OpenRaiser/Intern-Atlas huggingface dataset:
Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Venue: arXiv cs.SE 2018
Year: 2018

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.
More on the dynamics of the symbolic square root map
Venue: arXiv cs.FL 2018
Year: 2018

In our earlier paper [A square root map on Sturmian words, Electron. J. Combin. 24.1 (2017)], we introduced a symbolic square root map. Every optimal squareful infinite word $s$ contains exactly six minimal squares and can be written as a product of these squares: $s = X_1^2 X_2^2 \cdots$. The square root $\sqrt{s}$ of $s$ is the infinite word $X_1 X_2 \cdots$ obtained by deleting half of each square. We proved that the square root map preserves the languages of Sturmian words (which are optimal squareful words). The dynamics of the square root map on a Sturmian subshift are well understood. In our earlier work, we introduced another type of subshift of optimal squareful words which together with the square root map form a dynamical system. In this paper, we study these dynamical systems in more detail and compare their properties to the Sturmian case. The main results are characterizations of periodic points and the limit set. The results show that while there is some similarity it is possible for the square root map to exhibit quite different behavior compared to the Sturmian case.
Joint Content Delivery and Caching Placement via Dynamic Programming
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, downlink delivery of popular content is optimized with the assistance of wireless cache nodes. Specifically, the requests of one file is modeled as a Poisson point process with finite lifetime, and two downlink transmission modes are considered: (1) the base station multicasts file segments to the requesting users and selected cache nodes; (2) the base station proactively multicasts file segments to the selected cache nodes without requests from users. Hence the cache nodes with decoded files can help to offload the traffic upon the next file request via other air interfaces, e.g. WiFi. Without proactive caching placement, we formulate the downlink traffic offloading as a Markov decision process with random number of stages, and propose a revised Bellman's equation to obtain the optimal control policy. In order to address the prohibitively huge state space, we also introduce a low-complexity sub-optimal solution based on linear approximation of the value functions, where the gap between the approximated value functions and the real ones is bounded analytically. The approximated value functions can be calculated from analytical expressions given the spatial distribution of requesting users. Moreover, we propose a learning-based algorithm to evaluate the approximated value functions for unknown distribution of requesting users. Finally, a proactive caching placement algorithm is introduced to exploit the temporal diversity of shadowing effect. It is shown by simulation that the proposed low-complexity algorithm based on approximated value functions can significantly reduce the resource consumption at the base station, and the proactive caching placement can further improve the performance.
Online Job Scheduling in Distributed Machine Learning Clusters
Venue: arXiv cs.DC 2018
Year: 2018

Nowadays large-scale distributed machine learning systems have been deployed to support various analytics and intelligence services in IT firms. To train a large dataset and derive the prediction/inference model, e.g., a deep neural network, multiple workers are run in parallel to train partitions of the input dataset, and update shared model parameters. In a shared cluster handling multiple training jobs, a fundamental issue is how to efficiently schedule jobs and set the number of concurrent workers to run for each job, such that server resources are maximally utilized and model training can be completed in time. Targeting a distributed machine learning system using the parameter server framework, we design an online algorithm for scheduling the arriving jobs and deciding the adjusted numbers of concurrent workers and parameter servers for each job over its course, to maximize overall utility of all jobs, contingent on their completion times. Our online algorithm design utilizes a primal-dual framework coupled with efficient dual subroutines, achieving good long-term performance guarantees with polynomial time complexity. Practical effectiveness of the online algorithm is evaluated using trace-driven simulation and testbed experiments, which demonstrate its outperformance as compared to commonly adopted scheduling algorithms in today's cloud systems.
Sentence Object Notation: Multilingual sentence notation based on Wordnet
Venue: arXiv cs.CL 2018
Year: 2018

The representation of sentences is a very important task. It can be used as a way to exchange data inter-applications. One main characteristic, that a notation must have, is a minimal size and a representative form. This can reduce the transfer time, and hopefully the processing time as well. Usually, sentence representation is associated to the processed language. The grammar of this language affects how we represent the sentence. To avoid language-dependent notations, we have to come up with a new representation which don't use words, but their meanings. This can be done using a lexicon like wordnet, instead of words we use their synsets. As for syntactic relations, they have to be universal as much as possible. Our new notation is called STON "SenTences Object Notation", which somehow has similarities to JSON. It is meant to be minimal, representative and language-independent syntactic representation. Also, we want it to be readable and easy to be created. This simplifies developing simple automatic generators and creating test banks manually. Its benefit is to be used as a medium between different parts of applications like: text summarization, language translation, etc. The notation is based on 4 languages: Arabic, English, Franch and Japanese; and there are some cases where these languages don't agree on one representation. Also, given the diversity of grammatical structure of different world languages, this annotation may fail for some languages which allows more future improvements.
Graph Pattern Matching for Dynamic Team Formation
Venue: arXiv cs.DB 2018
Year: 2018

Finding a list of k teams of experts, referred to as top-k team formation, with the required skills and high collaboration compatibility has been extensively studied. However, existing methods have not considered the specific collaboration relationships among different team members, i.e., structural constraints, which are typically needed in practice. In this study, we first propose a novel graph pattern matching approach for top-k team formation, which incorporates both structural constraints and capacity bounds. Second, we formulate and study the dynamic top-k team formation problem due to the growing need of a dynamic environment. Third, we develop an unified incremental approach, together with an optimization technique, to handle continuous pattern and data updates, separately and simultaneously, which has not been explored before. Finally, using real-life and synthetic data, we conduct an extensive experimental study to show the effectiveness and efficiency of our graph pattern matching approach for (dynamic) top-k team formation.
Spot the Difference by Object Detection
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a simple yet effective solution to a change detection task that detects the difference between two images, which we call "spot the difference". Our approach uses CNN-based object detection by stacking two aligned images as input and considering the differences between the two images as objects to detect. An early-merging architecture is used as the backbone network. Our method is accurate, fast and robust while using very cheap annotation. We verify the proposed method on the task of change detection between the digital design and its photographic image of a book. Compared to verification based methods, our object detection based method outperforms other methods by a large margin and gives extra information of location. We compress the network and achieve 24 times acceleration while keeping the accuracy. Besides, as we synthesize the training data for detection using weakly labeled images, our method does not need expensive bounding box annotation.
Slowing Down Top Trees for Better Worst-Case Bounds
Venue: arXiv cs.DS 2018
Year: 2018

We consider the top tree compression scheme introduced by Bille et al. [ICALP 2013] and construct an infinite family of trees on $n$ nodes labeled from an alphabet of size $σ$, for which the size of the top DAG is $Θ(\frac{n}{\log_σn}\log\log_σn)$. Our construction matches a previously known upper bound and exhibits a weakness of this scheme, as the information-theoretic lower bound is $Ω(\frac{n}{\log_σn})$. This settles an open problem stated by Lohrey et al. [arXiv 2017], who designed a more involved version achieving the lower bound. We show that this can be also guaranteed by a very minor modification of the original scheme: informally, one only needs to ensure that different parts of the tree are not compressed too quickly. Arguably, our version is more uniform, and in particular, the compression procedure is oblivious to the value of $σ$.
A Voxel-based Rendering Pipeline for Large 3D Line Sets
Venue: arXiv cs.GR 2018
Year: 2018

We present a voxel-based rendering pipeline for large 3D line sets that employs GPU ray-casting to achieve scalable rendering including transparency and global illumination effects that cannot be achieved with GPU rasterization. Even for opaque lines we demonstrate superior rendering performance compared to GPU rasterization of lines, and when transparency is used we can interactively render large amounts of lines that are infeasible to be rendered via rasterization. To achieve this, we propose a direction-preserving encoding of lines into a regular voxel grid, along with the quantization of directions using face-to-face connectivity in this grid. On the regular grid structure, parallel GPU ray-casting is used to determine visible fragments in correct visibility order. To enable interactive rendering of global illumination effects like low-frequency shadows and ambient occlusions, illumination simulation is performed during ray-casting on a level-of-detail (LoD) line representation that considers the number of lines and their lengths per voxel. In this way we can render effects which are very difficult to render via GPU rasterization. A detailed performance and quality evaluation compares our approach to rasterization-based rendering of lines.
How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks
Venue: arXiv cs.SI 2018
Year: 2018

In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe the "tobacco strategy", which was used by the tobacco industry to influence policy makers regarding the health risks of tobacco products. The strategy involved two parts, consisting of (1) promoting and sharing independent research supporting the industry's preferred position and (2) funding additional research, but selectively publishing the results. We introduce a model of the Tobacco Strategy, and use it to argue that both prongs of the strategy can be extremely effective--even when policy makers rationally update on all evidence available to them. As we elaborate, this model helps illustrate the conditions under which the Tobacco Strategy is particularly successful. In addition, we show how journalists engaged in "fair" reporting can inadvertently mimic the effects of industry on public belief.
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
Venue: arXiv cs.CV 2018
Year: 2018

With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly rely on human operators to analyze the scene, typically composed of many object instances piled up in bulk. Helping them by robotizing the unitary extraction is a key challenge to speed up this tedious process. Whereas supervised deep learning has proven very efficient for such object-level scene understanding, e.g., generic object detection and segmentation in everyday scenes, it however requires large sets of per-pixel labeled images, that are hardly available for numerous application contexts, including industrial robotics. We thus propose a step towards a practical interactive application for generating an object-oriented robotic grasp, requiring as inputs only one depth map of the scene and one user click on the next object to extract. More precisely, we address in this paper the middle issue of object seg-mentation in top views of piles of bulk objects given a pixel location, namely seed, provided interactively by a human operator. We propose a twofold framework for generating edge-driven instance segments. First, we repurpose a state-of-the-art fully convolutional object contour detector for seed-based instance segmentation by introducing the notion of edge-mask duality with a novel patch-free and contour-oriented loss function. Second, we train one model using only synthetic scenes, instead of manually labeled training data. Our experimental results show that considering edge-mask duality for training an encoder-decoder network, as we suggest, outperforms a state-of-the-art patch-based network in the present application context.
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Venue: arXiv cs.CV 2018
Year: 2018

Semantic image segmentation is one of the most challenged tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: feature downsampling, combined feature upsampling and multiple predictions. We adopt a strategy of multiple steps of upsampling and combined feature maps in pooling layers with its corresponding unpooling layers. Then we bring out multiple pre-outputs, each pre-output is generated from an unpooling layer by one-step upsampling. Finally, we concatenate these pre-outputs to get the final output. As a result, our proposed network makes highly use of the feature information by fusing and reusing feature maps. In addition, when training our model, we add multiple soft cost functions on pre-outputs and final outputs. In this way, we can reduce the loss reduction when the loss is back propagated. We evaluate our model on three major segmentation datasets: CamVid, PASCAL VOC and ADE20K. We achieve a state-of-the-art performance on CamVid dataset, as well as considerable improvements on PASCAL VOC dataset and ADE20K dataset
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
Venue: arXiv cs.CV 2018
Year: 2018

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient L0-norm, which is named as L0TDL. The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the L0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed L0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.
Deep Random based Key Exchange protocol resisting unlimited MITM
Venue: arXiv cs.CR 2018
Year: 2018

We present a protocol enabling two legitimate partners sharing an initial secret to mutually authenticate and to exchange an encryption session key. The opponent is an active Man In The Middle (MITM) with unlimited computation and storage capacities. The resistance to unlimited MITM is obtained through the combined use of Deep Random secrecy, formerly introduced and proved as unconditionally secure against passive opponent for key exchange, and universal hashing techniques. We prove the resistance to MITM interception attacks, and show that (i) upon successful completion, the protocol leaks no residual information about the current value of the shared secret to the opponent, and (ii) that any unsuccessful completion is detectable by the legitimate partners. We also discuss implementation techniques.
Hygienic Source-Code Generation Using Functors
Venue: arXiv cs.PL 2018
Year: 2018

Existing source-code-generating tools such as Lex and Yacc suffer from practical inconveniences because they use disembodied code to implement actions. To prevent this problem, such tools could generate closed functors that are then instantiated by the programmer with appropriate action code. This results in all code being type checked in its appropriate context, and it assists the type checker in localizing errors correctly. We have implemented a lexer generator and parser generator based on this technique for Standard ML, OCaml, and Haskell.
Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning has achieved impressive results on many problems. However, it requires high degree of expertise or a lot of experience to tune well the hyperparameters, and such manual tuning process is likely to be biased. Moreover, it is not practical to try out as many different hyperparameter configurations in deep learning as in other machine learning scenarios, because evaluating each single hyperparameter configuration in deep learning would mean training a deep neural network, which usually takes quite long time. Hyperband algorithm achieves state-of-the-art performance on various hyperparameter optimization problems in the field of deep learning. However, Hyperband algorithm does not utilize history information of previous explored hyperparameter configurations, thus the solution found is suboptimal. We propose to combine Hyperband algorithm with Bayesian optimization (which does not ignore history when sampling next trial configuration). Experimental results show that our combination approach is superior to other hyperparameter optimization approaches including Hyperband algorithm.
Secrecy by Witness-Functions under Equational Theories
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we use the witness-functions to analyze cryptographic protocols for secrecy under nonempty equational theories. The witness-functions are safe metrics used to compute security. An analysis with a witness-function consists in making sure that the security of every atomic message does not decrease during its lifecycle in the protocol. The analysis gets more difficult under nonempty equational theories. Indeed, the intruder can take advantage of the algebraic properties of the cryptographic primitives to derive secrets. These properties arise from the use of mathematical functions, such as multiplication, addition, exclusive-or or modular exponentiation in the cryptosystems and the protocols. Here, we show how to use the witness-functions under nonempty equational theories and we run an analysis on the Needham-Schroeder-Lowe protocol under the cipher homomorphism. This analysis reveals that although this protocol is proved secure under the perfect encryption assumption, its security collapses under the homomorphic primitives. We show how the witness-functions help to illustrate an attack scenario on it and we propose an amended version to fix it.
Reasons and Means to Model Preferences as Incomplete
Venue: arXiv cs.AI 2018
Year: 2018

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences.
Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos
Venue: arXiv cs.CV 2018
Year: 2018

Vehicle detection is a technology which its aim is to locate and show the vehicle size in digital images. In this technology, vehicles are detected in presence of other things like trees and buildings. It has an important role in many computer vision applications such as vehicle tracking, analyzing the traffic scene and efficient traffic management. In this paper, vehicles detected based on the boosting technique by Viola Jones. Our proposed system is tested in some real scenes of surveillance videos with different light conditions. The experimental results show that the accuracy,completeness, and quality of the proposed vehicle detection method are better than the previous techniques (about 94%, 92%, and 87%, respectively). Thus, our proposed approach is robust and efficient to detect vehicles in surveillance videos and their applications.
Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey
Venue: arXiv cs.AI 2018
Year: 2018

Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.