OpenRaiser/Intern-Atlas huggingface dataset:
On Large-Scale Graph Generation with Validation of Diverse Triangle Statistics at Edges and Vertices
Venue: arXiv cs.DM 2018
Year: 2018

Researchers developing implementations of distributed graph analytic algorithms require graph generators that yield graphs sharing the challenging characteristics of real-world graphs (small-world, scale-free, heavy-tailed degree distribution) with efficiently calculable ground-truth solutions to the desired output. Reproducibility for current generators used in benchmarking are somewhat lacking in this respect due to their randomness: the output of a desired graph analytic can only be compared to expected values and not exact ground truth. Nonstochastic Kronecker product graphs meet these design criteria for several graph analytics. Here we show that many flavors of triangle participation can be cheaply calculated while generating a Kronecker product graph. Given two medium-sized scale-free graphs with adjacency matrices $A$ and $B$, their Kronecker product graph has adjacency matrix $C = A \otimes B$. Such graphs are highly compressible: $|{\cal E}|$ edges are represented in ${\cal O}(|{\cal E}|^{1/2})$ memory and can be built in a distributed setting from small data structures, making them easy to share in compressed form. Many interesting graph calculations have worst-case complexity bounds ${\cal O}(|{\cal E}|^p)$ and often these are reduced to ${\cal O}(|{\cal E}|^{p/2})$ for Kronecker product graphs, when a Kronecker formula can be derived yielding the sought calculation on $C$ in terms of related calculations on $A$ and $B$. We focus on deriving formulas for triangle participation at vertices, ${\bf t}_C$, a vector storing the number of triangles that every vertex is involved in, and triangle participation at edges, $Δ_C$, a sparse matrix storing the number of triangles at every edge.
Managing Large-Scale Transient Data in IoT Systems
Venue: arXiv cs.DC 2018
Year: 2018

The pervasive availability of streaming data is driving interest in distributed Fast Data platforms for streaming applications. Such latency-sensitive applications need to respond to dynamism in the input rates and task behavior using scale-in and -out on elastic Cloud resources. Platforms like Apache Storm do not provide robust capabilities for responding to such dynamism and for rapid task migration across VMs. We propose several dataflow checkpoint and migration approaches that allow a running streaming dataflow to migrate, without any loss of in-flight messages or their internal tasks states, while reducing the time to recover and stabilize. We implement and evaluate these migration strategies on Apache Storm using micro and application dataflows for scaling in and out on up to 2-21 Azure VMs. Our results show that we can migrate dataflows of large sizes within 50 sec, in comparison to Storm's default approach that takes over 100 sec. We also find that our approaches stabilize the application much earlier and there is no failure and re-processing of messages.
To overlap or not to overlap: Enabling Channel Bonding in High-Density WLANs
Venue: arXiv cs.NI 2018
Year: 2018

Wireless local area networks (WLANs) are the most popular kind of wireless Internet connection because of their simplicity of deployment and operation. As a result, the number of devices accessing the Internet through WLANs such as laptops, smartphones, or wearables, is increasing drastically at the same time that applications' throughput requirements do. To cope with these challenges, channel bonding (CB) techniques are used for enabling higher data rates by transmitting in wider channels, thus increasing spectrum efficiency. However, important issues like higher potential co-channel and adjacent channel interference arise when bonding channels. This may harm the performance of the carrier sense multiple access (CSMA) protocol because of recurrent backoff freezing while making nodes more sensitive to hidden node effects. In this paper, we address the following point at issue: is it convenient for high-density (HD) WLANs to use wider channels and potentially overlap in the spectrum? First, we highlight key aspects of DCB in toy scenarios through a continuous time Markov network (CTMN) model. Then, by means of extensive simulations covering a wide range of traffic loads and access point (AP) densities, we show that dynamic channel bonding (DCB) - which adapts the channel bandwidth on a per-packet transmission - significantly outperforms traditional single-channel on average. Nevertheless, results also corroborate that DCB is more prone to generate unfair situations where WLANs may starve. Contrary to most of the current thoughts pushing towards non-overlapping channels in HD deployments, we highlight the benefits of allocating channels as wider as possible to WLANs altogether with implementing adaptive access policies to cope with the unfairness situations that may appear.
A Dynamic-Adversarial Mining Approach to the Security of Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers - a) harder to evade, b) easier to detect changes in the data distribution over time, and c) be able to retrain and recover from model degradation. While most works in the security of machine learning has concentrated on the evasion resistance (a) problem, there is little work in the areas of reacting to attacks (b and c). Additionally, while streaming data research concentrates on the ability to react to changes to the data distribution, they often take an adversarial agnostic view of the security problem. This makes them vulnerable to adversarial activity, which is aimed towards evading the concept drift detection mechanism itself. In this paper, we analyze the security of machine learning, from a dynamic and adversarial aware perspective. The existing techniques of Restrictive one class classifier models, Complex learning models and Randomization based ensembles, are shown to be myopic as they approach security as a static task. These methodologies are ill suited for a dynamic environment, as they leak excessive information to an adversary, who can subsequently launch attacks which are indistinguishable from the benign data. Based on empirical vulnerability analysis against a sophisticated adversary, a novel feature importance hiding approach for classifier design, is proposed. The proposed design ensures that future attacks on classifiers can be detected and recovered from. The proposed work presents motivation, by serving as a blueprint, for future work in the area of Dynamic-Adversarial mining, which combines lessons learned from Streaming data mining, Adversarial learning and Cybersecurity.
Noise generation for compression algorithms
Venue: arXiv cs.CV 2018
Year: 2018

In various Computer Vision and Signal Processing applications, noise is typically perceived as a drawback of the image capturing system that ought to be removed. We, on the other hand, claim that image noise, just as texture, is important for visual perception and, therefore, critical for lossy compression algorithms that tend to make decompressed images look less realistic by removing small image details. In this paper we propose a physically and biologically inspired technique that learns a noise model at the encoding step of the compression algorithm and then generates the appropriate amount of additive noise at the decoding step. Our method can significantly increase the realism of the decompressed image at the cost of few bytes of additional memory space regardless of the original image size. The implementation of our method is open-sourced and available at https://github.com/google/pik.
Go-Smart: Open-Ended, Web-Based Modelling of Minimally Invasive Cancer Treatments via a Clinical Domain Approach
Venue: arXiv cs.CY 2018
Year: 2018

Clinicians benefit from online treatment planning systems, through off-site accessibility, data sharing and professional interaction. As well as enhancing clinical value, incorporation of simulation tools affords innovative avenues for open-ended, multi-disciplinary research collaboration. An extensible system for clinicians, technicians, manufacturers and researchers to build on a simulation framework is presented. This is achieved using a domain model that relates entities from theoretical, engineering and clinical domains, allowing algorithmic generation of simulation configuration for several open source solvers. The platform is applied to Minimally Invasive Cancer Treatments (MICTs), allowing interventional radiologists to upload patient data, segment patient images and validate simulated treatments of radiofrequency ablation, cryoablation, microwave ablation and irreversible electroporation. A traditional radiology software layout is provided in-browser for clinical use, with simple, guided simulation, primarily for training and research. Developers and manufacturers access a web-based system to manage their own simulation components (equipment, numerical models and clinical protocols) and related parameters. This system is tested by interventional radiologists at four centres, using pseudonymized patient data, as part of the Go-Smart Project (http://gosmart-project.eu). The simulation technology is released as a set of open source components http://github.com/go-smart.
FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces
Venue: arXiv cs.CV 2018
Year: 2018

With recent advances in computer vision and graphics, it is now possible to generate videos with extremely realistic synthetic faces, even in real time. Countless applications are possible, some of which raise a legitimate alarm, calling for reliable detectors of fake videos. In fact, distinguishing between original and manipulated video can be a challenge for humans and computers alike, especially when the videos are compressed or have low resolution, as it often happens on social networks. Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets. To this end, we introduce a novel face manipulation dataset of about half a million edited images (from over 1000 videos). The manipulations have been generated with a state-of-the-art face editing approach. It exceeds all existing video manipulation datasets by at least an order of magnitude. Using our new dataset, we introduce benchmarks for classical image forensic tasks, including classification and segmentation, considering videos compressed at various quality levels. In addition, we introduce a benchmark evaluation for creating indistinguishable forgeries with known ground truth; for instance with generative refinement models.
Optimal Spectrum Sensing Policy with Traffic Classification in RF-Powered CRNs
Venue: arXiv cs.IT 2018
Year: 2018

An orthogonal frequency division multiple access (OFDMA)-based primary user (PU) network is considered, which provides different spectral access/energy harvesting opportunities in RF-powered cognitive radio networks (CRNs). In this scenario, we propose an optimal spectrum sensing policy for opportunistic spectrum access/energy harvesting under both the PU collision and energy causality constraints. PU subchannels can have different traffic patterns and exhibit distinct idle/busy frequencies, due to which the spectral access/energy harvesting opportunities are application specific. Secondary user (SU) collects traffic pattern information through observation of the PU subchannels and classifies the idle/busy period statistics for each subchannel. Based on the statistics, we invoke stochastic models for evaluating SU capacity by which the energy detection threshold for spectrum sensing can be adjusted with higher sensing accuracy. To this end, we employ the Markov decision process (MDP) model obtained by quantizing the amount of SU battery and the duty cycle model obtained by the ratio of average harvested energy and energy consumption rates. We demonstrate the effectiveness of the proposed stochastic models through comparison with the optimal one obtained from an exhaustive method.
LTE-advanced simulation framework based on OMNeT++
Venue: arXiv cs.NI 2018
Year: 2018

Long Term Evolution-Advanced (LTE-Advanced) is the most recent mobile telecommunication technology proposed by 3GPP. LTE-Advanced is applied in some countries, but still in development and testing phase, because of that, a simulation model is needed to test this technology. This paper introduces a LTE-Advanced simulation framework based on OMNeT++ IDE (open source). The proposed framework test results showed that it is working properly and ready to be used. This framework can be considered as the beginning of the LTE-Advanced simulation model based on OMNeT++.
Minmax Centered k-Partitioning of Trees and Applications to Sink Evacuation with Dynamic Confluent Flows
Venue: arXiv cs.DS 2018
Year: 2018

Let $T=(V,E)$ be a tree with associated costs on its subtrees. A minmax $k$-partition of $T$ is a partition into $k$ subtrees, minimizing the maximum cost of a subtree over all possible partitions. In the centered version of the problem, the cost of a subtree cost is defined as the minimum cost of "servicing" that subtree using a center located within it. The problem motivating this work was the sink-evacuation problem on trees, i.e., finding a collection of $k$-sinks that minimize the time required by a confluent dynamic network flow to evacuate all supplies to sinks. This paper provides the first polynomial-time algorithm for solving this problem, running in $O\Bigl(\max(k,\log n) k n \log^4 n\Bigr)$ time. The technique developed can be used to solve any Minmax Centered $k$-Partitioning problem on trees in which the servicing costs satisfy some very general conditions. Solutions can be found for both the discrete case, in which centers must be on vertices, and the continuous case, in which centers may also be placed on edges. The technique developed also improves previous results for finding a minmax cost $k$-partition of a tree given the location of the sinks in advance.
A New Reconfigurable Antenna MIMO Architecture for mmWave Communication
Venue: arXiv cs.IT 2018
Year: 2018

The large spectrum available in the millimeter-Wave (mmWave) band has emerged as a promising solution for meeting the huge capacity requirements of the 5th generation (5G) wireless networks. However, to fully harness the potential of mmWave communications, obstacles such as severe path loss, channel sparsity and hardware complexity should be overcome. In this paper, we introduce a generalized reconfigurable antenna multiple-input multiple-output (MIMO) architecture that takes advantage of lens-based reconfigurable antennas. The considered antennas can support multiple radiation patterns simultaneously by using a single RF chain. The degrees of freedom provided by the reconfigurable antennas are used to, first, combat channel sparsity in MIMO mmWave systems. Further, to suppress high path loss and shadowing at mmWave frequencies, we use a rate-one space-time block code. Our analysis and simulations show that the proposed reconfigurable MIMO architecture achieves full-diversity gain by using linear receivers and without requiring channel state information at the transmitter. Moreover, simulations show that the proposed architecture outperforms traditional MIMO transmission schemes in mmWave channel settings.
Design Assurance Evaluation of Microcontrollers for safety critical Avionics
Venue: arXiv cs.SE 2018
Year: 2018

Dealing with Commercial off-the-shelf (COTS) com- ponents is a daily business for avionic system manufacturers. They are necessary ingredients for hardware designs, but are not built in accordance with the avionics consensus standard DO- 254 for Airborne Electronic Hardware (AEH) design. Especially for complex COTS hardware components used in safety critical AEH, like Microcontroller Units (MCUs), additional assurance activities have to be performed. All of them together shall form a convincing confident, that the hardware is safe in its intended operation environment. The focus of DO-254 is one approach called Design Assurance (DA). Its aim is to reduce design errors by adherence of prescribed process objectives for the entire design life cycle. The effort for certain COTS assurance activities could be reduced if it is possible to demonstrate, that the COTS design process is based on similar effective design process guide- lines to minimize desgin errors. In the last years, semiconductor manufacturers released safety MCUs in compliance to the ISO 26262 standard, dedicated for the development of functional safe automotive systems. These products are COTS components in the sense of avionics, but they are also developed according to a process that focuses on reduction of design errors. In this paper an evaluation is performed to figure out if the ISO 26262 prescribes a similar DA approach as the DO-254, in order to reduce the COTS assurance effort for coming avionic systems.
Clustering to Given Connectivities
Venue: arXiv cs.DS 2018
Year: 2018

We define a general variant of the graph clustering problem where the criterion of density for the clusters is (high) connectivity. In {\sc Clustering to Given Connectivities}, we are given an $n$-vertex graph $G$, an integer $k$, and a sequence $Λ=\langle λ_{1},\ldots,λ_{t}\rangle$ of positive integers and we ask whether it is possible to remove at most $k$ edges from $G$ such that the resulting connected components are {\sl exactly} $t$ and their corresponding edge connectivities are lower-bounded by the numbers in $Λ$. We prove that this problem, parameterized by $k$, is fixed parameter tractable i.e., can be solved by an $f(k)\cdot n^{O(1)}$-step algorithm, for some function $f$ that depends only on the parameter $k$. Our algorithm uses the recursive understanding technique that is especially adapted so to deal with the fact that, in out setting, we do not impose any restriction to the connectivity demands in $Λ$.
Alternative interpretation of the Plücker quadric's ambient space and its application
Venue: arXiv cs.CG 2018
Year: 2018

It is well-known that there exists a bijection between the set of lines of the projective 3-dimensional space $P^3$ and all real points of the so-called Plücker quadric $Ψ$. Moreover one can identify each point of the Plücker quadric's ambient space with a linear complex of lines in $P^3$. Within this paper we give an alternative interpretation for the points of $P^5$ as lines of an Euclidean 4-space $E^4$, which are orthogonal to a fixed direction. By using the quaternionic notation for lines, we study straight lines in $P^5$ which correspond in the general case to cubic 2-surfaces in $E^4$. We show that these surfaces are geometrically connected with circular Darboux 2-motions in $E^4$, as they are basic surfaces of the underlying line-symmetric motions. Moreover we extend the obtained results to line-elements of the Euclidean 3-space $E^3$, which can be represented as points of a cone over $Ψ$ sliced along the 2-dimensional generator space of ideal lines. We also study straight lines of its ambient space $P^6$ and show that they correspond to ruled surface strips composed of the mentioned 2-surfaces with circles on it. Finally we present an application of this interpretation in the context of interactive design of ruled surfaces and ruled surface strips/patches based on the algorithm of De Casteljau.
A Provably Correct Algorithm for Deep Learning that Actually Works
Venue: arXiv cs.LG 2018
Year: 2018

We describe a layer-by-layer algorithm for training deep convolutional networks, where each step involves gradient updates for a two layer network followed by a simple clustering algorithm. Our algorithm stems from a deep generative model that generates mages level by level, where lower resolution images correspond to latent semantic classes. We analyze the convergence rate of our algorithm assuming that the data is indeed generated according to this model (as well as additional assumptions). While we do not pretend to claim that the assumptions are realistic for natural images, we do believe that they capture some true properties of real data. Furthermore, we show that our algorithm actually works in practice (on the CIFAR dataset), achieving results in the same ballpark as that of vanilla convolutional neural networks that are being trained by stochastic gradient descent. Finally, our proof techniques may be of independent interest.
Deep Representation for Patient Visits from Electronic Health Records
Venue: arXiv cs.CY 2018
Year: 2018

We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models anticipated to drive personalized medicine and improve healthcare quality. These embeddings are learned using a deep neural network trained to predict ICD diagnosis categories. We show that our embeddings capture relevant clinical informations and can be used directly as input to standard machine learning algorithms like multi-output classifiers for ICD code prediction. We also show that important medical informations correspond to particular directions in our embedding space.
Infinitesimal Weak Rigidity, Formation Control of Three Agents, and Extension to 3-dimensional Space
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we introduce new concepts of weak rigidity matrix and infinitesimal weak rigidity for planar frameworks. The weak rigidity matrix is used to directly check if a framework is infinitesimally weakly rigid while previous work can check a weak rigidity of a framework indirectly. An infinitesimal weak rigidity framework can be uniquely determined up to a translation and a rotation (and a scaling also when the framework does not include any edge) by its inter-neighbor distances and angles. We apply the new concepts to a three-agent formation control problem with a gradient control law, and prove instability of the control system at any incorrect equilibrium point and convergence to a desired target formation. Also, we propose a modified Henneberg construction, which is a technique to generate minimally rigid (or weakly rigid) graphs. Finally, we extend the concept of the weak rigidity in R^2 to the concept in R^3.
Comparative Study Regarding Control of Wind Energy Conversion Systems Based on the Usage of Classical and Adaptive Neuro Fuzzy Controllers
Venue: arXiv cs.SY 2018
Year: 2018

The paper presents a comparative study regarding the control (using an adaptive neuro-fuzzy controller and a PD controller) based on the simulation of wind energy conversion systems functioning. There are considered several simulations based on asynchronous generator usage, by using the dedicated MATLAB-PSB (Power System Blockset) toolbox implementations.