OpenRaiser/Intern-Atlas huggingface dataset:
Traffic-aware Threshold Adjustment for NFV Scaling using DDPG
Venue: arXiv cs.NI 2018
Year: 2018

Current solutions mostly focus on how to predict traffic, rather than observing traffic characteristics in a specific NFV scenario. So, most of them use a uniform threshold to scale in/out. In real NFV scenario, each VNF may serve the one or more flows, and the characteristics of these flows are completely different, a uniform threshold used in this scenario is not suitable, because each VNF has a distinct processing logic depending on incident network traffic and events. Even if certain VNFs share packet processing functionality such as packet header analysis, the differences in upper-layer processing and implementation can exhibit unique resource usage patterns. We proposes a dynamic threshold scaling mechanism that can tailor thresholds according to each VNF's characteristic. As setting thresholds is a per-VNF task, and requires a deep understanding of workload trends and the diversity of each VNF, so we have added tailor-made features to the traditional dynamic mechanism. Besides, we also reserve resources by predicting workload and add an emergency module to cope with anomaly traffic, that is to say we develop a hybrid scaling policy combining proactive and reactive scaling together. Moreover, the sharp rise of network traffic not only can be caused by large amount of new incoming flows, but also can be induced by the growing of existing flows. If the traffic arises mainly due to the growing of existing flows, then only rerouting new flows can not alleviate the overload quickly and SLAs may be violated \cite{zhang2016co}. The only method to avoid SLA violations is to migrate flows and associated NF internal states quickly and safely from existing instances to new scaled instances, so state migration is an important part of the scaling procedure. We achieved the flow migration in scaling process on openNF to guarantee the accuracy and timeline of scaling.
Formal FocusST Specification of CAN
Venue: arXiv cs.FL 2018
Year: 2018

This paper presents a formal specification of the Controller Area Network (CAN) protocol using FocusST framework. We formally describe core components of the protocol, which provides a basis for further formal analysis using the Isabelle/HOL theorem prover.
Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Change detection has been a challenging visual task due to the dynamic nature of real-world scenes. Good performance of existing methods depends largely on prior background images or a long-term observation. These methods, however, suffer severe degradation when they are applied to detection of instantaneously occurred changes with only a few preceding frames provided. In this paper, we exploit spatio-temporal convolutional networks to address this challenge, and propose a novel retrospective convolution, which features efficient change information extraction between the current frame and frames from historical observation. To address the problem of foreground-specific over-fitting in learning-based methods, we further propose a data augmentation method, named static sample synthesis, to guide the network to focus on learning change-cued information rather than specific spatial features of foreground. Trained end-to-end with complex scenarios, our framework proves to be accurate in detecting instantaneous changes and robust in combating diverse noises. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods.
How You See Me
Venue: arXiv cs.CV 2018
Year: 2018

Convolution Neural Networks is one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while their internal working was left unexplored to much extent. They are often defined as black boxes that can map non-linear data very effectively. This paper tries to show how CNN has learned to look at an image. The proposed algorithm exploits the basic math of CNN to backtrack the important pixels it is considering to predict. This is a simple algorithm which does not involve any training of its own over a pre-trained CNN which can classify.
Unsupervised Learning of Shape Concepts - From Real-World Objects to Mental Simulation
Venue: arXiv cs.CV 2018
Year: 2018

An unsupervised shape analysis is proposed to learn concepts reflecting shape commonalities. Our approach is two-fold: i) a spatial topology analysis of point cloud segment constellations within objects is used in which constellations are decomposed and described in a hierarchical and symbolic manner. ii) A topology analysis of the description space is used in which segment decompositions are exposed in. Inspired by Persistent Homology, groups of shape commonality are revealed. Experiments show that extracted persistent commonality groups can feature semantically meaningful shape concepts; the generalization of the proposed approach is evaluated by different real-world datasets. We extend this by not only learning shape concepts using real-world data, but by also using mental simulation of artificial abstract objects for training purposes. This extended approach is unsupervised in two respects: label-agnostic (no label information is used) and instance-agnostic (no instances preselected by human supervision are used for training). Experiments show that concepts generated with mental simulation, generalize and discriminate real object observations. Consequently, a robot may train and learn its own internal representation of concepts regarding shape appearance in a self-driven and machine-centric manner while omitting the tedious process of supervised dataset generation including the ambiguity in instance labeling and selection.
HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings
Venue: arXiv cs.DB 2018
Year: 2018

To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed -- giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw). Given the increasing interest in using such decomposition methods in practice, a publicly accessible repository of decomposition software, as well as a large set of benchmarks, and a web-accessible workbench for inserting, analysing, and retrieving hypergraphs are called for. We address this need by providing (i) concrete implementations of hypergraph decompositions (including new practical algorithms), (ii) a new, comprehensive benchmark of hypergraphs stemming from disparate CQ and CSP collections, and (iii) HyperBench, our new web-inter\-face for accessing the benchmark and the results of our analyses. In addition, we describe a number of actual experiments we carried out with this new infrastructure.
FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions
Venue: arXiv cs.CR 2018
Year: 2018

Machine learning as a service has been widely deployed to utilize deep neural network models to provide prediction services. However, this raises privacy concerns since clients need to send sensitive information to servers. In this paper, we focus on the scenario where clients want to classify private images with a convolutional neural network model hosted in the server, while both parties keep their data private. We present FALCON, a fast and secure approach for CNN predictions based on Fourier Transform. Our solution enables linear layers of a CNN model to be evaluated simply and efficiently with fully homomorphic encryption. We also introduce the first efficient and privacy-preserving protocol for softmax function, which is an indispensable component in CNNs and has not yet been evaluated in previous works due to its high complexity. We implemented the FALCON and evaluated the performance on real-world CNN models. The experimental results show that FALCON outperforms the best known works in both computation and communication cost.
Human Intention Estimation based on Hidden Markov Model Motion Validation for Safe Flexible Robotized Warehouses
Venue: arXiv cs.RO 2018
Year: 2018

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results.
IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet
Venue: arXiv cs.CV 2018
Year: 2018

Accurate localization and segmentation of intervertebral disc (IVD) is crucial for the assessment of spine disease diagnosis. Despite the technological advances in medical imaging, IVD localization and segmentation are still manually performed, which is time-consuming and prone to errors. If, in addition, multi-modal imaging is considered, the burden imposed on disease assessments increases substantially. In this paper, we propose an architecture for IVD localization and segmentation in multi-modal MRI, which extends the well-known UNet. Compared to single images, multi-modal data brings complementary information, contributing to better data representation and discriminative power. Our contributions are three-fold. First, how to effectively integrate and fully leverage multi-modal data remains almost unexplored. In this work, each MRI modality is processed in a different path to better exploit their unique information. Second, inspired by HyperDenseNet, the network is densely-connected both within each path and across different paths, granting the model the freedom to learn where and how the different modalities should be processed and combined. Third, we improved standard U-Net modules by extending inception modules with two dilated convolutions blocks of different scale, which helps handling multi-scale context. We report experiments over the data set of the public MICCAI 2018 Challenge on Automatic Intervertebral Disc Localization and Segmentation, with 13 multi-modal MRI images used for training and 3 for validation. We trained IVD-Net on an NVidia TITAN XP GPU with 16 GBs RAM, using ADAM as optimizer and a learning rate of 10e-5 during 200 epochs. Training took about 5 hours, and segmentation of a whole volume about 2-3 seconds, on average. Several baselines, with different multi-modal fusion strategies, were used to demonstrate the effectiveness of the proposed architecture.
Visual SLAM-based Localization and Navigation for Service Robots: The Pepper Case
Venue: arXiv cs.RO 2018
Year: 2018

We propose a Visual-SLAM based localization and navigation system for service robots. Our system is built on top of the ORB-SLAM monocular system but extended by the inclusion of wheel odometry in the estimation procedures. As a case study, the proposed system is validated using the Pepper robot, whose short-range LIDARs and RGB-D camera do not allow the robot to self-localize in large environments. The localization system is tested in navigation tasks using Pepper in two different environments: a medium-size laboratory, and a large-size hall.
Design paradigms of intelligent control systems on a chip
Venue: arXiv cs.OH 2018
Year: 2018

This paper focuses on the Field Programmable Gate Array (FPGA) design and implementation of intelligent control system applications on a chip, specifically fuzzy logic and genetic algorithm processing units. Initially, an overview of the FPGA technology is presented, followed by design methodologies, development tools and the use of hardware description languages (HDL). Two FPGA design examples with the use of Hardware Description Languages (HDLs) of parameterized fuzzy logic controller cores are discussed. Thereinafter, a System-on-a-Chip (SoC) designed by the authors in previous work and realized on FPGA featuring a Digital Fuzzy Logic Controller (DFLC) and a soft processor core for the path tracking problem of mobile robots is discussed. Finally a Genetic Algorithm implementation (previously published by the authors) in FPGA chip for the Traveling Salesman Problem (TSP) is also discussed.
Neural Machine Translation with Adequacy-Oriented Learning
Venue: arXiv cs.CL 2018
Year: 2018

Although Neural Machine Translation (NMT) models have advanced state-of-the-art performance in machine translation, they face problems like the inadequate translation. We attribute this to that the standard Maximum Likelihood Estimation (MLE) cannot judge the real translation quality due to its several limitations. In this work, we propose an adequacy-oriented learning mechanism for NMT by casting translation as a stochastic policy in Reinforcement Learning (RL), where the reward is estimated by explicitly measuring translation adequacy. Benefiting from the sequence-level training of RL strategy and a more accurate reward designed specifically for translation, our model outperforms multiple strong baselines, including (1) standard and coverage-augmented attention models with MLE-based training, and (2) advanced reinforcement and adversarial training strategies with rewards based on both word-level BLEU and character-level chrF3. Quantitative and qualitative analyses on different language pairs and NMT architectures demonstrate the effectiveness and universality of the proposed approach.
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War
Venue: arXiv cs.LG 2018
Year: 2018

We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy. Here, a good strategy successfully counters the opponent's current and possible future strategies which can only be estimated using partial observations. We investigate whether we can utilize the full game state information during training time (in the form of an auxiliary prediction task) to increase performance. Experiments carried out within a StarCraft: Brood War bot against strong community bots show substantial win rate improvements over a fixed-strategy baseline and encouraging results when learning with the auxiliary task.
Encryption is Futile: Delay Attacks on High-Precision Clock Synchronization
Venue: arXiv cs.CR 2018
Year: 2018

Clock synchronization has become essential to modern societies since many critical infrastructures depend on a precise notion of time. This paper analyzes security aspects of high-precision clock synchronization protocols, particularly their alleged protection against delay attacks when clock synchronization traffic is encrypted using standard network security protocols such as IPsec, MACsec, or TLS. We use the Precision Time Protocol (PTP), the most widely used protocol for high-precision clock synchronization, to demonstrate that statistical traffic analysis can identify properties that support selective message delay attacks even for encrypted traffic. We furthermore identify a fundamental conflict in secure clock synchronization between the need of deterministic traffic to improve precision and the need to obfuscate traffic in order to mitigate delay attacks. A theoretical analysis of clock synchronization protocols isolates the characteristics that make these protocols vulnerable to delay attacks and argues that such attacks cannot be prevented entirely but only be mitigated. Knowledge of the underlying communication network in terms of one-way delays and knowledge on physical constraints of these networks can help to compute guaranteed maximum bounds for slave clock offsets. These bounds are essential for detecting delay attacks and minimizing their impact. In the general case, however, the precision that can be guaranteed in adversarial settings is orders of magnitude lower than required for high-precision clock synchronization in critical infrastructures, which, therefore, must not rely on a precise notion of time when using untrusted networks.
Bitcoin: A Natural Oligopoly
Venue: arXiv cs.CR 2018
Year: 2018

Although Bitcoin was intended to be a decentralized digital currency, in practice, mining power is quite concentrated. This fact is a persistent source of concern for the Bitcoin community. We provide an explanation using a simple model to capture miners' incentives to invest in equipment. In our model, $n$ miners compete for a prize of fixed size. Each miner chooses an investment $q_i$, incurring cost $c_i q_i$, and then receives reward $\frac{q_i^α}{\sum_j q_j^α}$, for some $α\geq 1$. When $c_i = c_j$ for all $i,j$, and $α= 1$, there is a unique equilibrium where all miners invest equally. However, we prove that under seemingly mild deviations from this model, equilibrium outcomes become drastically more centralized. In particular, (a) When costs are asymmetric, if miner $i$ chooses to invest, then miner $j$ has market share at least $1-\frac{c_j}{c_i}$. That is, if miner $j$ has costs that are (e.g.) $20\%$ lower than those of miner $i$, then miner $j$ must control at least $20\%$ of the \emph{total} mining power. (b) In the presence of economies of scale ($α> 1$), every market participant has a market share of at least $1-\frac{1}α$, implying that the market features at most $\fracα{α- 1}$ miners in total. We discuss the implications of our results for the future design of cryptocurrencies. In particular, our work further motivates the study of protocols that minimize "orphaned" blocks, proof-of-stake protocols, and incentive compatible protocols.
Contextualized Non-local Neural Networks for Sequence Learning
Venue: arXiv cs.CL 2018
Year: 2018

Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN$^{\textbf{3}}$), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighborhood. Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labeling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.
Compensated Integrated Gradients to Reliably Interpret EEG Classification
Venue: arXiv cs.LG 2018
Year: 2018

Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction. This method, however, requires an appropriate baseline for reliable determination of the contributions. We propose a compensated integrated gradients method that does not require a baseline. In fact, the method compensates the attributions calculated by integrated gradients at an arbitrary baseline using Shapley sampling. We prove that the method retrieves reliable attributions if the processes of input features in a classifier are mutually independent, and they are identical like shared weights in convolutional neural networks. Using three electroencephalogram datasets, we experimentally demonstrate that the attributions of the proposed method are more reliable than those of the original integrated gradients, and its computational complexity is much lower than that of Shapley sampling.
How visual discomfort changes with horizontal viewing angle on stereoscopic display
Venue: arXiv cs.HC 2018
Year: 2018

When viewing stereoscopic displays, people may not always be able to stay exactly in front of the display. It is known that viewing stereoscopic display from different vertical angles lead to different visual discomfort. However, the effects of horizontal viewing angle on stereoscopic visual discomfort have been rarely investigated, especially for household stereoscopic displays. In this study, subjects were required to view a stereoscopic display from various horizontal viewing angles, and assessed their visual discomfort during viewing. The visual stimuli have various amount of disparities: positive disparity, negative disparity or zero disparity. Results showed that the visual discomfort changes with horizontal viewing angle, and greater angles generally lead to more serious visual discomfort. Furthermore, the relationship between visual discomfort and horizontal viewing angle can be approximately expressed by a quadratic function.
Computational Decomposition of Style for Controllable and Enhanced Style Transfer
Venue: arXiv cs.CV 2018
Year: 2018

Neural style transfer has been demonstrated to be powerful in creating artistic image with help of Convolutional Neural Networks (CNN). However, there is still lack of computational analysis of perceptual components of the artistic style. Different from some early attempts which studied the style by some pre-processing or post-processing techniques, we investigate the characteristics of the style systematically based on feature map produced by CNN. First, we computationally decompose the style into basic elements using not only spectrum based methods including Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) but also latent variable models such Principal Component Analysis (PCA), Independent Component Analysis (ICA). Then, the decomposition of style induces various ways of controlling the style elements which could be embedded as modules in state-of-the-art style transfer algorithms. Such decomposition of style brings several advantages. It enables the computational coding of different artistic styles by our style basis with similar styles clustering together, and thus it facilitates the mixing or intervention of styles based on the style basis from more than one styles so that compound style or new style could be generated to produce styled images. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also sketch style transfer which indicates possible applications on picture-to-sketch problems.