OpenRaiser/Intern-Atlas huggingface dataset:
Stochastic Fairness and Language-Theoretic Fairness in Planning on Nondeterministic Domains
Venue: arXiv cs.AI 2019
Year: 2019

We address two central notions of fairness in the literature of planning on nondeterministic fully observable domains. The first, which we call stochastic fairness, is classical, and assumes an environment which operates probabilistically using possibly unknown probabilities. The second, which is language-theoretic, assumes that if an action is taken from a given state infinitely often then all its possible outcomes should appear infinitely often (we call this state-action fairness). While the two notions coincide for standard reachability goals, they diverge for temporally extended goals. This important difference has been overlooked in the planning literature, and we argue has led to confusion in a number of published algorithms which use reductions that were stated for state-action fairness, for which they are incorrect, while being correct for stochastic fairness. We remedy this and provide an optimal sound and complete algorithm for solving state-action fair planning for LTL/LTLf goals, as well as a correct proof of the lower bound of the goal-complexity (our proof is general enough that it provides new proofs also for the no-fairness and stochastic-fairness cases). Overall, we show that stochastic fairness is better behaved than state-action fairness.
ConstructAide: Analyzing and Visualizing Construction Sites through Photographs and Building Models
Venue: arXiv cs.GR 2019
Year: 2019

We describe a set of tools for analyzing, visualizing, and assessing architectural/construction progress with unordered photo collections and 3D building models. With our interface, a user guides the registration of the model in one of the images, and our system automatically computes the alignment for the rest of the photos using a novel Structure-from-Motion (SfM) technique; images with nearby viewpoints are also brought into alignment with each other. After aligning the photo(s) and model(s), our system allows a user, such as a project manager or facility owner, to explore the construction site seamlessly in time, monitor the progress of construction, assess errors and deviations, and create photorealistic architectural visualizations. These interactions are facilitated by automatic reasoning performed by our system: static and dynamic occlusions are removed automatically, rendering information is collected, and semantic selection tools help guide user input. We also demonstrate that our user-assisted SfM method outperforms existing techniques on both real-world construction data and established multi-view datasets.
THUEE system description for NIST 2019 SRE CTS Challenge
Venue: arXiv cs.SD 2019
Year: 2019

This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge. Six subsystems, including etdnn/ams, ftdnn/as, eftdnn/ams, resnet, multitask and c-vector are developed in this evaluation.
Next-Generation Big Data Federation Access Control: A Reference Model
Venue: arXiv cs.CR 2019
Year: 2019

This paper discusses one of the most significant challenges of next-generation big data (BD) federation platforms, namely, Hadoop access control. Privacy and security on a federation scale remain significant concerns among practitioners. Hadoop's current primitive access control presents security concerns and limitations, such as the complexity of deployment and the consumption of resources. However, this major concern has not been a subject of intensive study in the literature. This paper critically reviews and investigates these security limitations and provides a framework called BD federation access broker to address 8 main security limitations. This paper proposes the federated access control reference model (FACRM) to formalize the design of secure BD solutions within the Apache Hadoop stack. Furthermore, this paper discusses the implementation of the access broker and its usefulness for security breach detection and digital forensics investigations. The efficiency of the proposed access broker has not sustainably affected the performance overhead. The experimental results show only 1\% of each 100 MB read/write operation in a WebHDFS. Overall, the findings of the paper pave the way for a wide range of revolutionary and state-of-the-art enhancements and future trends within Hadoop stack security and privacy.
Ranking and Classification driven Feature Learning for Person Re_identification
Venue: arXiv cs.CV 2019
Year: 2019

Person re-identification has attracted many researchers' attention for its wide application, but it is still a very challenging task because only part of the image information can be used for personnel matching. Most of current methods uses CNN to learn to embeddings that can capture semantic similarity information among data points. Many of the state-of-the-arts methods use complex network structures with multiple branches that fuse multiple features while training or testing, using classification loss, Triplet loss or a combination of the two as loss function. However, the method that using Triplet loss as loss function converges slowly, and the method in which pull features of the same class as close as possible in features space leads to poor feature stability. This paper will combine the ranking motivated structured loss, proposed a new metric learning loss function that make the features of the same class are sparsely distributed into the range of small hyperspheres and the features of different classes are uniformly distributed at a clearly angle. And adopted a new single-branch network structure that only using global feature can also get great performance. The validity of our method is verified on the Market1501 and DukeMTMC-ReID person re-identification datasets. Finally acquires 90.9% rank-1 accuracy and 80.8% mAP on DukeMTMC-reID, 95.3% rank-1 accuracy and 88.7% mAP on Market1501. Codes and models are available in Github.https://github.com/Qidian213/Ranked_Person_ReID.
Discovering High Utility Episodes in Sequences
Venue: arXiv cs.DB 2019
Year: 2019

Sequence data, e.g., complex event sequence, is more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this paper, we propose an efficient utility mining approach namely UMEpi: Utility Mining of high-utility Episodes from complex event sequence. We propose the concept of remaining utility of episode, and achieve a tighter upper bound, namely episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequence, while the state-of-the-art algorithms fail to return the correct results. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability.
Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction
Venue: arXiv cs.IR 2019
Year: 2019

Recommender systems are designed to help mitigate information overload users experience during online shopping. Recent work explores neural language models to learn user and item representations from user reviews and combines such representations with rating information. Most existing convolutional-based neural models take pooling immediately after convolution and loses the interaction information between the latent dimension of convolutional feature vectors along the way. Moreover, these models usually take all feature vectors at higher levels as equal and do not take into consideration that some features are more relevant to this specific user-item context. To bridge these gaps, this paper proposes a convolutional quantum-like language model with mutual-attention for rating prediction (ConQAR). By introducing a quantum-like density matrix layer, interactions between latent dimensions of convolutional feature vectors are well captured. With the attention weights learned from the mutual-attention layer, final representations of a user and an item absorb information from both itself and its counterparts for making rating prediction. Experiments on two large datasets show that our model outperforms multiple state-of-the-art CNN-based models. We also perform an ablation test to analyze the independent effects of the two components of our model. Moreover, we conduct a case study and present visualizations of the quantum probabilistic distributions in one user and one item review document to show that the learned distributions capture meaningful information about this user and item, and can be potentially used as textual profiling of the user and item.
Planar Prior Assisted PatchMatch Multi-View Stereo
Venue: arXiv cs.CV 2019
Year: 2019

The completeness of 3D models is still a challenging problem in multi-view stereo (MVS) due to the unreliable photometric consistency in low-textured areas. Since low-textured areas usually exhibit strong planarity, planar models are advantageous to the depth estimation of low-textured areas. On the other hand, PatchMatch multi-view stereo is very efficient for its sampling and propagation scheme. By taking advantage of planar models and PatchMatch multi-view stereo, we propose a planar prior assisted PatchMatch multi-view stereo framework in this paper. In detail, we utilize a probabilistic graphical model to embed planar models into PatchMatch multi-view stereo and contribute a novel multi-view aggregated matching cost. This novel cost takes both photometric consistency and planar compatibility into consideration, making it suited for the depth estimation of both non-planar and planar regions. Experimental results demonstrate that our method can efficiently recover the depth information of extremely low-textured areas, thus obtaining high complete 3D models and achieving state-of-the-art performance.
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning has shown to be effective for depth inference in multi-view stereo (MVS). However, the scalability and accuracy still remain an open problem in this domain. This can be attributed to the memory-consuming cost volume representation and inappropriate depth inference. Inspired by the group-wise correlation in stereo matching, we propose an average group-wise correlation similarity measure to construct a lightweight cost volume. This can not only reduce the memory consumption but also reduce the computational burden in the cost volume filtering. Based on our effective cost volume representation, we propose a cascade 3D U-Net module to regularize the cost volume to further boost the performance. Unlike the previous methods that treat multi-view depth inference as a depth regression problem or an inverse depth classification problem, we recast multi-view depth inference as an inverse depth regression task. This allows our network to achieve sub-pixel estimation and be applicable to large-scale scenes. Through extensive experiments on DTU dataset and Tanks and Temples dataset, we show that our proposed network with Correlation cost volume and Inverse DEpth Regression (CIDER), achieves state-of-the-art results, demonstrating its superior performance on scalability and accuracy.
Power Efficient IRS-Assisted NOMA
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the capacity region when the channels are quasi-degraded. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.
Twinless articulation points and some related problems
Venue: arXiv cs.DS 2019
Year: 2019

Let $G=(V,E)$ be a twinless strongly connected graph. a vertex $v\in V$ is a twinless articulation point if the subrgraph obtained from $G$ by removing the vertex $v$ is not twinless strongly connected. An edge $e\in E$ is a twinless bridge if the subgraph obtained from $G$ by deleting $e$ is not twiless strongly connected graph. In this paper we study twinless articulation points and twinless bridges. We also study the problem of finding a minimum cardinality edge subset $E_{1} \subseteq E$ such that the subgraph $(V,E_{1})$ is twinless strongly connected. Moreover, we present an algorithm for computing the $2$-vertex-twinless connected components of $G$.
Anomalous Communications Detection in IoT Networks Using Sparse Autoencoders
Venue: arXiv cs.CR 2019
Year: 2019

Nowadays, IoT devices have been widely deployed for enabling various smart services, such as, smart home or e-healthcare. However, security remains as one of the paramount concern as many IoT devices are vulnerable. Moreover, IoT malware are constantly evolving and getting more sophisticated. IoT devices are intended to perform very specific tasks, so their networking behavior is expected to be reasonably stable and predictable. Any significant behavioral deviation from the normal patterns would indicate anomalous events. In this paper, we present a method to detect anomalous network communications in IoT networks using a set of sparse autoencoders. The proposed approach allows us to differentiate malicious communications from legitimate ones. So that, if a device is compromised only malicious communications can be dropped while the service provided by the device is not totally interrupted. To characterize network behavior, bidirectional TCP flows are extracted and described using statistics on the size of the first N packets sent and received, along with statistics on the corresponding inter-arrival times between packets. A set of sparse autoencoders is then trained to learn the profile of the legitimate communications generated by an experimental smart home network. Depending on the value of N, the developed model achieves attack detection rates ranging from 86.9% to 91.2%, and false positive rates ranging from 0.1% to 0.5%.
Benchmarking Adversarial Robustness
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms. In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks. After briefly reviewing plenty of representative attack and defense methods, we perform large-scale experiments with two robustness curves as the fair-minded evaluation criteria to fully understand the performance of these methods. Based on the evaluation results, we draw several important findings and provide insights for future research.
Quasi-Newton Trust Region Policy Optimization
Venue: arXiv cs.LG 2019
Year: 2019

We propose a trust region method for policy optimization that employs Quasi-Newton approximation for the Hessian, called Quasi-Newton Trust Region Policy Optimization QNTRPO. Gradient descent is the de facto algorithm for reinforcement learning tasks with continuous controls. The algorithm has achieved state-of-the-art performance when used in reinforcement learning across a wide range of tasks. However, the algorithm suffers from a number of drawbacks including: lack of stepsize selection criterion, and slow convergence. We investigate the use of a trust region method using dogleg step and a Quasi-Newton approximation for the Hessian for policy optimization. We demonstrate through numerical experiments over a wide range of challenging continuous control tasks that our particular choice is efficient in terms of number of samples and improves performance
GASOL: Gas Analysis and Optimization for Ethereum Smart Contracts
Venue: arXiv cs.PL 2019
Year: 2019

We present the main concepts, components, and usage of GASOL, a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. GASOL offers a wide variety of cost models that allow inferring the gas consumption associated to selected types of EVM instructions and/or inferring the number of times that such types of bytecode instructions are executed. Among others, we have cost models to measure only storage opcodes, to measure a selected family of gas-consumption opcodes following the Ethereum's classification, to estimate the cost of a selected program line, etc. After choosing the desired cost model and the function of interest, GASOL returns to the user an upper bound of the cost for this function. As the gas consumption is often dominated by the instructions that access the storage, GASOL uses the gas analysis to detect under-optimized storage patterns, and includes an (optional) automatic optimization of the selected function. Our tool can be used within an Eclipse plugin for Solidity which displays the gas and instructions bounds and, when applicable, the gas-optimized Solidity function.
Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video Streaming over HTTP
Venue: arXiv cs.CV 2019
Year: 2019

Dynamic Adaptive Streaming over HTTP (DASH) has demonstrated to be an emerging and promising multimedia streaming technique, owing to its capability of dealing with the variability of networks. Rate adaptation mechanism, a challenging and open issue, plays an important role in DASH based systems since it affects Quality of Experience (QoE) of users, network utilization, etc. In this paper, based on non-cooperative game theory, we propose a novel algorithm to optimally allocate the limited export bandwidth of the server to multi-users to maximize their QoE with fairness guaranteed. The proposed algorithm is proxy-free. Specifically, a novel user QoE model is derived by taking a variety of factors into account, like the received video quality, the reference buffer length, and user accumulated buffer lengths, etc. Then, the bandwidth competing problem is formulated as a non-cooperation game with the existence of Nash Equilibrium that is theoretically proven. Finally, a distributed iterative algorithm with stability analysis is proposed to find the Nash Equilibrium. Compared with state-of-the-art methods, extensive experimental results in terms of both simulated and realistic networking scenarios demonstrate that the proposed algorithm can produce higher QoE, and the actual buffer lengths of all users keep nearly optimal states, i.e., moving around the reference buffer all the time. Besides, the proposed algorithm produces no playback interruption.
Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Venue: arXiv cs.CL 2019
Year: 2019

Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data. In this work, we developed a new text representation Clinical XLNet for clinical notes which also leverages the temporal information of the sequence of the notes. We evaluated our models on prolonged mechanical ventilation prediction problem and our experiments demonstrated that Clinical XLNet outperforms the best baselines consistently.
A Two-Phase Scheme for Distributed TDMA Scheduling in WSNs with Flexibility to Trade-off between Schedule Length and Scheduling Time
Venue: arXiv cs.DC 2019
Year: 2019

The existing distributed TDMA-scheduling techniques can be classified as either static or dynamic. The primary purpose of static TDMA-scheduling algorithms is to improve the channel utilization by generating a schedule of shorter length. But, they usually take a longer time to schedule, and hence, are not suitable for WSNs, in which the network topology changes dynamically. On the other hand, dynamic TDMA-scheduling algorithms generate a schedule quickly, but they are not efficient in terms of generated schedule length. In this paper, we propose a new approach to TDMA scheduling for WSNs, that bridges the gap between the above two extreme types of TDMA-scheduling techniques, by providing the flexibility to trade-off between the schedule length and the time required to generate the schedule (scheduling time). The proposed TDMA scheduling works in two phases. In the first phase, we generate a TDMA schedule quickly, which need not have to be very efficient in terms of schedule length. In the second phase, we iteratively reduce the schedule length in a manner, such that the process of schedule length reduction can be terminated after the execution of an arbitrary number of iterations, and still be left with a valid schedule. This step provides the capability to trade-off between schedule length and scheduling time. We have used Castalia network simulator to evaluate the performance of proposed TDMA-scheduling scheme. The simulation result together with theoretical analysis shows that in addition to the advantage of trading-off the schedule length with scheduling time, the proposed TDMA scheduling approach achieves better performance than existing algorithms in terms of schedule length and scheduling time.
A sparsity augmented probabilistic collaborative representation based classification method
Venue: arXiv cs.CV 2019
Year: 2019

In order to enhance the performance of image recognition, a sparsity augmented probabilistic collaborative representation based classification (SA-ProCRC) method is presented. The proposed method obtains the dense coefficient through ProCRC, then augments the dense coefficient with a sparse one, and the sparse coefficient is attained by the orthogonal matching pursuit (OMP) algorithm. In contrast to conventional methods which require explicit computation of the reconstruction residuals for each class, the proposed method employs the augmented coefficient and the label matrix of the training samples to classify the test sample. Experimental results indicate that the proposed method can achieve promising results for face and scene images. The source code of our proposed SA-ProCRC is accessible at https://github.com/yinhefeng/SAProCRC.