OpenRaiser/Intern-Atlas huggingface dataset:
Improving embedding efficiency for digital steganography by exploiting similarities between secret and cover images
Venue: arXiv cs.MM 2020
Year: 2020

Digital steganography is becoming a common tool for protecting sensitive communications in various applications such as crime(terrorism) prevention whereby law enforcing personals need to remotely compare facial images captured at the scene of crime with faces databases of known criminals(suspects); exchanging military maps or surveillance video in hostile environment(situations); privacy preserving in the healthcare systems when storing or exchanging patient medical images(records); and prevent bank customers accounts(records) from being accessed illegally by unauthorized users. Existing digital steganography schemes for embedding secret images in cover image files tend not to exploit various redundancies in the secret image bit-stream to deal with the various conflicting requirements on embedding capacity, stego-image quality, and un-detectibility. This paper is concerned with the development of innovative image procedures and data hiding schemes that exploit, as well as increase, similarities between secret image bit-stream and the cover image LSB plane. This will be achieved in two novel steps involving manipulating both the secret and the cover images,prior to embedding, to achieve higher 0:1 ratio in both the secret image bit-stream and the cover image LSB plane. The above two steps strategy has been exploited to use a bit-plane(s) mapping technique, instead of bit-plane(s) replacement to make each cover pixel usable for secret embedding. This paper will demonstrate that this strategy produces stego-images that have minimal distortion, high embedding efficiency, reasonably good stego-image quality and robustness against 3 well-known targeted steganalysis tools.
CFR-RL: Traffic Engineering with Reinforcement Learning in SDN
Venue: arXiv cs.NI 2020
Year: 2020

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using Software-Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR-RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL achieves near-optimal performance by rerouting only 10%-21.3% of total traffic.
A novel multimodal approach for hybrid brain-computer interface
Venue: arXiv cs.HC 2020
Year: 2020

Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals. The fusion algorithm for EEG and NIRS sources is the key to implement them in real-life applications. In this research, we propose three fusion methods for the hybrid of the EEG and NIRS-based brain-computer interface system: linear fusion, tensor fusion, and $p$th-order polynomial fusion. Firstly, our results prove that the hybrid BCI system is more accurate, as expected. Secondly, the $p$th-order polynomial fusion has the best classification results out of the three methods, and also shows improvements compared with previous studies. For a motion imagery task and a mental arithmetic task, the best detection accuracy in previous papers were 74.20\% and 88.1\%, whereas our accuracy achieved was 77.53\% and 90.19\% . Furthermore, unlike complex artificial neural network methods, our proposed methods are not as computationally demanding.
DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
Venue: arXiv cs.SI 2020
Year: 2020

Next (or successive) point-of-interest (POI) recommendation has attracted increasing attention in recent years. Most of the previous studies attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, none of these approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. Also, we carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is of high efficiency among six neural-network- and attention-based methods.
An algorithmic weakening of the Erdős-Hajnal conjecture
Venue: arXiv cs.DS 2020
Year: 2020

We study the approximability of the Maximum Independent Set (MIS) problem in $H$-free graphs (that is, graphs which do not admit $H$ as an induced subgraph). As one motivation we investigate the following conjecture: for every fixed graph $H$, there exists a constant $δ> 0$ such that MIS can be $n^{1 - δ}$-approximated in $H$-free graphs, where $n$ denotes the number of vertices of the input graph. We first prove that a constructive version of the celebrated Erdős-Hajnal conjecture implies ours. We then prove that the set of graphs $H$ satisfying our conjecture is closed under the so-called graph substitution. This, together with the known polynomial-time algorithms for MIS in $H$-free graphs (e.g. $P_6$-free and fork-free graphs), implies that our conjecture holds for many graphs $H$ for which the Erdős-Hajnal conjecture is still open. We then focus on improving the constant $δ$ for some graph classes: we prove that the classical Local Search algorithm provides an $OPT^{1-\frac{1}{t}}$-approximation in $K_{t,t}$-free graphs (hence a $\sqrt{OPT}$-approximation in $C_4$-free graphs), and, while there is a simple $\sqrt{n}$-approximation in triangle-free graphs, it cannot be improved to $n^{\frac{1}{4}-\varepsilon}$ for any $\varepsilon > 0$ unless $NP \subseteq BPP$. More generally, we show that there is a constant $c$ such that MIS in graphs of girth $γ$ cannot be $n^{\frac{c}γ}$-approximated. Up to a constant factor in the exponent, this matches the ratio of a known approximation algorithm by Monien and Speckenmeyer, and by Murphy. To the best of our knowledge, this is the first strong (i.e., $Ω(n^δ)$ for some $δ> 0$) inapproximability result for Maximum Independent Set in a proper hereditary class.
EfficientPose: Scalable single-person pose estimation
Venue: arXiv cs.CV 2020
Year: 2020

Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more complex and inefficient models have also been introduced, which have caused tremendous increases in computational demands. To cope with these complexity and inefficiency challenges, we propose a novel convolutional neural network architecture, called EfficientPose, which exploits recently proposed EfficientNets in order to deliver efficient and scalable single-person pose estimation. EfficientPose is a family of models harnessing an effective multi-scale feature extractor and computationally efficient detection blocks using mobile inverted bottleneck convolutions, while at the same time ensuring that the precision of the pose configurations is still improved. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model both in terms of accuracy and computational efficiency. In particular, our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets.
On the Generalization Capability of Evolved Counter-propagation Neuro-controllers for Robot Navigation
Venue: arXiv cs.RO 2020
Year: 2020

Evolving Counter-Propagation Neuro-Controllers (CPNCs), rather than the traditional Feed-Forward Neuro-Controllers (FFNCs), has recently been suggested and tested using simulated robot navigation. It has been demon-strated that both convergence rate and final performance obtained by evolving CPNCs are superior to those obtained by evolving FFNCs. In this paper the maze generalization features of both types of evolved navigation controllers are examined. For this purpose the controllers are tested in an environment that drastically differs from the one used for their training. Moreover, a comparison is carried out of results obtained by single-objective and multi-objective evolution approaches. Using a simulated case-study, the maze generalization capability of the evolved CPNCs is highlighted in both the single and multi-objective cases. In contrast, the evolved FFNCs are found to lack such capabilities in both approaches.
Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the shape of an object; the reconstruction is often in a canonical frame, unsuitable for downstream robotics tasks. To this end, we leverage recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame. We demonstrate that our approach---dubbed reconstruct, rasterize and backprop (RRB) achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery. We further extend our results to an (offline) setup, where we demonstrate a dense monocular object-centric egomotion estimation system.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Venue: arXiv cs.CV 2020
Year: 2020

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation of LGE-MRIs is challenging due to its attenuated contrast. Since most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double, sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved far superior results than traditional methods and pipelines containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.
Improved User Fairness in Decode-Forward Relaying Non-orthogonal Multiple Access Schemes with Imperfect SIC
Venue: arXiv cs.IT 2020
Year: 2020

Non-orthogonal multiple access (NOMA) is one of the key technologies to serve in ultra-dense networks with massive connections which is crucial for Internet of Things (IoT) applications. Besides, NOMA provides better spectral efficiency compared to orthogonal multiple access (OMA) schemes. However, in NOMA, successive interference canceler (SIC) should be implemented for interference mitigation and mostly in the literature, perfect SIC is assumed for NOMA involved systems. Unfortunately, this is not the case for practical scenarios and this imperfect SIC effect limits the performance of NOMA involved systems. In addition, it causes unfairness between users. In this paper, we introduce reversed decode-forward relaying NOMA (R-DFNOMA) to improve user fairness compared to conventional DFNOMA (C-DFNOMA) which is widely analyzed in literature. In the analysis, we define imperfect SIC effect dependant to channel fading and with this imperfect SIC, we derive exact expressions for ergodic capacity (EC) and outage probability (OP). Moreover, we evaluate bit error performance of proposed R-DFNOMA and derive bit error probability (BEP) in closed-form which has not been also studied well in literature. Then, we define user fairness index in terms of all key performance indicators (KPIs) (i.e., EC, OP and BEP). Based on extensive simulations, all derived expressions are validated, and it is proved that proposed R-DFNOMA provides better user fairness than C-DFNOMA in terms of all KPIs. Finally, we discuss the effect of power allocations at the both source and relay on the performance metrics and user fairness
Warm-Start AlphaZero Self-Play Search Enhancements
Venue: arXiv cs.AI 2020
Year: 2020

Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level. AlphaZero is a large and complicated system with many parameters, and success requires much compute power and fine-tuning. Reproducing results in other games is a challenge, and many researchers are looking for ways to improve results while reducing computational demands. AlphaZero's design is purely based on self-play and makes no use of labeled expert data ordomain specific enhancements; it is designed to learn from scratch. We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE) and dynamically weighted combinations of these with the neural network, and Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that most of these enhancements improve the performance of their baseline player in three different (small) board games, with especially RAVE based variants playing strongly.
Monadic Decomposition in Integer Linear Arithmetic (Technical Report)
Venue: arXiv cs.LO 2020
Year: 2020

Monadic decomposability is a notion of variable independence, which asks whether a given formula in a first-order theory is expressible as a Boolean combination of monadic predicates in the theory. Recently, Veanes et al. showed the usefulness of monadic decomposability in the context of SMT (i.e. the input formula is quantifier-free), and found various interesting applications including string analysis. However, checking monadic decomposability is undecidable in general. Decidability for certain theories is known (e.g. Presburger Arithmetic, Tarski's Real-Closed Field), but there are very few results regarding their computational complexity. In this paper, we study monadic decomposability of integer linear arithmetic in the setting of SMT. We show that this decision problem is coNP-complete and, when monadically decomposable, a formula admits a decomposition of exponential size in the worst case. We provide a new application of our results to string constraint solving with length constraints. We then extend our results to variadic decomposability, where predicates could admit multiple free variables (in contrast to monadic decomposability). Finally, we give an application to quantifier elimination in integer linear arithmetic where the variables in a block of quantifiers, if independent, could be eliminated with an exponential (instead of the standard doubly exponential) blow-up.
An Efficient Index Method for the Optimal Route Query over Multi-Cost Networks
Venue: arXiv cs.DB 2020
Year: 2020

Smart city has been consider the wave of the future and the route recommendation in networks is a fundamental problem in it. Most existing approaches for the shortest route problem consider that there is only one kind of cost in networks. However, there always are several kinds of cost in networks and users prefer to select an optimal route under the global consideration of these kinds of cost. In this paper, we study the problem of finding the optimal route in the multi-cost networks. We prove this problem is NP-hard and the existing index techniques cannot be used to this problem. We propose a novel partition-based index with contour skyline techniques to find the optimal route. We propose a vertex-filtering algorithm to facilitate the query processing. We conduct extensive experiments on six real-life networks and the experimental results show that our method has an improvement in efficiency by an order of magnitude compared to the previous heuristic algorithms.
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
Venue: arXiv cs.CL 2020
Year: 2020

Recently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the cost of decreased relevance accompanied by diversity improvements. In this paper, we propose a novel multimodal response generation framework with exemplar augmentation and curriculum optimization to enhance relevance and diversity of generated responses. First, unlike existing VAE-based models that usually approximate a simple Gaussian posterior distribution, we present a Gaussian mixture posterior distribution (i.e, multimodal) to further boost response diversity, which helps capture complex semantics of responses. Then, to ensure that relevance does not decrease while diversity increases, we fully exploit similar examples (exemplars) retrieved from the training data into posterior distribution modeling to augment response relevance. Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard. Experimental results on widely used SwitchBoard and DailyDialog datasets demonstrate that our model achieves significant improvements compared to strong baselines in terms of diversity and relevance.
Semi-Supervised Neural System for Tagging, Parsing and Lematization
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes the ICS PAS system which took part in CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. The system consists of jointly trained tagger, lemmatizer, and dependency parser which are based on features extracted by a biLSTM network. The system uses both fully connected and dilated convolutional neural architectures. The novelty of our approach is the use of an additional loss function, which reduces the number of cycles in the predicted dependency graphs, and the use of self-training to increase the system performance. The proposed system, i.e. ICS PAS (Warszawa), ranked 3th/4th in the official evaluation obtaining the following overall results: 73.02 (LAS), 60.25 (MLAS) and 64.44 (BLEX).
Succinct Filters for Sets of Unknown Sizes
Venue: arXiv cs.DS 2020
Year: 2020

The membership problem asks to maintain a set $S\subseteq[u]$, supporting insertions and membership queries, i.e., testing if a given element is in the set. A data structure that computes exact answers is called a dictionary. When a (small) false positive rate $ε$ is allowed, the data structure is called a filter. The space usages of the standard dictionaries or filters usually depend on the upper bound on the size of $S$, while the actual set can be much smaller. Pagh, Segev and Wieder (FOCS'13) were the first to study filters with varying space usage based on the current $|S|$. They showed in order to match the space with the current set size $n=|S|$, any filter data structure must use $(1-o(1))n(\log(1/ε)+(1-O(ε))\log\log n)$ bits, in contrast to the well-known lower bound of $N\log(1/ε)$ bits, where $N$ is an upper bound on $|S|$. They also presented a data structure with almost optimal space of $(1+o(1))n(\log(1/ε)+O(\log\log n))$ bits provided that $n>u^{0.001}$, with expected amortized constant insertion time and worst-case constant lookup time. In this work, we present a filter data structure with improvements in two aspects: - it has constant worst-case time for all insertions and lookups with high probability; - it uses space $(1+o(1))n(\log (1/ε)+\log\log n)$ bits when $n>u^{0.001}$, achieving optimal leading constant for all $ε=o(1)$. We also present a dictionary that uses $(1+o(1))n\log(u/n)$ bits of space, matching the optimal space in terms of the current size, and performs all operations in constant time with high probability.
Experiments with LVT and FRE for Transformer model
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we experiment with Large Vocabulary Trick and Feature-rich encoding applied to the Transformer model for Text Summarization. We could not achieve better results, than the analogous RNN-based sequence-to-sequence model, so we tried more models to find out, what improves the results and what deteriorates them.