bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks
Authors: Qiu Wei , Chen Haipeng , An Bo
Published: July 2019

Over the past decades, Electronic Toll Collection (ETC) systems have been proved the capability of alleviating traffic congestion in urban areas. Dynamic Electronic Toll Collection (DETC) was recently proposed to further improve the efficiency of ETC, where tolls are dynamically set based on traffic dynamics. However, computing the optimal DETC scheme is computationally difficult and existing approaches are limited to small scale or partial road networks, which significantly restricts the adoption of DETC. To this end, we propose a novel multi-agent reinforcement learning (RL) approach for DETC. We make several key contributions: i) an enhancement over the state-of-the-art RL-based method with a deep neural network representation of the policy and value functions and a temporal difference learning framework to accelerate the update of target values, ii) a novel edge-based graph convolutional neural network (eGCN) to extract the spatio-temporal correlations of the road network state features, iii) a novel cooperative multi-agent reinforcement learning (MARL) which divides the whole road network into partitions according to their geographic and economic characteristics and trains a tolling agent for each partition. Experimental results show that our approach can scale up to realistic-sized problems with robust performance and significantly outperform the state-of-the-art method.
FireCast: Leveraging Deep Learning to Predict Wildfire Spread
Authors: Radke David , Hessler Anna , Ellsworth Dan
Published: July 2019

Destructive wildfires result in billions of dollars in damage each year and are expected to increase in frequency, duration, and severity due to climate change. The current state-of-the-art wildfire spread models rely on mathematical growth predictions and physics-based models, which are difficult and computationally expensive to run. We present and evaluate a novel system, FireCast. FireCast combines artificial intelligence (AI) techniques with data collection strategies from geographic information systems (GIS). FireCast predicts which areas surrounding a burning wildfire have high-risk of near-future wildfire spread, based on historical fire data and using modest computational resources. FireCast is compared to a random prediction model and a commonly used wildfire spread model, Farsite, outperforming both with respect to total accuracy, recall, and F-score.
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent
Authors: Shen Shuheng , Xu Linli , Liu Jingchang , Liang Xianfeng , Cheng Yifei
Published: July 2019

With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization challenges. Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup. In this paper, we propose a computation and communication decoupled stochastic gradient descent (CoCoD-SGD) algorithm to run computation and communication in parallel to reduce the communication cost. We prove that CoCoD-SGD has a linear iteration speedup with respect to the total computation capability of the hardware resources. In addition, it has a lower communication complexity and better time speedup comparing with traditional distributed SGD algorithms. Experiments on deep neural network training demonstrate the significant improvements of CoCoD-SGD: when training ResNet18 and VGG16 with 16 Geforce GTX 1080Ti GPUs, CoCoD-SGD is up to 2-3 x faster than traditional synchronous SGD.
Randomized Adversarial Imitation Learning for Autonomous Driving
Authors: Shin MyungJae , Kim Joongheon
Published: July 2019

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical. The autonomous driving system simultaneously activates multiple ADAS functions; and thus it is essential to coordinate various ADAS functions. This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors. The RAIL policies are trained through derivative-free optimization for the decision maker that coordinates the proper ADAS functions, e.g., smart cruise control and lane keeping system. Especially, the proposed method is also able to deal with the LIDAR data and makes decisions in complex multi-lane highways and multi-agent environments.
Scaling Fine-grained Modularity Clustering for Massive Graphs
Authors: Shiokawa Hiroaki , Amagasa Toshiyuki , Kitagawa Hiroyuki
Published: July 2019

Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is difficult to fully reproduce ground-truth clusters due to the resolution limit problem. (2) They are computationally expensive because all nodes and edges must be computed iteratively. This paper proposes gScarf, which outputs fine-grained clusters within a short running time. To overcome the aforementioned weaknesses, gScarf dynamically prunes unnecessary nodes and edges, ensuring that it captures fine-grained clusters. Experiments show that gScarf outperforms existing methods in terms of running time while finding clusters with high accuracy.
Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty
Authors: Li Minglong , Yang Wenjing , Cai Zhongxuan , Yang Shaowu , Wang Ji
Published: July 2019

The performance of decentralized multi-agent systems tends to benefit from information sharing and its effective utilization. However, too much or unnecessary sharing may hinder the performance due to the delay, instability and additional overhead of communications. Aiming to a satisfiable coordination performance, one would prefer the cost of communications as less as possible. In this paper, we propose an approach for improving the sharing utilization by integrating information sharing with prediction in decentralized planning. We present a novel planning algorithm by combining decision sharing and prediction based on decentralized Monte Carlo Tree Search called Dec-MCTS-SP. Each agent grows a search tree guided by the rewards calculated by the joint actions, which can not only be sampled from the shared probability distributions over action sequences, but also be predicted by a sufficiently-accurate and computationally-cheap heuristics-based method. Besides, several policies including sparse and discounted UCT and DIY-bonus are leveraged for performance improvement. We have implemented Dec-MCTS-SP in the case study on multi-agent information gathering under threat and uncertainty, which is formulated as Decentralized Partially Observable Markov Decision Process (Dec-POMDP). The factored belief vectors are integrated into Dec-MCTS-SP to handle the uncertainty. Comparing with the random, auction-based algorithm and Dec-MCTS, the evaluation shows that Dec-MCTS-SP can reduce communication cost significantly while still achieving a surprisingly higher coordination performance.
Node Embedding over Temporal Graphs
Authors: Singer Uriel , Guy Ido , Radinsky Kira
Published: July 2019

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.
Medical Concept Embedding with Multiple Ontological Representations
Authors: Song Lihong , Cheong Chin Wang , Yin Kejing , Cheung William K. , Fung Benjamin C. M. , Poon Jonathan
Published: July 2019

Learning representations of medical concepts from the Electronic Health Records (EHR) has been shown effective for predictive analytics in healthcare. Incorporation of medical ontologies has also been explored to further enhance the accuracy and to ensure better alignment with the known medical knowledge. Most of the existing work assumes that medical concepts under the same ontological category should share similar representations, which however does not always hold. In particular, the categorizations in medical ontologies were established with various factors being considered. Medical concepts even under the same ontological category may not follow similar occurrence patterns in the EHR data, leading to contradicting objectives for the representation learning. In this paper, we propose a deep learning model called MMORE which alleviates this conflicting objective issue by allowing multiple representations to be inferred for each ontological category via an attention mechanism. We apply MMORE to diagnosis prediction and our experimental results show that the representations obtained by MMORE can achieve better predictive accuracy and result in clinically meaningful sub-categorization of the existing ontological categories.
Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation
Authors: Xu Yanan , Zhu Yanmin , Shen Yanyan , Yu Jiadi
Published: July 2019

Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.
Dual-Path in Dual-Path Network for Single Image Dehazing
Authors: Yang Aiping , Wang Haixin , Ji Zhong , Pang Yanwei , Shao Ling
Published: July 2019

Recently, deep learning-based single image dehazing method has been a popular approach to tackle dehazing. However, the existing dehazing approaches are performed directly on the original hazy image, which easily results in image blurring and noise amplifying. To address this issue, the paper proposes a DPDP-Net (Dual-Path in Dual-Path network) framework by employing a hierarchical dual path network. Specifically, the first-level dual-path network consists of a Dehazing Network and a Denoising Network, where the Dehazing Network is responsible for haze removal in the structural layer, and the Denoising Network deals with noise in the textural layer, respectively. And the second-level dual-path network lies in the Dehazing Network, which has an AL-Net (Atmospheric Light Network) and a TM-Net (Transmission Map Network), respectively. Concretely, the AL-Net aims to train the non-uniform atmospheric light, while the TM-Net aims to train the transmission map that reflects the visibility of the image. The final dehazing image is obtained by nonlinearly fusing the output of the Denoising Network and the Dehazing Network. Extensive experiments demonstrate that our proposed DPDP-Net achieves competitive performance against the state-of-the-art methods on both synthetic and real-world images.
Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
Authors: Yu Shengze , Wang Xin , Zhu Wenwu , Cui Peng , Wang Jingdong
Published: July 2019

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Predicting dominance in multi-person videos
Authors: Bai Chongyang , Bolonkin Maksim , Kumar Srijan , Leskovec Jure , Burgoon Judee , Dunbar Norah , Subrahmanian V. S.
Published: July 2019

We consider the problems of predicting (i) the most dominant person in a group of people, and (ii) the more dominant of a pair of people, from videos depicting group interactions. We introduce a novel family of variables called Dominance Rank. We combine features not previously used for dominance prediction (e.g., facial action units, emotions), with a novel ensemble-based approach to solve these two problems. We test our models against four competing algorithms in the literature on two datasets and show that our results improve past performance. We show 2.4% to 16.7% improvement in AUC compared to baselines on one dataset, and a gain of 0.6% to 8.8% in accuracy on the other. Ablation testing shows that Dominance Rank features play a key role.
Procedural Generation of Initial States of Sokoban
Authors: Bento Dâmaris S. , Pereira André G. , Lelis Levi H. S.
Published: July 2019

Procedural generation of initial states of state-space search problems have applications in human and machine learning as well as in the evaluation of planning systems. In this paper we deal with the task of generating hard and solvable initial states of Sokoban puzzles. We propose hardness metrics based on pattern database heuristics and the use of novelty to improve the exploration of search methods in the task of generating initial states. We then present a system called Beta that uses our hardness metrics and novelty to generate initial states. Experiments show that Beta is able to generate initial states that are harder to solve by a specialized solver than those designed by human experts.
DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks
Authors: Chen Huili , Fu Cheng , Zhao Jishen , Koushanfar Farinaz
Published: July 2019

Deep Neural Networks (DNNs) are vulnerable to Neural Trojan (NT) attacks where the adversary injects malicious behaviors during DNN training. This type of ‘backdoor’ attack is activated when the input is stamped with the trigger pattern specified by the attacker, resulting in an incorrect prediction of the model. Due to the wide application of DNNs in various critical fields, it is indispensable to inspect whether the pre-trained DNN has been trojaned before employing a model. Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deployment. We propose DeepInspect, the first black-box Trojan detection solution with minimal prior knowledge of the model. DeepInspect learns the probability distribution of potential triggers from the queried model using a conditional generative model, thus retrieves the footprint of backdoor insertion. In addition to NT detection, we show that DeepInspect’s trigger generator enables effective Trojan mitigation by model patching. We corroborate the effectiveness, efficiency, and scalability of DeepInspect against the state-of-the-art NT attacks across various benchmarks. Extensive experiments show that DeepInspect offers superior detection performance and lower runtime overhead than the prior work.
VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities
Authors: Duan Xu , Wu Jingzheng , Ji Shouling , Rui Zhiqing , Luo Tianyue , Yang Mutian , Wu Yanjun
Published: July 2019

With the explosive development of information technology, vulnerabilities have become one of the major threats to computer security. Most vulnerabilities with similar patterns can be detected effectively by static analysis methods. However, some vulnerable and non-vulnerable code is hardly distinguishable, resulting in low detection accuracy. In this paper, we define the accurate identification of vulnerabilities in similar code as a fine-grained vulnerability detection problem. We propose VulSniper which is designed to detect fine-grained vulnerabilities more effectively. In VulSniper, attention mechanism is used to capture the critical features of the vulnerabilities. Especially, we use bottom-up and top-down structures to learn the attention weights of different areas of the program. Moreover, in order to fully extract the semantic features of the program, we generate the code property graph, design a 144-dimensional vector to describe the relation between the nodes, and finally encode the program as a feature tensor. VulSniper achieves F1-scores of 80.6% and 73.3% on the two benchmark datasets, the SARD Buffer Error dataset and the SARD Resource Management Error dataset respectively, which are significantly higher than those of the state-of-the-art methods.
Real-Time Adversarial Attacks
Authors: Gong Yuan , Li Boyang , Poellabauer Christian , Shi Yiyu
Published: July 2019

In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can observe the entire original sample and then add a perturbation at any point of the sample. These attack approaches are not applicable to situations where the target model takes streaming input, i.e., an attacker is only able to observe past data points and add perturbations to the remaining (unobserved) data points of the input. In this paper, we propose a real-time adversarial attack scheme for machine learning models with streaming inputs.
Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns
Authors: Liu Yong , Hu Yujing , Gao Yang , Chen Yingfeng , Fan Changjie
Published: July 2019

Many real-world problems, such as robot control and soccer game, are naturally modeled as sparse-interaction multi-agent systems. Reutilizing single-agent knowledge in multi-agent systems with sparse interactions can greatly accelerate the multi-agent learning process. Previous works rely on bisimulation metric to define Markov decision process (MDP) similarity for controlling knowledge transfer. However, bisimulation metric is costly to compute and is not suitable for high-dimensional state space problems. In this work, we propose more scalable transfer learning methods based on a novel MDP similarity concept. We start by defining the MDP similarity based on the N-step return (NSR) values of an MDP. Then, we propose two knowledge transfer methods based on deep neural networks called direct value function transfer and NSR-based value function transfer. We conduct experiments in image-based grid world, multi-agent particle environment (MPE) and Ms. Pac-Man game. The results indicate that the proposed methods can significantly accelerate multi-agent reinforcement learning and meanwhile get better asymptotic performance.
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
Authors: Hou Min , Wu Le , Chen Enhong , Li Zhi , Zheng Vincent W. , Liu Qi
Published: July 2019

In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on real-world datasets clearly demonstrate the effectiveness of our approach compared with the state-of-the-art methods.
Model-Agnostic Adversarial Detection by Random Perturbations
Authors: Huang Bo , Wang Yi , Wang Wei
Published: July 2019

Adversarial examples induce model classification errors on purpose, which has raised concerns on the security aspect of machine learning techniques. Many existing countermeasures are compromised by adaptive adversaries and transferred examples. We propose a model-agnostic approach to resolve the problem by analysing the model responses to an input under random perturbations, and study the robustness of detecting norm-bounded adversarial distortions in a theoretical framework. Extensive evaluations are performed on the MNIST, CIFAR-10 and ImageNet datasets. The results demonstrate that our detection method is effective and resilient against various attacks including black-box attacks and the powerful CW attack with four adversarial adaptations.
Musical Composition Style Transfer via Disentangled Timbre Representations
Authors: Hung Yun-Ning , Chiang I-Tung , Chen Yi-An , Yang Yi-Hsuan
Published: July 2019

Music creation involves not only composing the different parts (e.g., melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. While the former has received increasing attention, the latter has not been much investigated. This paper presents, to the best
of our knowledge, the first deep learning models for rearranging music of arbitrary genres. Specifically, we build encoders and decoders that take a
piece of polyphonic musical audio as input, and predict as output its musical score. We investigate disentanglement techniques such as adversarial
training to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation
(timbre) of the parts per short-time segment. By disentangling pitch and timbre, our models have an idea of how each piece was composed and arranged. Moreover, the models can realize “composition style transfer” by rearranging a musical piece without much affecting its pitch content. We
validate the effectiveness of the models by experiments on instrument activity detection and composition style transfer. To facilitate follow-up research,
we open source our code at https://github.com/biboamy/instrument-disentangle.
Multiple Policy Value Monte Carlo Tree Search
Authors: Lan Li-Cheng , Li Wei , Wei Ting-Han , Wu I-Chen
Published: July 2019

Many of the strongest game playing programs use a combination of Monte Carlo tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as policy or value evaluators. Given a limited budget, such as online playing or during the self-play phase of AlphaZero (AZ) training, a balance needs to be reached between accurate state estimation and more MCTS simulations, both of which are critical for a strong game playing agent. Typically, larger DNNs are better at generalization and accurate evaluation, while smaller DNNs are less costly, and therefore can lead to more MCTS simulations and bigger search trees with the same budget. This paper introduces a new method called the multiple policy value MCTS (MPV-MCTS), which combines multiple policy value neural networks (PV-NNs) of various sizes to retain advantages of each network, where two PV-NNs f_S and f_L are used in this paper. We show through experiments on the game NoGo that a combined f_S and f_L MPV-MCTS outperforms single PV-NN with policy value MCTS, called PV-MCTS. Additionally, MPV-MCTS also outperforms PV-MCTS for AZ training.
Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space
Authors: Li Linyi , Zhong Zexuan , Li Bo , Xie Tao
Published: July 2019

Machine learning techniques, especially deep neural networks (DNNs), have been widely adopted in various applications. However, DNNs are recently found to be vulnerable against adversarial examples, i.e., maliciously perturbed inputs that can mislead the models to make arbitrary prediction errors.
Empirical defenses have been studied, but many of them can be adaptively attacked again.
Provable defenses provide provable error bound of DNNs, while such bound so far is far from satisfaction.
To address this issue, in this paper, we present our approach named Robustra for effectively improving the provable error bound of DNNs.
We leverage the adversarial space of a reference model as the feasible region to solve the min-max game between the attackers and defenders.
We solve its dual problem by linearly approximating the attackers' best strategy and utilizing the monotonicity of the slack variables introduced by the reference model.
The evaluation results show that our approach can provide significantly better provable adversarial error bounds on MNIST and CIFAR10 datasets, compared to the state-of-the-art results. In particular, bounded by L^infty, with epsilon = 0.1, on MNIST we reduce the error bound from 2.74% to 2.09%; with epsilon = 0.3, we reduce the error bound from 24.19% to 16.91%.
Dilated Convolution with Dilated GRU for Music Source Separation
Authors: Liu Jen-Yu , Yang Yi-Hsuan
Published: July 2019

Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music source separation, whose goal is to separate different sound sources while maintain the quality of the separated sounds. Therefore, in this paper, we use stacked dilated convolutions as the backbone for music source separation. Although stacked dilated convolutions can reach wider context than standard convolutions do, their effective receptive fields are still fixed and might not be wide enough for complex music audio signals. To reach even further information at remote locations, we propose to combine a dilated convolution with a modified GRU called Dilated GRU to form a block. A Dilated GRU receives information from k-step before instead of the previous step for a fixed k. This modification allows a GRU unit to reach a location with fewer recurrent steps and run faster because it can execute in parallel partially. We show that the proposed model with a stack of such blocks performs equally well or better than the state-of-the-art for separating both vocals and accompaniment.
Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks
Authors: Liu Tianlong , Qi Yu , Shi Liang , Yan Jianan
Published: July 2019

Web attacks such as Cross-Site Scripting and SQL Injection are serious Web threats that lead to catastrophic data leaking and loss. Because attack payloads are often short segments hidden in URL requests/posts that can be very long, classical machine learning approaches have difficulties in learning useful patterns from them. In this study, we propose a novel Locate-Then-Detect (LTD) system that can precisely detect Web threats in real-time by using attention-based deep neural networks. Firstly, an efficient Payload Locating Network (PLN) is employed to propose most suspicious regions from large URL requests/posts. Then a Payload Classification Network (PCN) is adopted to accurately classify malicious regions from suspicious candidates. In this way, PCN can focus more on learning malicious segments and highly increase detection accuracy. The noise induced by irrelevant background strings can be largely eliminated. Besides, LTD can greatly reduce computational costs (82.6% less) by ignoring large irrelevant URL content. Experiments are carried out on both benchmarks and real Web traffic. The LTD outperforms an HMM-based approach, the Libinjection system, and a leading commercial rule-based Web Application Firewall. Our method can be efficiently implemented on GPUs with an average detection time of about 5ms and well qualified for real-time applications.
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Authors: Ma Yuzhe , Zhu Xiaojin , Hsu Justin
Published: July 2019

Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set. We consider differential privacy as a defensive measure against this type of attack. We show that private learners are resistant to data poisoning attacks when the adversary is only able to poison a small number of items. However, this protection degrades as the adversary is allowed to poison more data. We emprically evaluate this protection by designing attack algorithms targeting objective and output perturbation learners, two standard approaches to differentially-private machine learning. Experiments show that our methods are effective when the attacker is allowed to poison sufficiently many training items.
LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Authors: Meng Weibin , Liu Ying , Zhu Yichen , Zhang Shenglin , Pei Dan , Liu Yuqing , Chen Yihao , Zhang Ruizhi , Tao Shimin , Sun Pei , Zhou Rong
Published: July 2019

Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.
Decidability of Model Checking Multi-Agent Systems with Regular Expressions against Epistemic HS Specifications
Authors: Michaliszyn Jakub , Witkowski Piotr
Published: July 2019

Epistemic Halpern-Shoham logic (EHS) is an interval temporal logic defined to verify properties of Multi-Agent Systems. In this paper we show that the model checking Multi-Agent Systems with regular expressions against the EHS specifications is decidable. We achieve this by reducing the model checking problem to the satisfiability problem of Monadic Second-Order Logic on trees.
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
Authors: Lockhart Edward , Lanctot Marc , Pérolat Julien , Lespiau Jean-Baptiste , Morrill Dustin , TImbers Finbarr , Tuyls Karl
Published: July 2019

In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents. We prove that when following this optimization, the exploitability of a player's strategy converges asymptotically to zero, and hence when both players employ this optimization, the joint policies converge to a Nash equilibrium. Unlike fictitious play (XFP) and counterfactual regret minimization (CFR), our convergence result pertains to the policies being optimized rather than the average policies. Our experiments demonstrate convergence rates comparable to XFP and CFR in four benchmark games in the tabular case. Using function approximation, we find that our algorithm outperforms the tabular version in two of the games, which, to the best of our knowledge, is the first such result in imperfect information games among this class of algorithms.
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
Authors: Phan NhatHai , Vu Minh N. , Liu Yang , Jin Ruoming , Dou Dejing , Wu Xintao , Thai My T.
Published: July 2019

In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples. We first relax the constraint of the privacy budget in the traditional Gaussian Mechanism from (0, 1] to (0, infty), with a new bound of the noise scale to preserve differential privacy. The noise in our mechanism can be arbitrarily redistributed, offering a distinctive ability to address the trade-off between model utility and privacy loss. To derive provable robustness, our HGM is applied to inject Gaussian noise into the first hidden layer. Then, a tighter robustness bound is proposed. Theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of differentially private deep neural networks, compared with baseline approaches, under a variety of model attacks.
Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization
Authors: Reis Sofia , Abreu Rui , d'Amorim Marcelo
Published: July 2019

Several approaches have been proposed to reduce debugging costs through
automated software fault diagnosis. Dynamic Slicing (DS) and Spectrum-based
Fault Localization (SFL) are popular fault diagnosis techniques and normally
seen as complementary. This paper reports on a comprehensive
study to reassess the effects of combining DS with SFL. With this
combination, components that are often involved in failing but seldom in passing
test runs could be located and their suspiciousness reduced.
Results show that the DS-SFL combination, coined
as Tandem-FL, improves the diagnostic accuracy up
to 73.7% (13.4% on average). Furthermore, results
indicate that the risk of missing faulty statements,
which is a DS?s key limitation, is not high ? DS
misses faulty statements in 9% of the 260 cases. To
sum up, we found that the DS-SFL combination
was practical and effective and encourage new SFL
techniques to be evaluated against that optimization.
Equally-Guided Discriminative Hashing for Cross-modal Retrieval
Authors: Shi Yufeng , You Xinge , Zheng Feng , Wang Shuo , Peng Qinmu
Published: July 2019

Cross-modal hashing intends to project data from two modalities into a common hamming space to perform cross-modal retrieval efficiently. Despite satisfactory performance achieved on real applications, existing methods are incapable of effectively preserving semantic structure to maintain inter-class relationship and improving discriminability to make intra-class samples aggregated simultaneously, which thus limits the higher retrieval performance. To handle this problem, we propose Equally-Guided Discriminative Hashing (EGDH), which jointly takes into consideration semantic structure and discriminability. Specifically, we discover the connection between semantic structure preserving and discriminative methods. Based on it, we directly encode multi-label annotations that act as high-level semantic features to build a common semantic structure preserving classifier. With the common classifier to guide the learning of different modal hash functions equally, hash codes of samples are intra-class aggregated and inter-class relationship preserving. Experimental results on two benchmark datasets demonstrate the superiority of EGDH compared with the state-of-the-arts.
A Privacy Preserving Collusion Secure DCOP Algorithm
Authors: Tassa Tamir , Grinshpoun Tal , Yanay Avishai
Published: July 2019

In recent years, several studies proposed privacy-preserving algorithms for solving Distributed Constraint Optimization Problems (DCOPs). All of those studies assumed that agents do not collude. In this study we propose the first privacy-preserving DCOP algorithm that is immune against coalitions, under the assumption of honest majority. Our algorithm -- PC-SyncBB -- is based on the classical Branch and Bound DCOP algorithm. It offers constraint, topology and decision privacy. We evaluate its performance on different benchmarks, problem sizes, and constraint densities. We show that achieving security against coalitions is feasible. As all existing privacy-preserving DCOP algorithms base their security on assuming solitary conduct of the agents, we view this study as an essential first step towards lifting this potentially harmful assumption in all those algorithms.
Two-Stage Generative Models of Simulating Training Data at The Voxel Level for Large-Scale Microscopy Bioimage Segmentation
Authors: Wang Deli , Zhao Ting , Zheng Nenggan , Gong Zhefeng
Published: July 2019

Bioimage Informatics is a growing area that aims to extract biological knowledge from microscope images of biomedical samples automatically. Its mission is vastly challenging, however, due to the complexity of diverse imaging modalities and big scales of multi-dimensional images. One major challenge is automatic image segmentation, an essential step towards high-level modeling and analysis. While progresses in deep learning have brought the goal of automation much closer to reality, creating training data for producing powerful neural networks is often laborious. To provide a shortcut for this costly step, we propose a novel two-stage generative model for simulating voxel level training data based on a specially designed objective function of preserving foreground labels. Using segmenting neurons from LM (Light Microscopy) image stacks as a testing example, we showed that segmentation networks trained by our synthetic data were able to produce satisfactory results. Unlike other simulation methods available in the field, our method can be easily extended to many other applications because it does not involve sophisticated cell models and imaging mechanisms.
Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation
Authors: Wang Di , Xu Jinhui
Published: July 2019

In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a non-trivial lower bound on the non-interactive private minimax risk in the metric of squared spectral norm. We show that the lower bound is actually tight, as it matches a previous upper bound.
Our main technique for achieving this lower bound is a general framework, called General Private Assouad Lemma, which is a considerable generalization of the previous private Assouad lemma and can be used as a general method for bounding the private minimax risk of matrix-related estimation problems.
Principal Component Analysis in the Local Differential Privacy Model
Authors: Wang Di , Xu Jinhui
Published: July 2019

In this paper, we study the Principal Component Analysis (PCA) problem under the (distributed) non-interactive local differential privacy model. For the low dimensional case, we show the optimal rate for the private minimax risk of the k-dimensional PCA using the squared subspace distance as the measurement. For the high dimensional row sparse case, we first give a lower bound on the private minimax risk, . Then we provide an efficient algorithm to achieve a near optimal upper bound. Experiments on both synthetic and real world datasets confirm the theoretical guarantees of our algorithms.
Binarized Collaborative Filtering with Distilling Graph Convolutional Network
Authors: Wang Haoyu , Lian Defu , Ge Yong
Published: July 2019

The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative samples and the large number of candidate items. To address the challenges, we firstly introduce an improved Graph Convolutional Network~(GCN) model with high-order feature interaction considered. Then we distill the ranking information derived from GCN into binarized collaborative filtering, which makes use of binary representation to improve the efficiency of online recommendation. However, binary codes are not only hard to be optimized but also likely to incur the loss of information during the training processing. Therefore, we propose a novel framework to convert the binary constrained optimization problem into an equivalent continuous optimization problem with a stochastic penalty. The binarized collaborative filtering model is then easily optimized by many popular solvers like SGD and Adam. The proposed algorithm is finally evaluated on three real-world datasets and shown the superiority to the competing baselines.
Novel Collaborative Filtering Recommender Friendly to Privacy Protection
Authors: Wang Jun , Tang Qiang , Arriaga Afonso , Ryan Peter Y. A.
Published: July 2019

Nowadays, recommender system is an indispensable tool in many information services, and a large number of algorithms have been designed and implemented. However, fed with very large datasets, state-of-the-art recommendation algorithms often face an efficiency bottleneck, i.e., it takes huge amount of computing resources to train a recommendation model. In order to satisfy the needs of privacy-savvy users who do not want to disclose their information to the service provider, the complexity of most existing solutions becomes prohibitive. As such, it is an interesting research question to design simple and efficient recommendation algorithms that achieve reasonable accuracy and facilitate privacy protection at the same time.

In this paper, we propose an efficient recommendation algorithm, named CryptoRec, which has two nice properties: (1) can estimate a new user's preferences by directly using a model pre-learned from an expert dataset, and the new user's data is not required to train the model; (2) can compute recommendations with only addition and multiplication operations. As to the evaluation, we first test the recommendation accuracy on three real-world datasets and show that CryptoRec is competitive with state-of-the-art recommenders. Then, we evaluate the performance of the privacy-preserving variants of CryptoRec and show that predictions can be computed in seconds on a PC. In contrast, existing solutions will need tens or hundreds of hours on more powerful computers.
Adversarial Examples for Graph Data: Deep Insights into Attack and Defense
Authors: Wu Huijun , Wang Chen , Tyshetskiy Yuriy , Docherty Andrew , Lu Kai , Zhu Liming
Published: July 2019

Graph deep learning models, such as graph convolutional networks (GCN) achieve state-of-the-art performance for tasks on graph data. However, similar to other deep learning models, graph deep learning models are susceptible to adversarial attacks. However, compared with non-graph data the discrete nature of the graph connections and features provide unique challenges and opportunities for adversarial attacks and defenses. In this paper, we propose techniques for both an adversarial attack and a defense against adversarial attacks. Firstly, we show that the problem of discrete graph connections and the discrete features of common datasets can be handled by using the integrated gradient technique that accurately determines the effect of changing selected features or edges while still benefiting from parallel computations. In addition, we show that an adversarially manipulated graph using a targeted attack statistically differs from un-manipulated graphs. Based on this observation, we propose a defense approach which can detect and recover a potential adversarial perturbation. Our experiments on a number of datasets show the effectiveness of the proposed techniques.
Computational Aspects of Equilibria in Discrete Preference Games
Authors: Lolakapuri Phani Raj , Bhaskar Umang , Narayanam Ramasuri , Parija Gyana R , Dayama Pankaj S
Published: July 2019

We study the complexity of equilibrium computation in discrete preference games. These games were introduced by Chierichetti, Kleinberg, and Oren (EC '13, JCSS '18) to model decision-making by agents in a social network that choose a strategy from a finite, discrete set, balancing between their intrinsic preferences for the strategies and their desire to choose a strategy that is `similar' to their neighbours. There are thus two components: a social network with the agents as vertices, and a metric space of strategies. These games are potential games, and hence pure Nash equilibria exist. Since their introduction, a number of papers have studied various aspects of this model, including the social cost at equilibria, and arrival at a consensus. We show that in general, equilibrium computation in discrete preference games is PLS-complete, even in the simple case where each agent has a constant number of neighbours. If the edges in the social network are weighted, then the problem is PLS-complete even if each agent has a constant number of neighbours, the metric space has constant size, and every pair of strategies is at distance 1 or 2. Further, if the social network is directed, modelling asymmetric influence, an equilibrium may not even exist. On the positive side, we show that if the metric space is a tree metric, or is the product of path metrics, then the equilibrium can be computed in polynomial time.