bluuebunny/crossref_metadata_2025 huggingface dataset:
A Rule Mining-based Advanced Persistent Threats Detection System
Authors: Benabderrahmane Sidahmed , Berrada Ghita , Cheney James , Valtchev Petko
Published: August 2021

Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time.

Blocking all APTs is impossible, security experts caution, hence the importance of research on early detection and damage limitation.

Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur.

We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces.

Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.
A Novel Sequence-to-Subgraph Framework for Diagnosis Classification
Authors: Chen Jun , Yuan Quan , Lu Chao , Huang Haifeng
Published: August 2021

Text-based diagnosis classification is a critical problem in AI-enabled healthcare studies, which assists clinicians in making correct decision and lowering the rate of diagnostic errors. Previous studies follow the routine of sequence based deep learning models in NLP literature to deal with clinical notes. However, recent studies find that structural information is important in clinical contents that greatly impacts the predictions. In this paper, a novel sequence-to-subgraph framework is introduced to process clinical texts for classification, which changes the paradigm of managing texts. Moreover, a new classification model under the framework is proposed that incorporates subgraph convolutional network and hierarchical diagnostic attentive network to extract the layered structural features of clinical texts. The evaluation conducted on both the real-world English and Chinese datasets shows that the proposed method outperforms the state-of-the-art deep learning based diagnosis classification models.
Differentially Private Correlation Alignment for Domain Adaptation
Authors: Jin Kaizhong , Cheng Xiang , Yang Jiaxi , Shen Kaiyuan
Published: August 2021

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. As a simple and efficient method for domain adaptation, correlation alignment transforms the distribution of the source domain by utilizing the covariance matrix of the target domain, such that a model trained on the transformed source data can be applied to the target data. However, when source and target domains come from different institutes, exchanging information between the two domains might pose a potential privacy risk. In this paper, for the first time, we propose a differentially private correlation alignment approach for domain adaptation called PRIMA, which can provide privacy guarantees for both the source and target data. In PRIMA, to relieve the performance degradation caused by perturbing the covariance matrix in high dimensional setting, we present a random subspace ensemble based covariance estimation method which splits the feature spaces of source and target data into several low dimensional subspaces. Moreover, since perturbing the covariance matrix may destroy its positive semi-definiteness, we develop a shrinking based method for the recovery of positive semi-definiteness of the covariance matrix. Experimental results on standard benchmark datasets confirm the effectiveness of our approach.
CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space
Authors: Li Shuxin , Zhang Youzhi , Wang Xinrun , Xue Wanqi , An Bo
Published: August 2021

In many real-world scenarios, a team of agents must coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e.g., Counterfactual Regret Minimization (CFR). To address this problem, we propose a new framework of CFR: CFR-MIX. Firstly, we propose a new strategy representation that represents a joint action strategy using individual strategies of all agents and a consistency relationship to maintain the cooperation between agents. To compute the equilibrium with individual strategies under the CFR framework, we transform the consistency relationship between strategies to the consistency relationship between the cumulative regret values. Furthermore, we propose a novel decomposition method over cumulative regret values to guarantee the consistency relationship between the cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which employs a mixing layer to estimate cumulative regret values of joint actions as a non-linear combination of cumulative regret values of individual actions. Experimental results show that CFR-MIX outperforms existing algorithms on various games significantly.
Adapting Meta Knowledge with Heterogeneous Information Network for COVID-19 Themed Malicious Repository Detection
Authors: Qian Yiyue , Zhang Yiming , Ye Yanfang , Zhang Chuxu
Published: August 2021

As cyberattacks caused by malware have proliferated during the pandemic, building an automatic system to detect COVID-19 themed malware in social coding platforms is in urgent need. The existing methods mainly rely on file content analysis while ignoring structured information among entities in social coding platforms. Additionally, they usually require sufficient data for model training, impairing their performances over cases with limited data which is common in reality. To address these challenges, we develop Meta-AHIN, a novel model for COVID-19 themed malicious repository detection in GitHub. In Meta-AHIN, we first construct an attributed heterogeneous information network (AHIN) to model the code content and social coding properties in GitHub; and then we exploit attention-based graph convolutional neural network (AGCN) to learn repository embeddings and present a meta-learning framework for model optimization. To utilize unlabeled information in AHIN and to consider task influence of different types of repositories, we further incorporate node attribute-based self-supervised module and task-aware attention weight into AGCN and meta-learning respectively. Extensive experiments on the collected data from GitHub demonstrate that Meta-AHIN outperforms state-of-the-art methods.
Matchings with Group Fairness Constraints: Online and Offline Algorithms
Authors: S. Sankar Govind , Louis Anand , Nasre Meghana , Nimbhorkar Prajakta
Published: August 2021

We consider the problem of assigning items to platforms in the presence of group fairness constraints. In the input, each item belongs to certain categories, called classes in this paper. Each platform specifies the group fairness constraints through an upper bound on the number of items it can serve from each class. Additionally, each platform also has an upper bound on the total number of items it can serve. The goal is to assign items to platforms so as to maximize the number of items assigned while satisfying the upper bounds of each class. This problem models several important real-world problems like ad-auctions, scheduling, resource allocations, school choice etc. We show that if the classes are arbitrary, then the problem is NP-hard and has a strong inapproximability. We consider the problem in both online and offline settings under natural restrictions on the classes. Under these restrictions, the problem continues to remain NP-hard but admits approximation algorithms with small approximation factors. We also implement some of the algorithms. Our experiments show that the algorithms work well in practice both in terms of efficiency and the number of items that get assigned to some platform.
Keep the Structure: A Latent Shift-Reduce Parser for Semantic Parsing
Authors: Li Yuntao , Chen Bei , Liu Qian , Gao Yan , Lou Jian-Guang , Zhang Yan , Zhang Dongmei
Published: August 2021

Traditional end-to-end semantic parsing models treat a natural language utterance as a holonomic structure. However, hierarchical structures exist in natural languages, which also align with the hierarchical structures of logical forms. In this paper, we propose a latent shift-reduce parser, called LASP, which decomposes both natural language queries and logical form expressions according to their hierarchical structures and finds local alignment between them to enhance semantic parsing. LASP consists of a base parser and a shift-reduce splitter. The splitter dynamically separates an NL query into several spans. The base parser converts the relevant simple spans into logical forms, which are further combined to obtain the final logical form. We conducted empirical studies on two datasets across different domains and different types of logical forms. The results demonstrate that the proposed method significantly improves the performance of semantic parsing, especially on unseen scenarios.
A Streaming End-to-End Framework For Spoken Language Understanding
Authors: Potdar Nihal , Raymundo Avila Anderson , Xing Chao , Wang Dong , Cao Yiran , Chen Xiao
Published: August 2021

End-to-end spoken language understanding (SLU) recently attracted increasing interest. Compared to the conventional tandem-based approach that combines speech recognition and language understanding as separate modules, the new approach extracts users' intentions directly from the speech signals, resulting in joint optimization and low latency. Such an approach, however, is typically designed to process one intent at a time, which leads users to have to take multiple rounds to fulfill their requirements while interacting with a dialogue system. In this paper, we propose a streaming end-to-end framework that can process multiple intentions in an online and incremental way. The backbone of our framework is a unidirectional RNN trained with the connectionist temporal classification (CTC) criterion. By this design, an intention can be identified when sufficient evidence has been accumulated, and multiple intentions will be identified sequentially. We evaluate our solution on the Fluent Speech Commands (FSC) dataset and the detection accuracy is about 97 % on all multi-intent settings. This result is comparable to the performance of the state-of-the-art non-streaming models, but is achieved in an online and incremental way. We also employ our model to an keyword spotting task using the Google Speech Commands dataset, and the results are also highly promising.
UniMF: A Unified Framework to Incorporate Multimodal Knowledge Bases intoEnd-to-End Task-Oriented Dialogue Systems
Authors: Yang Shiquan , Zhang Rui , Erfani Sarah M. , Lau Jey Han
Published: August 2021

Knowledge bases (KBs) are usually essential for building practical dialogue systems. Recently we have seen rapidly growing interest in integrating knowledge bases into dialogue systems. However, existing approaches mostly deal with knowledge bases of a single modality, typically textual information. As today's knowledge bases become abundant with multimodal information such as images, audios and videos, the limitation of existing approaches greatly hinders the development of dialogue systems. In this paper, we focus on task-oriented dialogue systems and address this limitation by proposing a novel model that integrates external multimodal KB reasoning with pre-trained language models. We further enhance the model via a novel multi-granularity fusion mechanism to capture multi-grained semantics in the dialogue history. To validate the effectiveness of the proposed model, we collect a new large-scale (14K) dialogue dataset MMDialKB, built upon multimodal KB. Both automatic and human evaluation results on MMDialKB demonstrate the superiority of our proposed framework over strong baselines.
MRD-Net: Multi-Modal Residual Knowledge Distillation for Spoken Question Answering
Authors: You Chenyu , Chen Nuo , Zou Yuexian
Published: August 2021

Spoken question answering (SQA) has recently drawn considerable attention in the speech community. It requires systems to find correct answers from the given spoken passages simultaneously. The common SQA systems consist of the automatic speech recognition (ASR) module and text-based question answering module. However, previous methods suffer from severe performance degradation due to ASR errors. To alleviate this problem, this work proposes a novel multi-modal residual knowledge distillation method (MRD-Net), which further distills knowledge at the acoustic level from the audio-assistant (Audio-A). Specifically, we utilize the teacher (T) trained on manual transcriptions to guide the training of the student (S) on ASR transcriptions. We also show that introducing an Audio-A helps this procedure by learning residual errors between T and S. Moreover, we propose a simple yet effective attention mechanism to adaptively leverage audio-text features as the new deep attention knowledge to boost the network performance. Extensive experiments demonstrate that the proposed MRD-Net achieves superior results compared with state-of-the-art methods on three spoken question answering benchmark datasets.
ME-MCTS: Online Generalization by Combining Multiple Value Estimators
Authors: Baier Hendrik , Kaisers Michael
Published: August 2021

This paper addresses the challenge of online generalization in tree search. We propose Multiple Estimator Monte Carlo Tree Search (ME-MCTS), with a two-fold contribution: first, we introduce a formalization of online generalization that can represent existing techniques such as "history heuristics", "RAVE", or "OMA" -- contextual action value estimators or abstractors that generalize across specific contexts. Second, we incorporate recent advances in estimator averaging that enable guiding search by combining the online action value estimates of any number of such abstractors or similar types of action value estimators.

Unlike previous work, which usually proposed a single abstractor for either the selection or the rollout phase of MCTS simulations, our approach focuses on the combination of multiple estimators and applies them to all move choices in MCTS simulations. As the MCTS tree itself is just another value estimator -- unbiased, but without abstraction -- this blurs the traditional distinction between action choices inside and outside of the MCTS tree.

Experiments with three abstractors in four board games show significant improvements of ME-MCTS over MCTS using only a single abstractor, both for MCTS with random rollouts as well as for MCTS with static evaluation functions.

While we used deterministic, fully observable games, ME-MCTS naturally extends to more challenging settings.
LTL-Constrained Steady-State Policy Synthesis
Authors: Křetínský Jan
Published: August 2021

Decision-making policies for agents are often synthesized with the constraint that a formal specification of behaviour is satisfied. Here we focus on infinite-horizon properties. On the one hand, Linear Temporal Logic (LTL) is a popular example of a formalism for qualitative specifications. On the other hand, Steady-State Policy Synthesis (SSPS) has recently received considerable attention as it provides a more quantitative and more behavioural perspective on specifications, in terms of the frequency with which states are visited. Finally, rewards provide a classic framework for quantitative properties.

In this paper, we study Markov decision processes (MDP) with the specification combining all these three types. The derived policy maximizes the reward among all policies ensuring the LTL specification with the given probability and adhering to the steady-state constraints. To this end, we provide a unified solution reducing the multi-type specification to a multi-dimensional long-run average reward. This is enabled by Limit-Deterministic Büchi Automata (LDBA), recently studied in the context of LTL model checking on MDP, and allows for an elegant solution through a simple linear programme. The algorithm also extends to the general omega-regular properties and runs in time polynomial in the sizes of the MDP as well as the LDBA.
Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning
Authors: Lauer Pascal , Torralba Alvaro , Fišer Daniel , Höller Daniel , Wichlacz Julia , Hoffmann Jörg
Published: August 2021

Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases where the grounded representation is "small enough". Previous attempts to tackle this problem for the delete relaxation leveraged symmetries to reduce the blow-up. Here we take a more radical approach, applying an additional relaxation to obtain a heuristic function that runs in time polynomial in the size of the PDDL input. Our relaxation splits the predicates into smaller predicates of fixed arity K. We show that computing a relaxed plan is still NP-hard (in PDDL input size) for K>=2, but is polynomial-time for K=1. We implement a heuristic function for K=1 and show that it can improve the state of the art on benchmarks whose grounded representation is large.
Fair Pairwise Exchange among Groups
Authors: Sun Zhaohong , Todo Taiki , Walsh Toby
Published: August 2021

We study the pairwise organ exchange problem among groups motivated by real-world applications and consider two types of group formulations. Each

group represents either a certain type of patient-donor pairs who are compatible with the same set of organs, or a set of patient-donor pairs who reside

in the same region. We address a natural research question, which asks how to match a maximum number of pairwise compatible patient-donor

pairs in a fair and individually rational way. We first propose a natural fairness concept that is applicable to both types of group formulations and design

a polynomial-time algorithm that checks whether a matching exists that satisfies optimality, individual rationality, and fairness. We also present several

running time upper bounds for computing such matchings for different graph structures.
Building Affordance Relations for Robotic Agents - A Review
Authors: Ardón Paola , Pairet Èric , Lohan Katrin S. , Ramamoorthy Subramanian , Petrick Ron P. A.
Published: August 2021

Affordances describe the possibilities for an agent to perform actions with an object. While the significance of the affordance concept has been previously studied from varied perspectives, such as psychology and cognitive science, these approaches are not always sufficient to enable direct transfer, in the sense of implementations, to artificial intelligence (AI)-based systems and robotics. However, many efforts have been made to pragmatically employ the concept of affordances, as it represents great potential for AI agents to effectively bridge perception to action. In this survey, we review and find common ground amongst different strategies that use the concept of affordances within robotic tasks, and build on these methods to provide guidance for including affordances as a mechanism to improve autonomy. To this end, we outline common design choices for building representations of affordance relations, and their implications on the generalisation capabilities of an agent when facing previously unseen scenarios. Finally, we identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.
Recent Trends in Word Sense Disambiguation: A Survey
Authors: Bevilacqua Michele , Pasini Tommaso , Raganato Alessandro , Navigli Roberto
Published: August 2021

Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identifying the most suitable meaning from a predefined sense inventory. Recent breakthroughs in representation learning have fueled intensive WSD research, resulting in considerable performance improvements, breaching the 80% glass ceiling set by the inter-annotator agreement. In this survey, we provide an extensive overview of current advances in WSD, describing the state of the art in terms of i) resources for the task, i.e., sense inventories and reference datasets for training and testing, as well as ii) automatic disambiguation approaches, detailing their peculiarities, strengths and weaknesses. Finally, we highlight the current limitations of the task itself, but also point out recent trends that could help expand the scope and applicability of WSD, setting up new promising directions for the future.
Explanation in Constraint Satisfaction: A Survey
Authors: Dev Gupta Sharmi , Genc Begum , O'Sullivan Barry
Published: August 2021

Much of the focus on explanation in the field of artificial intelligence has focused on machine learning methods and, in particular, concepts produced by advanced methods such as neural networks and deep learning. However, there has been a long history of explanation generation in the general field of constraint satisfaction, one of the AI's most ubiquitous subfields. In this paper we survey the major seminal papers on the explanation and constraints, as well as some more recent works. The survey sets out to unify many disparate lines of work in areas such as model-based diagnosis, constraint programming, Boolean satisfiability, truth maintenance systems, quantified logics, and related areas.
Recent Advances in Heterogeneous Relation Learning for Recommendation
Authors: Huang Chao
Published: August 2021

Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload. In this survey, we review the development of recommendation frameworks with the focus on heterogeneous relational learning, which consists of different types of dependencies among users and items. The objective of this task is to map heterogeneous relational data into latent representation space, such that the structural and relational properties from both user and item domain can be well preserved. To address this problem, recent research developments can fall into three major categories: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation. We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information. Finally, we present exploratory outlook to highlight several promising directions and opportunities in heterogeneous relational learning frameworks for recommendation.
End-to-End Constrained Optimization Learning: A Survey
Authors: Kotary James , Fioretto Ferdinando , Van Hentenryck Pascal , Wilder Bryan
Published: August 2021

This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solvers and optimization methods with machine learning architectures.

These approaches hold the promise to develop new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. This paper presents a conceptual review of the recent advancements in this emerging area.
Policy Learning with Constraints in Model-free Reinforcement Learning: A Survey
Authors: Liu Yongshuai , Halev Avishai , Liu Xin
Published: August 2021

Reinforcement Learning (RL) algorithms have had tremendous success in simulated domains. These algorithms, however, often cannot be directly applied to physical systems, especially in cases where there are constraints to satisfy (e.g. to ensure safety or limit resource consumption). In standard RL, the agent is incentivized to explore any policy with the sole goal of maximizing reward; in the real world, however, ensuring satisfaction of certain constraints in the process is also necessary and essential. In this article, we overview existing approaches addressing constraints in model-free reinforcement learning. We model the problem of learning with constraints as a Constrained Markov Decision Process and consider two main types of constraints: cumulative and instantaneous. We summarize existing approaches and discuss their pros and cons. To evaluate policy performance under constraints, we introduce a set of standard benchmarks and metrics. We also summarize limitations of current methods and present open questions for future research.
Analogical Proportions: Why They Are Useful in AI
Authors: Prade Henri , Richard Gilles
Published: August 2021

This paper presents a survey of researches in analogical reasoning whose building block are analogical proportions which are statements of the form “a is to b as c is to d”. They have been developed in the last twenty years within an Artificial Intelligence perspective. After discussing their formal modeling with the associated inference mechanism, the paper reports the main results obtained in various AI domains ranging from computational linguistics to classification, including image processing, I.Q. tests, case based reasoning, preference learning, and formal concepts analysis. The last section discusses some new theoretical concerns, and the potential of analogical proportions in other areas such as argumentation, transfer learning, and XAI.
A Survey on Response Selection for Retrieval-based Dialogues
Authors: Tao Chongyang , Feng Jiazhan , Yan Rui , Wu Wei , Jiang Daxin
Published: August 2021

Building an intelligent dialogue system capable of naturally and coherently conversing with humans has been a long-standing goal of artificial intelligence. In the past decade, with the development of machine/deep learning technology and the explosive growth of available conversation data in social media, numerous neural models have been developed for context-response matching tasks in retrieval-based dialogue systems, with more fluent and informative responses compared with generative models. This paper presents a comprehensive survey of recent advances in response selection for retrieval-based dialogues. In particular, we first formulate the problem of response selection and review state-of-the-art context-response matching models categorized by their architecture.

Then we summarize some recent advances on the research of response selection, including incorporation with extra knowledge and exploration on more effective model learning. Finally, we highlight the challenges which are not yet well addressed in this task and present future research directions.
Graph Learning based Recommender Systems: A Review
Authors: Wang Shoujin , Hu Liang , Wang Yan , He Xiangnan , Sheng Quan Z. , Orgun Mehmet A. , Cao Longbing , Ricci Francesco , Yu Philip S.
Published: August 2021

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.
Automated Machine Learning on Graphs: A Survey
Authors: Zhang Ziwei , Wang Xin , Zhu Wenwu
Published: August 2021

Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.
Abstract Cores in Implicit Hitting Set MaxSat Solving (Extended Abstract)
Authors: Berg Jeremias , Bacchus Fahiem , Poole Alex
Published: August 2021

Maximum satisfiability (MaxSat) solving is an active area of research motivated by numerous successful applications to solving

NP-hard combinatorial optimization problems. One of the most successful approaches for solving MaxSat instances from real world domains

are the so called implicit hitting set (IHS) solvers. IHS solvers decouple MaxSat solving into separate core-extraction (i.e. reasoning) and optimization steps which are tackled by a Boolean satisfiability (SAT) and an integer linear programming (IP) solver, respectively. While the approach shows state-of-the-art performance on many industrial instances,

it is known that there exists instances on which IHS solvers need to extract an exponential number of cores before terminating. Motivated by the simplest of these problematic instances, we propose abstract cores, a compact representation for a potentially exponential number of regular cores. We demonstrate how to incorporate abstract core reasoning into the IHS algorithm and report on an empirical evaluation demonstrating, that including abstract cores into a state-of-the-art IHS solver improves its performance enough to surpass the best performing solvers of the 2019 MaxSat Evaluation.
Decentralized No-regret Learning Algorithms for Extensive-form Correlated Equilibria (Extended Abstract)
Authors: Celli Andrea , Marchesi Alberto , Farina Gabriele , Gatti Nicola
Published: August 2021

The existence of uncoupled no-regret learning dynamics converging to correlated equilibria in normal-form games is a celebrated result in the theory of multi-agent systems. Specifically, it has been known for more than 20 years that when all players seek to minimize their internal regret in a repeated normal-form game, the empirical frequency of play converges to a normal-form correlated equilibrium. Extensive-form games generalize normal-form games by modeling both sequential and simultaneous moves, as well as imperfect information. Because of the sequential nature and the presence of private information, correlation in extensive-form games possesses significantly different properties than in normal-form games. The extensive-form correlated equilibrium (EFCE) is the natural extensive-form counterpart to the classical notion of correlated equilibrium in normal-form games. Compared to the latter, the constraints that define the set of EFCEs are significantly more complex, as the correlation device ({\em a.k.a.} mediator) must take into account the evolution of beliefs of each player as they make observations throughout the game. Due to this additional complexity, the existence of uncoupled learning dynamics leading to an EFCE has remained a challenging open research question for a long time. In this article, we settle that question by giving the first uncoupled no-regret dynamics which provably converge to the set of EFCEs in n-player general-sum extensive-form games with perfect recall. We show that each iterate can be computed in time polynomial in the size of the game tree, and that, when all players play repeatedly according to our learning dynamics, the empirical frequency of play after T game repetitions is guaranteed to be a O(T^-1/2)-approximate EFCE with high probability, and an EFCE almost surely in the limit.
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search (Extended Abstract)
Authors: Lorenzen Stephan S. , Pham Ninh
Published: August 2021

Top-k maximum inner product search (MIPS) is a central task in many machine learning applications. This work extends top-k MIPS with a budgeted setting, that asks for the best approximate top-k MIPS given a limited budget of computational operations. We study recent advanced sampling methods, including wedge and diamond sampling, to solve budgeted top-k MIPS. First, we theoretically show that diamond sampling is essentially a combination of wedge sampling and basic sampling for top-k MIPS. Second, we propose dWedge, a simple deterministic variant of wedge sampling for budgeted top-k MIPS. Empirically, dWedge provides significantly higher accuracy than other budgeted top-k MIPS solvers while maintaining a similar speedup.
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild (Extended Abstract)
Authors: Wu Shangzhe , Rupprecht Christian , Vedaldi Andrea
Published: August 2021

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least approximately, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. Code and demo available at https://github.com/elliottwu/unsup3d.
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning
Authors: Yang He
Published: August 2021

The longstanding goals of federated learning (FL) require rigorous privacy guarantees and low communication overhead while holding a relatively high model accuracy. However, simultaneously achieving all the goals is extremely challenging. In this paper, we propose a novel framework called hierarchical federated learning (H-FL) to tackle this challenge. Considering the degradation of the model performance due to the statistic heterogeneity of the training data, we devise a runtime distribution reconstruction strategy, which reallocates the clients appropriately and utilizes mediators to rearrange the local training of the clients. In addition, we design a compression-correction mechanism incorporated into H-FL to reduce the communication overhead while not sacrificing the model performance. To further provide privacy guarantees, we introduce differential privacy while performing local training, which injects moderate amount of noise into only part of the complete model. Experimental results show that our H-FL framework achieves the state-of-art performance on different datasets for the real-world image recognition tasks.
Automatic Design of Heuristic Algorithms for Binary Optimization Problems
Authors: de Souza Marcelo
Published: August 2021

In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.
Combining Reinforcement Learning and Causal Models for Robotics Applications
Authors: Méndez-Molina Arquímides
Published: August 2021

The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. In this extended abstract of our Ph.D. research proposal, we present a way to combine both areas to improve their respective learning processes, especially in the context of our application area (service robotics). The preliminary results obtained so far are a good starting point for thinking about the success of our research project.
Learning from Multimedia Data with Incomplete Information
Authors: Tao Renshuai
Published: August 2021

Traditional deep learning methods are based on the condition that the data is of high-quality, which means the data information is highly available. However, data in these scenes often have the characteristics of large background noise, lack of sample content, small target, serious occlusion and a small number of samples. The application of related tasks in real open scenarios is very important, so it is urgent to make full use of these incomplete information data accurately.
Learning and Planning Under Uncertainty for Green Security
Authors: Xu Lily
Published: August 2021

Green security concerns the protection of the world's wildlife, forests, and fisheries from poaching, illegal logging, and illegal fishing. Unfortunately, conservation efforts in green security domains are constrained by the limited availability of defenders, who must patrol vast areas to protect from attackers. Artificial intelligence (AI) techniques have been developed for green security and other security settings, such as US Coast Guard patrols and airport screenings, but effective deployment of AI in these settings requires learning adversarial behavior and planning in complex environments where the true dynamics may be unknown. My research develops novel techniques in machine learning and game theory to enable the effective development and deployment of AI in these resource-constrained settings. Notably, my work has spanned the pipeline from learning in a supervised setting, planning in stochastic environments, sequential planning in uncertain environments, and deployment in the real world. The overarching goal is to optimally allocate scarce resources under uncertainty for environmental conservation.
An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts
Authors: Zhao Rui
Published: August 2021

We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.
Intelligent and Learning Agents: Four Investigations
Authors: Kalyanakrishnan Shivaram
Published: August 2021

My research is driven by my curiosity about the nature of intelligence. Of the several aspects that characterise the behaviour of intelligent agents, I primarily study sequential decision making, learning, and exploration. My interests also extend to broader questions on the effects of AI on life and society. In this paper, I present four distinct investigations drawn from my recent work, which range from theoretical to applied, and which involve both analysis and design. I also share my outlook as an early-career researcher.
Towards a New Generation of Cognitive Diagnosis
Authors: Liu Qi
Published: August 2021

Cognitive diagnosis is a type of assessment for automatically measuring individuals' proficiency profiles from their observed behaviors, e.g. quantifying the mastery level of examinees on specific knowledge concepts/skills. As one of the fundamental research tasks in domains like intelligent education, a number of Cognitive Diagnosis Models (CDMs) have been developed in the past decades. Though these solutions are usually well designed based on psychometric theories, they still suffer from the limited ability of the handcrafted diagnosis functions, especially when dealing with heterogeneous data. In this paper, I will share my personal understanding of cognitive diagnosis and review our recent developments of CDMs mostly from a machine learning perspective. Meanwhile, I will show the wide applications of cognitive diagnosis.
IIAS: An Intelligent Insurance Assessment System through Online Real-time Conversation Analysis
Authors: Zhou Mengdi , Peng Shuang , Yang Minghui , Li Nan , Wang Hongbin , Qiao Li , Mi Haitao , Wen Zujie , Xu Teng , Liu Lei
Published: August 2021

With the development of Chinese medical insurance industry, the amount of claim cases is growing rapidly. Ultimately, more claims necessarily indicate that the insurance company has to spend much time assessing claims and decides how much compensation the claimant should receive, which is a highly professional process that involves many complex operations. Therefore, the insurance assessor's role is essential. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure. In order to alleviate assessors' cognitive workload, we propose an Intelligent Insurance Assessment System (IIAS) that helps effectively collect claimant information through online real-time conversation analysis. With the assistance of IIAS, the average time cost of the insurance assessment procedure is reduced from 55 minutes to 35 minutes.
On Smoother Attributions using Neural Stochastic Differential Equations
Authors: Jha Sumit , Ewetz Rickard , Velasquez Alvaro , Jha Susmit
Published: August 2021

Several methods have recently been developed for computing attributions of a neural network's prediction over the input features. However, these existing approaches for computing attributions are noisy and not robust to small perturbations of the input. This paper uses the recently identified connection between dynamical systems and residual neural networks to show that the attributions computed over neural stochastic differential equations (SDEs) are less noisy, visually sharper, and quantitatively more robust. Using dynamical systems theory, we theoretically analyze the robustness of these attributions. We also experimentally demonstrate the efficacy of our approach in providing smoother, visually sharper and quantitatively robust attributions by computing attributions for ImageNet images using ResNet-50, WideResNet-101 models and ResNeXt-101 models.
Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering
Authors: Dang Long Hoang , Le Thao Minh , Le Vuong , Tran Truyen
Published: August 2021

Video Question Answering (Video QA) is a powerful testbed to develop new AI capabilities. This task necessitates learning to reason about objects, relations, and events across visual and linguistic domains in space-time. High-level reasoning demands lifting from associative visual pattern recognition to symbol like manipulation over objects, their behavior and interactions. Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects. At each stage of the video event flow, these objects interact with each other, and their interactions are reasoned about with respect to the query and under the overall context of a video. This mechanism is materialized into a family of general-purpose neural units and their multi-level architecture called Hierarchical Object-oriented Spatio-Temporal Reasoning (HOSTR) networks. This neural model maintains the objects' consistent lifelines in the form of a hierarchically nested spatio-temporal graph. Within this graph, the dynamic interactive object-oriented representations are built up along the video sequence, hierarchically abstracted in a bottom-up manner, and converge toward the key information for the correct answer. The method is evaluated on multiple major Video QA datasets and establishes new state-of-the-arts in these tasks. Analysis into the model's behavior indicates that object-oriented reasoning is a reliable, interpretable and efficient approach to Video QA.
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Authors: Guo Yangyang , Nie Liqiang , Cheng Zhiyong , Ji Feng , Zhang Ji , Del Bimbo Alberto
Published: August 2021

A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions based on the language shortcut. Some efforts have been devoted to overcoming this issue with delicate models. However, there is no research to address it from the view of the answer feature space learning, despite the fact that existing VQA methods all cast VQA as a classification task. Inspired by this, in this work, we attempt to tackle the language prior problem from the viewpoint of the feature space learning. An adapted margin cosine loss is designed to discriminate the frequent and the sparse answer feature space under each question type properly. In this way, the limited patterns within the language modality can be largely reduced to eliminate the language priors. We apply this loss function to several baseline models and evaluate its effectiveness on two VQA-CP benchmarks. Experimental results demonstrate that our proposed adapted margin cosine loss can enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.
Studying the Chaotic Dynamics Using Rossler-Chua Systems Combined with A Semiconductor Laser
Authors: Jamal Raied K. , Ali Falah H. , Mutlak Falah A-H.
Published: August 2021

In this paper, two different chaotic dynamic systems are coupled using a semiconductor laser to produce a new chaotic system. These two chaotic systems are Rossler and Chua systems. X-dynamic of Rossler system was coupled optically using optical fiber as a carrier of signal with x, y, and z-dynamics of Chua system. The results were analyzed and the behavior of Chua system was found to be changing in time series which, in turn, changed the attractor. The Chua attractor was converted from double scroll to single scroll. The results obtained from connecting two different systems in chaotic behavior showed a remarkable increase in the bandwidth of Chua system. This increase in bandwidth opens up a wide field for many applications, the most important of which is in the field of secure communications.