bluuebunny/crossref_metadata_2025 huggingface dataset:
V for Verification: Intelligent Algorithm of Checking Reliability of Smart Systems
Authors: Lukina Anna
Published: June 2022

Cyber-physical systems (CPS) are intended to receive information from the environment through sensors and perform appropriate actions using actuators of the controller. In the last years world of intelligent technologies has grown in an exponential fashion: from cruise control to smart ecosystems. Next we are facing the future of CPS involved in almost every aspect of our lives bringing higher comfortability and efficiency. Our goal is to help smart inventions adjust to this highly uncertain environment and guarantee safety for its inhabitants. The physical environment renders the problem of CPS verification extremely cumbersome. Due to a wealth of uncertainties introduced by physical processes, the system is best described by stochastic models. Approximate prediction techniques, such as Statistical Model Checking (SMC), have therefore recently become increasingly popular. As a result, verification of a CPS boils down to quantitative analysis of how close the system is to reaching bad states (safety property) or desired goal (liveness property). Controlling the systems, that is, computing appropriate response actions depending on the environment, involves probabilistic state estimation, as well as optimal action prediction, i.e., choosing the best next step by simulating the future. In my thesis, I develop a novel intelligent algorithm addressing existing deficiencies of SMC such as poor prediction of rare events (RE) and sampling divergence.
Modelling Familiarity for Intelligent Personalized Social Mobilization
Authors: Pan Zhengxiang
Published: June 2022

With the rise of the Internet and social media, social mobilization - large-scale mobilization manpower for scientific, social, and political activities through crowdsourcing - has become a widespread practice. Despite the success, social mobilization is not without its limitations. Local trapping of diffusion and the dependence on highly connected individuals to mobilize people in distance locations affect the effectiveness of social mobilization. Furthermore, as empirical studies on people's responses to various social mobilization approaches are lacking, it is a significant challenge for artificial intelligence (AI) researchers to design effective and efficient decision support mechanisms to help manage this emerging phenomenon. In my thesis, I conduct large-scale empirical studies to help the AI research community establish baseline personal variabilities in different people's response patterns to social mobilization approaches. Based on the collected dataset, I will further propose computational algorithmic crowdsourcing mechanisms which leverage the empirical evidence to improve the effectiveness and efficiency of social mobilization, towards achieving superlinear productivity. Throughout this process, I will also incorporate human factors into the computational models to benefit social mobilization efforts.
Improving Deep Reinforcement Learning with Knowledge Transfer
Authors: Glatt Ruben , Costa Anna
Published: June 2022

Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led to a wave of breakthrough developments in agent theory and established the field of Deep Reinforcement Learning (DRL). While DRL has shown great results for single task learning, the multi-task case is still underrepresented in the available literature. This D.Sc. research proposal aims at extending DRL to the multi- task case by leveraging the power of Transfer Learning algorithms to improve the training time and results for multi-task learning. Our focus lies on defining a novel framework for scalable DRL agents that detects similarities between tasks and balances various TL techniques, like parameter initialization, policy or skill transfer.
A Supervised Sparse Learning Framework to Solve EEG Inverse Problem for Discriminative Activations Pattern
Authors: Liu Feng
Published: June 2022

Electroencephalography (EEG) is one of the most important noninvasive neuroimaging tools that provides excellent temporal accuracy. As the EEG electrode sensors measure electrical potentials on the scalp instead of direct measuring activities of brain voxels deep inside the head, many approaches are proposed to infer the activated brain regions due to its significance in neuroscience research and clinical application. However, since mostly part of the brain activity is composed of the spontaneous neural activities or non-task related activations, task related activation patterns will be corrupted in strong background signal/noises. In our research, we proposed a sparse learning framework for solving EEG inverse problem which aims to explicitly extract the discriminative sources for different cognitive tasks by fusing the label information into the inverse model. The proposed framework is capable of estimation the discriminative brain sources under given different brain states where traditional inverse methods failed. We introduced two models, one is formulated as supervised sparse dictionary learning and the other one is the graph regularized discriminative source estimation model to promote the consistency within same class. Preliminary experimental results also validated that the proposed sparse learning framework is effective to discover the discriminative task-related brain activation sources, which shows the potential to advance the high resolution EEG source analysis for real-time non-invasive brain imaging research.
AniDraw: When Music and Dance Meet Harmoniously
Authors: Bu Yaohua , Taoran Tang , Jia Jia , Zhiyuan Ma , Songyao Wu , Yuming You
Published: June 2022

In this paper, we present a demo, AniDraw, which can help users practice the coordination between their hands, mouth and eyes by combing the elements of music, painting and dance. Users can sketch a cartoon character through multitouch screens and then hum songs, which will drive the cartoon character to dance to create a lively animation. In technical realization, we apply the mechanism of acoustic driving in which AniDraw extracts time-domain acoustic features to map to the intensity of dances, frequency-domain ones to map to the style of dances, and high-level ones including onesets and tempos to map to start, duration and speed of dances. AniDraw can not only stimulate users’ enthusiasm in artistic creation, but also enhance their esthetic ability on harmony.
An Event Reconstruction Tool for Conflict Monitoring Using Social Media
Authors: Liang Junwei , Fan Desai , Lu Han , Huang Poyao , Chen Jia , Jiang Lu , Hauptmann Alexander
Published: June 2022

What happened during the Boston Marathon in 2013? Nowadays, at any major event, lots of people take videos and share them on social media. To fully understand exactly what happened in these major events, researchers and analysts often have to examine thousands of these videos manually. To reduce this manual effort, we present an investigative system that automatically synchronizes these videos to a global timeline and localizes them on a map. In addition to alignment in time and space, our system combines various functions for analysis, including gunshot detection, crowd size estimation, 3D reconstruction and person tracking. To our best knowledge, this is the first time a unified framework has been built for comprehensive event reconstruction for social media videos.
ARTY: Fueling Creativity through Art, Robotics and Technology for Youth
Authors: Burhans Debra , Dantu Karthik
Published: June 2022

ARTY is a week-long program for middle school students to teach them programming of robots and allow them to express themselves artistically. It was started in 2013 and ran its fourth edition in 2016. We describe the ideas behind the inception of this program, its curriculum, our experiences during the 2016 workshop and challenges/future directions for the program. Our primary intent in this paper is to convey the program curriculum and its design, including the way in which robots can be viewed as vehicles for artistic expression. Some results from a brief attitudinal survey that was administered before and after the workshop are also included along with a discussion of outcomes assessment and issues.
AI Projects for Computer Science Capstone Classes (Extended Abstract)
Authors: Taylor Matthew , Arslan Ay Sakire
Published: June 2022

Capstone senior design projects provide students with a collaborative software design and development experience to reinforce learned material while allowing students latitude in developing real-world applications. Our two-semester capstone classes are required for all computer science majors. Students must have completed a software engineering course — capstone classes are typically taken during their last two semesters. Project proposals come from a variety of sources, including industry, WSU faculty (from our own and other departments), local agencies, and entrepreneurs. We have recently targeted projects in AI — although students typically have little background, they find the ideas and methods compelling. This paper outlines our instructional approach and reports our experiences with three projects.
Preference Elicitation For Participatory Budgeting
Authors: Benade Gerdus , Nath Swaprava , Procaccia Ariel , Shah Nisarg
Published: June 2022

Participatory budgeting enables the allocation of public funds by collecting and aggregating individual preferences; it has already had a sizable real-world impact. But making the most of this new paradigm requires a rethinking of some of the basics of computational social choice, including the very way in which individuals express their preferences. We analytically compare four preference elicitation methods -- knapsack votes, rankings by value or value for money, and threshold approval votes -- through the lens of implicit utilitarian voting, and find that threshold approval votes are qualitatively superior. This conclusion is supported by experiments using data from real participatory budgeting elections.
An Ambiguity Aversion Model for Decision Making under Ambiguity
Authors: Ma Wenjun , Luo Xudong , Jiang Yuncheng
Published: June 2022

In real life, decisions are often made under ambiguity, where it is difficult to estimate accurately the probability of each single possible consequence of a choice. However, this problem has not been solved well in existing work for the following two reasons. (i) Some of them cannot cover the Ellsberg paradox and the Machina Paradox. Thus, the choices that they predict could be inconsistent with empirical observations. (ii) Some of them rely on parameter tuning without offering explanations for the reasonability of setting such bounds of parameters. Thus, the prediction of such a model in new decision making problems is doubtful. To the end, this paper proposes a new decision making model based on D-S theory and the emotion of ambiguity aversion. Some insightful properties of our model and the validating on two famous paradoxes show that our model indeed is a better alternative for decision making under ambiguity.
Disarmament Games
Authors: Deng Yuan , Conitzer Vincent
Published: June 2022

Much recent work in the AI community concerns algorithms for computing optimal mixed strategies to commit to, as well as the deployment of such algorithms in real security applications. Another possibility is to commit not to play certain actions. If only one player makes such a commitment, then this is generally less powerful than completely committing to a single mixed strategy. However, if players can alternatingly commit not to play certain actions and thereby iteratively reduce their strategy spaces, then desirable outcomes can be obtained that would not have been possible with just a single player committing to a mixed strategy. We refer to such a setting as a disarmament game. In this paper, we study disarmament for two-player normal-form games. We show that deciding whether an outcome can be obtained with disarmament is NP-complete (even for a fixed number of rounds), if only pure strategies can be removed. On the other hand, for the case where mixed strategies can be removed, we provide a folk theorem that shows that all desirable utility profiles can be obtained, and give an efficient algorithm for (approximately) obtaining them.
Optimal Pricing for Submodular Valuations with Bounded Curvature
Authors: Maehara Takanori , Kawase Yasushi , Sumita Hanna , Tono Katsuya , Kawarabayashi Ken-ichi
Published: June 2022

The optimal pricing problem is a fundamental problem that arises in combinatorial auctions. Suppose that there is one seller who has indivisible items and multiple buyers who want to purchase a combination of the items. The seller wants to sell his items for the highest possible prices, and each buyer wants to maximize his utility (i.e., valuation minus payment) as long as his payment does not exceed his budget. The optimal pricing problem seeks a price of each item and an assignment of items to buyers such that every buyer achieves the maximum utility under the prices. The goal of the problem is to maximize the total payment from buyers. In this paper, we consider the case that the valuations are submodular. We show that the problem is computationally hard even if there exists only one buyer. Then we propose approximation algorithms for the unlimited budget case. We also extend the algorithm for the limited budget case when there exists one buyer and multiple buyers collaborate with each other.
Fans Economy and All-Pay Auctions with Proportional Allocations
Authors: Tang Pingzhong , Zeng Yulong , Zuo Song
Published: June 2022

In this paper, we analyze an emerging economic form, called fans economy, in which a fan donates money to the host and gets allocated proportional to the amount of his donation (normalized by the overall amount of donation). Fans economy is the major way live streaming apps monetize and includes a number of popular economic forms ranging from crowdfunding to mutual fund. We propose an auction game, coined all-pay auctions with proportional allocation (APAPA), to model the fans economy and analyze the auction from the perspective of revenue. Comparing to the standard all-pay auction, which normally has no pure Nash-Equilibrium in the complete information setting, we solve the pure Nash-Equilibrium of the APAPA in closed form and prove its uniqueness. Motivated by practical concerns, we then analyze the case where APAPA is equipped with a reserve and show that there might be multiple equilibria in this case. We give an efficient algorithm to compute all equilibria in this case. For either case, with or without reserve, we show that APAPA always extracts revenue that 2-approximates the second-highest valuation. Furthermore, we conduct experiments to show how revenue changes with respect to different reserves.
Algorithms for Max-Min Share Fair Allocation of Indivisible Chores
Authors: Aziz Haris , Rauchecker Gerhard , Schryen Guido , Walsh Toby
Published: June 2022

We consider Max-min Share (MmS) fair allocations of indivisible chores (items with negative utilities). We show that allocation of chores and classical allocation of goods (items with positive utilities) have some fundamental connections but also differences which prevent a straightforward application of algorithms for goods in the chores setting and vice-versa. We prove that an MmS allocation does not need to exist for chores and computing an MmS allocation - if it exists - is strongly NP-hard. In view of these non-existence and complexity results, we present a polynomial-time 2-approximation algorithm for MmS fairness for chores. We then introduce a new fairness concept called optimal MmS that represents the best possible allocation in terms of MmS that is guaranteed to exist. We use connections to parallel machine scheduling to give (1) a polynomial-time approximation scheme for computing an optimal MmS allocation when the number of agents is fixed and (2) an effective and efficient heuristic with an ex-post worst-case analysis.
Computing Least Cores of Supermodular Cooperative Games
Authors: Hatano Daisuke , Yoshida Yuichi
Published: June 2022

One of the goals of a cooperative game is to compute a valuedivision to the players from which they have no incentive todeviate. This concept is formalized as the notion of the core.To obtain a value division that motivates players to cooperate to a greater extent or that is more robust under noise, the notions of the strong least core and the weak least core have been considered. In this paper, we characterize the strong and the weak least cores of supermodular cooperative games using the theory of minimizing crossing submodular functions. We then apply our characterizations to two representative supermodular cooperative games, namely, the induced subgraph game generalized to hypergraphs and the airport game. For these games, we derive explicit forms of the strong and weak least core values, and provide polynomial-time algorithms that compute value divisions in the strong and weak least cores.
Teams in Online Scheduling Polls: Game-Theoretic Aspects
Authors: Bredereck Robert , Chen Jiehua , Niedermeier Rolf , Obraztsova Svetlana , Talmon Nimrod
Published: June 2022

Consider an important meeting to be held in a team-based organization. Taking availability constraints into account, an online scheduling poll is being used in order to decide upon the exact time of the meeting. Decisions are to be taken during the meeting, therefore each team would like to maximize its relative attendance (i.e. the proportional number of its team members attending the meeting). We introduce a corresponding game, where each team can declare a lower total availability in the scheduling poll in order to improve its relative attendance—the pay-off. We are especially interested in situations where teams can form coalitions. We provide an efficient algorithm that, given a coalition, finds an optimal way for each team in a coalition to improve its pay-off. In contrast, we show that deciding whether such a coalition exists is NP-hard. We also study the existence of Nash equilibria: Finding Nash equilibria for various small sizes of teams and coalitions can be done in polynomial time while it is coNP-hard if the coalition size is unbounded.
Tractable Algorithms for Approximate Nash Equilibria in Generalized Graphical Games with Tree Structure
Authors: Ortiz Luis , Irfan Mohammad
Published: June 2022

We provide the first fully polynomial time approximation scheme (FPTAS) for computing an approximate mixed-strategy Nash equilibrium in graphical multi-hypermatrix games (GMhGs), which are generalizations of normal-form games, graphical games, graphical polymatrix games, and hypergraphical games. Computing an exact mixed-strategy Nash equilibria in graphical polymatrix games is PPAD complete and thus generally believed to be intractable. In contrast, to the best of our knowledge, we are the first to establish an FPTAS for tree polymatrix games as well as tree graphical games when the number of actions is bounded by a constant. As a corollary, we give a quasi-polynomial time approximation scheme (quasi-PTAS) when the number of actions is bounded by a logarithm of the number of players.
Winner Determination in Huge Elections with MapReduce
Authors: Csar Theresa , Lackner Martin , Pichler Reinhard , Sallinger Emanuel
Published: June 2022

In computational social choice, we are concerned with the development of methods for joint decision making. A central problem in this field is the winner determination problem, which aims at identifying the most preferred alternative(s). With the rise of modern e-business platforms, processing of huge amounts of preference data has become an issue. In this work, we apply the MapReduce framework - which has been specifically designed for dealing with big data - to various versions of the winner determination problem. We obtain efficient and highly parallel algorithms and provide a theoretical analysis and experimental evaluation.
Preferences Single-Peaked on a Circle
Authors: Peters Dominik , Lackner Martin
Published: June 2022

We introduce the domain of preferences that are single-peaked on a circle, which is a generalization of the well-studied single-peaked domain. This preference restriction is useful, e.g., for scheduling decisions, and for one-dimensional decisions in the presence of extremist preferences. We give a fast recognition algorithm of this domain, provide a characterisation by finitely many forbidden subprofiles, and show that many popular single- and multi-winner voting rules are polynomial-time computable on this domain. In contrast, Kemeny's rule remains hard to evaluate, and several impossibility results from social choice theory can be proved using only profiles that are single-peaked on a circle
Engineering Agreement: The Naming Game with Asymmetric and Heterogeneous Agents
Authors: Gao Jie , Li Bo , Schoenebeck Grant , Yu Fang-Yi
Published: June 2022

Being popular in language evolution, cognitive science, and culture dynamics, the Naming Game has been widely used to analyze how agents reach global consensus via communications in multi-agent systems. Most prior work considered networks that are symmetric and homogeneous (e.g., vertex transitive). In this paper we consider asymmetric or heterogeneous settings that complement the current literature: 1) we show that increasing asymmetry in network topology can improve convergence rates. The star graph empirically converges faster than all previously studied graphs; 2) we consider graph topologies that are particularly challenging for naming game such as disjoint cliques or multi-level trees and ask how much extra homogeneity (random edges) is required to allow convergence or fast convergence. We provided theoretical analysis which was confirmed by simulations; 3) we analyze how consensus can be manipulated when stubborn nodes are introduced at different points of the process. Early introduction of stubborn nodes can easily influence the outcome in certain family of networks while late introduction of stubborn nodes has much less power.
Market Pricing for Data Streams
Authors: Abolhassani Melika , Esfandiari Hossein , Hajiaghayi MohammadTaghi , Lucier Brendan , Yami Hadi
Published: June 2022

Internet-enabled marketplaces such as Amazon deal with huge datasets registering transaction of merchandises between lots of buyers and sellers. It is important that algorithms become more time and space efficient as the size of datasets increase. An algorithm that runs in polynomial time may not have a reasonable running time for such large datasets. Here, we study the development of pricing algorithms that are appropriate for use with massive datasets. We especially focus on the streaming setting, the common model for big data analysis. We present an envy-free mechanism for social welfare maximization problem in the streaming setting using O(k2 l) space, where k is the number of different goods and l is the number of available items of each good. We also provide an α-approximation mechanism for revenue maximization in this setting given an α-approximation mechanism for the corresponding offline problem exists. Moreover, we provide mechanisms to approximate the optimum social welfare (or revenue) within 1 – ε factor, in space independent of l which would be favorable in case l is large compared to k. Finally, we present hardness results showing approximation of optimal prices that maximize social welfare (or revenue) in the streaming setting needs Ω(l) space. We achieve our results by developing a powerful sampling technique for bipartite networks. The simplicity of our sampling technique empowers us to maintain the sample over the input sequence. Indeed, one can construct this sample in the distributed setting (a.k.a, MapReduce) and get the same results in two rounds of computations, or one may simply apply this sampling technique to provide faster offline algorithms.
Capturing Dependencies among Labels and Features for Multiple Emotion Tagging of Multimedia Data
Authors: Wu Shan , Wang Shangfei , Ji Qiang
Published: June 2022

In this paper, we tackle the problem of emotion tagging of multimedia data by modeling the dependencies among multiple emotions in both the feature and label spaces. These dependencies, which carry crucial top-down and bottom-up evidence for improving multimedia affective content analysis, have not been thoroughly exploited yet. To this end, we propose two hierarchical models that independently and dependently learn the shared features and global semantic relationships among emotion labels to jointly tag multiple emotion labels of multimedia data. Efficient learning and inference algorithms of the proposed models are also developed. Experiments on three benchmark emotion databases demonstrate the superior performance of our methods to existing methods.
JAG: A Crowdsourcing Framework for Joint Assessment and Peer Grading
Authors: Labutov Igor , Studer Christoph
Published: June 2022

Generation and evaluation of crowdsourced content is commonly treated as two separate processes, performed at different times and by two distinct groups of people: content creators and content assessors. As a result, most crowdsourcing tasks follow this template: one group of workers generates content and another group of workers evaluates it. In an educational setting, for example, content creators are traditionally students that submit open-response answers to assignments (e.g., a short answer, a circuit diagram, or a formula) and content assessors are instructors that grade these submissions. Despite the considerable success of peer-grading in massive open online courses (MOOCs), the process of test-taking and grading are still treated as two distinct tasks which typically occur at different times, and require an additional overhead of grader training and incentivization. Inspired by this problem in the context of education, we propose a general crowdsourcing framework that fuses open-response test-taking (content generation) and assessment into a single, streamlined process that appears to students in the form of an explicit test, but where everyone also acts as an implicit grader. The advantages offered by our framework include: a common incentive mechanism for both the creation and evaluation of content, and a probabilistic model that jointly models the processes of contribution and evaluation, facilitating efficient estimation of the quality of the contributions and the competency of the contributors. We demonstrate the effectiveness and limits of our framework via simulations and a real-world user study.
What's Hot in Constraint Programming
Authors: Michel Laurent , Rueher Michel
Published: June 2022

The CP conference is the annual international conference on constraint programming. It is concerned with all aspects of computing with constraints, including theory, algorithms, environments, languages, models, systems, and applications such as decision-making, resource allocation, scheduling, configuration, and planning. The CP community is very keen to ensure it remains open to interdisciplinary research at the intersection between constraint programming and related fields. Hence, in addition to the usual technical and application tracks, the CP 2016 conference featured thematic tracks: Computational Sustainability, CP and Biology, Preferences, Social Choice and Optimization, and Testing and Verification. In this overview, we highlight several remarkable papers that have been selected by the senior program committee and papers with the most innovative methods and techniques, and a very high potential for applications (in our opinion).
What's Hot at CPAIOR (Extended Abstract)
Authors: Quimper Claude-Guy
Published: June 2022

The 13th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2016), was held in Banff, Canada, May 29 - June 1, 2016. In order to trigger exchanges between the constraint programming and the operations research community, CPAIOR was co-located with CORS 2016, the Canadian Operational Research society's conference.
Regret Ratio Minimization in Multi-Objective Submodular Function Maximization
Authors: Soma Tasuku , Yoshida Yuichi
Published: June 2022

Submodular function maximization has numerous applications in machine learning and artificial intelligence. Many real applications require multiple submodular objective func-tions to be maximized, and which function is regarded as important by a user is not known in advance. In such cases, it is desirable to have a small family of representative solutions that would satisfy any user’s preference. A traditional approach for solving such a problem is to enumerate the Pareto optimal solutions. However, owing to the massive number of Pareto optimal solutions (possibly exponentially many), it is difficult for a user to select a solution. In this paper, we propose two efficient methods for finding a small family of representative solutions, based on the notion of regret ratio. The first method outputs a family of fixed size with a nontrivial regret ratio. The second method enables us to choose the size of the output family, and in the biobjective case, it has a provable trade-off between the size and the regret ratio. Using real and synthetic data, we empirically demonstrate that our methods achieve a small regret ratio.
Learning to Prune Dominated Action Sequences in Online Black-Box Planning
Authors: Jinnai Yuu , Fukunaga Alex
Published: June 2022

Black-box domains where the successor states generated by applying an action are generated by a completely opaque simulator pose a challenge for domain-independent planning. The main computational bottleneck in search-based planning for such domains is the number of calls to the black-box simulation. We propose a method for significantly reducing the number of calls to the simulator by the search algorithm by detecting and pruning sequences of actions which are dominated by others. We apply our pruning method to Iterated Width and breadth-first search in domain-independent black-box planning for Atari 2600 games in the Arcade Learning Environment (ALE), adding our pruning method significantly improves upon the baseline algorithms.
Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery
Authors: Lu Heng-Yang , Xie Lu-Yao , Kang Ning , Wang Chong-Jun , Xie Jun-Yuan
Published: June 2022

In our daily life, short texts have been everywhere especially since the emergence of social network. There are countless short texts in online media like twitter, online Q&A sites and so on. Discovering topics is quite valuable in various application domains such as content recommendation and text characterization. Traditional topic models like LDA are widely applied for sorts of tasks, but when it comes to short text scenario, these models may get stuck due to the lack of words. Recently, a popular model named BTM uses word co-occurrence relationship to solve the sparsity problem and is proved effectively. However, both BTM and extended models ignore the inside relationship between words. From our perspectives, more related words should appear in the same topic. Based on this idea, we propose a model named RIBS-TM which makes use of RNN for relationship learning and IDF for filtering high-frequency words. Experiments on two real-world short text datasets show great utility of our model.
On the Computation of Paracoherent Answer Sets
Authors: Amendola Giovanni , Dodaro Carmine , Faber Wolfgang , Leone Nicola , Ricca Francesco
Published: June 2022

Answer Set Programming (ASP) is a well-established formalism for nonmonotonic reasoning. An ASP program can have no answer set due to cyclic default negation. In this case, it is not possible to draw any conclusion, even if this is not intended. Recently, several paracoherent semantics have been proposed that address this issue,and several potential applications for these semantics have been identified. However, paracoherent semantics have essentially been inapplicable in practice, due to the lack of efficient algorithms and implementations. In this paper, this lack is addressed, and several different algorithms to compute semi-stable and semi-equilibrium models are proposed and implemented into an answer set solving framework. An empirical performance comparison among the new algorithms on benchmarks from ASP competitions is given as well.
Small Is Beautiful: Computing Minimal Equivalent EL Concepts
Authors: Nikitina Nadeschda , Koopmann Patrick
Published: June 2022

In this paper, we present an algorithm and a tool for computing minimal, equivalent EL concepts wrt. a given ontology. Our tool can provide valuable support in manual development of ontologies and improve the quality of ontologies automatically generated by processes such as uniform interpolation, ontology learning, rewriting ontologies into simpler DLs, abduction and knowledge revision. Deciding whether there exist equivalent EL concepts of size less than k is known to be an NP-complete problem. We propose a minimisation algorithm that achieves reasonable computational performance also for larger ontologies and complex concepts. We evaluate our tool on several bio-medical ontologies with promising results.
Add Data into Business Process Verification: Bridging the Gap between Theory and Practice
Authors: De Masellis Riccardo , Di Francescomarino Chiara , Ghidini Chiara , Montali Marco , Tessaris Sergio
Published: June 2022

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM).On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.
Centralized versus Personalized Commitments and Their Influence on Cooperation in Group Interactions
Authors: Han The Anh , Moniz Pereira Luis , Martinez-Vaquero Luis A. , Lenaerts Tom
Published: June 2022

Before engaging in a group venture agents may seek commitments from other members in the group and, based on the level of participation (i.e. the number of actually committed participants), decide whether it is worth joining the venture. Alternatively, agents can delegate this costly process to a (beneficent or non-costly) third-party, who helps seek commitments from the agents. Using methods from Evolutionary Game Theory, this paper shows that, in the context of Public Goods Game, much higher levels of cooperation can be achieved through such centralized commitment management. It provides a more efficient mechanism for dealing with commitment free-riders, those who are not willing to bear the cost of arranging commitments whilst enjoying the benefits provided by the paying commitment proposers. We show that the participation level plays a crucial role in the decision of whether an agreement should be formed; namely, it needs to be more strict in terms of the level of participation required from players of the centralized system for the agreement to be formed; however, once it is done right, it is much more beneficial in terms of the level of cooperation and social welfare achieved. In short, our analysis provides important insights for the design of multi-agent systems that rely on commitments to monitor agents' cooperative behavior.
Improving Surveillance Using Cooperative Target Observation
Authors: Aswani Rashi , Munnangi Sai Krishna , Paruchuri Praveen
Published: June 2022

The Cooperative Target Observation (CTO) problem has been of great interest in the multi-agents and robotics literature due to the problem being at the core of a number of applications including surveillance. In CTO problem, the observer agents attempt to maximize the collective time during which each moving target is being observed by at least one observer in the area of interest. However, most of the prior works for the CTO problem consider the targets movement to be Randomized. Given our focus on surveillance domain, we modify this assumption to make the targets strategic and present two target strategies namely Straight-line strategy and Controlled Randomization strategy. We then modify the observer strategy proposed in the literature based on the K-means algorithm to introduce five variants and provide experimental validation. In surveillance domain, it is often reasonable to assume that the observers may themselves be a subject of observation for a variety of purposes by unknown adversaries whose model may not be known. Randomizing the observers actions can help to make their target observation strategy less predictable. As the fifth variant, we therefore introduce Adjustable Randomization into the best performing observer strategy where the observer can adjust the expected loss in reward due to randomization depending on the situation.
Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation
Authors: Xiao Cao , Zhang Ping , Chaovalitwongse W. , Hu Jianying , Wang Fei
Published: June 2022

Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. It usually causes preventable hospitalizations and deaths, while associated with a huge amount of cost. Traditional preclinical in vitro safety profiling and clinical safety trials are restricted in terms of small scale, long duration, huge financial costs and limited statistical signifi- cance. The availability of large amounts of drug and ADR data potentially allows ADR predictions during the drugs’ early preclinical stage with data analytics methods to inform more targeted clinical safety tests. Despite their initial success, existing methods have trade-offs among interpretability, predictive power and efficiency. This urges us to explore methods that could have all these strengths and provide practical solutions for real world ADR predictions. We cast the ADR-drug relation structure into a three-layer hierarchical Bayesian model. We interpret each ADR as a symbolic word and apply latent Dirichlet allocation (LDA) to learn topics that may represent certain biochemical mechanism that relates ADRs with drug structures. Based on LDA, we designed an equivalent regularization term to incorporate the hierarchical ADR domain knowledge. Finally, we developed a mixed input model leveraging a fast collapsed Gibbs sampling method that the complexity of each iteration of Gibbs sampling proportional only to the number of positive ADRs. Experiments on real world data show our models achieved higher prediction accuracy and shorter running time than the state-of-the-art alternatives.
Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation
Authors: Gao Li , Wu Jia , Zhou Chuan , Hu Yue
Published: June 2022

In many time-aware item recommender systems, modeling the accurate evolution of both user profiles and the contents of items over time is essential. However, most existing methods focus on learning users' dynamic interests, where the contents of items are assumed to be stable over time. They thus fail to capture the dynamic changes in the item's contents. In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user's interests and item's contents information via topic dynamics. Specifically, we propose a dynamic sparse topic model to track the evolution of topics for changes in items' contents over time and adapt a vector autoregressive model to profile users' dynamic interests. The item's topics and user's interests and their evolutions are learned collaboratively and simultaneously into a unified learning framework. Experimental results on two real-world data sets demonstrate the quality and effectiveness of the proposed method and show that our method can be used to make better future recommendations.
Neural Programming by Example
Authors: Shu Chengxun , Zhang Hongyu
Published: June 2022

Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neural network based components: a string encoder, an input-output analyzer, a program generator, and a symbol selector. We demonstrate the effectiveness of NPBE by training it end-to-end to solve some common string manipulation problems in spreadsheet systems. The results show that our model can induce string manipulation programs effectively. Our work is one step towards teaching DNN to generate computer programs.
Discriminative Semi-Supervised Dictionary Learning with Entropy Regularization for Pattern Classification
Authors: Yang Meng , Chen Lin
Published: June 2022

Dictionary learning has played an important role in the success of sparse representation, which triggers the rapid developments of unsupervised and supervised dictionary learning methods. However, in most practical applications, there are usually quite limited labeled training samples while it is relatively easy to acquire abundant unlabeled training samples. Thus semi-supervised dictionary learning that aims to effectively explore the discrimination of unlabeled training data has attracted much attention of researchers. Although various regularizations have been introduced in the prevailing semi-supervised dictionary learning, how to design an effective unified model of dictionary learning and unlabeled-data class estimating and how to well explore the discrimination in the labeled and unlabeled data are still open. In this paper, we propose a novel discriminative semi-supervised dictionary learning model (DSSDL) by introducing discriminative representation, an identical coding of unlabeled data to the coding of testing data final classification, and an entropy regularization term. The coding strategy of unlabeled data can not only avoid the affect of its incorrect class estimation, but also make the learned discrimination be well exploited in the final classification. The introduced regularization of entropy can avoid overemphasizing on some uncertain estimated classes for unlabeled samples. Apart from the enhanced discrimination in the learned dictionary by the discriminative representation, an extended dictionary is used to mainly explore the discrimination embedded in the unlabeled data. Extensive experiments on face recognition, digit recognition and texture classification show the effectiveness of the proposed method.
ESPACE: Accelerating Convolutional Neural Networks via Eliminating Spatial and Channel Redundancy
Authors: Lin Shaohui , Ji Rongrong , Chen Chao , Huang Feiyue
Published: June 2022

Recent years have witnessed an extensive popularity of convolutional neural networks (CNNs) in various computer vision and artificial intelligence applications. However, the performance gains have come at a cost of substantially intensive computation complexity, which prohibits its usage inresource-limited applications like mobile or embedded devices. While increasing attention has been paid to the acceleration of internal network structure, the redundancy of visual input is rarely considered. In this paper, we make the first attempt of reducing spatial and channel redundancy directly from the visual input for CNNs acceleration. The proposed method, termed ESPACE (Elimination of SPAtial and Channel rEdundancy), works by the following three steps: First, the 3D channel redundancy of convolutional layers is reduced by a set of low-rank approximation of convolutional filters. Second, a novel mask based selective processing scheme is proposed, which further speedups the convolution operations via skipping unsalient spatial locations of the visual input. Third, the accelerated network is fine-tuned using the training data via back-propagation. The proposed method is evaluated on ImageNet 2012 with implementations on two widely adopted CNNs, i.e. AlexNet and GoogLeNet. In comparison to several recent methods of CNN acceleration, the proposed scheme has demonstrated new state-of-the-art acceleration performance by a factor of 5.48* and 4.12* speedup on AlexNet and GoogLeNet, respectively, with a minimal decrease in classification accuracy.
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression
Authors: Hoang Quang Minh , Hoang Trong Nghia , Low Kian Hsiang
Published: June 2022

While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel. This paper presents such an effort to advance the state of the art of sparse spectrum GP models to achieve competitive predictive performance for massive datasets. Our generalized framework of stochastic variational Bayesian sparse spectrum GP (sVBSSGP) models addresses their shortcomings by adopting a Bayesian treatment of the spectral frequencies to avoid overfitting, modeling these frequencies jointly in its variational distribution to enable their interaction a posteriori, and exploiting local data for boosting the predictive performance. However, such structural improvements result in a variational lower bound that is intractable to be optimized. To resolve this, we exploit a variational parameterization trick to make it amenable to stochastic optimization. Interestingly, the resulting stochastic gradient has a linearly decomposable structure that can be exploited to refine our stochastic optimization method to incur constant time per iteration while preserving its property of being an unbiased estimator of the exact gradient of the variational lower bound. Empirical evaluation on real-world datasets shows that sVBSSGP outperforms state-of-the-art stochastic implementations of sparse GP models.
Exploring Commonality and Individuality for Multi-Modal Curriculum Learning
Authors: Gong Chen
Published: June 2022

Curriculum Learning (CL) mimics the cognitive process ofhumans and favors a learning algorithm to follow the logical learning sequence from simple examples to more difficult ones. Recent studies show that selecting the simplest curriculum examples from different modalities for graph-based label propagation can yield better performance than simply leveraging single modality. However, they forcibly requirethe curriculums generated by all modalities to be identical to a common curriculum, which discard the individuality ofevery modality and produce the inaccurate curriculum for the subsequent learning. Therefore, this paper proposes a novel multi-modal CL algorithm by comprehensively investigating both the individuality and commonality of different modalities. By considering the curriculums of multiple modalities altogether, their common preference on selecting the simplestexamples can be explored by a row-sparse matrix, and their distinct opinions are captured by a sparse noise matrix. As a consequence, a "soft" fusion of multiple curriculums from different modalities is achieved and the propagation quality can thus be improved. Comprehensive empirical studies reveal that our method can generate higher accuracy than the state-of-the-art multi-modal CL approach and label propagation algorithms on various image classification tasks.