OpenRaiser/Intern-Atlas huggingface dataset:
Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a novel method for understanding daily hand-object manipulation by developing computer vision-based techniques. Specifically, we focus on recognizing hand grasp types, object attributes and manipulation actions within an unified framework by exploring their contextual relationships. Our hypothesis is that it is necessary to jointly model hands, objects and actions in order to accurately recognize multiple tasks that are correlated to each other in hand-object manipulation. In the proposed model, we explore various semantic relationships between actions, grasp types and object attributes, and show how the context can be used to boost the recognition of each component. We also explore the spatial relationship between the hand and object in order to detect the manipulated object from hand in cluttered environment. Experiment results on all three recognition tasks show that our proposed method outperforms traditional appearance-based methods which are not designed to take into account contextual relationships involved in hand-object manipulation. The visualization and generalizability study of the learned context further supports our hypothesis.
RGBiD-SLAM for Accurate Real-time Localisation and 3D Mapping
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we present a complete SLAM system for RGB-D cameras, namely RGB-iD SLAM. The presented approach is a dense direct SLAM method with the main characteristic of working with the depth maps in inverse depth parametrisation for the routines of dense alignment or keyframe fusion. The system consists in 2 CPU threads working in parallel, which share the use of the GPU for dense alignment and keyframe fusion routines. The first thread is a front-end operating at frame rate, which processes every incoming frame from the RGB-D sensor to compute the incremental odometry and integrate it in a keyframe which is changed periodically following a covisibility-based strategy. The second thread is a back-end which receives keyframes from the front-end. This thread is in charge of segmenting the keyframes based on their structure, describing them using Bags of Words, trying to find potential loop closures with previous keyframes, and in such case perform pose-graph optimisation for trajectory correction. In addition, our system allows is able to compute the odometry both with unregistered and registered depth maps, allowing to use customised calibrations of the RGB-D sensor. As a consequence in the paper we also propose a detailed calibration pipeline to compute customised calibrations for particular RGB-D cameras. The experiments with our approach in the TUM RGB-D benchmark datasets show results superior in accuracy to the state-of-the-art in many of the sequences. The code has been made available on-line for research purposes https://github.com/dangut/RGBiD-SLAM.
Multi-scale Alignment and Contextual History for Attention Mechanism in Sequence-to-sequence Model
Venue: arXiv cs.CL 2018
Year: 2018

A sequence-to-sequence model is a neural network module for mapping two sequences of different lengths. The sequence-to-sequence model has three core modules: encoder, decoder, and attention. Attention is the bridge that connects the encoder and decoder modules and improves model performance in many tasks. In this paper, we propose two ideas to improve sequence-to-sequence model performance by enhancing the attention module. First, we maintain the history of the location and the expected context from several previous time-steps. Second, we apply multiscale convolution from several previous attention vectors to the current decoder state. We utilized our proposed framework for sequence-to-sequence speech recognition and text-to-speech systems. The results reveal that our proposed extension could improve performance significantly compared to a standard attention baseline.
Analysis of the possibility for time-optimal control of the scanning system of the GREEN-WAKE's project lidar
Venue: arXiv cs.SY 2018
Year: 2018

The monograph represents analysis of the possibility for time-optimal control of a prototype of an aircraft-mounted scanning-imaging system of a Light Detection and Ranging based wake vortex detection system. The study is a part of the research project "Demonstration of Light Detection and Ranging based wake vortex detection system incorporating an atmospheric hazard map" or GREEN-WAKE (Project ID 213254) of the European Union Seventh Framework Program for Research and Technological Development. The scanning system comprises two light mirror actuators. The study is decomposed into several group of problems. The first and second groups consider the mathematical models of the scanning system and the mirror actuators. The third group of problems deals with the design of closed loop tracking control systems of both the large and small mirror actuators. The control of each one system is synthesized as a near time-optimal control of the precise linear model of the mirror actuator. The control algorithms realize the state of the art method for synthesis of time-optimal control of any order for a class of linear time-optimal control problems developed in the author's dissertation. The last discovers and theoretically proves new properties of a class of linear time-optimal control problems. From the point of view of the control synthesis algorithms the main advantage is that the time-optimal control is produced by a multistage procedure within the class of problems but without the need of describing the hyper-surfaces of switching over. The fourth group of problems considers modeling the real scan picture but with inclusion also of the Coulomb's friction model. The fifth group of problems investigates the ways of improvement of the real scan picture. As a result an excellent repetition and clearness of the scanning alongside with symmetry and matching the scan pattern are seen.
Long-Term Forecasts of Military Technologies for a 20-30 Year Horizon: An Empirical Assessment of Accuracy
Venue: arXiv cs.CY 2018
Year: 2018

During the 1990s, while exploring the impact of the collapse of the Soviet Union on developments in future warfare, a number of authors offered forecasts of military technology appearing by the year 2020. This paper offers a quantitative assessment of the accuracy of this group of forecasts. The overall accuracy - by several measures - was assessed as quite high, thereby pointing to the potential value of such forecasts in managing investments in long-term research and development. Major differences in accuracy, with strong statistical significance, were found between forecasts pertaining primarily to information acquisition and processing technologies, as opposed to technologies that aim primarily at physical effects. This paper also proposes several recommendations regarding methodological aspects of forecast accuracy assessments. Although the assessments were restricted to information available in open literature, the expert assessors did not find this constraint a significant detriment to the assessment process.
MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games
Venue: arXiv cs.LG 2018
Year: 2018

Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also supports the prediction of the remaining time of the game, and then realizes the evaluation of relative advantage between teams.
Examining Scientific Writing Styles from the Perspective of Linguistic Complexity
Venue: arXiv cs.CL 2018
Year: 2018

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity; and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation
Venue: arXiv cs.CV 2018
Year: 2018

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for automated melanoma diagnosis. In recent years, segmentation methods based on fully convolutional networks (FCN) have achieved great success in general images. This success is primarily due to the leveraging of large labelled datasets to learn features that correspond to the shallow appearance as well as the deep semantics of the images. However, the dependence on large dataset does not translate well into medical images. To improve the FCN performance for skin lesion segmentations, researchers attempted to use specific cost functions or add post-processing algorithms to refine the coarse boundaries of the FCN results. However, the performance of these methods is heavily reliant on the tuning of many parameters and post-processing techniques. In this paper, we leverage the state-of-the-art image feature learning method of generative adversarial network (GAN) for its inherent ability to produce consistent and realistic image features by using deep neural networks and adversarial learning concept. We improve upon GAN such that skin lesion features can be learned at different level of complexities, in a controlled manner. The outputs from our method is then augmented to the existing FCN training data, thus increasing the overall feature diversity. We evaluated our method on the ISIC 2018 skin lesion segmentation challenge dataset and showed that it was more accurate and robust when compared to the existing skin lesion segmentation methods.
Learning to Play Pong using Policy Gradient Learning
Venue: arXiv cs.LG 2018
Year: 2018

Activities in reinforcement learning (RL) revolve around learning the Markov decision process (MDP) model, in particular, the following parameters: state values, V; state-action values, Q; and policy, pi. These parameters are commonly implemented as an array. Scaling up the problem means scaling up the size of the array and this will quickly lead to a computational bottleneck. To get around this, the RL problem is commonly formulated to learn a specific task using hand-crafted input features to curb the size of the array. In this report, we discuss an alternative end-to-end Deep Reinforcement Learning (DRL) approach where the DRL attempts to learn general task representations which in our context refers to learning to play the Pong game from a sequence of screen snapshots without game-specific hand-crafted features. We apply artificial neural networks (ANN) to approximate a policy of the RL model. The policy network, via Policy Gradients (PG) method, learns to play the Pong game from a sequence of frames without any extra semantics apart from the pixel information and the score. In contrast to the traditional tabular RL approach where the contents in the array have clear interpretations such as V or Q, the interpretation of knowledge content from the weights of the policy network is more illusive. In this work, we experiment with various Deep ANN architectures i.e., Feed forward ANN (FFNN), Convolution ANN (CNN) and Asynchronous Advantage Actor-Critic (A3C). We also examine the activation of hidden nodes and the weights between the input and the hidden layers, before and after the DRL has successfully learnt to play the Pong game. Insights into the internal learning mechanisms and future research directions are then discussed.
A Cache-based Optimizer for Querying Enhanced Knowledge Bases
Venue: arXiv cs.DB 2018
Year: 2018

With recent emerging technologies such as the Internet of Things (IoT), information collection on our physical world and environment can be achieved at a much higher granularity and such detailed knowledge will play a critical role in improving the productivity, operational effectiveness, decision making, and in identifying new business models for economic growth. Efficient discovery and querying such knowledge remains a key challenge due to the limited capability and high latency of connections to the interfaces of knowledge bases, e.g., the SPARQL endpoints. In this article, we present a querying system on SPARQL endpoints for knowledge bases that performs queries faster than the state-of-the-art systems. Our system features a cache-based optimization scheme to improve querying performance by prefetching and caching the results of predicted potential queries. The evaluations on query sets from SPARQL endpoints of DBpedia and Linked GeoData showcase the effectiveness of our approach.
Generalized Cops and Robbers: A Multi-Player Pursuit Game on Graphs
Venue: arXiv cs.DM 2018
Year: 2018

We introduce and study the Generalized Cops and Robbers game (GCR), an N-player pursuit game in graphs. The two-player version is essentially equivalent to the classic Cops and Robbers (CR) game. The three-player version can be understood as two CR games played simultaneously on the same graph; a player can be at the same time both pursuer and evader. The same is true for four or more players. We formulate GCR as a discounted stochastic game of perfect information and prove that, for three or more players, it has at least two Nash Equilibria: one in positional deterministic strategies and another in non-positional ones. We also study the capturing properties of GCR Nash Equilibria in connection to the cop-number of a graph. Finally, we briefly discuss GCR as a member of a wider family of multi-player graph pursuit games with rather interesting properties.
Improving Deep Models of Person Re-identification for Cross-Dataset Usage
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification (Re-ID) is the task of matching humans across cameras with non-overlapping views that has important applications in visual surveillance. Like other computer vision tasks, this task has gained much with the utilization of deep learning methods. However, existing solutions based on deep learning are usually trained and tested on samples taken from same datasets, while in practice one need to deploy Re-ID systems for new sets of cameras for which labeled data is unavailable. Here, we mitigate this problem for one state-of-the-art model, namely, metric embedding trained with the use of the triplet loss function, although our results can be extended to other models. The contribution of our work consists in developing a method of training the model on multiple datasets, and a method for its online practically unsupervised fine-tuning. These methods yield up to 19.1% improvement in Rank-1 score in the cross-dataset evaluation.
Invisible Steganography via Generative Adversarial Networks
Venue: arXiv cs.MM 2018
Year: 2018

Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as \isgan to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, Pascal VOC2012 and ImageNet datasets.
On Enumerating Distributions for Associated Vectors in the Entropy Space
Venue: arXiv cs.IT 2018
Year: 2018

This paper focuses on the problem of finding a distribution for an associated entropic vector in the entropy space nearest to a given, possibly non-entropic, target vector for random variables with a constraint on alphabet size. We show the feasibility to find distribution for associated vector via a sequence of perturbations in the probability mass function. Then we present an algorithm for numerically solving the problem together with extensions, applications, and comparison with the known results.
Ideal Preconditioners for Saddle Point Systems with a Rank-Deficient Leading Block
Venue: arXiv cs.NA 2018
Year: 2018

We consider the iterative solution of symmetric saddle point systems with a rank-deficient leading block. We develop two preconditioners that, under certain assumptions on the rank structure of the system, yield a preconditioned matrix with a constant number of eigenvalues. We then derive some properties of the inverse of a particular class of saddle point system and exploit these to develop a third preconditioner, which remains ideal even when the earlier assumptions on rank structure are relaxed.
Novel weak form quadrature elements for second strain gradient Euler-Bernoulli beam theory
Venue: arXiv cs.CE 2018
Year: 2018

Two novel version of weak form quadrature elements are proposed based on Lagrange and Hermite interpolations, respectively, for a sec- ond strain gradient Euler-Bernoulli beam theory. The second strain gradient theory is governed by eighth order partial differential equa- tion with displacement, slope, curvature and triple derivative of dis- placement as degrees of freedom. A simple and efficient differential quadrature frame work is proposed herein to implement these classi- cal and non-classical degrees of freedom. A novel procedure to com- pute the modified weighting coefficient matrices for the beam element is presented. The proposed elements have displacement as the only degree of freedom in the element domain and displacement, slope, cur- vature and triple derivative of displacement at the boundaries. The Gauss-Lobatto-Legender quadrature points are assumed as element nodes and also used for numerical integration of the element matrices. Numerical examples are presented to demonstrate the efficiency and accuracy of the proposed beam element.
Maximum-Likelihood Network Reconstruction for SIS Processes is NP-Hard
Venue: arXiv cs.CC 2018
Year: 2018

The knowledge of the network topology is imperative to precisely describing the viral dynamics of an SIS epidemic process. In scenarios for which the network topology is unknown, one resorts to reconstructing the network from observing the viral state trace. This work focusses on the impact of the viral state observations on the computational complexity of the resulting network reconstruction problem. We propose a novel method of constructing a specific class of viral state traces from which the inference of the presence or absence of links is either easy or difficult. In particular, we use this construction to prove that the maximum-likelihood SIS network reconstruction is NP-hard. The NP-hardness holds for any adjacency matrix of a graph which is connected.
$K$--User Interference Channel with Backhaul Cooperation: DoF vs. Backhaul Load Trade--Off
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider multiple-antenna $K$-user interference channels with backhaul collaboration in one side (among the transmitters or among the receivers) and investigate the trade-off between the rate in the channel versus the communication load in the backhaul. In this investigation, we focus on a first order approximation result, where the rate of the wireless channel is measured by the degrees of freedom (DoF) per user, and the load of the backhaul is measured by the entropy of backhaul messages per user normalized by $\log$ of transmit power, at high power regimes. This trade-off is fully characterized for the case of even values of $K$, and approximately characterized for the case of odd values of $K$, with vanishing approximation gap as $K$ grows. For full DoF, this result establishes the optimality (approximately) of the most straightforward scheme, called Centralized Scheme, in which the messages are collected at one of the nodes, centrally processed, and forwarded back to each node. In addition, this result shows that the gain of the schemes, relying on distributed processing, through pairwise communication among the nodes (e.g., cooperative alignment) does not scale with the size of the network. For the converse, we develop a new outer-bound on the trade-off based on splitting the set of collaborative nodes (transmitters or receivers) into two subsets, and assuming full cooperation within each group. In continue, we further investigate the trade-off for the cases, where the backhaul or the wireless links (interference channel) are not fully connected.
Impact of Relay Cooperation on the Performance of Large-scale Multipair Two-way Relay Networks
Venue: arXiv cs.IT 2018
Year: 2018

We consider a multipair two-way relay communication network, where pairs of user devices exchange information via a relay system. The communication between users employs time division duplex, with all users transmitting simultaneously to relays in one time slot and relays sending the processed information to all users in the next time slot. The relay system consists of a large number of single antenna units that can form groups. Within each group, relays exchange channel state information (CSI), signals received in the uplink and signals intended for downlink transmission. On the other hand, per-group CSI and uplink/downlink signals (data) are not exchanged between groups, which perform the data processing completely independently. Assuming that the groups perform zero-forcing in both uplink and downlink, we derive a lower bound for the ergodic sumrate of the described system as a function of the relay group size. By close observation of this lower bound, it is concluded that the sumrate is essentially independent of group size when the group size is much larger than the number of user pairs. This indicates that a very large group of cooperating relays can be substituted by a number of smaller groups, without incurring any significant performance reduction. Moreover, this result implies that relay cooperation is more efficient (in terms of resources spent on cooperation) when several smaller relay groups are used in contrast to a single, large group.