bluuebunny/crossref_metadata_2025 huggingface dataset:
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning
Authors: Chen Qianglong , Li Feng-Lin , Xu Guohai , Yan Ming , Zhang Ji , Zhang Yin
Published: July 2022

Although pre-trained language models (PLMs) have achieved state-of-the-art performance on various natural language processing (NLP) tasks, they are shown to be lacking in knowledge when dealing with knowledge driven tasks. Despite the many efforts made for injecting knowledge into PLMs, this problem remains open. To address the challenge, we propose DictBERT, a novel approach that enhances PLMs with dictionary knowledge which is easier to acquire than knowledge graph (KG). During pre-training, we present two novel pre-training tasks to inject dictionary knowledge into PLMs via contrastive learning: dictionary entry prediction and entry description discrimination. In fine-tuning, we use the pre-trained DictBERT as a plugin knowledge base (KB) to retrieve implicit knowledge for identified entries in an input sequence, and infuse the retrieved knowledge into the input to enhance its representation via a novel extra-hop attention mechanism. We evaluate our approach on a variety of knowledge driven and language understanding tasks, including NER, relation extraction, CommonsenseQA, OpenBookQA and GLUE. Experimental results demonstrate that our model can significantly improve typical PLMs: it gains a substantial improvement of 0.5%, 2.9%, 9.0%, 7.1% and 3.3% on BERT-large respectively, and is also effective on RoBERTa-large.
Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering
Authors: Deng Zhenyun , Zhu Yonghua , Chen Yang , Witbrock Michael , Riddle Patricia
Published: July 2022

Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers. Most existing approaches cannot provide an interpretable reasoning process to illustrate how these models arrive at an answer. In this paper, we propose a Question Decomposition method based on Abstract Meaning Representation (QDAMR) for multi-hop QA, which achieves interpretable reasoning by decomposing a multi-hop question into simpler subquestions and answering them in order. Since annotating the decomposition is expensive, we first delegate the complexity of understanding the multi-hop question to an AMR parser. We then achieve decomposition of a multi-hop question via segmentation of the corresponding AMR graph based on the required reasoning type. Finally, we generate sub-questions using an AMR-to-Text generation model and answer them with an off-the-shelf QA model. Experimental results on HotpotQA demonstrate that our approach is competitive for interpretable reasoning and that the sub-questions generated by QDAMR are well-formed, outperforming existing question-decomposition-based multihop QA approaches.
Proportional Budget Allocations: Towards a Systematization
Authors: Los Maaike , Christoff Zoé , Grossi Davide
Published: July 2022

We contribute to the programme of lifting proportionality axioms from the multi-winner voting setting to participatory budgeting. We define novel proportionality axioms for participatory budgeting and test them on known proportionality-driven rules such as Phragmén and Rule X. We investigate logical implications among old and new axioms and provide a systematic overview of proportionality criteria in participatory budgeting.
Logically Consistent Adversarial Attacks for Soft Theorem Provers
Authors: Gaskell Alexander , Miao Yishu , Toni Francesca , Specia Lucia
Published: July 2022

Recent efforts within the AI community have yielded impressive results towards “soft theorem proving” over natural language sentences using language models. We propose a novel, generative adversarial framework for probing and improving these models’ reasoning capabilities. Adversarial attacks in this domain suffer from the logical inconsistency problem, whereby perturbations to the input may alter the label. Our Logically consistent AdVersarial Attacker, LAVA, addresses this by combining a structured generative process with a symbolic solver, guaranteeing logical consistency. Our framework successfully generates adversarial attacks and identifies global weaknesses common across multiple target models. Our analyses reveal naive heuristics and vulnerabilities in these models’ reasoning capabilities, exposing an incomplete grasp of logical deduction under logic programs. Finally, in addition to effective probing of these models, we show that training on the generated samples improves the target model’s performance.
Leveraging the Wikipedia Graph for Evaluating Word Embeddings
Authors: Giesen Joachim , Kahlmeyer Paul , Nussbaum Frank , Zarrieß Sina
Published: July 2022

Deep learning models for different NLP tasks often rely on pre-trained word embeddings, that is, vector representations of words. Therefore, it is crucial to evaluate pre-trained word embeddings independently of downstream tasks. Such evaluations try to assess whether the geometry induced by a word embedding captures connections made in natural language, such as, analogies, clustering of words, or word similarities. Here, traditionally, similarity is measured by comparison to human judgment. However, explicitly annotating word pairs with similarity scores by surveying humans is expensive. We tackle this problem by formulating a similarity measure that is based on an agent for routing the Wikipedia hyperlink graph. In this graph, word similarities are implicitly encoded by edges between articles. We show on the English Wikipedia that our measure correlates well with a large group of traditional similarity measures, while covering a much larger proportion of words and avoiding explicit human labeling. Moreover, since Wikipedia is available in more than 300 languages, our measure can easily be adapted to other languages, in contrast to traditional similarity measures.
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Authors: Huang Rongjie , Lam Max W. Y. , Wang Jun , Su Dan , Yu Dong , Ren Yi , Zhao Zhou
Published: July 2022

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies with adaptive conditions. A noise schedule predictor is also adopted to reduce the sampling steps without sacrificing the generation quality. Based on FastDiff, we design an end-to-end text-to-speech synthesizer, FastDiff-TTS, which generates high-fidelity speech waveforms without any intermediate feature (e.g., Mel-spectrogram). Our evaluation of FastDiff demonstrates the state-of-the-art results with higher-quality (MOS 4.28) speech samples. Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time. We further show that FastDiff generalized well to the mel-spectrogram inversion of unseen speakers, and FastDiff-TTS outperformed other competing methods in end-to-end text-to-speech synthesis. Audio samples are available at https://FastDiff.github.io/.
Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation
Authors: Ke Pei , Ji Haozhe , Yang Zhenyu , Huang Yi , Feng Junlan , Zhu Xiaoyan , Huang Minlie
Published: July 2022

Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks. Existing works mostly utilize abundant unlabeled structured data to conduct unsupervised pre-training for task adaption, which fail to model the complex relationship between source structured data and target texts. Thus, we introduce self-training as a better few-shot learner than task-adaptive pre-training, which explicitly captures this relationship via pseudo-labeled data generated by the pre-trained model. To alleviate the side-effect of low-quality pseudo-labeled data during self-training, we propose a novel method called Curriculum-Based Self-Training (CBST) to effectively leverage unlabeled data in a rearranged order determined by the difficulty of text generation. Experimental results show that our method can outperform fine-tuning and task-adaptive pre-training methods, and achieve state-of-the-art performance in the few-shot setting of data-to-text generation.
Variational Learning for Unsupervised Knowledge Grounded Dialogs
Authors: Mishra Mayank , Madan Dhiraj , Pandey Gaurav , Contractor Danish
Published: July 2022

Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to be known during training and rely on the use of a retrieval system to fetch relevant documents from a large index. The documents used to generate the responses are modeled as latent variables whose prior probabilities need to be estimated. Models such as RAG, marginalize the document probabilities over the documents retrieved from the index to define the log-likelihood loss function which is optimized end-to-end.

In this paper, we develop a variational approach to the above technique wherein, we instead maximize the Evidence Lower bound (ELBO). Using a collection of three publicly available open-conversation datasets, we demonstrate how the posterior distribution, which has information from the ground-truth response, allows for a better approximation of the objective function during training. To overcome the challenges associated with sampling over a large knowledge collection, we develop an efficient approach to approximate the ELBO.

To the best of our knowledge, we are the first to apply variational training for open-scale unsupervised knowledge grounded dialog systems.
Enhancing Text Generation via Multi-Level Knowledge Aware Reasoning
Authors: Mu Feiteng , Li Wenjie
Published: July 2022

How to generate high-quality textual content is a non-trivial task. Existing methods generally generate text by grounding on word-level knowledge. However, word-level knowledge cannot express multi-word text units, hence existing methods may generate low-quality and unreasonable text.

In this paper, we leverage event-level knowledge to enhance text generation.

However, event knowledge is very sparse. To solve this problem, we split a coarse-grained event into fine-grained word components to obtain the word-level knowledge among event components. The word-level knowledge models the interaction among event components, which makes it possible to reduce the sparsity of events. Based on the event-level and the word-level knowledge, we devise a multi-level knowledge aware reasoning framework. Specifically, we first utilize event knowledge to make event-based content planning, i.e., select reasonable event sketches conditioned by the input text. Then, we combine the selected event sketches with the word-level knowledge for text generation. We validate our method on two widely used datasets, experimental results demonstrate the effectiveness of our framework to text generation.
Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation
Authors: Peng Wei , Hu Yue , Xing Luxi , Xie Yuqiang , Sun Yajing , Li Yunpeng
Published: July 2022

Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task. It requires the system to explore the cause of help-seeker's emotional distress and understand their psychological intention to provide supportive responses. However, existing methods mainly focus on the sequential contextual information, ignoring the hierarchical relationships with the global cause and local psychological intention behind conversations, thus leads to a weak ability of emotional support. In this paper, we propose a Global-to-Local Hierarchical Graph Network to capture the multi-source information (global cause, local intentions and dialog history) and model hierarchical relationships between them, which consists of a multi-source encoder, a hierarchical graph reasoner, and a global-guide decoder. Furthermore, a novel training objective is designed to monitor semantic information of the global cause. Experimental results on the emotional support conversation dataset, ESConv, confirm that the proposed GLHG has achieved the state-of-the-art performance on the automatic and human evaluations.
BiFSMN: Binary Neural Network for Keyword Spotting
Authors: Qin Haotong , Ma Xudong , Ding Yifu , Li Xiaoyang , Zhang Yang , Tian Yao , Ma Zejun , Luo Jie , Liu Xianglong
Published: July 2022

The deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. However, computational resources for these networks are significantly constrained since they usually run on-call on edge devices. In this paper, we present BiFSMN, an accurate and extreme-efficient binary neural network for KWS. We first construct a High-frequency Enhancement Distillation scheme for the binarization-aware training, which emphasizes the high-frequency information from the full-precision network's representation that is more crucial for the optimization of the binarized network. Then, to allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we also propose a Thinnable Binarization Architecture to further liberate the acceleration potential of the binarized network from the topology perspective. Moreover, we implement a Fast Bitwise Computation Kernel for BiFSMN on ARMv8 devices which fully utilizes registers and increases instruction throughput to push the limit of deployment efficiency. Extensive experiments show that BiFSMN outperforms existing binarization methods by convincing margins on various datasets and is even comparable with the full-precision counterpart (e.g., less than 3% drop on Speech Commands V1-12). We highlight that benefiting from the thinnable architecture and the optimized 1-bit implementation, BiFSMN can achieve an impressive 22.3x speedup and 15.5x storage-saving on real-world edge hardware.
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
Authors: Wu Shuhui , Shen Yongliang , Tan Zeqi , Lu Weiming
Published: July 2022

Nested named entity recognition (nested NER) is a fundamental task in natural language processing. Various span-based methods have been proposed to detect nested entities with span representations. However, span-based methods do not consider the relationship between a span and other entities or phrases, which is helpful in the NER task. Besides, span-based methods have trouble predicting long entities due to limited span enumeration length. To mitigate these issues, we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction network for nested NER. In the propose stage, we use a span-based predictor to generate some coarse entity predictions as entity proposals. In the refine stage, proposals interact with each other, and richer contextual information is incorporated into the proposal representations. The refined proposal representations are used to re-predict entity boundaries and classes. In this way, errors in coarse proposals can be eliminated, and the boundary prediction is no longer constrained by the span enumeration length limitation. Additionally, we build multi-scale sentence representations, which better model the hierarchical structure of sentences and provide richer contextual information than token-level representations. Experiments show that PnRNet achieves state-of-the-art performance on four nested NER datasets and one flat NER dataset.
Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation
Authors: Yadav Rohan Kumar , Jiao Lei , Granmo Ole-Christoffer , Goodwin Morten
Published: July 2022

The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution biases that reside in the training and testing data. This issue hugely impacts the performance of the models when exposed to out-of-distribution and counterfactual data. The root cause seems to be that many machine learning models are prone to learn the shortcuts, modelling simple correlations rather than more fundamental and general relationships. As a result, such text classifiers tend to perform poorly when a human makes minor modifications to the data, which raises questions regarding their robustness. In this paper, we employ a rule-based architecture called Tsetlin Machine (TM) that learns both simple and complex correlations by ANDing features and their negations. As such, it generates explainable AND-rules using negated and non-negated reasoning. Here, we explore how non-negated reasoning can be more prone to distribution biases than negated reasoning. We further leverage this finding by adapting the TM architecture to mainly perform negated reasoning using the specificity parameter s. As a result, the AND-rules becomes robust to spurious correlations and can also correctly predict counterfactual data. Our empirical investigation of the model's robustness uses the specificity s to control the degree of negated reasoning. Experiments on publicly available Counterfactually-Augmented Data demonstrate that the negated clauses are robust to spurious correlations and outperform Naive Bayes, SVM, and Bi-LSTM by up to 20 %, and ELMo by almost 6 % on counterfactual test data.
Robust Solutions for Multi-Defender Stackelberg Security Games
Authors: Mutzari Dolev , Aumann Yonatan , Kraus Sarit
Published: July 2022

Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature.

However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium.

In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters.

First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate (alpha) core, and provide an efficient construction thereof.

Lastly, we show that stronger types of the core may be empty.

Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.
Stage-wise Stylistic Headline Generation: Style Generation and Summarized Content Insertion
Authors: Zhan Jiaao , Gao Yang , Bai Yu , Liu Qianhui
Published: July 2022

A quality headline with a high click-rate should not only summarize the content of an article, but also reflect a style that attracts users. Such demand has drawn rising attention to the task of stylistic headline generation (SHG). An intuitive method is to first generate plain headlines leveraged by document-headline parallel data then transfer them to a target style. However, this inevitably suffers from error propagation. Therefore, to unify the two sub-tasks and explicitly decompose style-relevant attributes and summarize content, we propose an end-to-end stage-wise SHG model containing the style generation component and the content insertion component, where the former generates stylistic-relevant intermediate outputs and the latter receives these outputs then inserts the summarized content. The intermediate outputs are observable, making the style generation easy to control. Our system is comprehensively evaluated by both quantitative and qualitative metrics, and it achieves state-of-the-art results in SHG over three different stylistic datasets.
Charge Prediction by Constitutive Elements Matching of Crimes
Authors: Zhao Jie , Guan Ziyu , Xu Cai , Zhao Wei , Chen Enze
Published: July 2022

Charge prediction is to automatically predict the judgemental charges for legal cases. To convict a person/unit of a charge, the case description must contain matching instances of the constitutive elements (CEs) of that charge. This knowledge of CEs is a valuable guide for the judge in making final decisions. However, it is far from fully exploited for charge prediction in the literature. In this paper we propose a novel method named Constitutive Elements-guided Charge Prediction (CECP). CECP mimics human's charge identification process to extract potential instances of CEs and generate predictions accordingly. It avoids laborious labeling of matching instances of CEs by a novel reinforcement learning module which progressively selects potentially matching sentences for CEs and evaluates their relevance. The final prediction is generated based on the selected sentences and their relevant CEs. Experiments on two real-world datasets show the superiority of CECP over competitive baselines.
I Will Have Order! Optimizing Orders for Fair Reviewer Assignment
Authors: Payan Justin , Zick Yair
Published: July 2022

We study mechanisms that allocate reviewers to papers in a fair and efficient manner. We model reviewer assignment as an instance of a fair allocation problem, presenting an extension of the classic round-robin mechanism, called Reviewer Round Robin (RRR). Round-robin mechanisms are a standard tool to ensure envy-free up to one item (EF1) allocations. However, fairness often comes at the cost of decreased efficiency. To overcome this challenge, we carefully select an approximately optimal round-robin order. Applying a relaxation of submodularity, γ-weak submodularity, we show that greedily inserting papers into an order yields a (1+γ²)-approximation to the maximum welfare attainable by our round-robin mechanism under any order. Our Greedy Reviewer Round Robin (GRRR) approach outputs highly efficient EF1 allocations for three real conference datasets, offering comparable performance to state-of-the-art paper assignment methods in fairness, efficiency, and runtime, while providing the only EF1 guarantee.
Contrastive Graph Transformer Network for Personality Detection
Authors: Zhu Yangfu , Hu Linmei , Ge Xinkai , Peng Wanrong , Wu Bin
Published: July 2022

Personality detection is to identify the personality traits underlying social media posts. Most of the existing work is mainly devoted to learning the representations of posts based on labeled data. Yet the ground-truth personality traits are collected through time-consuming questionnaires. Thus, one of the biggest limitations lies in the lack of training data for this data-hungry task. In addition, the correlations among traits should be considered since they are important psychological cues that could help collectively identify the traits. In this paper, we construct a fully-connected post graph for each user and develop a novel Contrastive Graph Transformer Network model (CGTN) which distills potential labels of the graphs based on both labeled and unlabeled data. Specifically, our model first explores a self-supervised Graph Neural Network (GNN) to learn the post embeddings. We design two types of post graph augmentations to incorporate different priors based on psycholinguistic knowledge of Linguistic Inquiry and Word Count (LIWC) and post semantics. Then, upon the post embeddings of the graph, a Transformer-based decoder equipped with post-to-trait attention is exploited to generate traits sequentially. Experiments on two standard datasets demonstrate that our CGTN outperforms the state-of-the-art methods for personality detection.
Planning with Qualitative Action-Trajectory Constraints in PDDL
Authors: Bonassi Luigi , Gerevini Alfonso Emilio , Scala Enrico
Published: July 2022

In automated planning the ability of expressing constraints on the structure of the desired plans is important to deal with solution quality, as well as to express control knowledge. In PDDL3, this is supported through state-trajectory constraints corresponding to a class of LTLf formulae. In this paper, first we introduce a formalism to express trajectory constraints over actions in the plan, rather than over traversed states; Then we investigate compilation-based methods to deal with such constraints in propositional planning, and propose a new simple effective method. Finally, we experimentally study the usefulness of our action-trajectory constraints as a tool to express control knowledge. The experimental results show that the performance of a classical planner can be significantly improved by exploiting knowledge expressed by action constraints and handled by our compilation method, while the same knowledge turns out to be less beneficial when specified as state constraints and handled by two state-of-the-art systems supporting state constraints.
Fair, Individually Rational and Cheap Adjustment
Authors: Polevoy Gleb , Dziubiński Marcin
Published: July 2022

Consider the practical goal of making a desired action profile played,

when the planner can only change the payoffs, bound by

stringent constraints.

Applications include motivating people

to choose the closest school, the closest subway station, or to coordinate

on a communication protocol or an investment strategy.

Employing subsidies and tolls, we adjust the game so that choosing this predefined action profile

becomes strictly dominant.

Inspired mainly by the work of Monderer and Tennenholtz,

where the promised subsidies do not materialise in the not played

profiles, we provide a fair and individually rational game

adjustment, such that the total outside investments sum up

to zero at any profile, thereby facilitating easy and frequent

usage of our adjustment without bearing costs, even if some

players behave unexpectedly. The resultant action profile itself needs no

adjustment. Importantly, we also prove that our adjustment minimises

the general transfer among all such adjustments, counting the total subsidising and taxation.
Competitive Analysis for Multi-Commodity Ski-Rental Problem
Authors: Wu Binghan , Bao Wei , Yuan Dong , Zhou Bing
Published: July 2022

We investigate an extended version of the classical ski-rental problem with multiple commodities. A customer uses a set of commodities altogether, and he/she needs to choose payment options to cover the usage of each commodity without the knowledge of the future. The payment options of each commodity include (1) renting: to pay for an on-demand usage and (2) buying: to pay for the lifetime usage. It is a novel extension of the classical ski-rental problem which deals with only one commodity. To address this problem, we propose a new online algorithm called the Multi-Object Break-Even (MOBE) algorithm and conduct competitive analysis. We show that the tight lower and upper bounds of MOBE algorithm's competitive ratio are e/e-1 and 2 respectively against adaptive adversary under arbitrary renting and buying prices. We further prove that MOBE algorithm is an optimal online algorithm if commodities have the same rent-to-buy ratio. Numerical results verify our theoretical conclusion and demonstrate the advantages of MOBE in a real-world scenario.
An Online Learning Approach towards Far-sighted Emergency Relief Planning under Intentional Attacks in Conflict Areas
Authors: Yang Haoyu , Xiao Kaiming , Liu Lihua , Huang Hongbin , Zhang Weiming
Published: July 2022

A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i.i.d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.
Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms
Authors: Zhao Zishuo , Chen Xi , Zhang Xuefeng , Zhou Yuan
Published: July 2022

A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms.
Multi-robot Task Allocation in the Environment with Functional Tasks
Authors: Yan Fuhan , Di Kai
Published: July 2022

Multi-robot task allocation (MRTA) problem has long been a key issue in multi-robot systems. Previous studies usually assumed that the robots must complete all tasks with minimum time cost. However, in many real situations, some tasks can be selectively performed by robots and will not limit the achievement of the goal. Instead, completing these tasks will cause some functional effects, such as decreasing the time cost of completing other tasks. This kind of task can be called “functional task”. This paper studies the multi-robot task allocation in the environment with functional tasks. In the problem, neither allocating all functional tasks nor allocating no functional task is always optimal. Previous algorithms usually allocate all tasks and cannot suitably select the functional tasks. Because of the interaction and sequential influence, the total effects of the functional tasks are too complex to exactly calculate. We fully analyze this problem and then design a heuristic algorithm. The heuristic algorithm scores the functional tasks referring to linear threshold model (used to analyze the sequential influence of a functional task). The simulated experiments demonstrate that the heuristic algorithm can outperform the benchmark algorithms.
A Closed-Loop Perception, Decision-Making and Reasoning Mechanism for Human-Like Navigation
Authors: Zhang Wenqi , Zhao Kai , Li Peng , Zhu Xiao , Shen Yongliang , Ma Yanna , Chen Yingfeng , Lu Weiming
Published: July 2022

Reliable navigation systems have a wide range of applications in robotics and autonomous driving. Current approaches employ an open-loop process that converts sensor inputs directly into actions. However, these open-loop schemes are challenging to handle complex and dynamic real-world scenarios due to their poor generalization. Imitating human navigation, we add a reasoning process to convert actions back to internal latent states, forming a two-stage closed loop of perception, decision-making, and reasoning. Firstly, VAE-Enhanced Demonstration Learning endows the model with the understanding of basic navigation rules. Then, two dual processes in RL-Enhanced Interaction Learning generate reward feedback for each other and collectively enhance obstacle avoidance capability. The reasoning model can substantially promote generalization and robustness, and facilitate the deployment of the algorithm to real-world robots without elaborate transfers. Experiments show our method is more adaptable to novel scenarios compared with state-of-the-art approaches.
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
Authors: Bian Chao , Zhou Yawen , Qian Chao
Published: July 2022

Subset selection, which aims to select a subset from a ground set to maximize some objective function, arises in various applications such as influence maximization and sensor placement. In real-world scenarios, however, one often needs to find a subset which is robust against (i.e., is good over) a number of possible objective functions due to uncertainty, resulting in the problem of robust subset selection. This paper considers robust subset selection with monotone objective functions, relaxing the submodular property required by previous studies. We first show that the greedy algorithm can obtain an approximation ratio with respect to the correlation and submodularity ratios of the objective functions; and then propose EPORSS, an evolutionary Pareto optimization algorithm that can utilize more time to find better subsets. We prove that EPORSS can also be theoretically grounded, achieving a similar approximation guarantee to the greedy algorithm. In addition, we derive the lower bound of the correlation ratio for the application of robust influence maximization, and further conduct experiments to validate the performance of the greedy algorithm and EPORSS.
Efficient Budgeted Graph Search
Authors: Kaur Jasmeet , Sturtevant Nathan R.
Published: July 2022

Iterative Budgeted Exponential Search (IBEX) is a general search algorithm that can limit the number of re-expansions performed in common problems like iterative-deepening tree search and search with inconsistent heuristics. IBEX has been adapted into a specific tree algorithm, Budgeted Tree Search (BTS), which behaves like IDA* when the problem instance is well-behaved but keeps the worst-case guarantees when problems are not well-behaved. The analogous algorithms on graphs, Budgeted Graph Search (BGS), do not have these same properties. This paper reformulates BGS into Efficient Budgeted Graph Search (BGSe), showing how to implement the algorithm so that it behaves identically to A* when problems are well-behaved, and retains the best-case performance otherwise. Experimental results validate the performance of BGSe on a range of theoretical and practical problem instances.
Generalisation of Alpha-Beta Search for AND-OR Graphs With Partially Ordered Values
Authors: Li Junkang , Zanuttini Bruno , Cazenave Tristan , Ventos Véronique
Published: July 2022

We define a new setting related to the evaluation of AND-OR directed acyclic graphs with partially ordered values. Such graphs arise naturally when solving games with incomplete information (e.g. most card games such as Bridge) or games with multiple criteria. In particular, this setting generalises standard AND-OR graph evaluation and computation of optimal strategies in games with complete information.

Under this setting, we propose a new algorithm which uses both alpha-beta pruning and cached values. In this paper, we present our algorithm, prove its correctness, and give experimental results on a card game with incomplete information.
Summary Markov Models for Event Sequences
Authors: Bhattacharjya Debarun , Sihag Saurabh , Hassanzadeh Oktie , Bialik Liza
Published: July 2022

Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora. We propose a family of models for such event sequences -- summary Markov models -- where the probability of observing an event type depends only on a summary of historical occurrences of its influencing set of event types. This Markov model family is motivated by Granger causal models for time series, with the important distinction that only one event can occur in a position in an event sequence. We show that a unique minimal influencing set exists for any set of event types of interest and choice of summary function, formulate two novel models from the general family that represent specific sequence dynamics, and propose a greedy search algorithm for learning them from event sequence data. We conduct an experimental investigation comparing the proposed models with relevant baselines, and illustrate their knowledge acquisition and discovery capabilities through case studies involving sequences from text.
Ancestral Instrument Method for Causal Inference without Complete Knowledge
Authors: Cheng Debo , Li Jiuyong , Liu Lin , Zhang Jiji , Le Thuc Duy , Liu Jixue
Published: July 2022

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and untestable. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables (known as a conditioning set for a conditional IV). However, the criterion for finding a conditioning set for a conditional IV needs a directed acyclic graph (DAG) representing the causal relationships of both observed and unobserved variables. This makes it challenging to discover a conditioning set directly from data. In this paper, by leveraging maximal ancestral graphs (MAGs) for causal inference with latent variables, we study the graphical properties of ancestral IVs, a type of conditional IVs using MAGs, and develop the theory to support data-driven discovery of the conditioning set for a given ancestral IV in data under the pretreatment variable assumption. Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data. Extensive experiments on synthetic and real-world datasets demonstrate the performance of the algorithm in comparison with existing IV methods.
Automated Sifting of Stories from Simulated Storyworlds
Authors: Leong Wilkins , Porteous Julie , Thangarajah John
Published: July 2022

Story sifting (or story recognition) allows for the exploration of events, stories, and patterns that emerge from simulated storyworlds. The goal of this work is to reduce the authoring burden for creating sifting queries. In this paper, we use the event traces of simulated storyworlds to create Dynamic Character Networks that track the changing relationship scores between characters in a simulation. These networks allow for the fortunes between any two characters to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user’s query arc can be calculated using the Dynamic Time Warping algorithm. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype ARC SIFT. The results of a user study support our expectation that ARC SIFT is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events emerging from a simulated story world.
Universal Video Style Transfer via Crystallization, Separation, and Blending
Authors: Lu Haofei , Wang Zhizhong
Published: July 2022

Universal video style transfer aims to migrate arbitrary styles to input videos. However, how to maintain the temporal consistency of videos while achieving high-quality arbitrary style transfer is still a hard nut to crack. To resolve this dilemma, in this paper, we propose the CSBNet which involves three key modules: 1) the Crystallization (Cr) Module that generates several orthogonal crystal nuclei, representing hierarchical stability-aware content and style components, from raw VGG features; 2) the Separation (Sp) Module that separates these crystal nuclei to generate the stability-enhanced content and style features; 3) the Blending (Bd) Module to cross-blend these stability-enhanced content and style features, producing more stable and higher-quality stylized videos. Moreover, we also introduce a new pair of component enhancement losses to improve network performance. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness and superiority of our CSBNet. Compared with the state-of-the-art models, it not only produces temporally more consistent and stable results for arbitrary videos but also achieves higher-quality stylizations for arbitrary images.
Captioning Bosch: A Twitter Bot
Authors: Ferner Cornelia
Published: July 2022

The artworks by Dutch painter Hieronymus Bosch are well known for their incredible wealth of details. The popular BoschBot regularly posts small segments of the digitized paintings on Twitter, thus relieving their density and making them more accessible. CaptioningBoschBot, the Twitter bot presented in this demo, reverses the creative process of the artist: It uses the out-of-context painting segments as input for an encoder-decoder model to generate captions that interpret the painted objects. As the model was only trained on realistic, photographic images, curious interpretations of the otherworldly details can be observed. The generated captions are again posted on Twitter to encourage discussions about Bosch's masterpieces and the AI technology in general.
Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes
Authors: Ghosh Soumitra , Ekbal Asif , Bhattacharyya Pushpak
Published: July 2022

The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB labels. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance. For comprehensive evaluation of our proposed method, we compare it to several state-of-the-art approaches on the existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset. Empirical evaluation suggests that temporal and emotional information can substantially improve the detection of PB and TB.
AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society
Authors: Hu Yuxuan , Wu Shiqing , Jiang Chenting , Li Weihua , Bai Quan , Roehrer Erin
Published: July 2022

AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.
Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection
Authors: Kou Ziyi , Shang Lanyu , Zhang Yang , Yue Zhenrui , Zeng Huimin , Wang Dong
Published: July 2022

In this paper, we study an explainable COVID-19 misinformation detection problem where the goal is to accurately identify COVID-19 misleading posts on social media and explain the posts with natural language explanations (NLEs). Our problem is motivated by the limitations of current explainable misinformation detection approaches that cannot provide NLEs for COVID-19 posts due to the lack of sufficient professional COVID-19 knowledge for supervision. To address such a limitation, we develop CEA-COVID, a crowd-expert-AI framework that jointly exploits the common logical reasoning ability of online crowd workers and the professional knowledge of COVID-19 experts to effectively generate NLEs for detecting and explaining COVID-19 misinformation. We evaluate CEA-COVID using two public COVID-19 misinformation datasets on social media. Results demonstrate that CEA-COVID outperforms existing explainable misinformation detection models in terms of both explainability and detection accuracy.
Ownership Concentration and Wealth Inequality in Russia
Authors: Polovnikov Kirill , Pospelov Nikita , Skougarevskiy Dmitriy
Published: July 2022

Knowledge of beneficial owners of companies is key to monitoring and managing wealth inequality in any country. Here we propose a robust and scalable network-based algorithm to reveal hidden ultimate owners in public ownership data. Our approach is based on the idea of Katz centrality in complex networks and circumvents the problem of cyclic ownership used to obscure effective control

through closed chains of intermediaries. When applied to a country-scale directed ownership network with 6 million nodes, the algorithm identifies ultimate holders of every organisation in 2021’s Russia. The distribution of asset ownership in the country follows a power law, indicating strong wealth inequality with Gini index of 0.93. 51.7% of net assets of non-financial companies are ultimately held by the state and state-owned enterprises, 25.0% — by individuals (incl. 3.4% held by Forbes–200-listed individuals), and 11.3% are owned by foreign entities (incl. 5.7% in tax havens).
DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections
Authors: Relia Kunal
Published: July 2022

The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We analyze its computational complexity and develop a heuristic algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We also present an empirical analysis of feasibility and utility traded-off. 

Moreover, even when the attributes of candidates and voters coincide, it is important to treat them separately as diversity does not imply representation and vice versa. This is to say that having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.