bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-thyroidal second primary malignancy in papillary thyroid cancer patients
Authors: Borges Duarte Diana , Benido Silva Vânia , Assunção Guilherme , Couto Carvalho André , Freitas Cláudia
Published: June 2022

Introduction

The occurrence of non-thyroidal second primary malignancy (NTSPM) in patients with papillary thyroid cancer (PTC) is well documented, but epidemiological data are conflicting.

Objective

The aim of this study was to evaluate the incidence of NTSPM in a large series of patients with PTC and to assess its potential risk factors.

Methods

Single-center cohort study with retrospective data collection conducted on consecutive PTC patients diagnosed from 1988 to 2018 with a minimum follow-up time of 2 years. NTSPM was defined as any primary malignancy with histological confirmation occurring in an anatomical site other than the thyroid. According to the timing of occurrence, NTSPM were subdivided into anachronous, synchronous or metachronous (diagnosed >6 months before, within 6 months and >6 months after PTC diagnosis, respectively).

Results

We included 773 individuals (83.3% females), median age at PTC diagnosis was 47.0 (IQR: 37.0–58.0) years and median follow-up time was 9.9 (6.2–16.3) years. Incidence of NTSPM was 15.5% (n  = 120) and its standard incidence ratio (SIR) was higher when compared to the general population (SIR: 2.70). Family history of malignancy and younger age at diagnosis were associated respectively with 206 and 4% increased risk of developing metachronous neoplasia (HR: 2.06 (95% CI: 1.10–3.86) and 1.04 (95% CI: 1.02–1.05), respectively).

Conclusion

In our series, the occurrence of NTSPM was not uncommon and its incidence was higher compared to the general population. First-degree family history of malignancy was a strong risk factor for multiple primary malignancies.
Lectura etnocrítica como herramienta analítica interdisciplinaria e interseccional
Authors: Solano Rivera Silvia , Ramírez Caro Jorge
Published: June 2022

The main theoretical and methodological assumptions of ethnocriticism are presented as an analytical tool of an interdisciplinary and intersectional nature that conceives the text as an ethnotext and culture as an ethnoculture. This ethnotext condenses meaning, knowledge, and the crossover of approaches that leads to the materialization of different points of view and discriminatory ideologies. The one who anchors these ideologies in the text is its author, as it is the result of his imaginary. Aware or not, the author screens, in his esthetic work, representations contradicting his ethical, political, and ideological proposal. Ethnocriticism detects, analyzes and denounces those ideological biases.
The sturlung era in modern Icelandic historical prose: Einar Kárason
Authors: Markelova Olga
Published: June 2022

The period from 1220 to1262-64, the so-called Sturlung era, is significant in Icelandic culture, not only due to its dramatic political events, but also because by that time the great works of the Old Icelandic literature have been written down. In modern Icelandic historical fiction the most noticable text, devoted to the Sturlung era, is the tetralogy by Einar K?rason ("A Gathering of Foes“ 2001, "Fury“ 2008, "The Skald“ 2012 and "The Axe era“, 2014). It is based on the Old Icelandic "Sturlunga saga“, in particular, on "The Saga of Icelanders“ by Sturla Thordarson. The writing technique in the tetralogy s nevertheless antipodal to the saga manner: the events are described fragmentary, from the subjective point of view of a variety of persons, also the episodical ones; thus the complexity and the multifaceted character of the historical prcess is being stated. In each novel of the tetralogy there is one central character, the other narrators reflect upon. Snorri Sturluson, the most iconic figure of this era, is often mentioned, but is never among the narrators. The writing of sagas is being described (especially in "The Skald“), and the inner intentions of the 13th century scribes creating their texts turn out to be similiar to the intentions of modern authors. The narration manner and the universal thinking way of thecharacters neutralizes the historical distance between the narration and the readers. In modern Icelandic literature a firm narrative tradition of perception of the Sturlung era is absent, thus, the works of Einar K?rason can be regarded as the direct reception of the Old Icelandic sourse texts as they are, but not as an interplay with a set tradition of their perception. But such a tradition can be formed in future by this tetralogy.
Biomarkers in Heart Failure with Preserved Ejection Fraction
Authors: Bayes-Genis Antoni , Cediel Germán , Domingo Mar , Codina Pau , Santiago Evelyn , Lupón Josep
Published: June 2022

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous disorder developing from multiple aetiologies with overlapping pathophysiological mechanisms. HFpEF diagnosis may be challenging, as neither cardiac imaging nor physical examination are sensitive in this situation. Here, we review biomarkers of HFpEF, of which the best supported are related to myocardial stretch and injury, including natriuretic peptides and cardiac troponins. An overview of biomarkers of inflammation, extracellular matrix derangements and fibrosis, senescence, vascular dysfunction, anaemia/iron deficiency and obesity is also provided. Finally, novel biomarkers from -omics technologies, including plasma metabolites and circulating microRNAs, are outlined briefly. A cardiac-centred approach to HFpEF diagnosis using natriuretic peptides seems reasonable at present in clinical practice. A holistic approach including biomarkers that provide information on the non-cardiac components of the HFpEF syndrome may enrich our understanding of the disease and may be useful in classifying HFpEF phenotypes or endotypes that may guide patient selection in HFpEF trials.
Pet owners' feedback on psychological support service in an Italian veterinary hospital
Authors: Campigli Michela , Strizzolo Giulia , Furlanello Tommaso , Uccheddu Stefania
Published: June 2022

The emotional stress experienced by pet caregivers, induced by negative diagnoses, clinical problems, and/or long-term therapies should be taken into serious consideration by the veterinarians to prevent these individuals from the risk of experiencing incapability of dealing with the great burden and the eventual reduction of compliance. Understanding the client's affective state may enhance the veterinarian's empathic response and effective communication. To understand better the unmet needs of clients, we collected data on service requests at the clinic and emotions that caregivers felt in the veterinarian setting. Understanding these difficulties may represent a first step to initiate the development of a psychological intervention targeting problems experienced by the caregivers. Assuming these difficulties according to the service requested for the pet, the owners might be interested in psychological support provided directly by the veterinary hospital. Our survey showed that a large percentage of owners expressed feelings related to caregiving in the veterinary setting, irrespective of the type of assistance needed, including apparently easy procedures. At the same time, they expressed a strong intention to receive a professional support. However, the crucial role of the psychological support is still difficult to accept even when it can be perceived as a right choice.
New Normal or Old Problems? “Hibernation” and Planning for Music Careers in the Victorian Music Industries during COVID-19
Authors: Cannizzo Fabian , Strong Catherine
Published: June 2022

Compared to many nations in the global metropole, Australia experienced low per capita cases of the novel coronavirus during 2020. However, despite the nation’s geographical isolation, its dependence on international travel did result in a number of infections in early 2020, prompting federal and state governments to impose travel restrictions, social distancing orders, and eventually some state-wide lockdowns. The strategy to help affected businesses and workers was a combination of income support, tax relief and economic incentives to spur on spending as businesses were able to again operate—an approach that became known as “hibernation”. This article examines music workers’ expectations for their future, and the future of the music industries, post-“hibernation”. Through surveying and interviewing workers and business owners from across the Victorian music industries during a period of lockdown, it is explored how workers position themselves in relation to the idea that the sector could return to “normal” post-COVID, and these responses are situated within creative work research. Without common spaces of socialization and common economic objectives, workers within the hibernated music industries have demonstrated individualized approaches to their career planning, fragmented by the breakdown of daily rituals and routines. Some workers are orienting themselves to a future where the sector re-opens mostly unchanged, while others believe that the industry will be fundamentally different post-COVID. Workers’ activities in lockdown are shaped by these beliefs, with many exiting or preparing for an exit from music work, while those who anticipate staying undertake extensive labour to ensure the viability of their careers. The article concludes by considering what this might mean for the future of live music events in Victoria.
Aportaciones de las Humanidades Digitales a los estudios sobre teatro: una revisión sistemática (2001-2020)
Authors: Dabrowska Monika , Martínez-Nieto Roxana-Beatriz
Published: June 2022

El presente artículo se propone mostrar el panorama actual de las aportaciones de los estudios digitales a la interpretación de obras dramáticas, así como señalar las principales líneas de interés y posibles áreas de desarrollo en torno a este campo de estudio. La revisión sistemática abarca 31 artículos originales revisados por pares, en español y en inglés, publicados durante las últimas dos décadas (2001-2020) en revistas indexadas en Scopus y SCImago. La búsqueda sistemática se ha realizado en once bases de datos internacionales: Web of Science, Scopus, Science Direct, Mendeley-Elsevier, Dialnet, SciELO, Google Scholar, Jstor, ERIC, Zenodo y Humanities Commons. Se concluye que la mayor parte de la investigación tecnológica se centra en análisis cuantitativos, tratamiento digital de textos y ediciones críticas digitales. Se evidencia la escasez de estudios dedicados a la reconstrucción virtual de espacios teatrales en 3D y al sentido propedéutico de la acción teatral en ámbito didáctico.
Policaptil Gel Retard® reduces body weight and improves insulin sensitivity in obese subjects
Authors: Centorame Giorgia , Baldassarre Maria Pompea Antonia , Di Dalmazi Giulia , Gambacorta Francesca , Febo Fabrizio , Consoli Agostino , Formoso Gloria
Published: June 2022

Policaptil Gel Retard® (PGR), a natural fiber-based molecule, has been shown to prevent weight gain and ameliorate insulin-resistance indices in obese children and adolescents. The aim of this study was to compare the effects of 12 weeks of low calories and low glycemic index (LC-LGI) diet associated or not with the intake of PGR on anthropometric, bioimpedance, and metabolic parameters. Data from 20 obese adult subjects (10 per group) were analyzed. An LC-LGI diet with or without PGR intake reduced weight, BMI, and waist circumference. PGR intake elicited a reduction in fasting plasma insulin and insulin resistance index together with an improvement in insulin sensitivity.
Exploring the Contribution of Unlabeled Data in Financial Sentiment Analysis
Authors: Ren Jimmy , Wang Wei , Wang Jiawei , Liao Stephen
Published: June 2022

With the proliferation of its applications in various industries, sentiment analysis by using publicly available web data has become an active research area in text classification during these years. It is argued by researchers that semi-supervised learning is an effective approach to this problem since it is capable to mitigate the manual labeling effort which is usually expensive and time-consuming. However, there was a long-term debate on the effectiveness of unlabeled data in text classification. This was partially caused by the fact that many assumptions in theoretic analysis often do not hold in practice. We argue that this problem may be further understood by adding an additional dimension in the experiment. This allows us to address this problem in the perspective of bias and variance in a broader view. We show that the well-known performance degradation issue caused by unlabeled data can be reproduced as a subset of the whole scenario. We argue that if the bias-variance trade-off is to be better balanced by a more effective feature selection method unlabeled data is very likely to boost the classification performance. We then propose a feature selection framework in which labeled and unlabeled training samples are both considered. We discuss its potential in achieving such a balance. Besides, the application in financial sentiment analysis is chosen because it not only exemplifies an important application, the data possesses better illustrative power as well. The implications of this study in text classification and financial sentiment analysis are both discussed.
Preventing Unraveling in Social Networks Gets Harder
Authors: Chitnis Rajesh , Fomin Fedor , Golovach Petr
Published: June 2022

The behavior of users in social networks is often observed to be affected by the actions of their friends. Bhawalkar et al. (ICALP '12) introduced a formal mathematical model for user engagement in social networks where each individual derives a benefit proportional to the number of its friends which are engaged. Given a threshold degree k the equilibrium for this model is a maximal subgraph whose minimum degree is at least k. However the dropping out of individuals with degrees less than k might lead to a cascading effect of iterated withdrawals such that the size of equilibrium subgraph becomes very small. To overcome this some special vertices called "anchors" are introduced: these vertices need not have large degree. Bhawalkar et al. considered the Anchored k-Core problem: Given a graph G and integers b, k and p do there exist sets of vertices B, H such that B is a subset of H, size of B is at most b and size of H is at least p, and every vertex v which is in H but not in B has degree at least k in the induced subgraph G[H]. They showed that the problem is NP-hard for all k greater equal 2, and gave some inapproximability and fixed-parameter intractability results. In this paper we give improved hardness results for this problem. In particular we show that the Anchored k-Core problem is W[1]-hard parameterized by p, even for k=3. This improves the result of Bhawalkar et al.  (who show W[2]-hardness parameterized by b) as our parameter is always bigger since p is greater equal than b. Then we answer a question of Bhawalkar et al. by showing that the Anchored k-Core problem remains NP-hard on planar graphs for all k greater equal 3, even if the maximum degree of the graph is k+2. Finally we show that the problem is FPT on planar graphs parameterized by b for all k greater equal 7.
Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval
Authors: Zhai Xiaohua , Peng Yuxin , Xiao Jianguo
Published: June 2022

As the major component of big data, unstructured heterogeneous multimedia content such as text, image, audio, video and 3D increasing rapidly on the Internet. User demand a new type of cross-media retrieval where user can search results across various media by submitting query of any media. Since the query and the retrieved results can be of different media, how to learn a heterogeneous metric is the key challenge. Most existing metric learning algorithms only focus on a single media where all of the media objects share the same data representation. In this paper, we propose a joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graph regularization. In JGRHML, different media are complementary to each other and optimizing them simultaneously can make the solution smoother for both media and further improve the accuracy of the final metric. Based on the heterogeneous metric, we further learn a high-level semantic metric through label propagation. JGRHML is effective to explore the semantic relationship hidden across different modalities. The experimental results on two datasets with up to five media types show the effectiveness of our proposed approach.
OpenEval: Web Information Query Evaluation
Authors: Samadi Mehdi , Veloso Manuela , Blum Manuel
Published: June 2022

In this paper, we investigate information validation tasks that are initiated as queries from either automated agents or humans. We introduce OpenEval, a new online information validation technique, which uses information on the web to automatically evaluate the truth of queries that are stated as multi-argument predicate instances (e.g., DrugHasSideEffect(Aspirin,GI Bleeding)). OpenEval gets a small number of instances of a predicate as seed positive examples and automatically learns how to evaluate the truth of a new predicate instance by querying the web and processing the retrieved unstructured web pages. We show that OpenEval is able to respond to the queries within a limited amount of time while also achieving high F1 score. In addition, we show that the accuracy of responses provided by OpenEval is increased as more time is given for evaluation. We have extensively tested our model and shown empirical results that illustrate the effectiveness of our approach compared to related techniques.
A Tractable Leader-Follower MDP Model for Animal Disease Management
Authors: Sabbadin Régis , Viet Anne-France
Published: June 2022

Sustainable animal disease management requires to design and implement control policies at the regional scale. However, for diseases which are not regulated, individual farmers are responsible for the adoption and successful application of control policies at the farm scale. Organizations (groups of farmers, health institutions...) may try to influence farmers' control actions through financial incentives, in order to ensure sustainable (from the health and economical point of views) disease management policies. Economics / Operations Research frameworks have been proposed for modeling the effect of incentives on agents. The Leader-Follower Markov Decision Processes framework is one such framework, that combines Markov Decision Processes (MDP) and stochastic games frameworks. However, since finding equilibrium policies in stochastic games is hard when the number of players is large, LF-MDP problems are intractable. Our contribution, in this article, is to propose a tractable model of the animal disease management problem. The tractable model is obtained through a few simple modeling approximations which are acceptable when the problem is viewed from the organization side. As a result, we design a polynomial-time algorithm for animal disease management, which we evaluate on a case study inspired from the problem of controlling the spread of the Porcine Reproductive and Respiratory Syndrome (PRRS).
Autonomous Agents in Future Energy Markets: The 2012 Power Trading Agent Competition
Authors: Ketter Wolfgang , Peters Markus , Collins John
Published: June 2022

Sustainable energy systems of the future will need more than efficient, clean, and low-cost energy sources. They will also need efficient price signals that motivate sustainable energy consumption behaviors and a tight real-time alignment of energy demand with supply from renewable and traditional sources. The Power Trading Agent Competition (Power TAC) is a rich, competitive, open-source simulation platform for future retail power markets built on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making as well as the robustness of proposed market designs. Power TAC invites researchers to develop autonomous electricity broker agents and to pit them against best-in-class strategies in global competitions, the first of which will be held at AAAI 2013. Power TAC competitions provide compelling, actionable information for policy makers and industry leaders. We describe the competition scenario, demonstrate the realism of the Power TAC platform, and analyze key characteristics of successful brokers in one of our 2012 pilot competitions between seven research groups from five different countries.
Walking on Minimax Paths for k-NN Search
Authors: Kim Kye-Hyeon , Choi Seungjin
Published: June 2022

Link-based dissimilarity measures, such as shortest path or Euclidean commute time distance, base their distance on paths between nodes of a weighted graph. These measures are known to be better suited to data manifold with nonconvex-shaped clusters, compared to Euclidean distance, so that k-nearest neighbor (NN) search is improved in such metric spaces. In this paper we present a new link-based dissimilarity measure based on minimax paths between nodes. Two main benefits of minimax path-based dissimilarity measure are: (1) only a subset of paths is considered to make it scalable, while Euclidean commute time distance considers all possible paths; (2) it better captures nonconvex-shaped cluster structure, compared to shortest path distance. We define the total cost assigned to a path between nodes as Lp norm of intermediate costs of edges involving the path, showing that minimax path emerges from our Lp norm over paths framework. We also define minimax distance as the intermediate cost of the longest edge on the minimax path, then present a greedy algorithm to compute k smallest minimax distances between a query and N data points in O(log N + k log k) time. Numerical experiments demonstrate that our minimax k-NN algorithm reduce the search time by several orders of magnitude, compared to existing methods, while the quality of k-NN search is significantly improved over Euclidean distance.
Multi-Armed Bandit with Budget Constraint and Variable Costs
Authors: Ding Wenkui , Qin Tao , Zhang Xu-Dong , Liu Tie-Yan
Published: June 2022

We study the multi-armed bandit problems with budget constraint and variable costs (MAB-BV). In this setting, pulling an arm will receive a random reward together with a random cost, and the objective of an algorithm is to pull a sequence of arms in order to maximize the expected total reward with the costs of pulling those arms complying with a budget constraint. This new setting models many Internet applications (e.g., ad exchange, sponsored search, and cloud computing) in a more accurate manner than previous settings where the pulling of arms is either costless or with a fixed cost. We propose two UCB based algorithms for the new setting. The first algorithm needs prior knowledge about the lower bound of the expected costs when computing the exploration term. The second algorithm eliminates this need by estimating the minimal expected costs from empirical observations, and therefore can be applied to more real-world applications where prior knowledge is not available. We prove that both algorithms have nice learning abilities, with regret bounds of O(ln B). Furthermore, we show that when applying our proposed algorithms to a previous setting with fixed costs (which can be regarded as our special case), one can improve the previously obtained regret bound. Our simulation results on real-time bidding in ad exchange verify the effectiveness of the algorithms and are consistent with our theoretical analysis.
Cost-Optimal Planning by Self-Interested Agents
Authors: Nissim Raz , Brafman Ronen
Published: June 2022

As our world becomes better connected and autonomous agents no longer appear to be science fiction, a natural need arises for enabling groups of selfish agents to cooperate in generating plans for diverse tasks that none of them can perform alone in a cost-effective manner. While most work on planning for/by selfish agents revolves around finding stable solutions (e.g., Nash Equilibrium), this work combines techniques from mechanism design with a recently introduced method for distributed planning, in order to find cost optimal (and, thus, social welfare maximizing) solutions. Based on the Vickrey-Clarke-Groves mechanisms, we present both a centralized, and a privacy-preserving distributed mechanism.
On the Value of Using Group Discounts under Price Competition
Authors: Meir Reshef , Lu Tyler , Tennenholtz Moshe , Boutilier Craig
Published: June 2022

The increasing use of group discounts has provided opportunities for buying groups with diverse preferences to coordinate their behavior in order to exploit the best offers from multiple vendors. We analyze this problem from the viewpoint of the vendors, asking under what conditions a vendor should adopt a volume-based price schedule rather than posting a fixed price, either as a monopolist or when competing with other vendors. When vendors have uncertainty about buyers' valuations specified by a known distribution, we show that a vendor is always better off posting a fixed price, provided that buyers' types are i.i.d. and that other vendors also use fixed prices. We also show that these assumptions cannot be relaxed: if buyers are not i.i.d., or other vendors post discount schedules, then posting a schedule may yield higher profit for the vendor. We provide similar results under a distribution-free uncertainty model, where vendors minimize their maximum regret over all type realizations.
Reduce and Re-Lift: Bootstrapped Lifted Likelihood Maximization for MAP
Authors: Hadiji Fabian , Kersting Kristian
Published: June 2022

By handling whole sets of indistinguishable objects together, lifted belief propagation approaches have rendered large, previously intractable, probabilistic inference problems quickly solvable. In this paper, we show that Kumar and Zilberstein's likelihood maximization (LM) approach to MAP inference is liftable, too, and actually provides additional structure for optimization. Specifically, it has been recognized that some pseudo marginals may converge quickly, turning intuitively into pseudo evidence. This additional evidence typically changes the structure of the lifted network: it may expand or reduce it. The current lifted network, however, can be viewed as an upper bound on the size of the lifted network required to finish likelihood maximization. Consequently, we re-lift the network only if the pseudo evidence yields a reduced network, which can efficiently be computed on the current lifted network. Our experimental results on Ising models, image segmentation and relational entity resolution demonstrate that this bootstrapped LM via "reduce and re-lift" finds MAP assignments comparable to those found by the original LM approach, but in a fraction of the time.
Partial MUS Enumeration
Authors: Previti Alessandro , Marques-Silva Joao
Published: June 2022

Minimal explanations of infeasibility find a wide range of uses. In the Boolean domain, these are referred to as Minimal Unsatisfiable Subsets (MUSes). In some settings, one needs to enumerate MUSes of a Boolean formula. Most often the goal is to enumerate all MUSes. In cases where this is computationally infeasible, an alternative is to enumerate some MUSes. This paper develops a novel approach for partial enumeration of MUSes, that complements existing alternatives. If the enumeration of all MUSes is viable, then existing alternatives represent the best option. However, for formulas where the enumeration of all MUSes is unrealistic, our approach provides a solution for enumerating some MUSes within a given time bound. The experimental results focus on formulas for which existing solutions are unable to enumerate MUSes, and shows that the new approach can in most cases enumerate a non-negligible number of MUSes within a given time bound.
A Robust Bayesian Truth Serum for Non-Binary Signals
Authors: Radanovic Goran , Faltings Boi
Published: June 2022

Several mechanisms have been proposed for incentivizing truthful reports of a private signals owned by rational agents, among them the peer prediction method and the Bayesian truth serum. The robust Bayesian truth serum (RBTS) for small populations and binary signals is particularly interesting since it does not require a common prior to be known to the mechanism. We further analyze the problem of the common prior not known to the mechanism and give several results regarding the restrictions that need to be placed in order to have an incentive-compatible mechanism. Moreover, we construct a Bayes-Nash incentive-compatible scheme called multi-valued RBTS that generalizes RBTS to operate on both small populations and non-binary signals.
Causal Transportability with Limited Experiments
Authors: Bareinboim Elias , Pearl Judea
Published: June 2022

We address the problem of transferring causal knowledge learned in one environment to another, potentially different environment, when only limited experiments may be conducted at the source. This generalizes the treatment of transportability introduced in [Pearl and Bareinboim, 2011; Bareinboim and Pearl, 2012b], which deals with transferring causal information when any experiment can be conducted at the source. Given that it is not always feasible to conduct certain controlled experiments, we consider the decision problem whether experiments on a selected subset Z of variables together with qualitative assumptions encoded in a diagram may render causal effects in the target environment computable from the available data. This problem, which we call z-transportability, reduces to ordinary transportability when Z is all-inclusive, and, like the latter, can be given syntactic characterization using the do-calculus [Pearl, 1995; 2000]. This paper establishes a necessary and sufficient condition for causal effects in the target domain to be estimable from both the non-experimental information available and the limited experimental information transferred from the source. We further provides a complete algorithm for computing the transport formula, that is, a way of fusing experimental and observational information to synthesize an unbiased estimate of the desired causal relation.
A Hierarchical Aspect-Sentiment Model for Online Reviews
Authors: Kim Suin , Zhang Jianwen , Chen Zheng , Oh Alice , Liu Shixia
Published: June 2022

To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node itself is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and parameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is comparable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models.
Fraudulent Support Telephone Number Identification Based on Co-Occurrence Information on the Web
Authors: Li Xin , Liu Yiqun , Zhang Min , Ma Shaoping
Published: June 2022

"Fraudulent support phones" refers to the misleading telephone numbers placed on Web pages or other media that claim to provide services with which they are not associated. Most fraudulent support phone information is found on search engine result pages (SERPs), and such information substantially degrades the search engine user experience. In this paper, we propose an approach to identify fraudulent support telephone numbers on the Web based on the co-occurrence relations between telephone numbers that appear on SERPs. We start from a small set of seed official support phone numbers and seed fraudulent numbers. Then, we construct a co-occurrence graph according to the co-occurrence relationships of the telephone numbers that appear on Web pages. Additionally, we take the page layout information into consideration on the assumption that telephone numbers that appear in nearby page blocks should be regarded as more closely related. Finally, we develop a propagation algorithm to diffuse the trust scores of seed official support phone numbers and the distrust scores of the seed fraudulent numbers on the co-occurrence graph to detect additional fraudulent numbers. Experimental results based on over 1.5 million SERPs produced by a popular Chinese commercial search engine indicate that our approach outperforms TrustRank, Anti-TrustRank and Good-Bad Rank algorithms by achieving an AUC value of over 0.90.
Cross-Lingual Knowledge Validation Based Taxonomy Derivation from Heterogeneous Online Wikis
Authors: Wang Zhigang , Li Juanzi , Li Shuangjie , Li Mingyang , Tang Jie , Zhang Kuo , Zhang Kun
Published: June 2022

Creating knowledge bases based on the crowd-sourced wikis, like Wikipedia, has attracted significant research interest in the field of intelligent Web. However, the derived taxonomies usually contain many mistakenly imported taxonomic relations due to the difference between the user-generated subsumption relations and the semantic taxonomic relations. Current approaches to solving the problem still suffer the following issues: (i) the heuristic-based methods strongly rely on specific language dependent rules. (ii) the corpus-based methods depend on a large-scale high-quality corpus, which is often unavailable. In this paper, we formulate the cross-lingual taxonomy derivation problem as the problem of cross-lingual taxonomic relation prediction. We investigate different linguistic heuristics and language independent features, and propose a cross-lingual knowledge validation based dynamic adaptive boosting model to iteratively reinforce the performance of taxonomic relation prediction. The proposed approach successfully overcome the above issues, and experiments show that our approach significantly outperforms the designed state-of-the-art comparison methods.
Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation
Authors: Fang Hui , Bao Yang , Zhang Jie
Published: June 2022

Trust has been used to replace or complement rating-based similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.
How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance
Authors: Kang Yong-Bin , Pan Jeff Z. , Krishnaswamy Shonali , Sawangphol Wudhichart , Li Yuan-Fang
Published: June 2022

For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive task—2NEXPTIME-complete in the worst case. Hence, it is highly desirable to be able to accurately estimate classification time, especially for large and complex ontologies. Recently, machine learning techniques have been successfully applied to predicting the reasoning hardness category for a given (ontology, reasoner) pair. In this paper, we further develop predictive models to estimate actual classification time using regression techniques, with ontology metrics as features. Our large-scale experiments on 6 state-of-the-art OWL 2 DL reasoners and more than 450 significantly diverse ontologies demonstrate that the prediction models achieve high accuracy, good generalizability and statistical significance. Such prediction models have a wide range of applications. We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.
Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations
Authors: Ohsaka Naoto , Akiba Takuya , Yoshida Yuichi , Kawarabayashi Ken-ichi
Published: June 2022

Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been well-studied, it is still highly challenging to find solutions of high quality in large-scale networks of the day. While Monte-Carlo-simulation-based methods produce near-optimal solutions with a theoretical guarantee, they are prohibitively slow for large graphs. As a result, many heuristic methods without any theoretical guarantee have been developed, but all of them substantially compromise solution quality. To address this issue, we propose a new method for the influence maximization problem. Unlike other recent heuristic methods, the proposed method is a Monte-Carlo-simulation-based method, and thus it consistently produces solutions of high quality with the theoretical guarantee. On the other hand, unlike other previous Monte-Carlo-simulation-based methods, it runs as fast as other state-of-the-art methods, and can be applied to large networks of the day. Through our extensive experiments, we demonstrate the scalability and the solution quality of the proposed method.
Cross-View Feature Learning for Scalable Social Image Analysis
Authors: Xie Wenxuan , Peng Yuxin , Xiao Jianguo
Published: June 2022

Nowadays images on social networking websites (e.g., Flickr) are mostly accompanied with user-contributed tags, which help cast a new light on the conventional content-based image analysis tasks such as image classification and retrieval. In order to establish a scalable social image analysis system, two issues need to be considered: 1) Supervised learning is a futile task in modeling the enormous number of concepts in the world, whereas unsupervised approaches overcome this hurdle; 2) Algorithms are required to be both spatially and temporally efficient to handle large-scale datasets. In this paper, we propose a cross-view feature learning (CVFL) framework to handle the problem of social image analysis effectively and efficiently. Through explicitly modeling the relevance between image content and tags (which is empirically shown to be visually and semantically meaningful), CVFL yields more promising results than existing methods in the experiments. More importantly, being general and descriptive, CVFL and its variants can be readily applied to other large-scale multi-view tasks in unsupervised setting.
A Machine Learning Approach to Musically Meaningful Homogeneous Style Classification
Authors: Herlands William , Der Ricky , Greenberg Yoel , Levin Simon
Published: June 2022

Recent literature has demonstrated the difficulty of classifying between composers who write in extremely similar styles (homogeneous style). Additionally, machine learning studies in this field have been exclusively of technical import with little musicological interpretability or significance. We present a supervised machine learning system which addresses the difficulty of differentiating between stylistically homogeneous composers using foundational elements of music, their complexity and interaction. Our work expands on previous style classification studies by developing more complex features as well as introducing a new class of musical features which focus on local irregularities within musical scores. We demonstrate the discriminative power of the system as applied to Haydn and Mozart's string quartets. Our results yield interpretable musicological conclusions about Haydn's and Mozart's stylistic differences while distinguishing between the composers with higher accuracy than previous studies in this domain.
Learning Compositional Sparse Models of Bimodal Percepts
Authors: Kumar Suren , Dhiman Vikas , Corso Jason
Published: June 2022

Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal percepts that explicitly relates distinct projections across each modality and then jointly learns a bimodal sparse representation. The resulting model enables compositionality across these distinct projections and hence can generalize to unobserved percepts spanned by this compositional basis. For example, our model can be trained on 'red triangles' and 'blue squares'; yet, implicitly will also have learned 'red squares' and 'blue triangles'. The structure of the projections and hence the compositional basis is learned automatically for a given language model. To test our model, we have acquired a new bimodal dataset comprising images and spoken utterances of colored shapes in a tabletop setup. Our experiments demonstrate the benefits of explicitly leveraging compositionality in both quantitative and human evaluation studies.
Confident Reasoning on Raven's Progressive Matrices Tests
Authors: McGreggor Keith , Goel Ashok
Published: June 2022

We report a novel approach to addressing the Raven’s Progressive Matrices (RPM) tests, one based upon purely visual representations. Our technique introduces the calculation of confidence in an answer and the automatic adjustment of level of resolution if that confidence is insufficient. We first describe the nature of the visual analogies found on the RPM. We then exhibit our algorithm and work through a detailed example. Finally, we present the performance of our algorithm on the four major variants of the RPM tests, illustrating the impact of confidence. This is the first such account of any computational model against the entirety of the Raven’s.
Spatial Scan for Disease Mapping on a Mobile Population
Authors: Lan Liang , Malbasa Vuk , Vucetic Slobodan
Published: June 2022

In disease mapping, the spatial scan statistic is used to detect spatial regions where population is exposed to a significantly higher disease risk than expected. In this important application, the current residence is typically used to define the location of individuals from the population. Considering the mobility of humans at various temporal and spatial scales, using only information about the current residence may be an insufficiently informative proxy because it ignores a multitude of exposures that may occur away from home, or which had occurred at previous residences. In this paper, we propose a spatial scan statistic that is appropriate for disease mapping on mobile populations. We formulate a computationally efficient algorithm that uses the proposed statistic to find significant high-risk regions from mobile population's disease status data. The algorithm is applicable on large populations and over dense spatial grids. The experimental results demonstrate that the proposed algorithm is computationally efficient and outperforms the traditional disease clustering approaches at discovering high-risk regions in mobile populations.
A Region-Based Model for Estimating Urban Air Pollution
Authors: Jutzeler Arnaud , Li Jason , Faltings Boi
Published: June 2022

Air pollution has a direct impact to human health, and data-driven air quality models are useful for evaluating population exposure to air pollutants. In this paper, we propose a novel region-based Gaussian process model for estimating urban air pollution dispersion, and applied it to a large dataset of ultrafine particle (UFP) measurements collected from a network of sensors located on several trams in the city of Zurich. We show that compared to existing grid-based models, the region-based model produces better predictions across aggregates of all time scales. The new model is appropriate for many useful user applications such as exposure assessment and anomaly detection.
Mechanism Design for Scheduling with Uncertain Execution Time
Authors: Conitzer Vincent , Vidali Angelina
Published: June 2022

We study the problem where a task (or multiple unrelated tasks) must be executed, there are multiple machines/agents that can potentially perform the task, and our objective is to minimize the expected sum of the agents' processing times. Each agent does not know exactly how long it will take him to finish the task; he only knows the distribution from which this time is drawn. These times are independent across agents and the distributions fulfill the monotone hazard rate condition. Agents are selfish and will lie about their distributions if this increases their expected utility. We study different variations of the Vickrey mechanism that take as input the agents' reported distributions and the players' realized running times and that output a schedule that minimizes the expected sum of processing times, as well as payments that make it an ex-post equilibrium for the agents to both truthfully report their distributions and exert full effort to complete the task. We devise the ChPE mechanism, which is uniquely tailored to our problem, and has many desirable properties including: not rewarding agents that fail to finish the task and having non-negative payments.
Game-Theoretic Resource Allocation for Protecting Large Public Events
Authors: Yin Yue , An Bo , Jain Manish
Published: June 2022

High profile large scale public events are attractive targets for terrorist attacks. The recent Boston Marathon bombings on April 15, 2013 have further emphasized the importance of protecting public events. The security challenge is exacerbated by the dynamic nature of such events: e.g., the impact of an attack at different locations changes over time as the Boston marathon participants and spectators move along the race track. In addition, the defender can relocate security resources among potential attack targets at any time and the attacker may act at any time during the event. This paper focuses on developing efficient patrolling algorithms for such dynamic domains with continuous strategy spaces for both the defender and the attacker. We aim at computing optimal pure defender strategies, since an attacker does not have an opportunity to learn and respond to mixed strategies due to the relative infrequency of such events. We propose SCOUT-A, which makes assumptions on relocation cost, exploits payoff representation and computes optimal solutions efficiently. We also propose SCOUT-C to compute the exact optimal defender strategy for general cases despite the continuous strategy spaces. SCOUT-C computes the optimal defender strategy by constructing an equivalent game with discrete defender strategy space, then solving the constructed game. Experimental results show that both SCOUT-A and SCOUT-C significantly outperform other existing strategies.