OpenRaiser/Intern-Atlas huggingface dataset:
One-Shot Image-to-Image Translation via Part-Global Learning with a Multi-adversarial Framework
Venue: arXiv cs.CV 2019
Year: 2019

It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challenge for existing main-stream deep neural networks. Inspired by analogical reasoning in the human mind, a feasible strategy is to translate the abundant images of a rich source domain to enrich the relevant yet different target domain with insufficient image data. To achieve this goal, we propose a novel, effective multi-adversarial framework (MA) based on part-global learning, which accomplishes one-shot cross-domain image-to-image translation. In specific, we first devise a part-global adversarial training scheme to provide an efficient way for feature extraction and prevent discriminators being over-fitted. Then, a multi-adversarial mechanism is employed to enhance the image-to-image translation ability to unearth the high-level semantic representation. Moreover, a balanced adversarial loss function is presented, which aims to balance the training data and stabilize the training process. Extensive experiments demonstrate that the proposed approach can obtain impressive results on various datasets between two extremely imbalanced image domains and outperform state-of-the-art methods on one-shot image-to-image translation.
Static Analyzers and Potential Future Research Directions for Scala: An Overview
Venue: arXiv cs.SE 2019
Year: 2019

Static analyzers are tool sets which are proving to be indispensable to modern programmers. These enable the programmers to detect possible errors and security defects present in the current code base within the implementation phase of the development cycle, rather than relying on a standalone testing phase. Static analyzers typically highlight possible defects within the 'static' source code and thus does not require the source code to be compiled or executed. The Scala programming language has been gaining wider adoption across various industries in recent years. With such a wide adoption of tools of this nature, this paper presents an overview on the static analysis tools available, both commercial and open-source, for the Scala programming language. This paper discusses in detail about the types of defects that each of these tools can detect, limitations of these tools and also provide potential research direction that can improve the current state of static analyzers for the Scala programming language.
NFTracer: A Non-Fungible Token Tracking Proof-of-Concept Using Hyperledger Fabric
Venue: arXiv cs.DB 2019
Year: 2019

Various start-up developers and academic researchers have investigated the usage of blockchain as a data storage medium due to the advantages offered by its tamper-proof and decentralized nature. However, there have not been many attempts to provide a standard platform for virtually storing the states of unique tangible entities and their subsequent modifications. In this paper, we propose NFTracer, a non-fungible token tracking proof-of-concept based on Hyperledger Composer and Hyperledger Fabric Blockchain. To achieve the capabilities of our platform, we use NFTracer to build an artwork auction and a real estate auction, which vary in technical complexity and demonstrate the advantages of being able to track entities and their resulting modifications in a decentralized manner. We also present its accompanying modular architecture and system components, and discuss possible future works on NFTracer.
Identifying Security-Critical Cyber-Physical Components in Industrial Control Systems
Venue: arXiv cs.CR 2019
Year: 2019

In recent years, Industrial Control Systems (ICS) have become an appealing target for cyber attacks, having massive destructive consequences. Security metrics are therefore essential to assess their security posture. In this paper, we present a novel ICS security metric based on AND/OR graphs that represent cyber-physical dependencies among network components. Our metric is able to efficiently identify sets of critical cyber-physical components, with minimal cost for an attacker, such that if compromised, the system would enter into a non-operational state. We address this problem by efficiently transforming the input AND/OR graph-based model into a weighted logical formula that is then used to build and solve a Weighted Partial MAX-SAT problem. Our tool, META4ICS, leverages state-of-the-art techniques from the field of logical satisfiability optimisation in order to achieve efficient computation times. Our experimental results indicate that the proposed security metric can efficiently scale to networks with thousands of nodes and be computed in seconds. In addition, we present a case study where we have used our system to analyse the security posture of a realistic water transport network. We discuss our findings on the plant as well as further security applications of our metric.
A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models
Venue: arXiv cs.NE 2019
Year: 2019

Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cost of investment for a stock and compare different stocks. This paper proposes using sequence prediction models to forecast a value factor-the earning yield (EBIT/EV) of a company for stock selection. Two advanced sequence prediction models-Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are studied. These two models can overcome the inherent problems of a standard Recurrent Neural Network, i.e., vanishing and exploding gradients. This paper firstly introduces the theories of the networks. And then elaborates the workflow of stock pool creation, feature selection, data structuring, model setup and model evaluation. The LSTM and GRU models demonstrate superior performance of forecast accuracy over a traditional Feedforward Neural Network model. The GRU model slightly outperformed the LSTM model.
Synchronous Bidirectional Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Existing approaches to neural machine translation (NMT) generate the target language sequence token by token from left to right. However, this kind of unidirectional decoding framework cannot make full use of the target-side future contexts which can be produced in a right-to-left decoding direction, and thus suffers from the issue of unbalanced outputs. In this paper, we introduce a synchronous bidirectional neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and future information at the same time. Specifically, we first propose a new algorithm that enables synchronous bidirectional decoding in a single model. Then, we present an interactive decoding model in which left-to-right (right-to-left) generation does not only depend on its previously generated outputs, but also relies on future contexts predicted by right-to-left (left-to-right) decoding. We extensively evaluate the proposed SB-NMT model on large-scale NIST Chinese-English, WMT14 English-German, and WMT18 Russian-English translation tasks. Experimental results demonstrate that our model achieves significant improvements over the strong Transformer model by 3.92, 1.49 and 1.04 BLEU points respectively, and obtains the state-of-the-art performance on Chinese-English and English-German translation tasks.
Streaming Algorithms for Bin Packing and Vector Scheduling
Venue: arXiv cs.DS 2019
Year: 2019

Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value. We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For Bin Packing, we provide a streaming asymptotic $1+\varepsilon$-approximation with $\widetilde{O}\left(\frac{1}{\varepsilon}\right)$ memory, where $\widetilde{O}$ hides logarithmic factors. Moreover, such a space bound is essentially optimal. Our algorithm implies a streaming $d+\varepsilon$-approximation for Vector Bin Packing in $d$ dimensions, running in space $\widetilde{O}\left(\frac{d}{\varepsilon}\right)$. For the related Vector Scheduling problem, we show how to construct an input summary in space $\widetilde{O}(d^2\cdot m / \varepsilon^2)$ that preserves the optimum value up to a factor of $2 - \frac{1}{m} +\varepsilon$, where $m$ is the number of identical machines.
Towards a regularity theory for ReLU networks -- chain rule and global error estimates
Venue: arXiv cs.LG 2019
Year: 2019

Although for neural networks with locally Lipschitz continuous activation functions the classical derivative exists almost everywhere, the standard chain rule is in general not applicable. We will consider a way of introducing a derivative for neural networks that admits a chain rule, which is both rigorous and easy to work with. In addition we will present a method of converting approximation results on bounded domains to global (pointwise) estimates. This can be used to extend known neural network approximation theory to include the study of regularity properties. Of particular interest is the application to neural networks with ReLU activation function, where it contributes to the understanding of the success of deep learning methods for high-dimensional partial differential equations.
A simulation method for fatigue-driven delamination in layered structures involving non-negligible fracture process zones and arbitrarily shaped crack fronts
Venue: arXiv cs.CE 2019
Year: 2019

Most of the existing methods for fatigue-driven delamination are limited to two-dimensional (2D) applications or their predictive capabilities have not been validated in three-dimensional (3D) problems. This work presents a new cohesive zone-based computational method for simulating fatigue-driven delamination in the analysis of 3D structures without crack migration. The method accurately predicts fatigue propagation of non-nelgigible fracture process zones with arbitrarily shaped delamination fronts. The model does not require any kind of fitting parameter since all the input parameters are obtained experimentally from coupon tests. The evaluation of the energy release rate is done using two new techniques recently developed by the authors (the growth driving direction and the mode-decomposed J-integral) leading to an accurate prediction of delamination propagation under mixed-mode and non-self-similar growing conditions. The new method has been implemented as a UEL for Abaqus and validated against an experimental benchmark case with varying crack growth rate and shape and extension of the fracture process zone.
An extended Elrod-Adams model to account for backpressure and blow-by inception
Venue: arXiv cs.CE 2019
Year: 2019

The Piston-Ring-Liner system is the main tribological component of internal combustion engines. The Elrod-Adams model is customarily used to numerically assess the hydrodynamics of different ring designs and liner surface treatments. However, that model does not incorporate the backpressure boundary condition, which in this case corresponds to the combustion chamber pressure and is quite significant. In this contribution a model is proposed that imposes the combustion-chamber pressure in a mass-conserving way, together with an effective algorithm for its numerical approximation. The new model incorporates the pressure difference across the ring, which is shown to have a substantial effect on the predicted friction force and MFT. The model is further elaborated so as to provide a criterion for predicting blow-by inception.
Weakly-supervised Caricature Face Parsing through Domain Adaptation
Venue: arXiv cs.CV 2019
Year: 2019

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .
RFocus: Practical Beamforming for Small Devices
Venue: arXiv cs.NI 2019
Year: 2019

To reduce transmit power, increase throughput, and improve communication range, radio systems---such as IoT sensor networks, Wi-Fi and cellular networks---benefit from the ability to direct their signals, to ensure that more of the transmitted power reaches the receiver. Many modern systems beamform with antenna arrays for this purpose. However, a radio's ability to direct its signal is fundamentally limited by its size. Unfortunately practical challenges limit the size of modern radios, and consequently, their ability to beamform. In many settings, radios on devices must be small and inexpensive; today, these settings are unable to benefit from high-precision beamforming. To address this problem, we introduce RFocus, which moves beamforming functions from the radio endpoints to the environment. RFocus includes a two-dimensional surface with a rectangular array of simple elements, each of which functions as an RF switch. Each element either lets the signal through or reflects it. The surface does not emit any power of its own. The state of the elements is set by a software controller to maximize the signal strength at a receiver, with a novel optimization algorithm that uses signal strength measurements from the receiver. The RFocus surface can be manufactured as an inexpensive thin wallpaper, requiring no wiring. This solution requires only a method to communicate received signal strengths periodically to the RFocus controller. Our prototype implementation improves the median signal strength by 10.5x, and the median channel capacity by 2.1x.
Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics
Venue: arXiv cs.RO 2019
Year: 2019

We consider the problem of online adaptation of a neural network designed to represent vehicle dynamics. The neural network model is intended to be used by an MPC control law to autonomously control the vehicle. This problem is challenging because both the input and target distributions are non-stationary, and naive approaches to online adaptation result in catastrophic forgetting, which can in turn lead to controller failures. We present a novel online learning method, which combines the pseudo-rehearsal method with locally weighted projection regression. We demonstrate the effectiveness of the resulting Locally Weighted Projection Regression Pseudo-Rehearsal (LW-PR$^2$) method in simulation and on a large real world dataset collected with a 1/5 scale autonomous vehicle.
On Optimal Neighbor Discovery
Venue: arXiv cs.NI 2019
Year: 2019

Mobile devices apply neighbor discovery (ND) protocols to wirelessly initiate a first contact within the shortest possible amount of time and with minimal energy consumption. For this purpose, over the last decade, a vast number of ND protocols have been proposed, which have progressively reduced the relation between the time within which discovery is guaranteed and the energy consumption. In spite of the simplicity of the problem statement, even after more than 10 years of research on this specific topic, new solutions are still proposed even today. Despite the large number of known ND protocols, given an energy budget, what is the best achievable latency still remains unclear. This paper addresses this question and for the first time presents safe and tight, duty-cycle-dependent bounds on the worst-case discovery latency that no ND protocol can beat. Surprisingly, several existing protocols are indeed optimal, which has not been known until now. We conclude that there is no further potential to improve the relation between latency and duty-cycle, but future ND protocols can improve their robustness against beacon collisions.
Friendship Paradox Biases Perceptions in Directed Networks
Venue: arXiv cs.SI 2019
Year: 2019

How popular a topic or an opinion appears to be in a network can be very different from its actual popularity. For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i.e., its global popularity---can be dramatically different from how people see it in their social feeds---i.e., its perceived popularity---where the feeds aggregate their friends' posts. We trace the origin of this discrepancy to the friendship paradox in directed networks, which states that people are less popular than their friends (or followers) are, on average. We identify conditions on network structure that give rise to this perception bias, and validate the findings empirically using data from Twitter. Within messages posted by Twitter users in our sample, we identify topics that appear more frequently within the users' social feeds, than they do globally, i.e., among all posts. In addition, we present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic's global prevalence from biased perceptions of individuals. We characterize the bias of the polling estimate, provide an upper bound for its variance, and validate the algorithm's efficiency through synthetic polling experiments on our Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort social perceptions and resulting behaviors.
Disparity-Augmented Trajectories for Human Activity Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Numerous methods for human activity recognition have been proposed in the past two decades. Many of these methods are based on sparse representation, which describes the whole video content by a set of local features. Trajectories, being mid-level sparse features, are capable of describing the motion of an interest-point in 2D space. 2D trajectories might be affected by viewpoint changes, potentially decreasing their accuracy. In this paper, we initially propose and compare different 2D trajectory-based algorithms for human activity recognition. Moreover, we propose a new way of fusing disparity information with 2D trajectory information, without the calculation of 3D reconstruction. The obtained results show a 2.76\% improvement when using disparity-augmented trajectories, compared to using the classical 2D trajectory information only. Furthermore, we have also tested our method on the challenging Hollywood 3D dataset, and we have obtained competitive results, at a faster speed.
Robust Node ID Assignment for Mobile P2P Networks
Venue: arXiv cs.CR 2019
Year: 2019

The advancement of portable mobile wireless devices such as smart-phones, PDA, etc., brought mobile peer-to-peer (P2P) as an extension of traditional P2P networks to provide efficient, low-cost communication among them in a cellular network. It is challenging to assign a unique identifier to each user, as an adversary can target to disrupt the P2P system, by carefully selecting user IDs or obtaining many pseudo-IDs. This work proposes a robust node-ID assignment mechanism for secure peer joining in mobile P2P system called PJ-Sec. PJ-Sec facilitates to generate nodeID for a joining peer by a collaborative effort of an existing peer (within the vicinity) and pre-selected vicinity head. PJ-Sec is formally analyzed using AVISPA model checker and found to be attack resistant.
Comparison-limited Vector Quantization
Venue: arXiv cs.IT 2019
Year: 2019

A variation of the classic vector quantization problem is considered, in which the analog-to-digital (A2D) conversion is not constrained by the cardinality of the output but rather by the number of comparators available for quantization. More specifically, we consider the scenario in which a vector quantizer of dimension d is comprised of k comparators, each receiving a linear combination of the inputs and producing zero/one when this signal is above/below a threshold. Given a distribution of the inputs and a distortion criterion, the value of the linear combinations and thresholds are to be configured so as to minimize the distortion between the quantizer input and its reconstruction. This vector quantizer architecture naturally arises in many A2D conversion scenarios in which the quantizer's cost and energy consumption are severely restricted. For this novel vector quantizer architecture, we propose an algorithm to determine the optimal configuration and provide the first performance evaluation for the case of uniform and Gaussian sources.
Expression Conditional GAN for Facial Expression-to-Expression Translation
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.
Towards a Skeleton-Based Action Recognition For Realistic Scenarios
Venue: arXiv cs.RO 2019
Year: 2019

Understanding human actions is a crucial problem for service robots. However, the general trend in Action Recognition is developing and testing these systems on structured datasets. That's why this work presents a practical Skeleton-based Action Recognition framework which can be used in realistic scenarios. Our results show that although non-augmented and non-normalized data may yield comparable results on the test split of the dataset, it is far from being useful on another dataset which is a manually collected data.