bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Philosophy in Dante’s Divine Comedy
Authors: Ushkanova R. D.
Published: July 2020

The paper considers the role of antique philosophy in medieval theology. Dante’s «Divine Comedy» was chosen as an object for a comparative study of philosophy and theology. A «сomplicated» relationship between theology and philosophy is due to the question of the creation of the world. According to Aristotle and his medieval successors Averroes and Siger of Brabant, the world was not created by God but, he is needed as the source of its movement. Despite Aristotle’s overwhelming authority as a Philosopher and a Teacher, Dante placed antique thinkers led by Aristotle, in Limbo, Hell canopy. So they were unable to pass through Hell and Purgatory and face God one day. Ancient thinkers were to blame for living before Christ and therefore not knowing him. It is underlined, that all real-life hostilities become meaningless in Heaven, where everyone is equal before God. That’s why Thomas Aquinas and Siger de Brabantt are side by side in Heaven. The article states that the theme of wisdom is a keynote of Dante’s «Divine Comedy». It is noted, that Dante’s journey through the circles of Hell, ascension into Purgatory and «getting» to Heaven is because of Beatrice – allegory of philosophy. It was philosophy (in the form of beautiful Beatrice) «coming» to help when Dante found himself in an «impassable forest» of his soul. Philosophy in the form of Beatrice presents wisdom. Theology, in turn, is intended to purify the minds, which is possible just in Purgatory. Heaven is the embodiment of justice and perception of truth, the place of some conciliation between theology and philosophy.
Аspects of Post-Structural Anthropology of Eduardo Viveiros de Castro (Postmodernism as a Language of the Description of Amazonian Tribes’ Mythological Reality: the Trickster’s Simulacra in Shamanism)
Authors: Davydov I. P.
Published: July 2020

The purpose of the study is to trace the origins of E. V. de Castro’s «poststructural anthropology», and the possibility of its application in Religious Studies in contemporary Russia. For the purpose of the study, the methods of description, historical and comparative analysis and modelling were chosen. The objectives of the study are: 1) to create an analogous 3D model of «the shaman’s universe», according to the factography of E. V. de Castro’s «poststructural anthropology»; 2) to verify its heuristic potential using the material of the belief in the existence of werewolves; 3) to verify whether the postmodern thesaurus, as a language of description, could be adequately applied to the worldview of Amazonian shamans. The author concludes that: a) the immediate origins of «poststructural anthropology» lie in the views of J. Deleuze and F. Guattari, and that the achievements of the British school of social anthropology and Cl. Levi- Strauss’s structuralism served as the general background for E. V. de Castro’s and E. Kohn’s work; b) the conceptual framework of postmodernism is able to serve as an appropriate language of description of the shamanic experience which was clearly demonstrated by Amazonists E. V. de Castro and E. Kohn; c) the shamanic model of the universe (the Chaosmosis) has sufficient heuristic potential to be applied to other phenomena of religion, mytho-ritual, virtual fantasy worlds, fairy tales, and folklore.
Discovery of the Baldy toreva near urban areas along the southern Wasatch Range, Utah
Authors: Meyer Eric R. , Harris Ron A.
Published: July 2020

Structural and geomorphic studies, and lithostratigraphic and biostratigraphic mapping reveal that a giant toreva block (6.125 km3) slid off Mount Timpanogos toward what are now densely populated urban areas along the Wasatch Front of Utah. The block forms a prominent peak known as Big Baldy, which consists of steeply dipping and locally brecciated limestone and quartzarenite over nearly horizontal shale. Preferential erosion of this shale below overlying limestone and quartzarenite cliffs is most likely the cause of this particular landslide and potential future slides along the Wasatch Front. The low-angle contact at the base of the giant toreva block was initially mapped as a thrust, then as a low-angle normal fault. In both cases, these faults were inferred to have large amounts of displacement (900 meters), but no traces of such faults are found in adjacent canyons. The Baldy slide is associated with geomorphologic features, such as faceted spurs, landslide scarps, sackungen, and hummocky terrain. Limestone and quartzarenite beds in the block are back-rotated up to 80° and are locally broken and brecciated. No evidence of hydro-fracturing is found in the breccia or of multiple brecciation episodes, which indicates surficial rather than deep-crustal processes and perhaps a single event of slip. We speculate based on structural reconstructions of the slide block, and interpolation of maximum downcutting rates on nearby streams, that the slide initiated between 700 and 500 ka. Discovery of the Baldy slide attests to the importance of recognizing the influence of surficial processes in mountain front development and demonstrate the ongoing geologic hazard of mass wasting to communities along the seismically active Wasatch Front and similar horst blocks.
Visual Encoding and Decoding of the Human Brain Based on Shared Features
Authors: Li Chao , Liu Baolin , Wei Jianguo
Published: July 2020

Using a convolutional neural network to build visual encoding and decoding models of the human brain is a good starting point for the study on relationship between deep learning and human visual cognitive mechanism. However, related studies have not fully considered their differences. In this paper, we assume that only a portion of neural network features is directly related to human brain signals, which we call shared features. In the encoding process, we extract shared features from the lower and higher layers of the neural network, and then build a non-negative sparse map to predict brain activities. In the decoding process, we use back-propagation to reconstruct visual stimuli, and use dictionary learning and a deep image prior to improve the robustness and accuracy of the algorithm. Experiments on a public fMRI dataset confirm the rationality of the encoding models, and comparing with a recently proposed method, our reconstruction results obtain significantly higher accuracy.
Multi-Scale Spatial-Temporal Integration Convolutional Tube for Human Action Recognition
Authors: Wu Haoze , Liu Jiawei , Zhu Xierong , Wang Meng , Zha Zheng-Jun
Published: July 2020

Applying multi-scale representations leads to consistent performance improvements on a wide range of image recognition tasks. However, with the addition of the temporal dimension in video domain, directly obtaining layer-wise multi-scale spatial-temporal features will add a lot extra computational cost. In this work, we propose a novel and efficient Multi-Scale Spatial-Temporal Integration Convolutional Tube (MSTI) aiming at achieving accurate recognition of actions with lower computational cost. It firstly extracts multi-scale spatial and temporal features through the multi-scale convolution block. Considering the interaction of different-scales representations and the interaction of spatial appearance and temporal motion, we employ the cross-scale attention weighted blocks to perform feature recalibration by integrating multi-scale spatial and temporal features. An end-to-end deep network, MSTI-Net, is also presented based on the proposed MSTI tube for human action recognition. Extensive experimental results show that our MSTI-Net significantly boosts the performance of existing convolution networks and achieves state-of-the-art accuracy on three challenging benchmarks, i.e., UCF-101, HMDB-51 and Kinetics-400, with much fewer parameters and FLOPs.
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning
Authors: Guo Longteng , Liu Jing , Zhu Xinxin , He Xingjian , Jiang Jie , Lu Hanqing
Published: July 2020

Most image captioning models are autoregressive, i.e. they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAIC as a multi-agent reinforcement learning system where positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. Besides, we propose to utilize massive unlabeled images to boost captioning performance. Extensive experiments on MSCOCO image captioning benchmark show that our NAIC model achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup.
Overflow Aware Quantization: Accelerating Neural Network Inference by Low-bit Multiply-Accumulate Operations
Authors: Xie Hongwei , Song Yafei , Cai Ling , Li Mingyang
Published: July 2020

The inherent heavy computation of deep neural networks prevents their widespread applications. A widely used method for accelerating model inference is quantization, by replacing the input operands of a network using fixed-point values. Then the majority of computation costs focus on the integer matrix multiplication accumulation. In fact, high-bit accumulator leads to partially wasted computation and low-bit one typically suffers from numerical overflow. To address this problem, we propose an overflow aware quantization method by designing trainable adaptive fixed-point representation, to optimize the number of bits for each input tensor while prohibiting numeric overflow during the computation. With the proposed method, we are able to fully utilize the computing power to minimize the quantization loss and obtain optimized inference performance. To verify the effectiveness of our method, we conduct image classification, object detection, and semantic segmentation tasks on ImageNet, Pascal VOC, and COCO datasets, respectively. Experimental results demonstrate that the proposed method can achieve comparable performance with state-of-the-art quantization methods while accelerating the inference process by about 2 times.
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification
Authors: Rao Haocong , Wang Siqi , Hu Xiping , Tan Mingkui , Da Huang , Cheng Jun , Hu Bin
Published: July 2020

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID is an emerging open topic. Existing methods either adopt hand-crafted features or learn gait features by traditional supervised learning paradigms. Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner. Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait representations. Second, inspired by the fact that motion's continuity endows temporally adjacent skeletons with higher correlations (“locality”), we propose a locality-aware attention mechanism that encourages learning larger attention weights for temporally adjacent skeletons when reconstructing current skeleton, so as to learn locality when encoding gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations. AGEs are directly utilized to realize effective person Re-ID. Our approach typically improves existing skeleton-based methods by 10-20% Rank-1 accuracy, and it achieves comparable or even superior performance to multi-modal methods with extra RGB or depth information.
Temporal Adaptive Alignment Network for Deep Video Inpainting
Authors: Liu Ruixin , Weng Zhenyu , Zhu Yuesheng , Li Bairong
Published: July 2020

Video inpainting aims to synthesize visually pleasant and temporally consistent content in missing regions of video. Due to a variety of motions across different frames, it is highly challenging to utilize effective temporal information to recover videos. Existing deep learning based methods usually estimate optical flow to align frames and thereby exploit useful information between frames. However, these methods tend to generate artifacts once the estimated optical flow is inaccurate. To alleviate above problem, we propose a novel end-to-end Temporal Adaptive Alignment Network(TAAN) for video inpainting. The TAAN aligns reference frames with target frame via implicit motion estimation at a feature level and then reconstruct target frame by taking the aggregated aligned reference frame features as input. In the proposed network, a Temporal Adaptive Alignment (TAA) module based on deformable convolutions is designed to perform temporal alignment in a local, dense and adaptive manner. Both quantitative and qualitative evaluation results show that our method significantly outperforms existing deep learning based methods.
TRP: Trained Rank Pruning for Efficient Deep Neural Networks
Authors: Xu Yuhui , Li Yuxi , Zhang Shuai , Wen Wei , Wang Botao , Qi Yingyong , Chen Yiran , Lin Weiyao , Xiong Hongkai
Published: July 2020

To enable DNNs on edge devices like mobile phones, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several previous works attempted to directly approximate a pre-trained model by low-rank decomposition; however, small approximation errors in parameters can ripple over a large prediction loss. As a result, performance usually drops significantly and a sophisticated effort on fine-tuning is required to recover accuracy. Apparently, it is not optimal to separate low-rank approximation from training. Unlike previous works, this paper integrates low rank approximation and regularization into the training process. We propose Trained Rank Pruning (TRP), which alternates between low rank approximation and training. TRP maintains the capacity of the original network while imposing low-rank constraints during training. A nuclear regularization optimized by stochastic sub-gradient descent is utilized to further promote low rank in TRP. The TRP trained network inherently has a low-rank structure, and is approximated with negligible performance loss, thus eliminating the fine-tuning process after low rank decomposition. The proposed method is comprehensively evaluated on CIFAR-10 and ImageNet, outperforming previous compression methods using low rank approximation.
Video Question Answering on Screencast Tutorials
Authors: Zhao Wentian , Kim Seokhwan , Xu Ning , Jin Hailin
Published: July 2020

This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for a software. Unlike other video question answering works, all the answers in our dataset are grounded to the domain knowledge base. An one-shot recognition algorithm is designed to extract the visual cues, which helps enhance the performance of video question answering. We also propose several baseline neural network architectures based on various aspects of video contexts from the dataset. The experimental results demonstrate that our proposed models significantly improve the question answering performances by incorporating multi-modal contexts and domain knowledge.
Extended Conjunctive Normal Form and An Efficient Algorithm for Cardinality Constraints
Authors: Lei Zhendong , Cai Shaowei , Luo Chuan
Published: July 2020

Satisfiability (SAT) and Maximum Satisfiability (MaxSAT) are two basic and important constraint problems with many important applications. SAT and MaxSAT are expressed in CNF, which is difficult to deal with cardinality constraints. In this paper, we introduce Extended Conjunctive Normal Form (ECNF), which expresses cardinality constraints straightforward and does not need auxiliary variables or clauses. Then, we develop a simple and efficient local search solver LS-ECNF with a well designed scoring function under ECNF. We also develop a generalized Unit Propagation (UP) based algorithm to generate the initial solution for local search. We encode instances from Nurse Rostering and Discrete Tomography Problems into CNF with three different cardinality constraint encodings and ECNF respectively. Experimental results show that LS-ECNF has much better performance than state of the art MaxSAT, SAT, Pseudo-Boolean and ILP solvers, which indicates solving cardinality constraints with ECNF is promising.
On Irrelevant Literals in Pseudo-Boolean Constraint Learning
Authors: Le Berre Daniel , Marquis Pierre , Mengel Stefan , Wallon Romain
Published: July 2020

Learning pseudo-Boolean (PB) constraints in PB solvers exploiting cutting planes based inference is not as well understood as clause learning in conflict-driven clause learning solvers.

In this paper, we show that PB constraints derived using cutting planes may contain irrelevant literals, i.e., literals whose assigned values (whatever they are) never change the truth value of the constraint.

Such literals may lead to infer constraints that are weaker than they should be, impacting the size of the proof built by the solver, and thus also affecting its performance.

This suggests that current implementations of PB solvers based on cutting planes should be reconsidered to prevent the generation of irrelevant literals.

Indeed, detecting and removing irrelevant literals is too expensive in practice to be considered as an option (the associated problem is NP-hard).
Fast and Parallel Decomposition of Constraint Satisfaction Problems
Authors: Gottlob Georg , Okulmus Cem , Pichler Reinhard
Published: July 2020

Constraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP instance, and previous algorithms have shown their limitations in doing so. In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster. We thus advance the ability to compute optimal (i.e., minimal-width) GHDs for a significantly wider range of CSP instances on modern machines. This lays the foundation for more systems and applications in evaluating CSPs and related problems (such as Conjunctive Query answering) based on their structural properties.
Learning Sensitivity of RCPSP by Analyzing the Search Process
Authors: Ménard Marc-André , Quimper Claude-Guy , Gaudreault Jonathan
Published: July 2020

Solving the problem is an important part of optimization. An equally important part is the analysis of the solution where several questions can arise. For a scheduling problem, is it possible to obtain a better solution by increasing the capacity of a resource? What happens to the objective value if we start a speciﬁc task earlier? Answering such questions is important to provide explanations and increase the acceptability of a solution. A lot of research has been done on sensitivity analysis, but few techniques can be applied to constraint programming. We present a new method for sensitivity analysis applied to constraint programming. It collects information, during the search, about the propagation of the CUMULATIVE constraint, the ﬁltering of the variables, and the solution returned by the solver. Using machine learning algorithms, we predict if increasing/decreasing the capacity of the cumulative resource allows a better solution. We also predict the impact on the objective value of forcing a task to ﬁnish earlier. We experimentally validate our method with the RCPSP problem.
Learning Optimal Decision Trees with MaxSAT and its Integration in AdaBoost
Authors: Hu Hao , Siala Mohamed , Hebrard Emmanuel , Huguet Marie-José
Published: July 2020

Recently, several exact methods to compute decision trees have been introduced. On the one hand, these approaches can find optimal trees for various objective functions including total size, depth or accuracy on the training set and therefore. On the other hand, these methods are not yet widely used in practice and classic heuristics are often still the methods of choice.

In this paper we show how the SAT model proposed by [Narodytska et.al 2018] can be lifted to a MaxSAT approach, making it much more practically relevant.

In particular, it scales to much larger data sets; the objective function can easily be adapted to take into account combinations of size, depth and accuracy on the training set; and the fine-grained control of the objective function it offers makes it particularly well suited for boosting.

Our experiments show promising results.

In particular, we show that the prediction quality of our approach often exceeds state of the art heuristics. We also show that the MaxSAT formulation is well adapted for boosting using the well-known AdaBoost Algorithm.
NLocalSAT: Boosting Local Search with Solution Prediction
Authors: Zhang Wenjie , Sun Zeyu , Zhu Qihao , Li Ge , Cai Shaowei , Xiong Yingfei , Zhang Lu
Published: July 2020

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.
Optimal Region Search with Submodular Maximization
Authors: Chen Xuefeng , Cao Xin , Zeng Yifeng , Fang Yixiang , Yao Bin
Published: July 2020

Region search is an important problem in location-based services due to its wide applications. In this paper, we study the problem of optimal region search with submodular maximization (ORS-SM). This problem considers a region as a connected subgraph. We compute an objective value over the locations in the region using a submodular function and a budget value by summing up the costs of edges in the region, and aim to search the region with the largest objective score under a budget value constraint. ORS-SM supports many applications such as the most diversified region search. We prove that the problem is NP-hard and develop two approximation algorithms with guaranteed error bounds. We conduct experiments on two applications using three real-world datasets. The results demonstrate that our algorithms can achieve high-quality solutions and are faster than a state-of-the-art method by orders of magnitude.
Assume-Guarantee Synthesis for Prompt Linear Temporal Logic
Authors: Fijalkow Nathanaël , Maubert Bastien , Murano Aniello , Vardi Moshe
Published: July 2020

Prompt-LTL extends Linear Temporal Logic with a bounded version of the ``eventually'' operator to express temporal requirements such as bounding waiting times. We study assume-guarantee synthesis for prompt-LTL: the goal is to construct a system such that for all environments satisfying a first prompt-LTL formula (the assumption) the system composed with this environment satisfies a second prompt-LTL formula (the guarantee). This problem has been open for a decade. We construct an algorithm for solving it and show that, like classical LTL synthesis, it is 2-EXPTIME-complete.
Discrete Embedding for Latent Networks
Authors: Yang Hong , Chen Ling , Lei Minglong , Niu Lingfeng , Zhou Chuan , Zhang Peng
Published: July 2020

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Authors: Chen Yu , Wu Lingfei , Zaki Mohammed J.
Published: July 2020

Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. Moreover, when reasoning over passage text, most of them simply treat it as a word sequence without exploring rich semantic relationships among words. In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs. The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks. In addition, visualization experiments show that our proposed model can offer good interpretability for the reasoning process.
Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact
Authors: Goodge Adam , Hooi Bryan , Ng See Kiong , Ng Wee Siong
Published: July 2020

Detecting anomalies is an important task in a wide variety of applications and domains. Deep learning methods have achieved state-of-the-art performance in anomaly detection in recent years; unsupervised methods being particularly popular. However, deep learning methods can be fragile to small perturbations in the input data. This can be exploited by an adversary to deliberately hinder model performance; an adversarial attack. This phenomena has been widely studied in the context of supervised image classification since its discovery, however such studies for an anomaly detection setting are sorely lacking. Moreover, the plethora of defense mechanisms that have been proposed are often not applicable to unsupervised anomaly detection models. In this work, we study the effect of adversarial attacks on the performance of anomaly-detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies. An adversary would attempt to disguise these points as normal through adversarial perturbations. To combat this, we propose the Approximate Projection Autoencoder (APAE), which incorporates two defenses against such attacks into a general autoencoder. One of these involves a novel technique to improve robustness under adversarial impact by optimising latent representations for better reconstruction outputs.
Speeding up Very Fast Decision Tree with Low Computational Cost
Authors: Sun Jian , Jia Hongyu , Hu Bo , Huang Xiao , Zhang Hao , Wan Hai , Zhao Xibin
Published: July 2020

Very Fast Decision Tree (VFDT) is one of the most widely used online decision tree induction algorithms, and it provides high classification accuracy with theoretical guarantees. In VFDT, the split-attempt operation is essential for leaf-split. It is computation-intensive since it computes the heuristic measure of all attributes of a leaf. To reduce split-attempts, VFDT tries to split at constant intervals (for example, every 200 examples). However, this mechanism introduces split-delay for split can only happen at fixed intervals, which slows down the growth of VFDT and finally lowers accuracy. To address this problem, we first devise an online incremental algorithm that computes the heuristic measure of an attribute with a much lower computational cost. Then a subset of attributes is carefully selected to find a potential split timing using this algorithm. A split-attempt will be carried out once the timing is verified. By the whole process, computational cost and split-delay are lowered significantly. Comprehensive experiments are conducted using multiple synthetic and real datasets. Compared with state-of-the-art algorithms, our method reduces split-attempts by about 5 to 10 times on average with much lower split-delay, which makes our algorithm run faster and more accurate.
Inductive Anomaly Detection on Attributed Networks
Authors: Ding Kaize , Li Jundong , Agarwal Nitin , Liu Huan
Published: July 2020

Anomaly detection on attributed networks has attracted a surge of research attention due to its broad applications in various high-impact domains, such as security, finance, and healthcare. Nonetheless, most of the existing efforts do not naturally generalize to unseen nodes, leading to the fact that people have to retrain the detection model from scratch when dealing with newly observed data. In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks with a novel unsupervised framework: Aegis (adversarial graph differentiation networks). Specifically, we design a new graph neural layer to learn anomaly-aware node representations and further employ generative adversarial learning to detect anomalies among new data. Extensive experiments on various attributed networks demonstrate the efficacy of the proposed approach.
Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression
Authors: Li Peiyan , Wang Honglian , Böhm Christian , Shao Junming
Published: July 2020

Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classification techniques are focused on the single-label case, while only a few multi-label stream classification algorithms exist, and they are mainly trained on labeled instances. In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label predictions in a semi-supervised setting and is robust to evolving label distributions. Specifically, to capture the high-order label relationship and to build a compact target space for regression, OnSeML compresses the label set into a low-dimensional space by a fixed orthogonal label encoder. Then a locally defined regression function for each incoming instance is obtained with a closed-form solution. Targeting the evolving label distribution problem, we propose an adaptive decoding scheme to adequately integrate newly arriving labeled data. Extensive experiments provide empirical evidence for the effectiveness of our approach.
Network Schema Preserving Heterogeneous Information Network Embedding
Authors: Zhao Jianan , Wang Xiao , Shi Chuan , Liu Zekuan , Ye Yanfang
Published: July 2020

As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous structure, has drawn increasing attention in recent years. Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. However, the selection of meta-paths is still an open problem, which either depends on domain knowledge or is learned from label information. As a uniform blueprint of HIN, the network schema comprehensively embraces the high-order structure and contains rich semantics. In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE. In NSHE, a network schema sampling method is first proposed to generate sub-graphs (i.e., schema instances), and then multi-task learning task is built to preserve the heterogeneous structure of each schema instance. Besides preserving pairwise structure information, NSHE is able to retain high-order structure (i.e., network schema). Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods.
Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification
Authors: Wu Lianwei , Rao Yuan , Yang Xiong , Wang Wanzhen , Nazir Ambreen
Published: July 2020

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.
Unsatisfiability Proofs for Weight 16 Codewords in Lam's Problem
Authors: Bright Curtis , Cheung Kevin K.H. , Stevens Brett , Kotsireas Ilias , Ganesh Vijay
Published: July 2020

In the 1970s and 1980s, searches performed by L. Carter, C. Lam, L. Thiel, and S. Swiercz showed that projective planes of order ten with weight 16 codewords do not exist. These searches required highly specialized and optimized computer programs and required about 2,000 hours of computing time on mainframe and supermini computers. In 2010, these searches were verified by D. Roy using an optimized C program and 16,000 hours on a cluster of desktop machines. We performed a verification of these searches by reducing the problem to the Boolean satisfiability problem (SAT). Our verification uses the cube-and-conquer SAT solving paradigm, symmetry breaking techniques using the computer algebra system Maple, and a result of Carter that there are ten nonisomorphic cases to check. Our searches completed in about 30 hours on a desktop machine and produced nonexistence proofs of about 1 terabyte in the DRAT (deletion resolution asymmetric tautology) format.
Vertex Weighting-Based Tabu Search for p-Center Problem
Authors: Zhang Qingyun , Lü Zhipeng , Su Zhouxing , Li Chumin , Fang Yuan , Ma Fuda
Published: July 2020

The p-center problem consists of choosing p centers from a set of candidates to minimize the maximum cost between any client and its assigned facility. In this paper, we transform the p-center problem into a series of set covering subproblems, and propose a vertex weighting-based tabu search (VWTS) algorithm to solve them. The proposed VWTS algorithm integrates distinguishing features such as a vertex weighting technique and a tabu search strategy to help the search to jump out of the local optima. Computational experiments on 138 most commonly used benchmark instances show that VWTS is highly competitive comparing to the state-of-the-art methods in spite of its simplicity. As a well-known NP-hard problem which has already been studied for over half a century, it is a challenging task to break the records on these classic datasets. Yet VWTS improves the best known results for 14 out of 54 large instances, and matches the optimal results for all remaining 84 ones. In addition, the computational time taken by VWTS is much shorter than other algorithms in the literature.
Exploration Based Language Learning for Text-Based Games
Authors: Madotto Andrea , Namazifar Mahdi , Huizinga Joost , Molino Piero , Ecoffet Adrien , Zheng Huaixiu , Papangelis Alexandros , Yu Dian , Khatri Chandra , Tur Gokhan
Published: July 2020

This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games.

These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents. Moreover, they provide a learning setting in which these skills can be acquired through interactions with an environment rather than using fixed corpora.

One aspect that makes these games particularly challenging for learning agents is the combinatorially large action space. Existing methods for solving text-based games are limited to games that are either very simple or have an action space restricted to a predetermined set of admissible actions.

In this work, we propose to use the exploration approach of Go-Explore for solving text-based games. More specifically, in an initial exploration phase, we first extract trajectories with high rewards, after which we train a policy to solve the game by imitating these trajectories.

Our experiments show that this approach outperforms existing solutions in solving text-based games, and it is more sample efficient in terms of the number of interactions with the environment.

Moreover, we show that the learned policy can generalize better than existing solutions to unseen games without using any restriction on the action space.
Learning to Complement Humans
Authors: Wilder Bryan , Horvitz Eric , Kamar Ece
Published: July 2020

A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. To date, systems aimed at complementing the skills of people have employed models trained to be as accurate as possible in isolation. We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams by considering the distinct abilities of people and machines. The goal is to focus machine learning on problem instances that are difficult for humans, while recognizing instances that are difficult for the machine and seeking human input on them. We demonstrate in two real-world domains (scientific discovery and medical diagnosis) that human-machine teams built via these methods outperform the individual performance of machines and people. We then analyze conditions under which this complementarity is strongest, and which training methods amplify it. Taken together, our work provides the first systematic investigation of how machine learning systems can be trained to complement human reasoning.
Answering Counting Queries over DL-Lite Ontologies
Authors: Bienvenu Meghyn , Manière Quentin , Thomazo Michaël
Published: July 2020

Ontology-mediated query answering (OMQA) is a promising approach to data access and integration that has been actively studied in the knowledge representation and database communities for more than a decade. The vast majority of work on OMQA focuses on conjunctive queries, whereas more expressive queries that feature counting or other forms of aggregation remain largely unexplored. In this paper, we introduce a general form of counting query, relate it to previous proposals, and study the complexity of answering such queries in the presence of DL-Lite ontologies. As it follows from existing work that query answering is intractable and often of high complexity, we consider some practically relevant restrictions, for which we establish improved complexity bounds.
Diagnosing Software Faults Using Multiverse Analysis
Authors: Chatterjee Prantik , Chatterjee Abhijit , Campos Jose , Abreu Rui , Roy Subhajit
Published: July 2020

Spectrum-based Fault Localization (SFL) approaches aim to efficiently localize faulty components from examining program behavior. This is done by collecting the execution patterns of various

combinations of components and the corresponding outcomes into a spectrum. Efficient fault localization depends heavily on the quality of the spectra. Previous approaches, including the current

state-of-the-art Density- Diversity-Uniqueness (DDU) approach, attempt to generate “good” test-suites by improving certain structural properties of the spectra. In this work, we propose a different

approach, Multiverse Analysis, that considers multiple hypothetical universes, each corresponding to a scenario where one of the components is assumed to be faulty, to generate a spectrum that

attempts to reduce the expected worst-case wasted effort over all the universes. Our experiments show that the Multiverse Analysis not just improves the efficiency of fault localization but also achieves better coverage and generates smaller test-suites over DDU, the current state-of-the-art technique. On average, our approach reduces the developer effort over DDU by over 16% for more than 92% of the instances. Further, the improvements over DDU are indeed statistically significant on the paired Wilcoxon Signed-rank test.
Deductive Module Extraction for Expressive Description Logics
Authors: Koopmann Patrick , Chen Jieying
Published: July 2020

In deductive module extraction, we determine a small subset of an ontology for a given vocabulary that preserves all logical entailments that can be expressed in that vocabulary. While in the literature stronger module notions have been discussed, we argue that for applications in ontology analysis and ontology reuse, deductive modules, which are decidable and potentially smaller, are often sufficient. We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse. An evaluation of our implementation shows that the modules computed by our method are often significantly smaller than those computed by existing methods.
A Framework for Reasoning about Dynamic Axioms in Description Logics
Authors: Bednarczyk Bartosz , Demri Stephane , Mansutti Alessio
Published: July 2020

Description logics are well-known logical formalisms for knowledge

representation. We propose to enrich knowledge bases (KBs) with dynamic

axioms that specify how the satisfaction of statements from the KBs

evolves when the interpretation is decomposed or recomposed, providing

a natural means to predict the evolution of interpretations.

Our dynamic axioms borrow logical connectives from separation logics,

well-known specification languages to verify programs with

dynamic data structures.

In the paper, we focus on ALC and EL augmented

with dynamic axioms, or to their subclass of positive dynamic axioms.

The knowledge base consistency problem in the presence of dynamic axioms

is investigated, leading to interesting complexity results, among which

the problem for EL with positive dynamic axioms is tractable,

whereas EL with dynamic axioms is undecidable.
Overcoming the Grounding Bottleneck Due to Constraints in ASP Solving: Constraints Become Propagators
Authors: Cuteri Bernardo , Dodaro Carmine , Ricca Francesco , Schüller Peter
Published: July 2020

Answer Set Programming (ASP) is a well-known formalism for Knowledge Representation and Reasoning, successfully employed to solve many AI problems, also thanks to the availability of efficient implementations. Traditionally, ASP systems are based on the ground&solve approach, where the grounding transforms a general input program into its propositional counterpart, whose stable models are then computed by the solver using the CDCL algorithm. This approach suffers an intrinsic limitation: the grounding of one or few constraints may be unaffordable from a computational point of view; a problem known as grounding bottleneck. In this paper, we develop an innovative approach for evaluating ASP programs, where some of the constraints of the input program are not grounded but automatically translated into propagators of the CDCL algorithm that work on partial interpretations. We implemented the new approach on top of the solver WASP and carried out an experimental analysis on different benchmarks. Results show that our approach consistently outperforms state-of-the-art ASP systems by overcoming the grounding bottleneck.
A Logic of Directions
Authors: Du Heshan , Alechina Natasha , Cohn Anthony G.
Published: July 2020

We propose a logic of directions for points (LD) over 2D Euclidean space, which formalises primary direction relations east (E), west (W), and indeterminate east/west (Iew), north (N), south (S) and indeterminate north/south (Ins). We provide a sound and complete axiomatisation of it, and prove that its satisfiability problem is NP-complete.
Automatic Synthesis of Generalized Winning Strategies of Impartial Combinatorial Games Using SMT Solvers
Authors: Wu Kaisheng , Fang Liangda , Xiong Liping , Lai Zhao-Rong , Qiao Yong , Chen Kaidong , Rong Fei
Published: July 2020

Strategy representation and reasoning has recently received much attention in artificial intelligence. Impartial combinatorial games (ICGs) are a type of elementary and fundamental games in game theory. One of the challenging problems of ICGs is to construct winning strategies, particularly, generalized winning strategies for possibly infinitely many instances of ICGs. In this paper, we investigate synthesizing generalized winning strategies for ICGs. To this end, we first propose a logical framework to formalize ICGs based on the linear integer arithmetic fragment of numeric part of PDDL. We then propose an approach to generating the winning formula that exactly captures the states in which the player can force to win. Furthermore, we compute winning strategies for ICGs based on the winning formula. Experimental results on several games demonstrate the effectiveness of our approach.