OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Role Binding in Blockchain-Based Collaborative Business Processes
Venue: arXiv cs.SE 2018
Year: 2018

Blockchain technology enables the execution of collaborative business processes involving mutually untrusted parties. Existing platforms allow such processes to be modeled using high-level notations and compiled into smart contracts that can be deployed on blockchain platforms. However, these platforms brush aside the question of who is allowed to execute which tasks in the process, either by deferring the question altogether or by adopting a static approach where all actors are bound to roles upon process instantiation. Yet, a key advantage of blockchains is their ability to support dynamic sets of actors. This paper presents a model for dynamic binding of actors to roles in collaborative processes and an associated binding policy specification language. The proposed language is endowed with a Petri net semantics, thus enabling policy consistency verification. The paper also outlines an approach to compile policy specifications into smart contracts for enforcement. An experimental evaluation shows that the cost of policy enforcement increases linearly with the number of roles and constraints.
More or Less? Predict the Social Influence of Malicious URLs on Social Media
Venue: arXiv cs.SI 2018
Year: 2018

Users of Online Social Networks (OSNs) interact with each other more than ever. In the context of a public discussion group, people receive, read, and write comments in response to articles and postings. In the absence of access control mechanisms, OSNs are a great environment for attackers to influence others, from spreading phishing URLs, to posting fake news. Moreover, OSN user behavior can be predicted by social science concepts which include conformity and the bandwagon effect. In this paper, we show how social recommendation systems affect the occurrence of malicious URLs on Facebook. We exploit temporal features to build a prediction framework, having greater than 75% accuracy, to predict whether the following group users' behavior will increase or not. Included in this work, we demarcate classes of URLs, including those malicious URLs classified as creating critical damage, as well as those of a lesser nature which only inflict light damage such as aggressive commercial advertisements and spam content. It is our hope that the data and analyses in this paper provide a better understanding of OSN user reactions to different categories of malicious URLs, thereby providing a way to mitigate the influence of these malicious URL attacks.
Per-Flow Cardinality Estimation Based On Virtual LogLog Sketching
Venue: arXiv cs.IT 2018
Year: 2018

Flow cardinality estimation is the problem of estimating the number of distinct elements in a data flow, often with a stringent memory constraint. It has wide applications in network traffic measurement and in database systems. The virtual LogLog algorithm proposed recently by Xiao, Chen, Chen and Ling estimates the cardinalities of a large number of flows with a compact memory. The purpose of this thesis is to explore two new perspectives on the estimation process of this algorithm. Firstly, we propose and investigate a family of estimators that generalizes the original vHLL estimator and evaluate the performance of the vHLL estimator compared to other estimators in this family. Secondly, we propose an alternative solution to the estimation problem by deriving a maximum-likelihood estimator. Empirical evidence from both perspectives suggests the near-optimality of the vHLL estimator for per-flow estimation, analogous to the near-optimality of the HLL estimator for single-flow estimation.
Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems
Venue: arXiv cs.CY 2018
Year: 2018

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance concepts and techniques. At the same time, open problems and the relevant technical fields are not organized. To establish standard quality assurance frameworks, it is necessary to visualize and organize these open problems in an interdisciplinary way, so that the experts from many different technical fields may discuss these problems in depth and develop solutions. In the present study, we identify, classify, and explore the open problems in quality assurance of safety-critical machine-learning systems, and their relevant corresponding industry and technological trends, using automated-driving vehicles as an example. Our results show that addressing these open problems requires incorporating knowledge from several different technological and industrial fields, including the automobile industry, statistics, software engineering, and machine learning.
Analysis of tagging latency when comparing event-related potentials
Venue: arXiv cs.HC 2018
Year: 2018

Event-related potentials (ERPs) are very small voltage produced by the brain in response to external stimulation. In order to detect and evaluate an ERP in an ongoing electroencephalogram (EEG), it is necessary to tag the EEG with the exact onset time of the stimulus. We define the latency as the delay between the time the tagging command is sent and the detection of the stimulus on the screen. Failing to control sequencing in the tagging pipeline causes problems when interpreting latency, in particular when comparing ERPs generated from stimuli displayed by different systems. In this work, we present number of technical aspects which can influence latency such as the refresh rate of the screen or the display of a stimulus at different screen location. A few propositions are suggested to estimate and correct this latency.
mD-Track: Leveraging Multi-Dimensionality in Passive Indoor Wi-Fi Tracking
Venue: arXiv cs.NI 2018
Year: 2018

Wi-Fi localization and tracking face accuracy limitations dictated by antenna count (for angle-of-arrival methods) and frequency bandwidth (for time-of-arrival methods). This paper presents mD-Track a device-free Wi-Fi tracking system capable of jointly fusing information from as many dimensions as possible to overcome the resolution limit of each individual dimension. Through a novel path separation algorithm, mD-Track can resolve multipath at a much finer-grained resolution, isolating signals reflected off targets of interest. mD-Track can localize human passively at a high accuracy with just a single Wi-Fi transceiver pair. mD-Track also introduces novel methods to greatly streamline its estimation algorithms, achieving real-time operation. We implement mD-Track on both WARP and cheap off-the-shelf commodity Wi-Fi hardware and evaluate its performance in different indoor environments.
Kernelization of Packing Problems
Venue: arXiv cs.DS 2018
Year: 2018

Kernelization algorithms are polynomial-time reductions from a problem to itself that guarantee their output to have a size not exceeding some bound. For example, d-Set Matching for integers d>2 is the problem of finding a matching of size at least k in a given d-uniform hypergraph and has kernels with O(k^d) edges. Bodlaender et al. [JCSS 2009], Fortnow and Santhanam [JCSS 2011], Dell and Van Melkebeek [JACM 2014] developed a framework for proving lower bounds on the kernel size for certain problems, under the complexity-theoretic hypothesis that coNP is not contained in NP/poly. Under the same hypothesis, we show tight lower bounds for the kernelization of d-Set Matching and other packing problems. Our bounds are tight for d-Set Matching: It does not have kernels with O(k^{d-ε}) edges for any ε>0 unless the hypothesis fails. By reduction, this transfers to a bound of O(k^{d-1-ε}) for the problem of finding k vertex-disjoint cliques of size d in standard graphs. Obtaining tight bounds for graph packing problems is challenging: We make first progress in this direction by showing non-trivial kernels with O(k^2.5) edges for the problem of finding k vertex-disjoint paths of three edges each. If the paths have d edges each, we improve the straightforward O(k^{d+1}) kernel to a uniform polynomial kernel where the exponent of the kernel size is independent of k. Most of our lower bound proofs follow a general scheme that we discover: To exclude kernels of size O(k^{d-ε}) for a problem in d-uniform hypergraphs, one should reduce from a carefully chosen d-partite problem that is still NP-hard. As an illustration, we apply this scheme to the vertex cover problem, which allows us to replace the number-theoretical construction by Dell and Van Melkebeek [JACM 2014] with shorter elementary arguments.
Scheduling a Human Channel
Venue: arXiv cs.IT 2018
Year: 2018

We consider a system where a human operator processes a sequence of tasks that are similar in nature under a total time constraint. In these systems, the performance of the operator depends on its past utilization. This is akin to $\textit{state-dependent}$ channels where the past actions of the transmitter affects the future quality of the channel (also known as $\textit{action-dependent}$ or $\textit{use-dependent}$ channels). For $\textit{human channels}$, a well-known psychological phenomena, known as $\textit{Yerkes-Dodson law}$, states that a human operator performs worse when he/she is over-utilized or under-utilized. Over such a $\textit{use-dependent}$ human channel, we consider the problem of maximizing a utility function, which is monotonically increasing and concave in the time allocated for each task, under explicit minimum and maximum $\textit{utilization}$ constraints. We show that the optimal solution is to keep the utilization ratio of the operator as high as possible, and to process all the tasks. We prove that the optimal policy consists of two major strategies: utilize the operator without resting until reaching the maximum allowable utilization ratio, and then alternate between working and resting the operator each time reaching the maximum allowable utilization at the end of work-period. We show that even though the tasks are similar in difficulty, the time allocated for the tasks can be different depending on the strategy in which a task is processed; however, the tasks processed in the same strategy are processed equally.
Dice in the Black Box: User Experiences with an Inscrutable Algorithm
Venue: arXiv cs.HC 2018
Year: 2018

We demonstrate that users may be prone to place an inordinate amount of trust in black box algorithms that are framed as intelligent. We deploy an algorithm that purportedly assesses the positivity and negativity of a users' writing emotional writing. In actuality, the algorithm responds in a random fashion. We qualitatively examine the paths to trust that users followed while testing the system. In light of the ease with which users may trust systems exhibiting "intelligent behavior" we recommend corrective approaches.
The $k$-conversion number of regular graphs
Venue: arXiv cs.DM 2018
Year: 2018

Given a graph $G=(V,E)$ and a set $S_0\subseteq V$, an irreversible $k$-threshold conversion process on $G$ is an iterative process wherein, for each $t=1,2,\dots$, $S_t$ is obtained from $S_{t-1}$ by adjoining all vertices that have at least $k$ neighbours in $S_{t-1}$. We call the set $S_0$ the seed set of the process, and refer to $S_0$ as an irreversible $k$-threshold conversion set, or a $k$-conversion set, of $G$ if $S_t=V(G)$ for some $t\geq 0$. The $k$-conversion number $c_{k}(G)$ is the size of a minimum $k$-conversion set of $G$. A set $X\subseteq V$ is a decycling set, or feedback vertex set, if and only if $G[V-X]$ is acyclic. It is known that $k$-conversion sets in $(k+1)$-regular graphs coincide with decycling sets. We characterize $k$-regular graphs having a $k$-conversion set of size $k$, discuss properties of $(k+1)$-regular graphs having a $k$-conversion set of size $k$, and obtain a lower bound for $c_k(G)$ for $(k+r)$-regular graphs. We present classes of cubic graphs that attain the bound for $c_2(G)$, and others that exceed it---for example, we construct classes of $3$-connected cubic graphs $H_m$ of arbitrary girth that exceed the lower bound for $c_2(H_m)$ by at least $m$.
No Peek: A Survey of private distributed deep learning
Venue: arXiv cs.LG 2018
Year: 2018

We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of neural networks. We study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends.
Medical Simulation and Training: "Haptic" Liver
Venue: arXiv cs.HC 2018
Year: 2018

Tactile perception plays an important role in medical simulation and training, specifically in surgery. The surgeon must feel organic tissue hardness, evaluate anatomical structures, measure tissue properties, and apply appropriate force control actions for safe tissue manipulation. Development of novel cost effective haptic-based simulators and their introduction in the minimally invasive surgery learning cycle can absorb the learning curve for residents. Receiving pre-training in a core set of surgical skills can reduce skill acquisition time and risks. We present the development of a cost-effective visuo-haptic simulator for the liver tissue, designed to improve practice-based education in minimally invasive surgery. Such systems can positively affect the next generations of learners by enhancing their knowledge in connection with real-life situations while they train in mandatory safe conditions.
Control of learning in anti-coordination network games
Venue: arXiv cs.SY 2018
Year: 2018

We consider control of heterogeneous players repeatedly playing an anti-coordination network game. In an anti-coordination game, each player has an incentive to differentiate its action from its neighbors. At each round of play, players take actions according to a learning algorithm that mimics the iterated elimination of strictly dominated strategies. We show that the learning dynamics may fail to reach anti-coordination in certain scenarios. We formulate an optimization problem with the objective to reach maximum anti-coordination while minimizing the number of players to control. We consider both static and dynamic control policy formulations. Relating the problem to a minimum vertex cover problem on bipartite networks, we develop a feasible dynamic policy that is efficient to compute. Solving for optimal policies on benchmark networks show that the vertex cover based policy can be a loose upper bound when there is a potential to make use of cascades caused by the learning dynamics of uncontrolled players. We propose an algorithm that finds feasible, though possibly suboptimal, policies by sequentially adding players to control considering their cascade potential. Numerical experiments on random networks show the cascade-based algorithm can lower the control effort significantly compared to simpler control schemes.
AutoGAN: Robust Classifier Against Adversarial Attacks
Venue: arXiv cs.LG 2018
Year: 2018

Classifiers fail to classify correctly input images that have been purposefully and imperceptibly perturbed to cause misclassification. This susceptability has been shown to be consistent across classifiers, regardless of their type, architecture or parameters. Common defenses against adversarial attacks modify the classifer boundary by training on additional adversarial examples created in various ways. In this paper, we introduce AutoGAN, which counters adversarial attacks by enhancing the lower-dimensional manifold defined by the training data and by projecting perturbed data points onto it. AutoGAN mitigates the need for knowing the attack type and magnitude as well as the need for having adversarial samples of the attack. Our approach uses a Generative Adversarial Network (GAN) with an autoencoder generator and a discriminator that also serves as a classifier. We test AutoGAN against adversarial samples generated with state-of-the-art Fast Gradient Sign Method (FGSM) as well as samples generated with random Gaussian noise, both using the MNIST dataset. For different magnitudes of perturbation in training and testing, AutoGAN can surpass the accuracy of FGSM method by up to 25\% points on samples perturbed using FGSM. Without an augmented training dataset, AutoGAN achieves an accuracy of 89\% compared to 1\% achieved by FGSM method on FGSM testing adversarial samples.
Binary Input Layer: Training of CNN models with binary input data
Venue: arXiv cs.LG 2018
Year: 2018

For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always excluded, as it leads to a significant error increase. Here, we present the novel concept of binary input layer (BIL), which allows the usage of binary input data by learning bit specific binary weights. The concept is evaluated on three datasets (PAMAP2, SVHN, CIFAR-10). Our results show that this approach is in particular beneficial for multimodal datasets (PAMAP2) where it outperforms networks using full precision weights in the first layer by 1:92 percentage points (pp) while consuming only 2 % of the chip area.
Comixify: Transform video into a comics
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a solution to transform a video into a comics. We approach this task using a neural style algorithm based on Generative Adversarial Networks (GANs). Several recent works in the field of Neural Style Transfer showed that producing an image in the style of another image is feasible. In this paper, we build up on these works and extend the existing set of style transfer use cases with a working application of video comixification. To that end, we train an end-to-end solution that transforms input video into a comics in two stages. In the first stage, we propose a state-of-the-art keyframes extraction algorithm that selects a subset of frames from the video to provide the most comprehensive video context and we filter those frames using image aesthetic estimation engine. In the second stage, the style of selected keyframes is transferred into a comics. To provide the most aesthetically compelling results, we selected the most state-of-the art style transfer solution and based on that implement our own ComixGAN framework. The final contribution of our work is a Web-based working application of video comixification available at http://comixify.ii.pw.edu.pl.
Working Principles of Binary Differential Evolution
Venue: arXiv cs.NE 2018
Year: 2018

We conduct a first fundamental analysis of the working principles of binary differential evolution (BDE), an optimization heuristic for binary decision variables that was derived by Gong and Tuson (2007) from the very successful classic differential evolution (DE) for continuous optimization. We show that unlike most other optimization paradigms, it is stable in the sense that neutral bit values are sampled with probability close to $1/2$ for a long time. This is generally a desirable property, however, it makes it harder to find the optima for decision variables with small influence on the objective function. This can result in an optimization time exponential in the dimension when optimizing simple symmetric functions like OneMax. On the positive side, BDE quickly detects and optimizes the most important decision variables. For example, dominant bits converge to the optimal value in time logarithmic in the population size. This enables BDE to optimize the most important bits very fast. Overall, our results indicate that BDE is an interesting optimization paradigm having characteristics significantly different from classic evolutionary algorithms or estimation-of-distribution algorithms (EDAs). On the technical side, we observe that the strong stochastic dependencies in the random experiment describing a run of BDE prevent us from proving all desired results with the mathematical rigor that was successfully used in the analysis of other evolutionary algorithms. Inspired by mean-field approaches in statistical physics we propose a more independent variant of BDE, show experimentally its similarity to BDE, and prove some statements rigorously only for the independent variant. Such a semi-rigorous approach might be interesting for other problems in evolutionary computation where purely mathematical methods failed so far.
Deep Spectral Reflectance and Illuminant Estimation from Self-Interreflections
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we propose a CNN-based approach to estimate the spectral reflectance of a surface and the spectral power distribution of the light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate the spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than the classical approaches. Our results show that the proposed approach outperforms the state of the art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.
Towards quantification of incompleteness in the pairwise comparisons method
Venue: arXiv cs.DM 2018
Year: 2018

Alongside consistency, completeness of information is one of the key factors influencing data quality. The objective of this paper is to define ways of treating missing entries in pairwise comparisons (PC) method with respect to inconsistency and sensitivity. Two important factors related to the incompleteness of PC matrices have been identified, namely the number of missing pairwise comparisons and their arrangements. Accordingly, four incompleteness indices have been developed, simple to calculate, each of them take into account both: the total number of missing data and their distribution in the PC matrix. A numerical study of the properties of these indices has been also conducted using a series of Montecarlo experiments. It demonstrated that both incompleteness and inconsistency of data equally contribute to the sensitivity of the PC matrix. Although incompleteness is only just one of the factors influencing sensitivity, a relative simplicity of the proposed indices may help decision makers to quickly estimate the impact of missing comparisons on the quality of final result.
Does the Web of Science Accurately Represent Chinese Scientific Performance?
Venue: arXiv cs.DL 2018
Year: 2018

The purpose of this study is to compare Web of Science (WoS) with a Chinese bibliometric database in terms of authors and their performance, demonstrate the extent of the overlap between the two groups of Chinese most productive authors in both international and Chinese bibliometric databases, and determine how different disciplines may affect this overlap. The results of this study indicate that Chinese bibliometric databases, or a combination of WoS and Chinese bibliometric databases, should be used to evaluate Chinese research performance except in few disciplines in which Chinese research performance could be assessed using WoS only.