bluuebunny/crossref_metadata_2025 huggingface dataset:
Nutrient and Trace Element Contributions from Drained Islands in the Sacramento–San Joaquin Delta, California
Authors:   University of California, Santa Cruz, Richardson Christina , Fackrell Joseph ,   University of California, Santa Cruz, Kraus Tamara E. C. ,   US Geological Survey, Young Megan ,   US Geological Survey, Paytan Adina ,   University of California, Santa Cruz
Published: June 2022

Inventorying nutrient and trace element sources in the Sacramento-San Joaquin Delta (the Delta) is critical to understanding how changes—including alterations to point source inputs such as upgrades to the Sacramento Regional Wastewater Treatment Plant (SRWTP) and landscape-scale changes related to wetland restoration—may alter the Delta’s water quality. While island drains are a ubiquitous feature of the Delta, limited data exist to evaluate island drainage mass fluxes in this system. To better constrain inputs from island drains, we measured monthly discharge along with nutrient and trace element concentrations in island drainage on three Delta islands and surrounding rivers from June 2017 to September 2018. These data were used to calculate island-level fluxes and then upscaled to estimate Delta-wide contributions from island drains. Based on these results, we present (1) new estimates of gross and net nutrient and trace element fluxes from Delta island drains, and (2) concomitant N stable isotope data to improve our understanding of island N cycling. Over 60% of nearly all island drainage gross nutrient and trace element loads occurred in winter and spring. Upscaled island drainage net annual total nitrogen (TN), total dissolved nitrogen (TDN), and NH4+ loads comprised an estimated 9%, 7%, and 4%, respectively, of annual inputs to this system in 2018, before the SRWTP upgrade. Under a post-upgrade scenario, we estimated net annual island drainage TDN contributions to increase to 11% and NH4+ contributions to 45% of total Delta inputs as the SRWTP NH4+ load diminished to near zero. Our results suggest that island drainage is a measurable N source that has likely become increasingly important now that the SRWTP upgrade is complete. With over 200 potential active outfalls, these inputs may affect aquatic biogeochemical cycling in many regions of the Delta, especially in areas with long residence times.
Cross-Cultural Marriages in the Context of the Transformation of the Family Model: Russia and Iran
Authors: Rostovskaya Tamara K. , Kuchmaeva Oksana V. , Afzali Mehdi , Irsetskaya Elena A.
Published: June 2022

Introduction. The relevance of the study of cross-cultural marriages in Russia and Iran is due to the fact that Russia and Iran are characterized by both attention to the observance of cultural needs and the desire for innovative development. These contradictory manifestations are also manifested in family and marriage relations. There is a shortage of empirical studies that reflect the range of opinions of various social strata in Russia and Iran, allowing a comparative analysis of attitudes towards cross-cultural marriages to determine the prospects for state policy to support the institution of marriage in a cross-cultural environment. The purpose of the public research is to analyze the relationship with cross-cultural marriages in Russia and Iran against the background of the proposed family model.

Materials and Methods. Sociological research is carried out by the method of a quantitative survey. 153 people were interviewed in total, urban residents of Russia and Iran aged 18 to 60 who are in cross-cultural marriages or an interethnic, interfaith environment. To analyze the results, descriptive statistics methods, frequency distributions, and a tabular data visualization method were used.

Results. The survey results have revealed the presence of different ideas about the desired model of family and marriage both in Iran and Russia (both traditional and modern). The study has revealed a friendly attitude towards interethnic marriages in both countries (primarily for urban residents). In many ways, the success of cross-cultural marriages is determined by the attitude of relatives and the local community towards them.

Discussion and Conclusion. The results obtained can be used to develop strategies for the development of socio-cultural interaction between Russia and Iran. It is essential to consider the study’s conclusions when supporting cross-cultural families in the process of adaptation and acquaintance with the norms, traditions, and customs of a new culture spouse. It is necessary to conduct a study on a more representative sample to assess the differentiation and determinants of people’s opinions in interethnic marriages living in Iran and Russia.
The City in Theory and in Practice: Factors of Social Identity Formation of the Capital of Republic within the Russian Federation
Authors: Bogatova Olga A. , Dolgaeva Evgenia I.
Published: June 2022

Introduction. The paper is devoted to the theoretically and practically relevant issue of constructing the social identity of the administrative centers of the Russian republics, considered by the authors as one of the factors of positive regional self-identification of the population of the republics. The purpose of the article is, based on the materials of the study, to identify the specifics and factors in the formation of the capital’s identity of the republic within the Russian Federation on the example of Saransk (the Republic of Mordovia).

Materials and Methods. The empirical basis of the article consists of the data of quantitative (mass survey of the population of Saransk, n = 443) and qualitative (2 focus groups) field studies carried out in November-December 2020, with the help of which specific features, main structural components and factors of formation of a positive metropolitan identity have been identified.

Results. In the structure of the capital identity of the population of Saransk, the system-forming component is identification with the capital status of the city, indirectly based on identification with the status of Mordovia as a republic and its representations in the urban environment. The paper demonstrates that a positive identification with the republican capital is formed on the basis of not so much symbolic politics but factors that create urban identity in general, including economic success and the quality of the urban environment.

Discussion and Conclusions. The authors conclude that the main limitations of the formation and maintenance of the capital identity of Saransk are associated with a shortage of urban identity in general, due to a low assessment of the state and prospects, a low level of self-organization at the place of residence and the rotation of the urban population in each generation, which forms the “transit city” identity for a part of the population. The basis of urban identity in Saransk, as in larger cities, is made up of networks of “weak ties”, but the quality of the urban environment, in the opinion of its residents, does not contribute to the formation and development of such contacts. On the basis of results of researches, the authors claim the necessity of studying of city activism for the cities which are not among largest, as an independent research problem.
Formation of the Social Identity of the Youth of the Region: The Potential of Aesthetic Information
Authors: Vorobev Vladimir P. , Ivchenkov Sergey G. , Eremina Ekaterina V. , Retinskaya Vera N. , Murzina Irina A.
Published: June 2022

Introduction. An urgent interdisciplinary problem is the identification of factors that determine the processes of constructing social identity, which is the socio-cultural foundation of the existence of any community. The aim of the work is to identify the actual role and potential of artistic and aesthetic components of social space in the process of forming the regional identity of the youth.

Materials and Methods. The empirical basis of the study was the data of a questionnaire survey of the youth of Moscow and the Moscow region, Penza and Saratov regions (n = 1000) and interviews with representatives of the student youth of Saratov and Penza (n = 30), devoted to the analysis of the identification hierarchy of the youth and the influence of aesthetic components of the social space of the region on the formation of its regional identity. These methods revealed the important role of artistic and aesthetic components of the regional social space in the construction of regional identity and regional patriotism of the youth of the region.

Results. Based on the results of the research, a conclusion has been made about the connection between “local” patriotism and specific social attitudes focused on the development of the territorial community and the improvement of its image, as well as about the partial determinism of the regional components of the respondents’ social identity by figurative and aesthetic factors. It is shown that at the theoretical level, taking into account the aesthetic factors of the construction of social identity implies a transition from deterministic models of interaction in the system “social environment – emerging personality” to stochastic ones.

Discussion and Conclusion. The authors point out when modeling the process of forming the social identity of young people, theoretical models based on the concepts of social constructivism should take into account the unique, intimate-personal interactions of a particular individual with separate socio-cultural phenomena symbolizing collective identity. The results of the study can be used by all agents of socialization in the practice of applying aesthetic components of regional social space in the process of forming regional patriotism.
Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment
Authors: Hai Ha Duong , Thi Thanh Ngo Huong , Tran Van Phong , Nguyen Duc Dam , Avand Mohammadtaghi , Nguyen Huu Duy , Amiri Mahdis , Van Le Hiep , Prakash Indra , Binh Thai Pham
Published: June 2022

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones.

One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the Average Merit (AM) of the rainfall factor was the highest (68.039), and river density was the lowest (53,969). Performance evaluation of ML models was done using standard statistical indicators, including Area Under the Receiver Operating Characteristic (ROC) curve (AUC). The results showed that all the four models performed well in the training (AUC ≥ 0.967) and testing (AUC ≥ 0.734) phases, but the performance of the ABRF (AUC=0.992) model is the best in the training phase, whereas LBRF is the best in the testing phase (AUC=0.776). The present model study would be helpful in the proper groundwater potential assessment and management of groundwater resources for sustainable development.
Egocentric Video Search via Physical Interactions
Authors: Miyanishi Taiki , Hirayama Jun-ichiro , Kong Quan , Maekawa Takuya , Moriya Hiroki , Suyama Takayuki
Published: June 2022

Retrieving past egocentric videos about personal daily life is important to support and augment human memory. Most previous retrieval approaches have ignored the crucial feature of human-physical world interactions, which is greatly related to our memory and experience of daily activities. In this paper, we propose a gesture-based egocentric video retrieval framework, which retrieves past visual experience using body gestures as non-verbal queries. We use a probabilistic framework based on a canonical correlation analysis that models physical interactions through a latent space and uses them for egocentric video retrieval and re-ranking search results. By incorporating physical interactions into the retrieval models, we address the problems resulting from the variability of human motions. We evaluate our proposed method on motion and egocentric video datasets about daily activities in household settings and demonstrate that our egocentric video retrieval framework robustly improves retrieval performance when retrieving past videos from personal and even other persons' video archives.
Learning Market Parameters Using Aggregate Demand Queries
Authors: Bei Xiaohui , Chen Wei , Garg Jugal , Hoefer Martin , Sun Xiaoming
Published: June 2022

We study efficient algorithms for a natural learning problem in markets. There is one seller with m divisible goods and n buyers with unknown individual utility functions and budgets of money. The seller can repeatedly announce prices and observe aggregate demand bundles requested by the buyers. The goal of the seller is to learn the utility functions and budgets of the buyers. Our scenario falls into the classic domain of ''revealed preference'' analysis. Problems with revealed preference have recently started to attract increased interest in computer science due to their fundamental nature in understanding customer behavior in electronic markets. The goal of revealed preference analysis is to observe rational agent behavior, to explain it using a suitable model for the utility functions, and to predict future agent behavior. Our results are the first polynomial-time algorithms to learn utility and budget parameters via revealed preference queries in classic Fisher markets with multiple buyers. Our analysis concentrates on linear, CES, and Leontief markets, which are the most prominent classes studied in the literature. Some of our results extend to general Arrow-Debreu exchange markets.
Who Can Win a Single-Elimination Tournament?
Authors: Kim Michael , Suksompong Warut , Williams Virginia
Published: June 2022

A single-elimination (SE) tournament is a popular way to select a winner in both sports competitions and in elections. A natural and well-studied question is the tournament fixing problem (TFP): given the set of all pairwise match outcomes, can a tournament organizer rig an SE tournament by adjusting the initial seeding so that their favorite player wins? We prove new sufficient conditions on the pairwise match outcome information and the favorite player, under which there is guaranteed to be a seeding where the player wins the tournament. Our results greatly generalize previous results. We also investigate the relationship between the set of players that can win an SE tournament under some seeding (so called SE winners) and other traditional tournament solutions. In addition, we generalize and strengthen prior work on probabilistic models for generating tournaments. For instance, we show that every player in an n player tournament generated by the Condorcet Random Model will be an SE winner even when the noise is as small as possible, p = Θ(ln n/n); prior work only had such results for p ≥ Ω( ln n/n). We also establish new results for significantly more general generative models.
On the Complexity of mCP-nets
Authors: Lukasiewicz Thomas , Malizia Enrico
Published: June 2022

mCP-nets are an expressive and intuitive formalism based on CP-nets to reason about preferences of groups of agents. The dominance semantics of mCP-nets is based on the concept of voting, and different voting schemes give rise to different dominance semantics for the group. Unlike CP-nets, which received an extensive complexity analysis, mCP-nets, as reported multiple times in the literature, lack a precise study of the voting tasks' complexity. Prior to this work, only a complexity analysis of brute-force algorithms for these tasks was available, and this analysis only gave EXPTIME upper bounds for most of those problems. In this paper, we start to fill this gap by carrying out a precise computational complexity analysis of voting tasks on acyclic binary polynomially connected mCP-nets whose constituents are standard CP-nets. Interestingly, all these problems actually belong to various levels of the polynomial hierarchy, and some of them even belong to PTIME or LOGSPACE. Furthermore, for most of these problems, we provide completeness results, which show tight lower bounds for problems that (up to date) did not have any explicit non-obvious lower bound.
Incentives for Strategic Behavior in Fisher Market Games
Authors: Chen Ning , Deng Xiaotie , Tang Bo , Zhang Hongyang
Published: June 2022

In a Fisher market game, a market equilibrium is computed in terms of the utility functions and money endowments that agents reported. As a consequence, an individual buyer may misreport his private information to obtain a utility gain. We investigate the extent to which an agent's utility can be increased by unilateral strategic plays and prove that the percentage of this improvement is at most 2 for markets with weak gross substitute utilities. Equivalently, we show that truthfully reporting is a 0.5-approximate Nash equilibrium in this game. To identify sufficient conditions for truthfully reporting being close to Nash equilibrium, we conduct a parameterized study on strategic behaviors and further show that the ratio of utility gain decreases linearly as buyer's initial endowment increases or his maximum share of an item decreases. Finally, we consider collusive behavior of a coalition and prove that the utility gain is bounded by 1/(1 - maximum share of the collusion). Our findings justify the truthful reporting assumption in Fisher markets by a quantitative study on participants incentive, and imply that under large market assumption, the utility gain of a buyer from manipulations diminishes to 0.
Graphical Hedonic Games of Bounded Treewidth
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 problems of finding stable, optimal, or fair outcomes tend to be computationally intractable in even severely restricted instances of hedonic games. We introduce the notion of a graphical hedonic game and show that, in contrast, on classes of graphical hedonic games whose underlying graphs are of bounded treewidth and degree, such problems become easy. In particular, problems that can be specified through quantification over agents, coalitions, and (connected) partitions can be decided in linear time. The proof is by reduction to monadic second order logic. We also provide faster algorithms in special cases, and show that the extra condition of the degree bound cannot be dropped. Finally, we note that the problem of allocating indivisible goods can be modelled as a hedonic game, so that our results imply tractability of finding fair and efficient allocations on appropriately restricted instances.
Computing Possible and Necessary Equilibrium Actions (and Bipartisan Set Winners)
Authors: Brill Markus , Freeman Rupert , Conitzer Vincent
Published: June 2022

In many multiagent environments, a designer has some, but limited control over the game being played. In this paper, we formalize this by considering incompletely specified games, in which some entries of the payoff matrices can be chosen from a specified set. We show that it is NP-hard for the designer to make this choices optimally, even in zero-sum games. In fact, it is already intractable to decide whether a given action is (potentially or necessarily) played in equilibrium. We also consider incompletely specified symmetric games in which all completions are required to be symmetric. Here, hardness holds even in weak tournament games (symmetric zero-sum games whose entries are all -1, 0, or 1) and in tournament games (symmetric zero-sum games whose non-diagonal entries are all -1 or 1). The latter result settles the complexity of the possible and necessary winner problems for a social-choice-theoretic solution concept known as the bipartisan set. We finally give a mixed-integer linear programming formulation for weak tournament games and evaluate it experimentally.
Variations on the Hotelling-Downs Model
Authors: Feldman Michal , Fiat Amos , Obraztsova Svetlana
Published: June 2022

In this paper we expand the standard Hotelling-Downs model of spatial competition to a setting where clients do not necessarily choose their closest candidate (retail product or political). Specifically, we consider a setting where clients may disavow all candidates if there is no candidate that is sufficiently close to the client preferences. Moreover, if there are multiple candidates that are sufficiently close, the client may choose amongst them at random. We show the existence of Nash Equilibria for some such models, and study the price of anarchy and stability in such scenarios.
A Deep Choice Model
Authors: Otsuka Makoto , Osogami Takayuki
Published: June 2022

Human choice is complex in two ways. First, human choice often shows complex dependency on available alternatives. Second, human choice is often made after examining complex items such as images. The recently proposed choice model based on the restricted Boltzmann machine (RBM choice model) has been proved to represent three typical phenomena of human choice, which addresses the first complexity. We extend the RBM choice model to a deep choice model (DCM) to deal with the features of items, which are ignored in the RBM choice model. We then use deep learning to extract latent features from images and plug those latent features as input to the DCM. Our experiments show that the DCM adequately learns the choice that involves both of the two complexities in human choice.
Nested Monte Carlo Search for Two-Player Games
Authors: Cazenave Tristan , Saffidine Abdallah , Schofield Michael , Thielscher Michael
Published: June 2022

The use of the Monte Carlo playouts as an evaluation function has proved to be a viable, general technique for searching intractable game spaces. This facilitate the use of statistical techniques like Monte Carlo Tree Search (MCTS), but is also known to require significant processing overhead. We seek to improve the quality of information extracted from the Monte Carlo playout in three ways. Firstly, by nesting the evaluation function inside another evaluation function; secondly, by measuring and utilising the depth of the playout; and thirdly, by incorporating pruning strategies that eliminate unnecessary searches and avoid traps. Our experimental data, obtained on a variety of two-player games from past General Game Playing (GGP) competitions and others, demonstrate the usefulness of these techniques in a Nested Player when pitted against a standard, optimised UCT player.
Learning to Branch in Mixed Integer Programming
Authors: Khalil Elias , Le Bodic Pierre , Song Le , Nemhauser George , Dilkina Bistra
Published: June 2022

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine learning (ML) framework for variable branching in MIP.Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. Based on the collected data, we learn an easy-to-evaluate surrogate function that mimics the SB strategy, by means of solving a learning-to-rank problem, common in ML. The learned ranking function is then used for branching. The learning is instance-specific, and is performed on-the-fly while executing a branch-and-bound search to solve the MIP instance. Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver.
A Model for Learning Description Logic Ontologies Based on Exact Learning
Authors: Konev Boris , Ozaki Ana , Wolter Frank
Published: June 2022

We investigate the problem of learning description logic (DL) ontologies in Angluin et al.’s framework of exact learning via queries posed to an oracle. We consider membership queries of the form “is a tuple a of individuals a certain answer to a data retrieval query q in a given ABox and the unknown target ontology?” and completeness queries of the form “does a hypothesis ontology entail the unknown target ontology?” Given a DL L and a data retrieval query language Q, we study polynomial learnability of ontologies in L using data retrieval queries in Q and provide an almost complete classification for DLs that are fragments of EL with role inclusions and of DL-Lite and for data retrieval queries that range from atomic queries and EL/ELI-instance queries to conjunctive queries. Some results are proved by non-trivial reductions to learning from subsumption examples.
Resistance to Corruption of Strategic Argumentation
Authors: Maher Michael
Published: June 2022

Strategic argumentation provides a simple model of disputation. We investigate it in the context of Dung's abstract argumentation. We show that strategic argumentation under the grounded semantics is resistant tocorruption -- specifically, collusion and espionage — in a sense similar to Bartholdi et al's notion of a voting scheme resistant to manipulation. Under the stable semantics, strategic argumentation is resistant to espionage, but its resistance to collusion varies according to the aims of the disputants. These results are extended to a variety of concrete languages for argumentation.
Boolean Functions with Ordered Domains in Answer Set Programming
Authors: Alviano Mario , Faber Wolfgang , Strass Hannes
Published: June 2022

Boolean functions in Answer Set Programming have proven a useful modelling tool. They are usually specified by means of aggregates or external atoms. A crucial step in computing answer sets for logic programs containing Boolean functions is verifying whether partial interpretations satisfy a Boolean function for all possible values of its undefined atoms. In this paper, we develop a new methodology for showing when such checks can be done in deterministic polynomial time. This provides a unifying view on all currently known polynomial-time decidability results, and furthermore identifies promising new classes that go well beyond the state of the art. Our main technique consists of using an ordering on the atoms to significantly reduce the necessary number of model checks. For many standard aggregates, we show how this ordering can be automatically obtained.
Affinity Preserving Quantization for Hashing: A Vector Quantization Approach to Learning Compact Binary Codes
Authors: Wang Zhe , Duan Ling-Yu , Huang Tiejun , Wen Gao
Published: June 2022

Hashing techniques are powerful for approximate nearest neighbour (ANN) search.Existing quantization methods in hashing are all focused on scalar quantization (SQ) which is inferior in utilizing the inherent data distribution.In this paper, we propose a novel vector quantization (VQ) method named affinity preserving quantization (APQ) to improve the quantization quality of projection values, which has significantly boosted the performance of state-of-the-art hashing techniques.In particular, our method incorporates the neighbourhood structure in the pre- and post-projection data space into vector quantization.APQ minimizes the quantization errors of projection values as well as the loss of affinity property of original space.An effective algorithm has been proposed to solve the joint optimization problem in APQ, and the extension to larger binary codes has been resolved by applying product quantization to APQ.Extensive experiments have shown that APQ consistently outperforms the state-of-the-art quantization methods, and has significantly improved the performance of various hashing techniques.
Complexity Results and Algorithms for Extension Enforcement in Abstract Argumentation
Authors: Wallner Johannes , Niskanen Andreas , Järvisalo Matti
Published: June 2022

Understanding the dynamics of argumentation frameworks (AFs) is important in the study of argumentation in AI. In this work, we focus on the so-called extension enforcement problem in abstract argumentation. We provide a nearly complete computational complexity map of fixed-argument extension enforcement under various major AF semantics, with results ranging from polynomial-time algorithms to completeness for the second-level of the polynomial hierarchy. Complementing the complexity results, we propose algorithms for NP-hard extension enforcement based on constrained optimization. Going beyond NP, we propose novel counterexample-guided abstraction refinement procedures for the second-level complete problems and present empirical results on a prototype system constituting the first approach to extension enforcement in its generality.
The Complexity of LTL on Finite Traces: Hard and Easy Fragments
Authors: Fionda Valeria , Greco Gianluigi
Published: June 2022

This paper focuses on LTL on finite traces (LTLf) for which satisfiability is known to be PSPACE-complete. However, little is known about the computational properties of fragments of LTLf. In this paper we fill this gap and make the following contributions. First, we identify several LTLf fragments for which the complexity of satisfiability drops to NP-complete or even P, by considering restrictions on the temporal operators and Boolean connectives being allowed. Second, we study a semantic variant of LTLf, which is of interest in the domain of business processes, where models have the property that precisely one propositional variable evaluates true at each time instant. Third, we introduce a reasoner for LTLf and compare its performance with the state of the art.
Generating CP-Nets Uniformly at Random
Authors: Allen Thomas , Goldsmith Judy , Justice Hayden , Mattei Nicholas , Raines Kayla
Published: June 2022

Conditional preference networks (CP-nets) are a commonly studied compact formalism for modeling preferences. To study the properties of CP-nets or the performance of CP-net algorithms on average, one needs to generate CP-nets in an equiprobable manner. We discuss common problems with naive generation, including sampling bias, which invalidates the base assumptions of many statistical tests and can undermine the results of an experimental study. We provide a novel algorithm for provably generating acyclic CP-nets uniformly at random. Our method is computationally efficient and allows for multi-valued domains and arbitrary bounds on the indegree in the dependency graph.
Metaphysics of Planning Domain Descriptions
Authors: Srivastava Siddharth , Russell Stuart , Pinto Alessandro
Published: June 2022

STRIPS-like languages (SLLs) have fostered immense advances in automated planning. In practice, SLLs are used to express highly abstract versions of real-world planning problems, leading to more concise models and faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to SLL models that are unsolvable, SLL models whose solutions are incorrect with respect to the real-world problem, or models that are inexpressible in SLLs. There is some evidence that such limitations have restricted the applicability of AI planning technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power — for example, allowing angelic nondeterminism in action effects — and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.
Strengthening Agents Strategic Ability with Communication
Authors: Huang Xiaowei , Chen Qingliang , Su Kaile
Published: June 2022

The current frameworks of reasoning about agents' collective strategy are either too conservative or too liberal in terms of the sharing of local information between agents. In this paper, we argue that in many cases, a suitable amount of information is required to be communicated between agents to both enforce goals and keep privacy. Several communication operators are proposed to work with an epistemic strategy logic ATLK. The complexity of model checking resulting logics is studied, and surprisingly, we found that the additional expressiveness from the communication operators comes for free.
Is It Harmful When Advisors Only Pretend to Be Honest?
Authors: Wang Dongxia , Muller Tim , Zhang Jie , Liu Yang
Published: June 2022

In trust systems, unfair rating attacks — where advisors provide ratings dishonestly — influence the accuracy of trust evaluation. A secure trust system should function properly under all possible unfair rating attacks; including dynamic attacks. In the literature, camouflage attacks are the most studied dynamic attacks. But an open question is whether more harmful dynamic attacks exist. We propose random processes to model and measure dynamic attacks. The harm of an attack is influenced by a user's ability to learn from the past. We consider three types of users: blind users, aware users, and general users. We found for all the three types, camouflage attacks are far from the most harmful. We identified the most harmful attacks, under which we found the ratings may still be useful to users.
Frugal Bribery in Voting
Authors: Dey Palash , Misra Neeldhara , Narahari Y.
Published: June 2022

Bribery in elections is an important problem in computational social choice theory. We introduce and study two important special cases of the bribery problem, namely, FRUGAL-BRIBERY and FRUGAL-$BRIBERY where the briber is frugal in nature. By this, we mean that the briber is only able to influence voters who benefit from the suggestion of the briber. More formally, a voter is vulnerable if the outcome of the election improves according to her own preference when she accepts the suggestion of the briber. In the FRUGAL-BRIBERY problem, the goal is to make a certain candidate win the election by changing only the vulnerable votes. In the FRUGAL-$BRIBERY problem, the vulnerable votes have prices and the goal is to make a certain candidate win the election by changing only the vulnerable votes, subject to a budget constraint. We show that both the FRUGAL-BRIBERY and the FRUGAL-$BRIBERY problems are intractable for many commonly used voting rules for weighted as well as unweighted elections. These intractability results demonstrate that bribery is a hard computational problem, in the sense that several special cases of this problem continue to be computationally intractable. This strengthens the view that bribery, although a possible attack on an election in principle, may be infeasible in practice.
Robust Execution of BDI Agent Programs by Exploiting Synergies Between Intentions
Authors: Yao Yuan , Logan Brian , Thangarajah John
Published: June 2022

A key advantage the reactive planning approach adopted by BDI-based agents is the ability to recover from plan execution failures, and almost all BDI agent programming languages and platforms provide some form of failure handling mechanism. In general, these consist of simply choosing an alternative plan for the failed subgoal (e.g., JACK, Jadex). In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent's intentions. A positive interaction occurs when the execution of an action in one intention assists the execution of actions in other intentions (e.g., by (re)establishing their preconditions). We have implemented our approach in a scheduling algorithm for BDI agents which we call SP. The results of a preliminary empirical evaluation of SP suggest our approach out-performs existing failure handling mechanisms used by state-of-the-art BDI languages. Moreover, the computational overhead of SP is modest.
Efficient Computation of Emergent Equilibrium in Agent-Based Simulation
Authors: Hu Zehong , Sha Meng , Jarrah Moath , Zhang Jie , Xi Hui
Published: June 2022

In agent-based simulation, emergent equilibrium describes the macroscopic steady states of agents' interactions. While the state of individual agents might be changing, the collective behavior pattern remains the same in macroscopic equilibrium states. Traditionally, these emergent equilibriums are calculated using Monte Carlo methods. However, these methods require thousands of repeated simulation runs, which are extremely time-consuming. In this paper, we propose a novel three-layer framework to efficiently compute emergent equilibriums. The framework consists of a macro-level pseudo-arclength equilibrium solver (PAES), a micro-level simulator (MLS) and a macro-micro bridge (MMB). It can adaptively explore parameter space and recursively compute equilibrium states using the predictor-corrector scheme. We apply the framework to the popular opinion dynamics and labour market models. The experimental results show that our framework outperformed Monte Carlo experiments in terms of computation efficiency while maintaining the accuracy.
Complexity of Shift Bribery in Committee Elections
Authors: Bredereck Robert , Faliszewski Piotr , Niedermeier Rolf , Talmon Nimrod
Published: June 2022

We study the (parameterized) complexity of Shift Bribery for multiwinner voting rules. We focus on the SNTV, Bloc, k-Borda, and Chamberlin-Courant rules, as well as on approximate variants of the Chamberlin-Courant rule, since the original rule is NP-hard to compute. We show that Shift Bribery tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where Shift Bribery is easy in the single-winner cases, but is hard (and hard to approximate) in the multiwinner ones. We show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin--Courant rule sometimes affects the complexity of Shift Bribery.
Learning a Hybrid Architecture for Sequence Regression and Annotation
Authors: Zhang Yizhe , Henao Ricardo , Carin Lawrence , Zhong Jianling , Hartemink Alexander
Published: June 2022

When learning a hidden Markov model (HMM), sequential observations can often be complemented by real-valued summary response variables generated from the path of hidden states. Such settings arise in numerous domains, including many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible framework for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is compatible with a rich set of mapping functions. Results show that the availability of additional continuous response variables can simultaneously improve the annotation of the sequential observations and yield good prediction performance in both synthetic data and real-world datasets.
Simultaneous Feature and Sample Reduction for Image-Set Classification
Authors: Zhang Man , He Ran , Cao Dong , Sun Zhenan , Tan Tieniu
Published: June 2022

Image-set classification is the assignment of a label to a given image set. In real-life scenarios such as surveillance videos, each image set often contains much redundancy in terms of features and samples. This paper introduces a joint learning method for image-set classification that simultaneously learns compact binary codes and removes redundant samples. The joint objective function of our model mainly includes two parts. The first part seeks a hashing function to generate binary codes that have larger inter-class and smaller intra-class distances. The second one reduces redundant samples with discrete constraints in a low-rank way. A kernel method based on anchor points is further used to reduce sample variations. The proposed discrete objective function is simplified to a series of sub-problems that admit an analytical solution, resulting in a high-quality discrete solution with a low computational cost. Experiments on three commonly used image-set datasets show that the proposed method for the tasks of face recognition from image sets is efficient and effective.
Semisupervised Autoencoder for Sentiment Analysis
Authors: Zhai Shuangfei , Zhang Zhongfei
Published: June 2022

In this paper, we investigate the usage of autoencoders in modeling textual data. Traditional autoencoders suffer from at least two aspects: scalability with the high dimensionality of vocabulary size and dealing with task-irrelevant words. We address this problem by introducing supervision via the loss function of autoencoders. In particular, we first train a linear classifier on the labeled data, then define a loss for the autoencoder with the weights learned from the linear classifier. To reduce the bias brought by one single classifier, we define a posterior probability distribution on the weights of the classifier, and derive the marginalized loss of the autoencoder with Laplace approximation. We show that our choice of loss function can be rationalized from the perspective of Bregman Divergence, which justifies the soundness of our model. We evaluate the effectiveness of our model on six sentiment analysis datasets, and show that our model significantly outperforms all the competing methods with respect to classification accuracy. We also show that our model is able to take advantage of unlabeled dataset and get improved performance. We further show that our model successfully learns highly discriminative feature maps, which explains its superior performance.
Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation
Authors: Gong Chen , Tao Dacheng , Yang Jie , Liu Wei
Published: June 2022

Multi-label propagation aims to transmit the multi-label information from labeled examples to unlabeled examples based on a weighted graph. Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagationin an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. To address this problem, this paper associates each possible label with a "teacher", and proposesa "Multi-Label Teaching-to-Learn and Learning-to-Teach" (ML-TLLT) algorithm, so that the entire propagationprocess is guided by the teachers and manipulated from simple examples to more difficult ones. In the teaching-to-learn step, the teachers select the simplest examples for the current propagation by investigating both the definitiveness of each possible label of the unlabeled examples, and the dependencies between labels revealed by the labeled examples. In the learning-to-teach step, the teachers reversely learn from the learner’s feedback to properly select the simplest examples for the next propagation. Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets.
Shakeout: A New Regularized Deep Neural Network Training Scheme
Authors: Kang Guoliang , Li Jun , Tao Dacheng
Published: June 2022

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice.We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR-10 show that Shakeout deals with over-fitting effectively.
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization
Authors: Singla Adish , Tschiatschek Sebastian , Krause Andreas
Published: June 2022

We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ratings. For summarization tasks with the goal of maximizing coverage and diversity, submodular set functions are a natural choice. When the underlying submodular function is unknown, users' feedback can provide noisy evaluations of the function that we seek to maximize. We provide a generic algorithm — ExpGreedy — for maximizing an unknown submodular function under cardinality constraints. This algorithm makes use of a novel exploration module— TopX — that proposes good elements based on adaptively sampling noisy function evaluations. TopX is able to accommodate different kinds of observation models such as value queries and pairwise comparisons. We provide PAC-style guarantees on the quality and sampling cost of the solution obtained by ExpGreedy. We demonstrate the effectiveness of our approach in an interactive, crowdsourced image collection summarization application.
Near-Optimal Active Learning of Multi-Output Gaussian Processes
Authors: Zhang Yehong , Hoang Trong Nghia , Low Kian Hsiang , Kankanhalli Mohan
Published: June 2022

This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena. In contrast to existing works, our active learning problem involves selecting not just the most informative sampling locations to be observed but also the types of measurements at each selected location for minimizing the predictive uncertainty (i.e., posterior joint entropy) of a target phenomenon of interest given a sampling budget. Unfortunately, such an entropy criterion scales poorly in the numbers of candidate sampling locations and selected observations when optimized. To resolve this issue, we first exploit a structure common to sparse MOGP models for deriving a novel active learning criterion. Then, we exploit a relaxed form of submodularity property of our new criterion for devising a polynomial-time approximation algorithm that guarantees a constant-factor approximation of that achieved by the optimal set of selected observations. Empirical evaluation on real-world datasets shows that our proposed approach outperforms existing algorithms for active learning of MOGP and single-output GP models.