bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition
Authors: Zhu Yaohui , Liu Chenlong , Jiang Shuqiang
Published: July 2020

The goal of few-shot image recognition is to distinguish different categories with only one or a few training samples. Previous works of few-shot learning mainly work on general object images. And current solutions usually learn a global image representation from training tasks to adapt novel tasks. However, fine-gained categories are distinguished by subtle and local parts, which could not be captured by global representations effectively. This may hinder existing few-shot learning approaches from dealing with fine-gained categories well. In this work, we propose a multi-attention meta-learning (MattML) method for few-shot fine-grained image recognition (FSFGIR). Instead of using only base learner for general feature learning, the proposed meta-learning method uses attention mechanisms of the base learner and task learner to capture discriminative parts of images. The base learner is equipped with two convolutional block attention modules (CBAM) and a classifier. The two CBAM can learn diverse and informative parts. And the initial weights of classifier are attended by the task learner, which gives the classifier a task-related sensitive initialization. For adaptation, the gradient-based meta-learning approach is employed by updating the parameters of two CBAM and the attended classifier, which facilitates the updated base learner to adaptively focus on discriminative parts. We experimentally analyze the different components of our method, and experimental results on four benchmark datasets demonstrate the effectiveness and superiority of our method.
Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms
Authors: Li Boyang , Cheng Yurong , Yuan Ye , Wang Guoren , Chen Lei
Published: July 2020

In recent years, 3D spatial crowdsourcing platforms become popular, in which users and workers travel together to their assigned workplaces for services, such as InterestingSport and Nanguache. A typical problem over 3D spatial crowdsourcing platforms is to match users with suitable workers and workplaces. Existing studies all ignored that the workers and users assigned to the same workplace should arrive almost at the same time, which is very practical in the real world. Thus, in this paper, we propose a new Simultaneous Arrival Matching (SAM), which enables workers and users to arrive at their assigned workplace within a given tolerant time. We find that the new considered arriving time constraint breaks the monotonic additivity of the result set. Thus, it brings a large challenge in designing effective and efficient algorithms for the SAM. We design Sliding Window algorithm and Threshold Scanning algorithm to solve the SAM. We conduct the experiments on real and synthetic datasets, experimental results show the effectiveness and efficiency of our algorithms.
Multi-Channel Graph Neural Networks
Authors: Zhou Kaixiong , Song Qingquan , Huang Xiao , Zha Daochen , Zou Na , Hu Xia
Published: July 2020

The classification of graph-structured data has be-come increasingly crucial in many disciplines. It has been observed that the implicit or explicit hierarchical community structures preserved in real-world graphs could be useful for downstream classification applications. A straightforward way to leverage the hierarchical structure is to make use the pooling algorithms to cluster nodes into fixed groups, and shrink the input graph layer by layer to learn the pooled graphs.However, the pool shrinking discards the graph details to make it hard to distinguish two non-isomorphic graphs, and the fixed clustering ignores the inherent multiple characteristics of nodes. To compensate the shrinking loss and learn the various nodes’ characteristics, we propose the multi-channel graph neural networks (MuchGNN). Motivated by the underlying mechanisms developed in convolutional neural networks, we define the tailored graph convolutions to learn a series of graph channels at each layer, and shrink the graphs hierarchically to en-code the pooled structures. Experimental results on real-world datasets demonstrate the superiority of MuchGNN over the state-of-the-art methods.
Rumor Detection on Social Media with Graph Structured Adversarial Learning
Authors: Yang Xiaoyu , Lyu Yuefei , Tian Tian , Liu Yifei , Liu Yudong , Zhang Xi
Published: July 2020

The wide spread of rumors on social media has caused tremendous effects in both the online and offline world. In addition to text information, recent detection methods began to exploit the graph structure in the propagation network. However, without a rigorous design, rumors may evade such graph models using various camouflage strategies by perturbing the structured data. Our focus in this work is to develop a robust graph-based detector to identify rumors on social media from an adversarial perspective. We first build a heterogeneous information network to model the rich information among users, posts, and user comments for detection. We then propose a graph adversarial learning framework, where the attacker tries to dynamically add intentional perturbations on the graph structure to fool the detector, while the detector would learn more distinctive structure features to resist such perturbations. In this way, our model would be enhanced in both robustness and generalization. Experiments on real-world datasets demonstrate that our model achieves better results than the state-of-the-art methods.
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Authors: Zhang Chenwei , Li Yaliang , Du Nan , Fan Wei , Yu Philip S.
Published: July 2020

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SynonymNet to determine whether or not two given entities are synonym with each other. Instead of using entities features, SynonymNet makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve and 3.19% in terms of Mean Average Precision compared to the best baseline method.
Pitfalls of Learning a Reward Function Online
Authors: Armstrong Stuart , Leike Jan , Orseau Laurent , Legg Shane
Published: July 2020

In some agent designs like inverse reinforcement learning an agent needs to learn its own reward function. Learning the reward function and optimising for it are typically two different processes, usually performed at different stages. We consider a continual (``one life'') learning approach where the agent both learns the reward function and optimises for it at the same time. We show that this comes with a number of pitfalls, such as deliberately manipulating the learning process in one direction, refusing to learn, ``learning'' facts already known to the agent, and making decisions that are strictly dominated (for all relevant reward functions). We formally introduce two desirable properties: the first is `unriggability', which prevents the agent from steering the learning process in the direction of a reward function that is easier to optimise. The second is `uninfluenceability', whereby the reward-function learning process operates by learning facts about the environment. We show that an uninfluenceable process is automatically unriggable, and if the set of possible environments is sufficiently large, the converse is true too.
Evaluating Committees for Representative Democracies: the Distortion and Beyond
Authors: Jaworski Michał , Skowron Piotr
Published: July 2020

We study a model where a group of representatives is elected to make a series of decisions on behalf of voters. The quality of such a representative committee is judged based on the extent to which the decisions it makes are consistent with the voters' preferences. We assume the set of issues on which the committee will make the decisions is unknown---a committee is elected based on the preferences of the voters over the candidates, which only reflect how similar are the preferences of the voters and candidates regarding the issues. In this model we theoretically and experimentally assess qualities of various multiwinner election rules.
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Authors: Åkerblom Niklas , Chen Yuxin , Haghir Chehreghani Morteza
Published: July 2020

Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance. Finally, we demonstrate the performance of our methods via several real-world experiments on Luxembourg SUMO Traffic dataset.
Batch Decorrelation for Active Metric Learning
Authors: Kumari Priyadarshini , Goru Ritesh , Chaudhuri Siddhartha , Chaudhuri Subhasis
Published: July 2020

We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.
Tensor-based multi-view label enhancement for multi-label learning
Authors: Zhang Fangwen , Jia Xiuyi , Li Weiwei
Published: July 2020

Label enhancement (LE) is a procedure of recovering the label distributions from the logical labels in the multi-label data, the purpose of which is to better represent and mine the label ambiguity problem through the form of label distribution. Existing LE work mainly concentrates on how to leverage the topological information of the feature space and the correlation among the labels, and all are based on single view data. In view of the fact that there are many multi-view data in real-world applications, which can provide richer semantic information from different perspectives, this paper first presents a multi-view label enhancement problem and proposes a tensor-based multi-view label enhancement method, named TMV-LE. Firstly, we introduce the tensor factorization to get the common subspace which contains the high-order relationships among different views. Secondly, we use the common representation and multiple views to jointly mine a more comprehensive topological structure in the dataset. Finally, the topological structure of the feature space is migrated to the label space to get the label distributions. Extensive comparative studies validate that the performance of multi-view multi-label learning can be improved significantly with TMV-LE.
Learning Personalized Itemset Mapping for Cross-Domain Recommendation
Authors: Zhang Yinan , Liu Yong , Han Peng , Miao Chunyan , Cui Lizhen , Li Baoli , Tang Haihong
Published: July 2020

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.
Accelerating Stratified Sampling SGD by Reconstructing Strata
Authors: Liu Weijie , Qian Hui , Zhang Chao , Shen Zebang , Xie Jiahao , Zheng Nenggan
Published: July 2020

In this paper, a novel stratified sampling strategy is designed to accelerate the mini-batch SGD. We derive a new iteration-dependent surrogate which bound the stochastic variance from above. To keep the strata minimizing this surrogate with high probability, a stochastic stratifying algorithm is adopted in an adaptive manner, that is, in each iteration, strata are reconstructed only if an easily verifiable condition is met. Based on this novel sampling strategy, we propose an accelerated mini-batch SGD algorithm named SGD-RS. Our theoretical analysis shows that the convergence rate of SGD-RS is superior to the state-of-the-art. Numerical experiments corroborate our theory and demonstrate that SGD-RS achieves at least 3.48-times speed-ups compared to vanilla minibatch SGD.
Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation
Authors: Xie Ruobing , Qiu Zhijie , Rao Jun , Liu Yi , Zhang Bo , Lin Leyu
Published: July 2020

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.
Partial Adversarial Behavior Deception in Security Games
Authors: Nguyen Thanh H. , Sinha Arunesh , He He
Published: July 2020

Learning attacker behavior is an important research topic in security games as security agencies are often uncertain about attackers' decision making. Previous work has focused on developing various behavioral models of attackers based on historical attack data. However, a clever attacker can manipulate its attacks to fail such attack-driven learning, leading to ineffective defense strategies. We study attacker behavior deception with three main contributions. First, we propose a new model, named partial behavior deception model, in which there is a deceptive attacker (among multiple attackers) who controls a portion of attacks. Our model captures real-world security scenarios such as wildlife protection in which multiple poachers are present. Second, we introduce a new scalable algorithm, GAMBO, to compute an optimal deception strategy of the deceptive attacker. Our algorithm employs the projected gradient descent and uses the implicit function theorem for the computation of gradient. Third, we conduct a comprehensive set of experiments, showing a significant benefit for the attacker and loss for the defender due to attacker deception.
Learning From Multi-Dimensional Partial Labels
Authors: Wang Haobo , Liu Weiwei , Zhao Yang , Hu Tianlei , Chen Ke , Chen Gang
Published: July 2020

Multi-dimensional classification has attracted huge attention from the community. Though most studies consider fully annotated data, in real practice obtaining fully labeled data in MDC tasks is usually intractable. In this paper, we propose a novel learning paradigm: MultiDimensional Partial Label Learning (MDPL) where the ground-truth labels of each instance are concealed in multiple candidate label sets. We first introduce the partial hamming loss for MDPL that incurs a large loss if the predicted labels are not in candidate label sets, and provide an empirical risk minimization (ERM) framework. Theoretically, we rigorously prove the conditions for ERM learnability of MDPL in both independent and dependent cases. Furthermore, we present two MDPL algorithms under our proposed ERM framework. Comprehensive experiments on both synthetic and real-world datasets validate the effectiveness of our proposals.
A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation
Authors: Zhu Feng , Wang Yan , Chen Chaochao , Liu Guanfeng , Zheng Xiaolin
Published: July 2020

The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed to improve the recommendation accuracies on both domains simultaneously. However, dual-target CDR faces two new challenges: (1) how to generate more representative user and item embeddings, and (2) how to effectively optimize the user/item embeddings on each domain. To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR. In GA-DTCDR, we first construct two separate heterogeneous graphs based on the rating and content information from two domains to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common users learned from both domains. Both steps significantly enhance the quality of user and item embeddings and thus improve the recommendation accuracy on each domain. Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches.
RDF-to-Text Generation with Graph-augmented Structural Neural Encoders
Authors: Gao Hanning , Wu Lingfei , Hu Po , Xu Fangli
Published: July 2020

The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.
One-Shot Neural Architecture Search via Novelty Driven Sampling
Authors: Zhang Miao , Li Huiqi , Pan Shirui , Liu Taoping , Su Steven
Published: July 2020

One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to search for the best performing architecture, adopting a bilevel optimization pattern with assuming this validation accuracy approximates to the test accuracy after re-training. However, recent works have found that there is no positive correlation between the above validation accuracy and test accuracy for these One-Shot NAS methods, and this reward based sampling for supernet training also entails the rich-get-richer problem. To handle this deceptive problem, this paper presents a new approach, Efficient Novelty-driven Neural Architecture Search, to sample the most abnormal architecture to train the supernet. Specifically, a single-path supernet is adopted, and only the weights of a single architecture sampled by our novelty search are optimized in each step to reduce the memory demand greatly. Experiments demonstrate the effectiveness and efficiency of our novelty search based architecture sampling method.
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
Authors: Wen Jie , Zhang Zheng , Xu Yong , Zhang Bob , Fei Lunke , Xie Guo-Sen
Published: July 2020

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, \emph{i.e.}, learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.
BERT-PLI: Modeling Paragraph-Level Interactions for Legal Case Retrieval
Authors: Shao Yunqiu , Mao Jiaxin , Liu Yiqun , Ma Weizhi , Satoh Ken , Zhang Min , Ma Shaoping
Published: July 2020

Legal case retrieval is a specialized IR task that involves retrieving supporting cases given a query case. Compared with traditional ad-hoc text retrieval, the legal case retrieval task is more challenging since the query case is much longer and more complex than common keyword queries. Besides that, the definition of relevance between a query case and a supporting case is beyond general topical relevance and it is therefore difficult to construct a large-scale case retrieval dataset, especially one with accurate relevance judgments. To address these challenges, we propose BERT-PLI, a novel model that utilizes BERT to capture the semantic relationships at the paragraph-level and then infers the relevance between two cases by aggregating paragraph-level interactions. We fine-tune the BERT model with a relatively small-scale case law entailment dataset to adapt it to the legal scenario and employ a cascade framework to reduce the computational cost. We conduct extensive experiments on the benchmark of the relevant case retrieval task in COLIEE 2019. Experimental results demonstrate that our proposed method outperforms existing solutions.
Hierarchical Matching Network for Heterogeneous Entity Resolution
Authors: Fu Cheng , Han Xianpei , He Jiaming , Sun Le
Published: July 2020

Entity resolution (ER) aims to identify data records referring to the same real-world entity. Most existing ER approaches rely on the assumption that the entity records to be resolved are homogeneous, i.e., their attributes are aligned. Unfortunately, entities in real-world datasets are often heterogeneous, usually coming from different sources and being represented using different attributes. Furthermore, the entities’ attribute values may be redundant, noisy, missing, misplaced, or misspelled—we refer to it as the dirty data problem. To resolve the above problems, this paper proposes an end-to-end hierarchical matching network (HierMatcher) for entity resolution, which can jointly match entities in three levels—token, attribute, and entity. At the token level, a cross-attribute token alignment and comparison layer is designed to adaptively compare heterogeneous entities. At the attribute level, an attribute-aware attention mechanism is proposed to denoise dirty attribute values. Finally, the entity level matching layer effectively aggregates all matching evidence for the final ER decisions. Experimental results show that our method significantly outperforms previous ER methods on homogeneous, heterogeneous and dirty datasets.
Generating Reasonable Legal Text through the Combination of Language Modeling and Question Answering
Authors: Huang Weijing , Liao Xianfeng , Xie Zhiqiang , Qian Jiang , Zhuang Bojin , Wang Shaojun , Xiao Jing
Published: July 2020

Due to the improvement of Language Modeling, the emerging NLP assistant tools aiming for text generation greatly reduce the human workload on writing documents. However, the generation of legal text faces greater challenges than ordinary texts because of its high requirement for keeping logic reasonable, which can not be guaranteed by Language Modeling right now. To generate reasonable legal documents, we propose a novel method CoLMQA, which (1) combines Language Modeling and Question Answering, (2) generates text with slots by Language Modeling, and (3) fills the slots by our proposed Question Answering method named Transformer-based Key-Value Memory Networks. In CoLMQA, the slots represent the text part that needs to be highly constrained by logic, such as the name of the law and the number of the law article. And the Question Answering fills the slots in context with the help of Legal Knowledge Base to keep logic reasonable. The experiment verifies the quality of legal documents generated by CoLMQA, surpassing the documents generated by pure Language Modeling.
A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition
Authors: Tan Conghui , Jiang Di , Peng Jinhua , Wu Xueyang , Xu Qian , Yang Qiang
Published: July 2020

Due to the rising awareness of privacy protection and the voluminous scale of speech data, it is becoming infeasible for Automatic Speech Recognition (ASR) system developers to train the acoustic model with complete data as before. In this paper, we propose a novel Divide-and-Merge paradigm to solve salient problems plaguing the ASR field. In the Divide phase, multiple acoustic models are trained based upon different subsets of the complete speech data, while in the Merge phase two novel algorithms are utilized to generate a high-quality acoustic model based upon those trained on data subsets. We first propose the Genetic Merge Algorithm (GMA), which is a highly specialized algorithm for optimizing acoustic models but suffers from low efficiency. We further propose the SGD-Based Optimizational Merge Algorithm (SOMA), which effectively alleviates the efficiency bottleneck of GMA and maintains superior performance. Extensive experiments on public data show that the proposed methods can significantly outperform the state-of-the-art.
Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction
Authors: Liu Jie , Chen Shaowei , Wang Bingquan , Zhang Jiaxin , Li Na , Xu Tong
Published: July 2020

Joint entity and relation extraction is critical for many natural language processing (NLP) tasks, which has attracted increasing research interest. However, it is still faced with the challenges of identifying the overlapping relation triplets along with the entire entity boundary and detecting the multi-type relations. In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges. The key of our model is devising a supervised multi-head self-attention mechanism as the relation detection module to learn the token-level correlation for each relation type separately. With the attention mechanism, our model can effectively identify overlapping relations and flexibly predict the relation type with its corresponding intensity. To verify the effectiveness of our model, we conduct comprehensive experiments on two benchmark datasets. The experimental results demonstrate that our model achieves state-of-the-art performances.
Hierarchical Multi-task Learning for Organization Evaluation of Argumentative Student Essays
Authors: Song Wei , Song Ziyao , Liu Lizhen , Fu Ruiji
Published: July 2020

Organization evaluation is an important dimension of automated essay scoring.

This paper focuses on discourse element (i.e., functions of sentences and paragraphs) based organization evaluation.

Existing approaches mostly separate discourse element identification and organization evaluation.

In contrast, we propose a neural hierarchical multi-task learning approach for jointly optimizing sentence and paragraph level discourse element identification and organization evaluation.

We represent the organization as a grid to simulate the visual layout of an essay and integrate discourse elements at multiple linguistic levels.

Experimental results show that the multi-task learning based organization evaluation can achieve significant improvements compared with existing work and pipeline baselines.

Multiple level discourse element identification also benefits from multi-task learning through mutual enhancement.
Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach
Authors: Chan Hau , Tran-Thanh Long , Viswanathan Vignesh
Published: July 2020

Standard disaster response involves using drones (or helicopters) for reconnaissance and using people on the ground to mitigate the damage. In this paper, we look at the problem of wildfires and propose an efficient resource allocation strategy to cope with both dynamically changing environment and uncertainty. In particular, we propose Firefly, a new resource allocation algorithm, that can provably achieve optimal or near optimal solutions with high probability by first efficiently allocating observation drones to collect information to reduce uncertainty, and then allocate the firefighting units to extinguish fire. For the former, Firefly uses a combination of maximum set coverage formulation and a novel utility estimation technique, and it uses a knapsack formulation to calculate the allocation for the latter. We also demonstrate empirically by using a real-world dataset that Firefly achieves up to 80-90% performance of the offline optimal solution, even with a small amount of drones, in most of the cases.
Almost Group Envy-free Allocation of Indivisible Goods and Chores
Authors: Aziz Haris , Rey Simon
Published: July 2020

We consider a multi-agent resource allocation setting in which an agent's utility may decrease or increase when an item is allocated. We take the group envy-freeness concept that is well-established in the literature and present stronger and relaxed versions that are especially suitable for the allocation of indivisible items. Of particular interest is a concept called group envy-freeness up to one item (GEF1). We then present a clear taxonomy of the fairness concepts. We study which fairness concepts guarantee the existence of a fair allocation under which preference domain. For two natural classes of additive utilities, we design polynomial-time algorithms to compute a GEF1 allocation. We also prove that checking whether a given allocation satisfies GEF1 is coNP-complete when there are either only goods, only chores or both.
Forecasting Avian Migration Patterns using a Deep Bidirectional RNN Augmented with an Auxiliary Task
Authors: Owoeye Kehinde
Published: July 2020

Early forecasting of bird migration patterns has important application for example in reducing avian biodiversity loss. An estimated 100 million to 1 billion birds are known to die yearly during migration due to fatal collisions with human made infrastructures such as buildings, high tension lines, wind turbines and aircrafts thus raising a huge concern for conservationists. Building models that can forecast accurate migration patterns is therefore important to enable the optimal management of these critical infrastructures with the sole aim of reducing biodiversity loss. While previous works have largely focused on the task of forecasting migration intensities and the onset of just one migration state, predicting several migration states at even finer granularity is more useful towards optimally managing the infrastructures that causes these deaths. In this work, we consider the task of forecasting migration patterns of the popular Turkey Vulture (Cathartes aura) collected with the aid of satellite telemetry for multiple years at a resolution of one hour. We use a deep Bidirectional-GRU recurrent neural network augmented with an auxiliary task where the state information of one layer is used to initialise the other. Empirical results on a variety of experiments with our approach show we can accurately forecast migration up to one week in advance performing better than a variety of baselines.
BitcoinHeist: Topological Data Analysis for Ransomware Prediction on the Bitcoin Blockchain
Authors: Akcora Cuneyt G. , Li Yitao , Gel Yulia R. , Kantarcioglu Murat
Published: July 2020

Recent proliferation of cryptocurrencies that allow for pseudo-anonymous transactions has resulted in a spike of various e-crime activities and, particularly, cryptocurrency payments in hacking attacks demanding ransom by encrypting sensitive user data. Currently, most hackers use Bitcoin for payments, and existing ransomware detection tools depend only on a couple of heuristics and/or tedious data gathering steps. By capitalizing on the recent advances in Topological Data Analysis, we propose a novel efficient and tractable framework to automatically predict new ransomware transactions in a ransomware family, given only limited records of past transactions. Moreover, our new methodology exhibits high utility to detect emergence of new ransomware families, that is, detecting ransomware with no past records of transactions.
Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection
Authors: Cai Xia
Published: July 2020

Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.
The Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert
Authors: WANG Cheng
Published: July 2020

As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80\% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94\% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1\%).
Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models
Authors: Ma Pingchuan , Wang Shuai , Liu Jin
Published: July 2020

Natural language processing (NLP) models have been increasingly used in sensitive application domains including credit scoring, insurance, and loan assessment. Hence, it is critical to know that the decisions made by NLP models are free of unfair bias toward certain subpopulation groups. In this paper, we propose a novel framework employing metamorphic testing, a well-established software testing scheme, to test NLP models and find discriminatory inputs that provoke fairness violations. Furthermore, inspired by recent breakthroughs in the certified robustness of machine learning, we formulate NLP model fairness in a practical setting as (ε, k)-fairness and accordingly smooth the model predictions to mitigate fairness violations. We demonstrate our technique using popular (commercial) NLP models, and successfully flag thousands of discriminatory inputs that can cause fairness violations. We further enhance the evaluated models by adding certified fairness guarantee at a modest cost.
A Unified Model for Financial Event Classification, Detection and Summarization
Authors: Li Quanzhi , Zhang Qiong
Published: July 2020

There is massive amount of news on financial events every day. In this paper, we present a unified model for detecting, classifying and summarizing financial events. This model exploits a multi-task learning approach, in which a pre-trained BERT model is used to encode the news articles, and the encoded information are shared by event type classification, detection and summarization tasks. For event summarization, we use a Transformer structure as the decoder. In addition to the input document encoded by BERT, the decoder also utilizes the predicted event type and cluster information, so that it can focus on the specific aspects of the event when generating summary. Our experiments show that our approach outperforms other methods.
Methodological Issues in Recommender Systems Research (Extended Abstract)
Authors: Ferrari Dacrema Maurizio , Cremonesi Paolo , Jannach Dietmar
Published: July 2020

The development of continuously improved machine learning algorithms for personalized item ranking lies at the core of today's research in the area of recommender systems.

Over the years, the research community has developed widely-agreed best practices for comparing algorithms and demonstrating progress with offline experiments. Unfortunately, we find this accepted research practice can easily lead to phantom progress due to the following reasons: limited reproducibility, comparison with complex but weak and non-optimized baseline algorithms, over-generalization from a small set of experimental configurations. To assess the extent of such problems, we analyzed 18 research papers published recently at top-ranked conferences. Only 7 were reproducible with reasonable effort, and 6 of them could often be outperformed by relatively simple heuristic methods, e.g., nearest neighbors. In this paper, we discuss these observations in detail, and reflect on the related fundamental problem of over-reliance on offline experiments in recommender systems research.
Explanation Perspectives from the Cognitive Sciences---A Survey
Authors: Srinivasan Ramya , Chander Ajay
Published: July 2020

With growing adoption of AI across fields such as healthcare, finance, and the justice system, explaining an AI decision has become more important than ever before. Development of human-centric explainable AI (XAI) systems necessitates an understanding of the requirements of the human-in-the-loop seeking the explanation. This includes the cognitive behavioral purpose that the explanation serves for its recipients, and the structure that the explanation uses to reach those ends. An understanding of the psychological foundations of explanations is thus vital for the development of effective human-centric XAI systems. Towards this end, we survey papers from the cognitive science literature that address the following broad questions: (1) what is an explanation, (2) what are explanations for, and 3) what are the characteristics of good and bad explanations. We organize the insights gained therein by means of highlighting the advantages and shortcomings of various explanation structures and theories, discuss their applicability across different domains, and analyze their utility to various types of humans-in-the-loop. We summarize the key takeaways for human-centric design of XAI systems, and recommend strategies to bridge the existing gap between XAI research and practical needs. We hope this work will spark the development of novel human-centric XAI systems.
The Blind Men and the Elephant: Integrated Offline/Online Optimization Under Uncertainty
Authors: Filippo Allegra De , Lombardi Michele , Milano Michela
Published: July 2020

Optimization problems under uncertainty are traditionally solved either via offline or online methods. Offline approaches can obtain high-quality robust solutions, but have a considerable computational cost. Online algorithms can react to unexpected events once they are observed, but often run under strict time constraints, preventing the computation of optimal solutions. Many real world problems, however, have both offline and online elements: a substantial amount of time and information is frequently available (offline) before an online problem is solved (e.g. energy production forecasts, or historical travel times in routing problems); in other cases both offline (i.e. strategic) and online (i.e. operational) decisions need to be made. Surprisingly, the interplay of these offline and online phases has received little attention: like in the blind men and the elephant tale, we risk missing the whole picture, and the benefits that could come from integrated offline/online optimization. In this survey we highlight the potential shortcomings of pure methods when applied to mixed offline/online problems, we review the strategies that have been designed to take advantage of this integration, and we suggest directions for future research.
Recent Developments in Boolean Matrix Factorization
Authors: Miettinen Pauli , Neumann Stefan
Published: July 2020

The goal of Boolean Matrix Factorization (BMF) is to approximate a given binary matrix as the product of two low-rank binary factor matrices, where the product of the factor matrices is computed under the Boolean algebra. While the problem is computationally hard, it is also attractive because the binary nature of the factor matrices makes them highly interpretable. In the last decade, BMF has received a considerable amount of attention in the data mining and formal concept analysis communities and, more recently, the machine learning and the theory communities also started studying BMF. In this survey, we give a concise summary of the efforts of all of these communities and raise some open questions which in our opinion require further investigation.
Pure-Past Linear Temporal and Dynamic Logic on Finite Traces
Authors: De Giacomo Giuseppe , Stasio Antonio Di , Fuggitti Francesco , Rubin Sasha
Published: July 2020

We review PLTLf and PLDLf, the pure-past versions of the well-known logics on finite traces LTLf and LDLf, respectively. PLTLf and PLDLf are logics about the past, and so scan the trace backwards from the end towards the beginning. Because of this, we can exploit a foundational result on reverse languages to get an exponential improvement, over LTLf /LDLf , for computing the corresponding DFA. This exponential improvement is reflected in several forms of sequential decision making involving temporal specifications, such as planning and decision problems in non-deterministic and non-Markovian domains. Interestingly, PLTLf (resp., PLDLf ) has the same expressive power as LTLf (resp., LDLf ), but transforming a PLTLf (resp., PLDLf ) formula into its equivalent LTLf (resp.,LDLf) is quite expensive. Hence, to take advantage of the exponential improvement, properties of interest must be directly expressed in PLTLf /PLDLf .
Learning for Graph Matching and Related Combinatorial Optimization Problems
Authors: Yan Junchi , Yang Shuang , Hancock Edwin
Published: July 2020

This survey gives a selective review of recent development of machine learning (ML) for combinatorial optimization (CO), especially for graph matching. The synergy of these two well-developed areas (ML and CO) can potentially give transformative change to artificial intelligence, whose foundation relates to these two building blocks. For its representativeness and wide-applicability, this paper is more focused on the problem of weighted graph matching, especially from the learning perspective. For graph matching, we show that many learning techniques e.g. convolutional neural networks, graph neural networks, reinforcement learning can be effectively incorporated in the paradigm for extracting the node features, graph structure features, and even the matching engine. We further present outlook for the new settings for learning graph matching, and direction towards more integrated combinatorial optimization solvers with prediction models, and also the mutual embrace of traditional solver and machine learning components.