OpenRaiser/Intern-Atlas huggingface dataset:
Reconfiguration Algorithms for High Precision Communications in Time Sensitive Networks: Time-Aware Shaper Configuration with IEEE 802.1Qcc (Extended Version)
Venue: arXiv cs.NI 2019
Year: 2019

As new networking paradigms emerge for different networking applications, e.g., cyber-physical systems, and different services are handled under a converged data link technology, e.g., Ethernet, certain applications with mission critical traffic cannot coexist on the same physical networking infrastructure using traditional Ethernet packet-switched networking protocols. The IEEE 802.1Q Time Sensitive Networking (TSN) task group is developing protocol standards to provide deterministic properties on Ethernet based packet-switched networks. In particular, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv, Time-Aware Shaper, can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. In this paper, we investigate the effects of using the IEEE 802.1Qcc management protocol to accurately and precisely configure TAS enabled switches (with transmission windows governed by gate control lists (GCLs) with gate control entries (GCEs)) ensuring ultra-low latency, zero packet loss, and minimal jitter for scheduled TSN traffic. We examine both a centralized network/distributed user model (hybrid model) and a fully-distributed (decentralized) 802.1Qcc model on a typical industrial control network with the goal of maximizing scheduled traffic streams.
Smallest graphs achieving the Stinson bound
Venue: arXiv cs.CR 2019
Year: 2019

Perfect secret sharing scheme is a method of distribute a secret information $s$ among participants such that only predefined coalitions, called qualified subsets of the participants can recover the secret, whereas any other coalitions, the unqualified subsets cannot determine anything about the secret. The most important property is the efficiency of the system, which is measured by the information ratio. It can be shown that for graphs the information ratio is at most $(δ+1)/2$ where $δ$ is the maximal degree of the graph. Blundo et al. constructed a family of $δ$-regular graphs with information ratio $(δ+1)/2$ on at least $c\cdot 6^δ$ vertices. We improve this result by constructing a significantly smaller graph family on $c\cdot 2^δ$ vertices achieving the same upper bound both in the worst and the average case.
Simple Natural Language Processing Tools for Danish
Venue: arXiv cs.CL 2019
Year: 2019

This technical note describes a set of baseline tools for automatic processing of Danish text. The tools are machine-learning based, using natural language processing models trained over previously annotated documents. They are maintained at ITU Copenhagen and will always be freely available.
Good for Games Automata: From Nondeterminism to Alternation
Venue: arXiv cs.FL 2019
Year: 2019

A word automaton recognizing a language $L$ is good for games (GFG) if its composition with any game with winning condition $L$ preserves the game's winner. While all deterministic automata are GFG, some nondeterministic automata are not. There are various other properties that are used in the literature for defining that a nondeterministic automaton is GFG, including "history-deterministic", "compliant with some letter game", "good for trees", and "good for composition with other automata". The equivalence of these properties has not been formally shown. We generalize all of these definitions to alternating automata and show their equivalence. We further show that alternating GFG automata are as expressive as deterministic automata with the same acceptance conditions and indices. We then show that alternating GFG automata over finite words, and weak automata over infinite words, are not more succinct than deterministic automata, and that determinizing Büchi and co-Büchi alternating GFG automata involves a $2^{Θ(n)}$ state blow-up. We leave open the question of whether alternating GFG automata of stronger acceptance conditions allow for doubly-exponential succinctness compared to deterministic automata.
Artificial Intelligence: the global landscape of ethics guidelines
Venue: arXiv cs.CY 2019
Year: 2019

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.
Airways: Optimization-Based Planning of Quadrotor Trajectories according to High-Level User Goals
Venue: arXiv cs.HC 2019
Year: 2019

In this paper we propose a computational design tool that al-lows end-users to create advanced quadrotor trajectories witha variety of application scenarios in mind. Our algorithm al-lows novice users to create quadrotor based use-cases withoutrequiring deep knowledge in either quadrotor control or theunderlying constraints of the target domain. To achieve thisgoal we propose an optimization-based method that gener-ates feasible trajectories which can be flown in the real world.Furthermore, the method incorporates high-level human ob-jectives into the planning of flight trajectories. An easy touse 3D design tool allows for quick specification and edit-ing of trajectories as well as for intuitive exploration of theresulting solution space. We demonstrate the utility of our ap-proach in several real-world application scenarios, includingaerial-videography, robotic light-painting and drone racing.
Optimizing for Aesthetically Pleasing Quadrotor Camera Motion
Venue: arXiv cs.GR 2019
Year: 2019

In this paper we first contribute a large scale online study (N=400) to better understand aesthetic perception of aerial video. The results indicate that it is paramount to optimize smoothness of trajectories across all keyframes. However, for experts timing control remains an essential tool. Satisfying this dual goal is technically challenging because it requires giving up desirable properties in the optimization formulation. Second, informed by this study we propose a method that optimizes positional and temporal reference fit jointly. This allows to generate globally smooth trajectories, while retaining user control over reference timings. The formulation is posed as a variable, infinite horizon, contour-following algorithm. Finally, a comparative lab study indicates that our optimization scheme outperforms the state-of-the-art in terms of perceived usability and preference of resulting videos. For novices our method produces smoother and better looking results and also experts benefit from generated timings.
On the Sparseness of Certain MRD Codes
Venue: arXiv cs.IT 2019
Year: 2019

We determine the proportion of $[3\times 3;3]$-MRD codes over ${\mathbb F}_q$ within the space of all $3$-dimensional $3\times3$-rank-metric codes over the same field. This shows that for these parameters MRD codes are sparse in the sense that the proportion tends to $0$ as $q\rightarrow\infty$. This is so far the only parameter case for which MRD codes are known to be sparse. The computation is accomplished by reducing the space of all such rank-metric codes to a space of specific bases and subsequently making use of a result by Menichetti (1973) on 3-dimensional semifields.
A Constant-Factor Approximation Algorithm for Online Coverage Path Planning with Energy Constraint
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we study the problem of coverage planning by a mobile robot with a limited energy budget. The objective of the robot is to cover every point in the environment while minimizing the traveled path length. The environment is initially unknown to the robot. Therefore, it needs to avoid the obstacles in the environment on-the-fly during the exploration. As the robot has a specific energy budget, it might not be able to cover the complete environment in one traversal. Instead, it will need to visit a static charging station periodically in order to recharge its energy. To solve the stated problem, we propose a budgeted depth-first search (DFS)-based exploration strategy that helps the robot to cover any unknown planar environment while bounding the maximum path length to a constant-factor of the shortest-possible path length. Our $O(1)$-approximation guarantee advances the state-of-the-art of log-approximation for this problem. Simulation results show that our proposed algorithm outperforms the current state-of-the-art algorithm both in terms of the traveled path length and run time in all the tested environments with concave and convex obstacles.
Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations
Venue: arXiv cs.DL 2019
Year: 2019

Identifying academic plagiarism is a pressing task for educational and research institutions, publishers, and funding agencies. Current plagiarism detection systems reliably find instances of copied and moderately reworded text. However, reliably detecting concealed plagiarism, such as strong paraphrases, translations, and the reuse of nontextual content and ideas is an open research problem. In this paper, we extend our prior research on analyzing mathematical content and academic citations. Both are promising approaches for improving the detection of concealed academic plagiarism primarily in Science, Technology, Engineering and Mathematics (STEM). We make the following contributions: i) We present a two-stage detection process that combines similarity assessments of mathematical content, academic citations, and text. ii) We introduce new similarity measures that consider the order of mathematical features and outperform the measures in our prior research. iii) We compare the effectiveness of the math-based, citation-based, and text-based detection approaches using confirmed cases of academic plagiarism. iv) We demonstrate that the combined analysis of math-based and citation-based content features allows identifying potentially suspicious cases in a collection of 102K STEM documents. Overall, we show that analyzing the similarity of mathematical content and academic citations is a striking supplement for conventional text-based detection approaches for academic literature in the STEM disciplines.
Deep Learning-Based Classification Of the Defective Pistachios Via Deep Autoencoder Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Pistachio nut is mainly consumed as raw, salted or roasted because of its high nutritional properties and favorable taste. Pistachio nuts with shell and kernel defects, besides not being acceptable for a consumer, are also prone to insects damage, mold decay, and aflatoxin contamination. In this research, a deep learning-based imaging algorithm was developed to improve the sorting of nuts with shell and kernel defects that indicate the risk of aflatoxin contamination, such as dark stains, oily stains, adhering hull, fungal decay and Aspergillus molds. This paper presents an unsupervised learning method to classify defective and unpleasant pistachios based on deep Auto-encoder neural networks. The testing of the designed neural network on a validation dataset showed that nuts having dark stain, oily stain or adhering hull with an accuracy of 80.3% can be distinguished from normal nuts. Due to the limited memory available in the HPC of university, the results are reasonable and justifiable.
Style Generator Inversion for Image Enhancement and Animation
Venue: arXiv cs.CV 2019
Year: 2019

One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GANs) have been able to generate images of remarkable quality. Unfortunately, adversarially-trained unconditional generator networks have not been successful as image priors. One of the main requirements for a network to act as a generative image prior, is being able to generate every possible image from the target distribution. Adversarial learning often experiences mode-collapse, which manifests in generators that cannot generate some modes of the target distribution. Another requirement often not satisfied is invertibility i.e. having an efficient way of finding a valid input latent code given a required output image. In this work, we show that differently from earlier GANs, the very recently proposed style-generators are quite easy to invert. We use this important observation to propose style generators as general purpose image priors. We show that style generators outperform other GANs as well as Deep Image Prior as priors for image enhancement tasks. The latent space spanned by style-generators satisfies linear identity-pose relations. The latent space linearity, combined with invertibility, allows us to animate still facial images without supervision. Extensive experiments are performed to support the main contributions of this paper.
From Data Quality to Model Quality: an Exploratory Study on Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Nowadays, people strive to improve the accuracy of deep learning models. However, very little work has focused on the quality of data sets. In fact, data quality determines model quality. Therefore, it is important for us to make research on how data quality affects on model quality. In this paper, we mainly consider four aspects of data quality, including Dataset Equilibrium, Dataset Size, Quality of Label, Dataset Contamination. We deign experiment on MNIST and Cifar-10 and try to find out the influence the four aspects make on model quality. Experimental results show that four aspects all have decisive impact on the quality of models. It means that decrease in data quality in these aspects will reduce the accuracy of model.
Classifying logistic vehicles in cities using Deep learning
Venue: arXiv cs.CV 2019
Year: 2019

Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Classical methods for counting vehicles use mechanical, electromagnetic or pneumatic sensors, but these devices are costly, difficult to implement and only detect the presence of vehicles without giving information about their category, model or trajectory. This paper proposes a Deep Learning tool for classifying vehicles in a given image while considering different categories of logistic vehicles, namely: light-duty, medium-duty and heavy-duty vehicles. The proposed approach yields two main contributions: first we developed an architecture to create an annotated and balanced database of logistic vehicles, reducing manual annotation efforts. Second, we built a classifier that accurately classifies the logistic vehicles passing through a given road. The results of this work are: first, a database of 72 000 images for 4 vehicles classes; and second two retrained convolutional neural networks (InceptionV3 and MobileNetV2) capable of classifying vehicles with accuracies over 90%.
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
Venue: arXiv cs.CR 2019
Year: 2019

A rise in Advanced Persistent Threats (APTs) has introduced a need for robustness against long-running, stealthy attacks which circumvent existing cryptographic security guarantees. FlipIt is a security game that models attacker-defender interactions in advanced scenarios such as APTs. Previous work analyzed extensively non-adaptive strategies in FlipIt, but adaptive strategies rise naturally in practical interactions as players receive feedback during the game. We model the FlipIt game as a Markov Decision Process and introduce QFlip, an adaptive strategy for FlipIt based on temporal difference reinforcement learning. We prove theoretical results on the convergence of our new strategy against an opponent playing with a Periodic strategy. We confirm our analysis experimentally by extensive evaluation of QFlip against specific opponents. QFlip converges to the optimal adaptive strategy for Periodic and Exponential opponents using associated state spaces. Finally, we introduce a generalized QFlip strategy with composite state space that outperforms a Greedy strategy for several distributions including Periodic and Uniform, without prior knowledge of the opponent's strategy. We also release an OpenAI Gym environment for FlipIt to facilitate future research.
Packing Boundary-Anchored Rectangles and Squares
Venue: arXiv cs.CG 2019
Year: 2019

Consider a set $P$ of $n$ points on the boundary of an axis-aligned square $Q$. We study the boundary-anchored packing problem on $P$ in which the goal is to find a set of interior-disjoint axis-aligned rectangles in $Q$ such that each rectangle is anchored (has a corner at some point in $P$), each point in $P$ is used to anchor at most one rectangle, and the total area of the rectangles is maximized. Here, a rectangle is anchored at a point $p$ in $P$ if one of its corners coincides with $p$. In this paper, we show how to solve this problem in time linear in $n$, provided that the points of $P$ are given in sorted order along the boundary of $Q$. We also consider the problem for anchoring squares and give an $O(n^4)$-time algorithm when the points in $P$ lie on two opposite sides of $Q$.
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle
Venue: arXiv cs.NI 2019
Year: 2019

The research literature on cybersecurity incident detection & response is very rich in automatic detection methodologies, in particular those based on the anomaly detection paradigm. However, very little attention has been devoted to the diagnosis ability of the methods, aimed to provide useful information on the causes of a given detected anomaly. This information is of utmost importance for the security team to reduce the time from detection to response. In this paper, we present Multivariate Big Data Analysis (MBDA), a complete intrusion detection approach based on 5 steps to effectively handle massive amounts of disparate data sources. The approach has been designed to deal with the main characteristics of Big Data, that is, the high volume, velocity and variety. The core of the approach is the Multivariate Statistical Network Monitoring (MSNM) technique proposed in a recent paper. Unlike in state of the art machine learning methodologies applied to the intrusion detection problem, when an anomaly is identified in MBDA the output of the system includes the detail of the logs of raw information associated to this anomaly, so that the security team can use this information to elucidate its root causes. MBDA is based in two open software packages available in Github: the MEDA Toolbox and the FCParser. We illustrate our approach with two case studies. The first one demonstrates the application of MBDA to semistructured sources of information, using the data from the VAST 2012 mini challenge 2. This complete case study is supplied in a virtual machine available for download. In the second case study we show the Big Data capabilities of the approach in data collected from a real network with labeled attacks.
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases?
Access Balancing in Storage Systems by Labeling Partial Steiner Systems
Venue: arXiv cs.IT 2019
Year: 2019

Storage architectures ranging from minimum bandwidth regenerating encoded distributed storage systems to declustered-parity RAIDs can be designed using dense partial Steiner systems in order to support fast reads, writes, and recovery of failed storage units. In order to ensure good performance, popularities of the data items should be taken into account and the frequencies of accesses to the storage units made as uniform as possible. A proposed combinatorial model ranks items by popularity and assigns data items to elements in a dense partial Steiner system so that the sums of ranks of the elements in each block are as equal as possible. By developing necessary conditions in terms of independent sets, we demonstrate that certain Steiner systems must have a much larger difference between the largest and smallest block sums than is dictated by an elementary lower bound. In contrast, we also show that certain dense partial $S(t, t+1, v)$ designs can be labeled to realize the elementary lower bound. Furthermore, we prove that for every admissible order $v$, there is a Steiner triple system $(S(2, 3, v))$ whose largest difference in block sums is within an additive constant of the lower bound.
A linear method for camera pair self-calibration and multi-view reconstruction with geometrically verified correspondences
Venue: arXiv cs.CV 2019
Year: 2019

We examine 3D reconstruction of architectural scenes in unordered sets of uncalibrated images. We introduce a linear method to self-calibrate and find the metric reconstruction of a camera pair. We assume unknown and different focal lengths but otherwise known internal camera parameters and a known projective reconstruction of the camera pair. We recover two possible camera configurations in space and use the Cheirality condition, that all 3D scene points are in front of both cameras, to disambiguate the solution. We show in two Theorems, first that the two solutions are in mirror positions and then the relations between their viewing directions. Our new method performs on par (median rotation error $ΔR = 3.49^{\circ}$) with the standard approach of Kruppa equations ($ΔR = 3.77^{\circ}$) for self-calibration and 5-Point algorithm for calibrated metric reconstruction of a camera pair. We reject erroneous image correspondences by introducing a method to examine whether point correspondences appear in the same order along $x, y$ image axes in image pairs. We evaluate this method by its precision and recall and show that it improves the robustness of point matches in architectural and general scenes. Finally, we integrate all the introduced methods to a 3D reconstruction pipeline. We utilize the numerous camera pair metric recontructions using rotation-averaging algorithms and a novel method to average focal length estimates.