bluuebunny/crossref_metadata_2025 huggingface dataset:
Nonparametric Stochastic Contextual Bandits
Authors: Guan Melody , Jiang Heinrich
Published: June 2022

We analyze the K-armed bandit problem where the reward for each arm is a noisy realization based on an observed context under mild nonparametric assumptions.We attain tight results for top-arm identification and a sublinear regret of Õ(T1+D/(2+D), where D is the context dimension, for a modified UCB algorithm that is simple to implement. We then give global intrinsic dimension dependent and ambient dimension independent regret bounds. We also discuss recovering topological structures within the context space based on expected bandit performance and provide an extension to infinite-armed contextual bandits. Finally, we experimentally show the improvement of our algorithm over existing approaches for both simulated tasks and MNIST image classification.
Lagrangian Constrained Community Detection
Authors: Ganji Mohadeseh , Bailey James , Stuckey Peter
Published: June 2022

Semi-supervised or constrained community detection incorporates side information to findcommunities of interest in complex networks. The supervision is often represented as constraints such as known labels and pairwise constraints. Existing constrained community detection approaches often fail to fully benefit from the available side information. This results in poor performance for scenarios such as: when the constraints are required to be fully satisfied, when there is a high confidence about the correctness of the supervision information, and in situations where the side information is expensive or hard to achieve and is only available in a limited amount. In this paper, we propose a new constrained community detection algorithm based on Lagrangian multipliers to incorporate and fully satisfy the instance level supervisio nconstraints. Our proposed algorithm can more fully utilise available side information and find better quality solutions. Our experiments on real and synthetic data sets show our proposed LagCCD algorithm outperforms existing algorithms in terms of solution quality, ability to satisfy the constraints and noise resistance.
Data Poisoning Attacks on Multi-Task Relationship Learning
Authors: Zhao Mengchen , An Bo , Yu Yaodong , Liu Sulin , Pan Sinno
Published: June 2022

Multi-task learning (MTL) is a machine learning paradigm that improves the performance of each task by exploiting useful information contained in multiple related tasks. However, the relatedness of tasks can be exploited by attackers to launch data poisoning attacks, which has been demonstrated a big threat to single-task learning. In this paper, we provide the first study on the vulnerability of MTL. Specifically, we focus on multi-task relationship learning (MTRL) models, a popular subclass of MTL models where task relationships are quantized and are learned directly from training data. We formulate the problem of computing optimal poisoning attacks on MTRL as a bilevel program that is adaptive to arbitrary choice of target tasks and attacking tasks. We propose an efficient algorithm called PATOM for computing optimal attack strategies. PATOM leverages the optimality conditions of the subproblem of MTRL to compute the implicit gradients of the upper level objective function. Experimental results on real-world datasets show that MTRL models are very sensitive to poisoning attacks and the attacker can significantly degrade the performance of target tasks, by either directly poisoning the target tasks or indirectly poisoning the related tasks exploiting the task relatedness. We also found that the tasks being attacked are always strongly correlated, which provides a clue for defending against such attacks.
On Trivial Solution and High Correlation Problems in Deep Supervised Hashing
Authors: Guo Yuchen , Zhao Xin , Ding Guiguang , Han Jungong
Published: June 2022

Deep supervised hashing (DSH), which combines binary learning and convolutional neural network, has attracted considerable research interests and achieved promising performance for highly efficient image retrieval. In this paper, we show that the widely used loss functions, pair-wise loss and triplet loss, suffer from the trivial solution problem and usually lead to highly correlated bits in practice, limiting the performance of DSH. One important reason is that it is difficult to incorporate proper constraints into the loss functions under the mini-batch based optimization algorithm. To tackle these problems, we propose to adopt ensemble learning strategy for deep model training. We found out that this simple strategy is capable of effectively decorrelating different bits, making the hashcodes more informative. Moreover, it is very easy to parallelize the training and support incremental model learning, which are very useful for real-world applications but usually ignored by existing DSH approaches. Experiments on benchmarks demonstrate the proposed ensemble based DSH can improve the performance of DSH approaches significant.
WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation
Authors: Yu Lu , Zhang Chuxu , Pei Shichao , Sun Guolei , Zhang Xiangliang
Published: June 2022

Top-N item recommendation techniques, e.g., pairwise models, learn the rank of users' preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for "cold-start" users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and high-order proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for cold-start users with 6% on average.
A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
Authors: Cheng Weiyu , Shen Yanyan , Zhu Yanmin , Huang Linpeng
Published: June 2022

Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limited due to the high cost. Thus, inferring fine-grained urban air quality information is becoming an essential issue for both government and people. In this paper, we propose a generic neural approach, named ADAIN, for urban air quality inference. We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology. ADAIN combines feedforward and recurrent neural networks for modeling static and sequential features as well as capturing deep feature interactions effectively. A novel attempt of ADAIN is an attention-based pooling layer that automatically learns the weights of features from different monitoring stations, to boost the performance. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with various state-of-the-art solutions.
Learning User Preferences to Incentivize Exploration in the Sharing Economy
Authors: Hirnschall Christoph , Singla Adish , Tschiatschek Sebastian , Krause Andreas
Published: June 2022

We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction of options does not have any reviews available. Such options are frequently neglected as viable choices, and in turn are unlikely to be evaluated, creating a vicious cycle. Platforms can engage users to deviate from their preferred choice by offering monetary incentives for choosing a different option instead. To efficiently learn the optimal incentives to offer, we consider structural information in user preferences and introduce a novel algorithm---Coordinated Online Learning (CoOL)---for learning with structural information modeled as convex constraints. We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb. Our findings suggest that our approach is well-suited to learn appropriate incentives and increase exploration on the investigated platform.
Modeling Attention and Memory for Auditory Selection in a Cocktail Party Environment
Authors: Xu Jiaming , Shi Jing , Liu Guangcan , Chen Xiuyi , Xu Bo
Published: June 2022

Developing a computational auditory model to solve the cocktail party problem has long bedeviled scientists, especially for a single microphone recording. Although recent deep learning based frameworks have made significant progress in multi-talker mixed speech separation, most existing deep learning based methods, focusing on separating all the speech channels rather than selectively attending the target speech and ignoring other sounds, may fail to offer a satisfactory solution in a complex auditory scene where the number of input sounds is usually uncertain and even dynamic. In this work, we employ ideas from auditory selective attention of behavioral and cognitive neurosciences and from recent advances of memory-augmented neural networks. Specifically, a unified Auditory Selection framework with Attention and Memory (dubbed ASAM) is proposed. Our ASAM first accumulates the prior knowledge (that is the acoustic feature to one specific speaker) into a life-long memory during the training phase, meanwhile a speech perceptor is trained to extract the temporal acoustic feature and update the memory online when a salient speech is given. Then, the learned memory is utilized to interact with the mixture input to attend and filter the target frequency out from the mixture stream. Finally, the network is trained to minimize the reconstruction error of the attended speech. We evaluate the proposed approach on WSJ0 and THCHS-30 datasets and the experimental results demonstrate that our approach successfully conducts two auditory selection tasks: the top-down task-specific attention (e.g. to follow a conversation with friend) and the bottom-up stimulus-driven attention (e.g. be attracted by a salient speech). Compared with deep clustering based methods, our method conducts competitive advantages especially in a real noise environment (e.g. street junction). Our code is available at https://github.com/jacoxu/ASAM.
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Authors: Ballinger Brandon , Hsieh Johnson , Singh Avesh , Sohoni Nimit , Wang Jack , Tison Geoffrey , Marcus Gregory , Sanchez Jose , Maguire Carol , Olgin Jeffrey , Pletcher Mark
Published: June 2022

We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised training methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.
Group-Pair Convolutional Neural Networks for Multi-View Based 3D Object Retrieval
Authors: Gao Zan , Wang Deyu , He Xiangnan , Zhang Hua
Published: June 2022

In recent years, research interest in object retrieval has shifted from 2D towards 3D data. Despite many well-designed approaches, we point out that limitations still exist and there is tremendous room for improvement, including the heavy reliance on hand-crafted features, the separated optimization of feature extraction and object retrieval, and the lack of sufficient training samples. In this work, we address the above limitations for 3D object retrieval by developing a novel end-to-end solution named Group Pair Convolutional Neural Network (GPCNN). It can jointly learn the visual features from multiple views of a 3D model and optimize towards the object retrieval task. To tackle the insufficient training data issue, we innovatively employ a pair-wise learning scheme, which learns model parameters from the similarity of each sample pair, rather than the traditional way of learning from sparse label–sample matching. Extensive experiments on three public benchmarks show that our GPCNN solution significantly outperforms the state-of-the-art methods with 3% to 42% improvement in retrieval accuracy.
The Geometric Block Model
Authors: Galhotra Sainyam , Mazumdar Arya , Pal Soumyabrata , Saha Barna
Published: June 2022

To capture the inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a Geometric Block Model. The geometric block model generalizes the random geometric graphs in the same way that the well-studied stochastic block model generalizes the Erdös-Renyi random graphs. It is also a natural extension of random community models inspired by the recent theoretical and practical advancement in community detection. While being a topic of fundamental theoretical interest, our main contribution is to show that many practical community structures are better explained by the geometric block model. We also show that a simple triangle-counting algorithm to detect communities in the geometric block model is near-optimal. Indeed, even in the regime where the average degree of the graph grows only logarithmically with the number of vertices (sparse-graph), we show that this algorithm performs extremely well, both theoretically and practically. In contrast, the triangle-counting algorithm is far from being optimum for the stochastic block model. We simulate our results on both real and synthetic datasets to show superior performance of both the new model as well as our algorithm.
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning With Confidence
Authors: Xie Ruobing , Liu Zhiyuan , Lin Fen , Lin Leyu
Published: June 2022

Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that still grows explosively and changes frequently, knowledge construction and update inevitably involve automatic mechanisms with less human supervision, which usually bring in plenty of noises and conflicts to KGs. However, most conventional knowledge representation learning methods assume that all triple facts in existing KGs share the same significance without any noises. To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Specifically, we introduce the triple confidence to conventional translation-based methods for knowledge representation learning. To make triple confidence more flexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple confidences considering both local and global structural information. In experiments, We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classification. Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.
Multi-Channel Encoder for Neural Machine Translation
Authors: Xiong Hao , He Zhongjun , Hu Xiaoguang , Wu Hua
Published: June 2022

Attention-based Encoder-Decoder has the effective architecture for neural machine translation (NMT), which typically relies on recurrent neural networks (RNN) to build the blocks that will be lately called by attentive reader during the decoding process. This design of encoder yields relatively uniform composition on source sentence, despite the gating mechanism employed in encoding RNN. On the other hand, we often hope the decoder to take pieces of source sentence at varying levels suiting its own linguistic structure: for example, we may want to take the entity name in its raw form while taking an idiom as a perfectly composed unit. Motivated by this demand, we propose Multi-channel Encoder (MCE), which enhances encoding components with different levels of composition. More specifically, in addition to the hidden state of encoding RNN, MCE takes 1) the original word embedding for raw encoding with no composition, and 2) a particular design of external memory in Neural Turing Machine NTM) for more complex composition, while all three encoding strategies are properly blended during decoding. Empirical study on Chinese-English translation shows that our model can improve by 6.52 BLEU points upon a strong open source NMT system: DL4MT1. On the WMT14 English-French task, our single shallow system achieves BLEU=38.8, comparable with the state-of-the-art deep models.
Event Representations With Tensor-Based Compositions
Authors: Weber Noah , Balasubramanian Niranjan , Chambers Nathanael
Published: June 2022

Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events for such understanding, and has recently attracted renewed attention. However, obtaining effective representations for modeling script-like event sequences is challenging. It requires representations that can capture event-level and scenario-level semantics. We propose a new tensor-based composition method for creating event representations. The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. With the continuous representations, we also devise a simple schema generation method which produces better schemas compared to a prior discrete representation based method. Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations.
FEEL: Featured Event Embedding Learning
Authors: Lee I-Ta , Goldwasser Dan
Published: June 2022

Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.
Search Engine Guided Neural Machine Translation
Authors: Gu Jiatao , Wang Yong , Cho Kyunghyun , Li Victor O.K.
Published: June 2022

In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. In the first stage –retrieval stage–, an off-the-shelf, black-box search engine is used to retrieve a small subset of sentence pairs from a training set given a source sentence. These pairs are further filtered based on a fuzzy matching score based on edit distance. In the second stage–translation stage–, a novel translation model, called search engine guided NMT (SEG-NMT), seamlessly uses both the source sentence and a set of retrieved sentence pairs to perform the translation. Empirical evaluation on three language pairs (En-Fr, En-De, and En-Es) shows that the proposed approach significantly outperforms the baseline approach and the improvement is more significant when more relevant sentence pairs were retrieved.
CoLink: An Unsupervised Framework for User Identity Linkage
Authors: Zhong Zexuan , Cao Yong , Cao Yong , Guo Mu , Guo Mu , Nie Zaiqing , Nie Zaiqing
Published: June 2022

Nowadays, it is very common for one person to be in different social networks. Linking identical users across different social networks, also known as the User Identity Linkage (UIL) problem, is fundamental for many applications. There are two major challenges in the UIL problem. First, it's extremely expensive to collect manually linked user pairs as training data. Second, the user attributes in different networks are usually defined and formatted very differently which makes attribute alignment very hard. In this paper we propose CoLink, a general unsupervised framework for the UIL problem. CoLink employs a co-training algorithm, which manipulates two independent models, the attribute-based model and the relationship-based model, and makes them reinforce each other iteratively in an unsupervised way. We also propose the sequence-to-sequence learning as a very effective implementation of the attribute-based model, which can well handle the challenge of the attribute alignment by treating it as a machine translation problem. We apply CoLink to a UIL task of mapping the employees in an enterprise network to their LinkedIn profiles. The experiment results show that CoLink generally outperforms the state-of-the-art unsupervised approaches by an F1 increase over 20%.
Learning Latent Opinions for Aspect-level Sentiment Classification
Authors: Wang Bailin , Lu Wei
Published: June 2022

Aspect-level sentiment classification aims at detecting the sentiment expressed towards a particular target in a sentence. Based on the observation that the sentiment polarity is often related to specific spans in the given sentence, it is possible to make use of such information for better classification. On the other hand, such information can also serve as justifications associated with the predictions.We propose a segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer. The model simulates human's process of inferring sentiment information when reading: when given a target, humans tend to search for surrounding relevant text spans in the sentence before making an informed decision on the underlying sentiment information.We perform sentiment classification tasks on publicly available datasets on online reviews across different languages from SemEval tasks and social comments from Twitter. Extensive experiments show that our model achieves the state-of-the-art performance while extracting interpretable sentiment expressions.
cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information
Authors: Cao Shaosheng , Lu Wei , Zhou Jun , Li Xiaolong
Published: June 2022

We propose cw2vec, a novel method for learning Chinese word embeddings. It is based on our observation that exploiting stroke-level information is crucial for improving the learning of Chinese word embeddings. Specifically, we design a minimalist approach to exploit such features, by using stroke n-grams, which capture semantic and morphological level information of Chinese words. Through qualitative analysis, we demonstrate that our model is able to extract semantic information that  cannot be captured by existing methods. Empirical results on the word similarity, word analogy, text classification and named entity recognition tasks show that the proposed approach consistently outperforms state-of-the-art approaches such as word-based word2vec and GloVe, character-based CWE, component-based JWE and pixel-based GWE.
Scale Up Event Extraction Learning via Automatic Training Data Generation
Authors: Zeng Ying , Feng Yansong , Ma Rong , Wang Zheng , Yan Rui , Shi Chongde , Zhao Dongyan
Published: June 2022

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of highquality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.
Learning Multimodal Word Representation via Dynamic Fusion Methods
Authors: Wang Shaonan , Zhang Jiajun , Zong Chengqing
Published: June 2022

Multimodal models have been proven to outperform text-based models on learning semantic word representations. Almost all previous multimodal models typically treat the representations from different modalities equally. However, it is obvious that information from different modalities contributes differently to the meaning of words. This motivates us to build a multimodal model that can dynamically fuse the semantic representations from different modalities according to different types of words. To that end, we propose three novel dynamic fusion methods to assign importance weights to each modality, in which weights are learned under the weak supervision of word association pairs. The extensive experiments have demonstrated that the proposed methods outperform strong unimodal baselines and state-of-the-art multimodal models.
Investigating Inner Properties of Multimodal Representation and Semantic Compositionality With Brain-Based Componential Semantics
Authors: Wang Shaonan , Zhang Jiajun , Lin Nan , Zong Chengqing
Published: June 2022

Multimodal models have been proven to outperform text-based approaches on learning semantic representations. However, it still remains unclear what properties are encoded in multimodal representations, in what aspects do they outperform the single-modality representations, and what happened in the process of semantic compositionality in different input modalities. Considering that multimodal models are originally motivated by human concept representations, we assume that correlating multimodal representations with brain-based semantics would interpret their inner properties to answer the above questions. To that end, we propose simple interpretation methods based on brain-based componential semantics. First we investigate the inner properties of multimodal representations by correlating them with corresponding brain-based property vectors. Then we map the distributed vector space to the interpretable brain-based componential space to explore the inner properties of semantic compositionality. Ultimately, the present paper sheds light on the fundamental questions of natural language understanding, such as how to represent the meaning of words and how to combine word meanings into larger units.
Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation
Authors: Cai Xiaoyan , Han Junwei , Yang Libin
Published: June 2022

Network representation has been recently exploited for many applications, such as citation recommendation, multi-label classification and link prediction. It learns low-dimensional vector representation for each vertex in networks. Existing network representation methods only focus on incomplete aspects of vertex information (i.e., vertex content, network structure or partial integration), moreover they are commonly designed for homogeneous information networks where all the vertices of a network are of the same type. In this paper, we propose a deep network representation model that integrates network structure and the vertex content information into a unified framework by exploiting generative adversarial network, and represents different types of vertices in the heterogeneous network in a continuous and common vector space. Based on the proposed model, we can obtain heterogeneous bibliographic network representation for efficient citation recommendation. The proposed model also makes personalized citation recommendation possible, which is a new issue that a few papers addressed in the past. When evaluated on the AAN and DBLP datasets, the performance of the proposed heterogeneous bibliographic network based citation recommendation approach is comparable with that of the other network representation based citation recommendation approaches. The results also demonstrate that the personalized citation recommendation approach is more effective than the non-personalized citation recommendation approach.
Improving Neural Fine-Grained Entity Typing With Knowledge Attention
Authors: Xin Ji , Lin Yankai , Liu Zhiyuan , Sun Maosong
Published: June 2022

Fine-grained entity typing aims to identify the semantic type of an entity in a particular plain text. It is an important task which can be helpful for a lot of natural language processing (NLP) applications. Most existing methods typically extract features separately from the entity mention and context words for type classification. These methods inevitably fail to model complex correlations between entity mentions and context words. They also neglect rich background information about these entities in knowledge bases (KBs). To address these issues, we take information from KBs into consideration to bridge entity mentions and their context together, and thereby propose Knowledge-Attention Neural Fine-Grained Entity Typing. Experimental results and case studies on real-world datasets demonstrate that our model significantly outperforms other state-of-the-art methods, revealing the effectiveness of incorporating KB information for entity typing. Code and data for this paper can be found at https://github.com/thunlp/KNET.
Byte-Level Machine Reading Across Morphologically Varied Languages
Authors: Kenter Tom , Jones Llion , Hewlett Daniel
Published: June 2022

The machine reading task, where a computer reads a document and answers questions about it, is important in artificial intelligence research. Recently, many models have been proposed to address it. Word-level models, which have words as units of input and output, have proven to yield state-of-the-art results when evaluated on English datasets. However, in morphologically richer languages, many more unique words exist than in English due to highly productive prefix and suffix mechanisms. This may set back word-level models, since vocabulary sizes too big to allow for efficient computing may have to be employed. Multiple alternative input granularities have been proposed to avoid large input vocabularies, such as morphemes, character n-grams, and bytes. Bytes are advantageous as they provide a universal encoding format across languages, and allow for a small vocabulary size, which, moreover, is identical for every input language. In this work, we investigate whether bytes are suitable as input units across morphologically varied languages. To test this, we introduce two large-scale machine reading datasets in morphologically rich languages, Turkish and Russian. We implement 4 byte-level models, representing the major types of machine reading models and introduce a new seq2seq variant, called encoder-transformer-decoder. We show that, for all languages considered, there are models reading bytes outperforming the current state-of-the-art word-level baseline. Moreover, the newly introduced encoder-transformer-decoder performs best on the morphologically most involved dataset, Turkish. The large-scale Turkish and Russian machine reading datasets are released to public.
Assertion-Based QA With Question-Aware Open Information Extraction
Authors: Yan Zhao , Tang Duyu , Duan Nan , Liu Shujie , Wang Wendi , Jiang Daxin , Zhou Ming , Li Zhoujun
Published: June 2022

We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments. An assertion conveys more evidences than a short answer span in reading comprehension, and it is more concise than a tedious passage in passage-based QA. These advantages make ABQA more suitable for human-computer interaction scenarios such as voice-controlled speakers. Further progress towards improving ABQA requires richer supervised dataset and powerful models of text understanding. To remedy this, we introduce a new dataset called WebAssertions, which includes hand-annotated QA labels for 358,427 assertions in 55,960 web passages. To address ABQA, we develop both generative and extractive approaches. The backbone of our generative approach is sequence to sequence learning. In order to capture the structure of the output assertion, we introduce a hierarchical decoder that first generates the structure of the assertion and then generates the words of each field. The extractive approach is based on learning to rank. Features at different levels of granularity are designed to measure the semantic relevance between a question and an assertion. Experimental results show that our approaches have the ability to infer question-aware assertions from a passage. We further evaluate our approaches by incorporating the ABQA results as additional features in passage-based QA. Results on two datasets show that ABQA features significantly improve the accuracy on passage-based QA.
Cross-Lingual Propagation for Deep Sentiment Analysis
Authors: Dong Xin , De Melo Gerard
Published: June 2022

Across the globe, people are voicing their opinion in social media and various other online fora. Given such data, modern deep learning-based sentiment analysis methods excel at determining the sentiment polarity of what is being said about companies, products, etc. Unfortunately, such deep methods require significant training data, while for many languages, resources and training data are scarce. In this work, we present a cross-lingual propagation algorithm that yields sentiment embedding vectors for numerous languages. We then rely on a dual-channel convolutional neural architecture to incorporate them into the network. This allows us to achieve gains in deep sentiment analysis across a range of languages and domains.
Scheduling in Visual Fog Computing: NP-Completeness and Practical Efficient Solutions
Authors: Chu Hong-Min , Yang Shao-Wen , Pillai Padmanabhan , Chen Yen-Kuang
Published: June 2022

The visual fog paradigm envisions tens of thousands of heterogeneous, camera-enabled edge devices distributed across the Internet, providing live sensing for a myriad of different visual processing applications. The scale, computational demands, and bandwidth needed for visual computing pipelines necessitates offloading intelligently to distributed computing infrastructure, including the cloud, Internet gateway devices, and the edge devices themselves. This paper focuses on the visual fog scheduling problem of assigning the visual computing tasks to various devices to optimize network utilization. We first prove this problem is NP-complete, and then formulate a practical, efficient solution. We demonstrate sub-minute computation time to optimally schedule 20,000 tasks across over 7,000 devices, and just 7-minute execution time to place 60,000 tasks across 20,000 devices, showing our approach is ready to meet the scale challenges introduced by visual fog.
Semi-Black Box: Rapid Development of Planning Based Solutions
Authors: Katz Michael , Moshkovich Dany , Karpas Erez
Published: June 2022

Software developers nowadays not infrequently face a challenge of solving problems that essentially sum up to finding a sequence of deterministic actions leading from a given initial state to a goal. This is the problem of deterministic planning, one of the most basic and well studied problems in artificial intelligence. Two of the best known approaches to deterministic planning are the black box approach, in which a programmer implements a successor generator, and the model-based approach, in which a user describes the problem symbolically, e.g., in PDDL. While the black box approach is usually easier for programmers who are not experts in AI to understand, it does not scale up without informative heuristics. We propose an approach that we baptize as semi-black box (SBB) that combines the strength of both. SBB is implemented as a set of Java classes, which a programmer can inherit from when implementing a successor generator. Using the known characteristics of these classes, we then automatically derive heuristics for the problem. Our empirical evaluation shows that these heuristics allow the planner to scale up significantly better than the traditional black box approach.
Unsupervised Selection of Negative Examples for Grounded Language Learning
Authors: Pillai Nisha , Matuszek Cynthia
Published: June 2022

There has been substantial work in recent years on grounded language acquisition, in which language and sensor data are used to create a model relating linguistic constructs to the perceivable world. While powerful, this approach is frequently hindered by ambiguities, redundancies, and omissions found in natural language. We describe an unsupervised system that learns language by training visual classifiers, first selecting important terms from object descriptions, then automatically choosing negative examples from a paired corpus of perceptual and linguistic data. We evaluate the effectiveness of each stage as well as the system's performance on the overall learning task.
On Cryptographic Attacks Using Backdoors for SAT
Authors: Semenov Alexander , Zaikin Oleg , Otpuschennikov Ilya , Kochemazov Stepan , Ignatiev Alexey
Published: June 2022

Propositional satisfiability (SAT) is at the nucleus of state-of-the-art approaches to a variety of computationally hard problems, one of which is cryptanalysis. Moreover, a number of practical applications of SAT can only be tackled efficiently by identifying and exploiting a subset of formula's variables called backdoor set (or simply backdoors). This paper proposes a new class of backdoor sets for SAT used in the context of cryptographic attacks, namely guess-and-determine attacks. The idea is to identify the best set of backdoor variables subject to a statistically estimated hardness of the guess-and-determine attack using a SAT solver. Experimental results on weakened variants of the renowned encryption algorithms exhibit advantage of the proposed approach compared to the state of the art in terms of the estimated hardness of the resulting guess-and-determine attacks.
Learning Robust Search Strategies Using a Bandit-Based Approach
Authors: Xia Wei , Yap Roland
Published: June 2022

Effective solving of constraint problems often requires choosing good or specific search heuristics. However, choosing or designing a good search heuristic is non-trivial and is often a manual process. In this paper, rather than manually choosing/designing search heuristics, we propose the use of bandit-based learning techniques to automatically select search heuristics. Our approach is online where the solver learns and selects from a set of heuristics during search. The goal is to obtain automatic search heuristics which give robust performance. Preliminary experiments show that our adaptive technique is more robust than the original search heuristics. It can also outperform the original heuristics.
Enhancing Constraint-Based Multi-Objective Combinatorial Optimization
Authors: Terra-Neves Miguel , Lynce Inês , Manquinho Vasco
Published: June 2022

Minimal Correction Subsets (MCSs) have been successfully applied to find approximate solutions to several real-world single-objective optimization problems. However, only recently have MCSs been used to solve Multi-Objective Combinatorial Optimization (MOCO) problems. In particular, it has been shown that all optimal solutions of MOCO problems with linear objective functions can be found by an MCS enumeration procedure. In this paper, we show that the approach of MCS enumeration can also be applied to MOCO problems where objective functions are divisions of linear expressions. Hence, it is not necessary to use a linear approximation of these objective functions. Additionally, we also propose the integration of diversification techniques on the MCS enumeration process in order to find better approximations of the Pareto front of MOCO problems. Finally, experimental results on the Virtual Machine Consolidation (VMC) problem show the effectiveness of the proposed techniques.
Face Sketch Synthesis From Coarse to Fine
Authors: Zhang Mingjin , Wang Nannan , Li Yunsong , Wang Ruxin , Gao Xinbo
Published: June 2022

Synthesizing fine face sketches from photos is a valuable yet challenging problem in digital entertainment. Face sketches synthesized by conventional methods usually exhibit coarse structures of faces, whereas fine details are lost especially on some critical facial components. In this paper, by imitating the coarse-to-fine drawing process of artists, we propose a novel face sketch synthesis framework consisting of a coarse stage and a fine stage. In the coarse stage, a mapping relationship between face photos and sketches is learned via the convolutional neural network. It ensures that the synthesized sketches keep coarse structures of faces. Given the test photo and the coarse synthesized sketch, a probabilistic graphic model is designed to synthesize the delicate face sketch which has fine and critical details. Experimental results on public face sketch databases illustrate that our proposed framework outperforms the state-of-the-art methods in both quantitive and visual comparisons.
Domain-Shared Group-Sparse Dictionary Learning for Unsupervised Domain Adaptation
Authors: Yang Baoyao , Ma Andy , Yuen Pong
Published: June 2022

Unsupervised domain adaptation has been proved to be a promising approach to solve the problem of dataset bias. To employ source labels in the target domain, it is required to align the joint distributions of source and target data. To do this, the key research problem is to align conditional distributions across domains without target labels. In this paper, we propose a new criterion of domain-shared group-sparsity that is an equivalent condition for conditional distribution alignment. To solve the problem in joint distribution alignment, a domain-shared group-sparse dictionary learning method is developed towards joint alignment of conditional and marginal distributions. A classifier for target domain is trained using the domain-shared group-sparse coefficients and the target-specific information from the target data. Experimental results on cross-domain face and object recognition show that the proposed method outperforms eight state-of-the-art unsupervised domain adaptation algorithms.
Graph Correspondence Transfer for Person Re-Identification
Authors: Zhou Qin , Fan Heng , Zheng Shibao , Su Hang , Li Xinzhe , Wu Shuang , Ling Haibin
Published: June 2022

In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification. Unlike existing methods, the GCT model formulates person re-identification as an off-line graph matching and on-line correspondence transferring problem. In specific, during training, the GCT model aims to learn off-line a set of correspondence templates from positive training pairs with various pose-pair configurations via patch-wise graph matching. During testing, for each pair of test samples, we select a few training pairs with the most similar pose-pair configurations as references, and transfer the correspondences of these references to test pair for feature distance calculation. The matching score is derived by aggregating distances from different references. For each probe image, the gallery image with the highest matching score is the re-identifying result. Compared to existing algorithms, our GCT can handle spatial misalignment caused by large variations in view angles and human poses owing to the benefits of patch-wise graph matching. Extensive experiments on five benchmarks including VIPeR, Road, PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over other state-of-the-art methods.
SqueezedText: A Real-Time Scene Text Recognition by Binary Convolutional Encoder-Decoder Network
Authors: Liu Zichuan , Li Yixing , Ren Fengbo , Goh Wang Ling , Yu Hao
Published: June 2022

A new approach for real-time scene text recognition is proposed in this paper. A novel binary convolutional encoder-decoder network (B-CEDNet) together with a bidirectional recurrent neural network (Bi-RNN). The B-CEDNet is engaged as a visual front-end to provide elaborated character detection, and a back-end Bi-RNN performs character-level sequential correction and classification based on learned contextual knowledge. The front-end B-CEDNet can process multiple regions containing characters using a one-off forward operation, and is trained under binary constraints with significant compression. Hence it leads to both remarkable inference run-time speedup as well as memory usage reduction. With the elaborated character detection, the back-end Bi-RNN merely processes a low dimension feature sequence with category and spatial information of extracted characters for sequence correction and classification. By training with over 1,000,000 synthetic scene text images, the B-CEDNet achieves a recall rate of 0.86, precision of 0.88 and F-score of 0.87 on ICDAR-03 and ICDAR-13. With the correction and classification by Bi-RNN, the proposed real-time scene text recognition achieves state-of-the-art accuracy while only consumes less than 1-ms inference run-time. The flow processing flow is realized on GPU with a small network size of 1.01 MB for B-CEDNet and 3.23 MB for Bi-RNN, which is much faster and smaller than the existing solutions.