bluuebunny/crossref_metadata_2025_split huggingface dataset:
Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization
Authors: Gu Bin , Xian Wenhan , Huang Heng 
Published: July 2019

Asynchronous parallel stochastic optimization for non-convex problems becomes more and more important in machine learning especially due to the popularity of deep learning. The Frank-Wolfe (a.k.a. conditional gradient) algorithms has regained much interest because of its projection-free property and the ability of handling structured constraints. However, our understanding of asynchronous stochastic Frank-Wolfe algorithms is extremely limited especially in the non-convex setting. To address this challenging problem, in this paper, we propose our asynchronous stochastic Frank-Wolfe algorithm (AsySFW) and its variance reduction version (AsySVFW) for solving the constrained non-convex optimization problems. More importantly, we prove the fast convergence rates of AsySFW and AsySVFW in the non-convex setting. To the best of our knowledge, AsySFW and AsySVFW are the first asynchronous parallel stochastic algorithms with convergence guarantees for solving the constrained non-convex optimization problems. The experimental results on real high-dimensional gray-scale images not only confirm the fast convergence of our algorithms, but also show a near-linear speedup on a parallel system with shared memory due to the lock-free implementation.
How Hard Is the Manipulative Design of Scoring Systems?
Authors: Baumeister Dorothea , Hogrebe Tobias 
Published: July 2019

In an election, votes are often given as ordered lists over candidates. A common way of determining the winner is then to apply some scoring system, where each position is associated with a specific score. This setting is also transferable to other situations, such as sports tournaments. The design of such systems, i.e., the choice of the score values, may have a crucial influence on the outcome. We study the computational complexity of two related decision problems. In addition, we provide a case study of data from Formula 1 using ILP formulations. Our results show that under some mild conditions there are cases where the actual scoring system has no influence, whereas in other cases very small changes may lead to a different winner. This may be seen as a measure of robustness of the winning candidate.
Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Authors: Liu Chunlei , Ding Wenrui , Xia Xin , Hu Yuan , Zhang Baochang , Liu Jianzhuang , Zhuang Bohan , Guo Guodong 
Published: July 2019

Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant performance gap between them. In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified framework. In particular, we use a GAN to train the 1-bit binary network with the guidance of its corresponding full-precision model, which significantly improves the performance of BCNNs. The rectified convolutional layers are generic and flexible, and can be easily incorporated into existing DCNNs such as WideResNets and ResNets. Extensive experiments demonstrate the superior performance of the proposed RBCNs over state-of-the-art BCNNs. In particular, our method shows strong generalization on the object tracking task.
Talking Face Generation by Conditional Recurrent Adversarial Network
Authors: Song Yang , Zhu Jingwen , Li Dawei , Wang Andy , Qi Hairong 
Published: July 2019

Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generate the talking face video with accurate lip synchronization. Existing works either do not consider temporal dependency across video frames thus yielding abrupt facial and lip movement or are limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional recurrent generation network that incorporates both image and audio features in the recurrent unit for temporal dependency. To achieve both image- and video-realism, a pair of spatial-temporal discriminators are included in the network for better image/video quality. Since accurate lip synchronization is essential to the success of talking face video generation, we also construct a lip-reading discriminator to boost the accuracy of lip synchronization. We also extend the network to model the natural pose and expression of talking face on the Obama Dataset. Extensive experimental results demonstrate the superiority of our framework over the state-of-the-arts in terms of visual quality, lip sync accuracy, and smooth transition pertaining to both lip and facial movement.
Strategy Logic with Simple Goals: Tractable Reasoning about Strategies
Authors: Belardinelli Francesco , Jamroga Wojciech , Kurpiewski Damian , Malvone Vadim , Murano Aniello 
Published: July 2019

In this paper we introduce Strategy Logic with simple goals (SL[SG]), a fragment of Strategy Logic that strictly extends the well-known Alternating-time Temporal Logic ATL by introducing arbitrary quantification over the agents' strategies. Our motivation comes from game-theoretic applications, such as expressing Stackelberg equilibria in games, coercion in voting protocols, as well as module checking for simple goals. Most importantly, we prove that the model checking problem for SL[SG] is PTIME-complete, the same as ATL. Thus, the extra expressive power comes at no computational cost as far as verification is concerned.
Color-Sensitive Person Re-Identification
Authors: Wang Guan'an , Yang Yang , Cheng Jian , Wang Jinqiao , Hou Zengguang 
Published: July 2019

Recent deep Re-ID models mainly focus on learning high-level semantic features, while failing to explicitly explore color information which is one of the most important cues for person Re-ID. In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information. On one hand, we train our model with real and fake images. By using the extra fake images, more color information can be exploited and it can avoid overfitting during training. On the other hand, we also train our model with images of the same person with different colors. By doing so, features can be forced to focus on the color difference in regions. To generate fake images with specified colors, we propose a novel Color Translation GAN (CTGAN) to learn mappings between different clothing colors and preserve identity consistency among the same clothing color. Extensive evaluations on two benchmark datasets show that our approach significantly outperforms state-of-the-art Re-ID models.
Transferable Adversarial Attacks for Image and Video Object Detection
Authors: Wei Xingxing , Liang Siyuan , Chen Ning , Cao Xiaochun 
Published: July 2019

Identifying adversarial examples is beneficial for understanding deep networks and developing robust models. However, existing attacking methods for image object detection have two limitations: weak transferability---the generated adversarial examples often have a low success rate to attack other kinds of detection methods, and high computation cost---they need much time to deal with video data, where many frames need polluting. To address these issues, we present a generative method to obtain adversarial images and videos, thereby significantly reducing the processing time. To enhance transferability, we manipulate the feature maps extracted by a feature network, which usually constitutes the basis of object detectors. Our method is based on the Generative Adversarial Network (GAN) framework, where we combine a high-level class loss and a low-level feature loss to jointly train the adversarial example generator. Experimental results on PASCAL VOC and ImageNet VID datasets show that our method efficiently generates image and video adversarial examples, and more importantly, these adversarial examples have better transferability, therefore being able to simultaneously attack two kinds of representative object detection models: proposal based models like Faster-RCNN and regression based models like SSD.
Athanor: High-Level Local Search Over Abstract Constraint Specifications in Essence
Authors: Attieh Saad , Dang Nguyen , Jefferson Christopher , Miguel Ian , Nightingale Peter 
Published: July 2019

This paper presents Athanor, a novel local search solver that operates on abstract constraint specifications of combinatorial problems in the Essence language. It is unique in that it operates directly on the high level, nested types in Essence, such as set of partitions or multiset of sequences, without refining such types into low level representations. This approach has two main advantages. First, the structure present in the high level types allows high quality neighbourhoods for local search to be automatically derived. Second, it allows Athanor to scale much better than solvers that operate on the equivalent, but much larger, low-level representations. The paper details how Athanor operates, covering incremental evaluation, dynamic unrolling of quantified expressions and neighbourhood construction. A series of case studies show the performance of Athanor, benchmarked against several local search solvers on a range of problem classes.
An Experimental View on Committees Providing Justified Representation
Authors: Bredereck Robert , Faliszewski Piotr , Kaczmarczyk Andrzej , Niedermeier Rolf 
Published: July 2019

We provide an experimental study of committees that achieve (proportional/extended) justified representation (JR/PJR/EJR). In particular, we ask how many such committees exist and how varied they are in terms of voter satisfaction and coverage. We find that under many natural distributions of preferences a large fraction of randomly selected JR committees also provide PJR and EJR. Further, we find that the sets of JR committees for our elections are very varied and include both high-quality ones and not-so-appealing ones.
DeltaDou: Expert-level Doudizhu AI through Self-play
Authors: Jiang Qiqi , Li Kuangzheng , Du Boyao , Chen Hao , Fang Hai 
Published: July 2019

Artificial Intelligence has seen several breakthroughs in two-player
perfect information game. Nevertheless, Doudizhu, a three-player
imperfect information game, is still quite challenging. In this paper,
we present a Doudizhu AI by applying deep reinforcement learning from
games of self-play. The algorithm combines an asymmetric MCTS on nodes
of information set of each player, a policy-value network that
approximates the policy and value on each decision node, and inference
on unobserved hands of other players by given policy. Our results show
that self-play can significantly improve the performance of our agent in
this multi-agent imperfect information game. Even starting with a weak
AI, our agent can achieve human expert level after days of self-play
and training.
Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces
Authors: Kishimoto Akihiro , Botea Adi , Marinescu Radu 
Published: July 2019

Computing cycle-free solutions in cyclic AND/OR search spaces is an important AI problem. Previous work on optimal depth-first search strongly assumes the use of consistent heuristics, the need to keep all examined states in a transposition table, and the existence of solutions. We give a new theoretical analysis under relaxed assumptions where previous results no longer hold. We then present a generic approachto proving unsolvability, and apply it to RBFAOO and BLDFS, two state-of-the-art algorithms. We demonstrate the performance in domain-independent nondeterministic planning
MiSC: Mixed Strategies Crowdsourcing
Authors: Ko Ching Yun , Lin Rui , Li Shu , Wong Ngai 
Published: July 2019

Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation. Recently, another cohort of models regard crowdsourced annotations as incomplete tensors and recover unfilled labels by tensor completion. However, mixed strategies of the two methodologies have never been comprehensively investigated, leaving them as rather independent approaches. In this work, we propose MiSC ( Mixed Strategies Crowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques. In particular, we propose a novel iterative Tucker label aggregation algorithm that outperforms state-of-the-art methods in extensive experiments.
Why Can’t You Do That HAL? Explaining Unsolvability of Planning Tasks
Authors: Sreedharan Sarath , Srivastava Siddharth , Smith David , Kambhampati Subbarao 
Published: July 2019

Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations of solutions to planning problems, explaining the absence of solutions remains an open and under-studied problem, even though such situations can be the hardest to understand or debug. In this paper, we show that hierarchical abstractions can be used to efficiently generate reasons for unsolvability of planning problems. In contrast to related work on computing certificates of unsolvability, we show that these methods can generate compact, human-understandable reasons for unsolvability. Empirical analysis and user studies show the validity of our methods as well as their computational efficacy on a number of benchmark planning domains.
Personalized Multimedia Item and Key Frame Recommendation
Authors: Wu Le , Chen Lei , Yang Yonghui , Hong Richang , Ge Yong , Xie Xing , Wang Meng 
Published: July 2019

When recommending or advertising items to users, an emerging trend is to present each multimedia item with a key frame image (e.g., the poster of a movie). As each multimedia item can be represented as multiple fine-grained visual images (e.g., related images of the movie), personalized key frame recommendation is necessary in these applications to attract users' unique visual preferences. However, previous personalized key frame recommendation models relied on users' fine grained image behavior of multimedia items (e.g., user-image interaction behavior), which is often not available in real scenarios. In this paper, we study the general problem of joint multimedia item and key frame recommendation in the absence of the fine-grained user-image behavior. We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior. To tackle this challenge, we leverage users' item behavior by projecting users(items) in two latent spaces: a collaborative latent space and a visual latent space. We further design a model to discern both the collaborative and visual dimensions of users, and model how users make decisive item preferences from these two spaces. As a result, the learned user visual profiles could be directly applied for key frame recommendation. Finally, experimental results on a real-world dataset clearly show the effectiveness of our proposed model on the two recommendation tasks.
Possibilistic Games with Incomplete Information
Authors: Ben Amor Nahla , Fargier Helene , Sabbadin Régis , Trabelsi Meriem 
Published: July 2019

Bayesian games offer a suitable framework for
games where the utility degrees are additive in
essence. This approach does nevertheless not apply to ordinal games, where the utility degrees do
not capture more than a ranking, nor to situations
of decision under qualitative uncertainty. This paper proposes a representation framework for ordinal games under possibilistic incomplete information (π-games) and extends the fundamental notion
of Nash equilibrium (NE) to this framework. We
show that deciding whether a NE exists is a difficult
problem (NP-hard) and propose a Mixed Integer Linear Programming (MILP) encoding.
Experiments on variants of the GAMUT problems
confirm the feasibility of this approach.
Oblivious and Semi-Oblivious Boundedness for Existential Rules
Authors: Bourhis Pierre , Leclère Michel , Mugnier Marie-Laure , Tison Sophie , Ulliana Federico , Gallois Lily 
Published: July 2019

We study the notion of boundedness in the context positive existential rules, that is, wether there exists an upper bound to the depth of the chase procedure, that is independent from the initial instance. By focussing our attention on the oblivious and the semi-oblivious chase variants, we give a characterization of boundedness in terms of FO-rewritability and chase termination. We show that it is decidable to recognize if a set of rules is bounded for several classes of rules and outline the complexity of the problem.
Reasoning about Quality and Fuzziness of Strategic Behaviours
Authors: Bouyer Patricia , Kupferman Orna , Markey Nicolas , Maubert Bastien , Murano Aniello , Perelli Giuseppe 
Published: July 2019

We introduce and study SL[F], a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0,1], reflecting ``how much'' or ``how well'' the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for ourlogic, based on a quantitative extension of Quantified CTL*.
Causal Discovery with Cascade Nonlinear Additive Noise Model
Authors: Cai Ruichu , Qiao Jie , Zhang Kun , Zhang Zhenjie , Hao Zhifeng 
Published: July 2019

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive--even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints; as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.
Election with Bribe-Effect Uncertainty: A Dichotomy Result
Authors: Chen Lin , Xu Lei , Xu Shouhuai , Gao Zhimin , Shi Weidong 
Published: July 2019

We consider the electoral bribery problem in computational social choice. In this context, extensive studies have been carried out to analyze the computational vulnerability of various voting (or election) rules. However, essentially all prior studies assume a deterministic model where each voter has an associated threshold value, which is used as follows. A voter will take a bribe and vote according to the attacker's (i.e., briber's) preference when the amount of the bribe is above the threshold, and a voter will not take a bribe when the amount of the bribe is not above the threshold (in this case, the voter will vote according to its own preference, rather than the attacker's). In this paper, we initiate the study of a more realistic model where each voter is associated with a willingness function, rather than a fixed threshold value. The willingness function characterizes the likelihood a bribed voter would vote according to the attacker's preference; we call this bribe-effect uncertainty. We characterize the computational complexity of the electoral bribery problem in this new model. In particular, we discover a dichotomy result: a certain mathematical property of the willingness function dictates whether or not the computational hardness can serve as a deterrence to bribery attackers.
Rational Inference Relations from Maximal Consistent Subsets Selection
Authors: Konieczny Sébastien , Marquis Pierre , Vesic Srdjan 
Published: July 2019

When one wants to draw non-trivial inferences from an inconsistent belief base, a very natural approach is to take advantage of the maximal consistent subsets of the base. But few inference relations from maximal consistent subsets exist. In this paper we point out new such relations based on selection of some of the maximal consistent subsets, leading thus to inference relations with a stronger inferential power. The selection process must obey some principles to ensure that it leads to an inference relation which is rational. We define a general class of monotonic selection relations for comparing maximal consistent sets. And we show that it corresponds to the class of rational inference relations.
Monitoring of a Dynamic System Based on Autoencoders
Authors: Osmani Aomar , Hamidi Massinissa , Bouhouche Salah 
Published: July 2019

Monitoring industrial infrastructures are undergoing a critical transformation with industry 4.0. Monitoring solutions must follow the system behavior in real time and must adapt to its continuous change. We propose in this paper an autoencoder model-based approach for tracking abnormalities in industrial application. A set of sensors collects data from turbo-compressors and an original two-level machine learning LSTM autoencoder architecture defines a continuous nominal vibration model. Normalized thresholds (ISO 20816) between the model and the system generates a possible abnormal situation to diagnose. Experimental results, including hyper-parameter optimization on large real data and domain expert analysis, show that our proposed solution gives promising results.
TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics
Authors: Yang Shihui , Tian Jidong , Zhang Honglun , Yan Junchi , He Hao , Jin Yaohui 
Published: July 2019

Knowledge graph embedding, which projects the symbolic relations and entities onto low-dimension continuous spaces, is essential to knowledge graph completion. Recently, translation-based embedding models (e.g. TransE) have aroused increasing attention for their simplicity and effectiveness. These models attempt to translate semantics from head entities to tail entities with the relations and infer richer facts outside the knowledge graph. In this paper, we propose a novel knowledge graph embedding method named TransMS, which translates and transmits multidirectional semantics: i) the semantics of head/tail entities and relations to tail/head entities with nonlinear functions and ii) the semantics from entities to relations with linear bias vectors. Our model has merely one additional parameter α than TransE for each triplet, which results in its better scalability in large-scale knowledge graph. Experiments show that TransMS achieves substantial improvements against state-of-the-art baselines, especially the Hit@10s of head entity prediction for N-1 relations and tail entity prediction for 1-N relations improved by about 27.1% and 24.8% on FB15K database respectively.