they fail to answer many questions for which the answers cannot be pre-formulated. This paper presents a question answering system which aims at answering nonfactoid questions in the DIY domain using logic-based reasoning. Specifically, the system uses Answer Set Programming to derive an answer by combining various types of knowledge such as domain and commonsense knowledge. We showcase the system by answering one specific type of questions — questions about alternatives. The evaluation result shows that our logic-based reasoning together with the KB (constructed from texts using Information Extraction) significantly improves the user experience.
The Digital Economy and Productivity
Authors: Byrne David M.
Published: June 2022

After reviewing the state of digitalization---the use of digital information technology (IT) throughout the economy---we consider the slippery concept of a distinct digital economy and efforts to record it in national accounts. We then anchor the digital economy in a growth accounting framework, augmenting the conventional measure of the IT contribution to productivity---innovation in the production of IT capital plus labor-saving use of IT throughout the economy---with the contribution from the digital platforms that help users navigate the sprawling information landscape. We discuss the difficult measurement issues that thwart a full accounting of the scope and productivity of the digital economy remain. These include quantifying the intangible assets created by platforms and their users, measuring the consumption of intangible services provided by platforms---often provided for free---and identifying platforms within the existing statistical system, which does not treat their activity as a distinct industry.
Analytical Capabilities of EDXRF for Determination of Rare Earth Elements
Authors: Kurniawati Syukria , Santoso Muhayatun , Lestiani Diah Dwiana , Adventini Natalia , Syahfitri Woro Yatu Niken
Published: June 2022

Rare earth elements (REE) are present in the lanthanide range and are widely used in high-tech and clean technology applications that are predicted to grow significantly in the coming decades. Therefore, there will be a high demand for REE in the future that will impact the needs for development of sensitive and selective methods for determination of REEs such as X-Ray Fluorescence (XRF). Reliability of REEs analysis results depend on analytical capability of XRF instrument’s performance. Analytical capability shows the ability of the instrument to perform sample analysis with high accuracy and precision and proven by validation so that the results obtained are reliable. The aim of this study is to assess the analytical capability of XRF for REEs analysis in samples particularly Lanthanum (La), Cerium (Ce), Neodymium (Nd), Samarium (Sm) and Yttrium (Y), by performing method validation of energy dispersive x-ray spectrometers (ED-XRF). Four measurement conditions that covered the selected elements were defined. Accuracy, precision and detection limits determination were performed by measuring the CRM In House Monazite Sand. The yield of the selected element corresponds to its certified value, with a %recovery between 95.99 to 103.1%. The %RSD values ranging from 0.59 to 5.19%. The detection limits (LLD) of ED-XRF ranged from 8.78 to 67.4 ppm. The results showed the good analytical capability of ED-XRF method for REEs analysis.
Catalytic hydrogen combustion using platinum supported on anodized aluminium oxide adhered to metallic aluminium
Authors: du Preez S , Kozhukhova A , Bessarabov D
Published: June 2022

The implementation of catalytic hydrogen combustion (CHC) can mitigate the dependence on solid fuel combustion typically used in Sub-Saharan Africa as a means of heating. However, low-cost and readily available materials for catalyst fabrication are relatively unexplored. Considering the economic constraints regarding platinum group metals generally used for CHC, the cost of support materials and its functionalization as catalyst support has to be minimized. Typically, materials such as silicon carbide foam, extruded aluminium oxide-based ceramics, and titanium oxide mesh/sintered plates are used to withstand the high temperatures associated with CHC. In this paper, nanostructured anodized aluminium oxide (AAO) was synthesized as the support material via the anodization of high purity (>99%) aluminium (Al). The AAO layer intimately adhered to a metallic Al core acted as a thermal conduit to disperse heat throughout the catalyst. Platinum (Pt), considered as the reactive metal, was deposited to the AAO as nanoparticles with a diameter of approximately 19.3 nm. The CHC reaction proceeded spontaneously on the surface of the Pt/AAO catalyst. A combustion temperature of408 ± 18 °C was maintained for 70 h at a 100 normal (N) mL min-1 hydrogen flow rate. The Pt-particles showed a relatively appreciable increase in particle size (from 19.3 nm to 25.0 nm), but the significance thereof was not evident during the 70 h CHC procedure.