OpenRaiser/Intern-Atlas huggingface dataset:
Decentralized Robust Control for Damping Inter-area Oscillations in Power Systems
Venue: arXiv cs.SY 2017
Year: 2017

As power systems become more and more interconnected, the inter-area oscillations has become a serious factor limiting large power transfer among different areas. Underdamped (Undamped) inter-area oscillations may cause system breakup and even lead to large-scale blackout. Traditional damping controllers include Power System Stabilizer (PSS) and Flexible AC Transmission System (FACTS) controller, which adds additional damping to the inter-area oscillation modes by affecting the real power in an indirect manner. However, the effectiveness of these controllers is restricted to the neighborhood of a prescribed set of operating conditions. In this paper, decentralized robust controllers are developed to improve the damping ratios of the inter-area oscillation modes by directly affecting the real power through the turbine governing system. The proposed control strategy requires only local signals and is robust to the variations in operation condition and system topology. The effectiveness of the proposed robust controllers is illustrated by detailed case studies on two different test systems.
Coupled Compound Poisson Factorization
Venue: arXiv cs.LG 2017
Year: 2017

We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model and, as examples of our framework, implement three different data-generating models---a mixture model, linear regression, and factor analysis---to robustly model non-random missing data in the context of clustering, prediction, and matrix factorization. In all three cases, we test our framework against models that ignore the missing-data mechanism on large scale studies with non-random missing data, and we show that explicitly modeling the missing-data mechanism substantially improves the quality of the results, as measured using data log likelihood on a held-out test set.
A dynamic intranet-based online-portal support for Computer Science teaching
Venue: arXiv cs.CY 2017
Year: 2017

The paper is a suggested experiment in effectively teaching subjects in Computer Science. The paper addresses effective content-delivery with the help of a university intranet. The proposal described herein is for teaching a subject like Combinatorics and Graph Theory - the main idea is to supplement lectures with a teacher-moderated online forum against an associated intranet portal. Keywords and phrases -computer-assisted learning; learning portal; active learning; OEIS; intranet portal; undergraduate teaching; Combinatorics and Graph theory
Type homogeneity is not a restriction for safe recursion schemes
Venue: arXiv cs.LO 2017
Year: 2017

Knapik et al. introduced the safety restriction which constrains both the types and syntax of the production rules defining a higher-order recursion scheme. This restriction gives rise to an equi-expressivity result between order-n pushdown automata and order-n safe recursion schemes, when such devices are used as tree generators. We show that the typing constraint of safety, called homogeneity, is unnecessary in the sense that imposing the syntactic restriction alone is sufficient to prove the equi-expressivity result for trees.
Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining
Venue: arXiv cs.AI 2017
Year: 2017

One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the Internet explosion and the massive diffusion of mobile smart devices lead to the creation of a worldwide system, which information is daily checked and fueled by the contribution of millions of users who interacts in a collaborative way. Search engines, continually exploring the Web, are a natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is possible to generalize the semantic similarity, under the assumption that semantically similar terms behave similarly, and define collaborative proximity measures based on the indexing information returned by search engines. The PMING Distance is a proximity measure used in data mining and information retrieval, which collaborative information express the degree of relationship between two terms, using only the number of documents returned as result for a query on a search engine. In this work, the PMINIG Distance is updated, providing a novel formal algebraic definition, which corrects previous works. The novel point of view underlines the features of the PMING to be a locally normalized linear combination of the Pointwise Mutual Information and Normalized Google Distance. The analyzed measure dynamically reflects the collaborative change made on the web resources.
Finding AND-OR Hierarchies in Workflow Nets
Venue: arXiv cs.LO 2017
Year: 2017

This paper presents the notion of AND-OR reduction, which reduces a WF net to a smaller net by iteratively contracting certain well-formed subnets into single nodes until no more such contractions are possible. This reduction can reveal the hierarchical structure of a WF net, and since it preserves certain semantical properties such as soundness, it can help with analysing and understanding why a WF net is sound or not. The reduction can also be used to verify if a WF net is an AND-OR net. This class of WF nets was introduced in earlier work, and arguably describes nets that follow good hierarchical design principles. It is shown that the AND-OR reduction is confluent up to isomorphism, which means that despite the inherent non-determinism that comes from the choice of subnets that are contracted, the final result of the reduction is always the same up to the choice of the identity of the nodes. Based on this result, a polynomial-time algorithm is presented that computes this unique result of the AND-OR reduction. Finally, it is shown how this algorithm can be used to verify if a WF net is an AND-OR net.
Visual Multiple-Object Tracking for Unknown Clutter Rate
Venue: arXiv cs.CV 2017
Year: 2017

In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this paper we are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. Recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalized labeled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios.
Energy Harvesting Communication Using Finite-Capacity Batteries with Internal Resistance
Venue: arXiv cs.IT 2017
Year: 2017

Modern systems will increasingly rely on energy harvested from their environment. Such systems utilize batteries to smoothen out the random fluctuations in harvested energy. These fluctuations induce highly variable battery charge and discharge rates, which affect the efficiencies of practical batteries that typically have non-zero internal resistances. In this paper, we study an energy harvesting communication system using a finite battery with non-zero internal resistance. We adopt a dual-path architecture, in which harvested energy can be directly used, or stored and then used. In a frame, both time and power can be split between energy storage and data transmission. For a single frame, we derive an analytical expression for the rate optimal time and power splitting ratios between harvesting energy and transmitting data. We then optimize the time and power splitting ratios for a group of frames, assuming non-causal knowledge of harvested power and fading channel gains, by giving an approximate solution. When only the statistics of the energy arrivals and channel gains are known, we derive a dynamic programming based policy and, propose three sub-optimal policies, which are shown to perform competitively. In summary, our study suggests that battery internal resistance significantly impacts the design and performance of energy harvesting communication systems and must be taken into account.
SIPHON: Towards Scalable High-Interaction Physical Honeypots
Venue: arXiv cs.CR 2017
Year: 2017

In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we focus on the adaptation of Honeypots for improving the security of IoTs. Low-interaction honeypots are used so far in the context of IoT. Such honeypots are limited and easily detectable, and thus, there is a need to find ways how to develop high-interaction, reliable, IoT honeypots that will attract skilled attackers. In this work, we propose the SIPHON architecture - a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture leverages IoT devices that are physically at one location and are connected to the Internet through so-called wormholes distributed around the world. The resulting architecture allows exposing few physical devices over a large number of geographically distributed IP addresses. We demonstrate the proposed architecture in a large scale experiment with 39 wormhole instances in 16 cities in 9 countries. Based on this setup, six physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months. A preliminary analysis of the collected traffic indicates that devices in some cities attracted significantly more traffic than others (ranging from 600 000 incoming TCP connections for the most popular destination to less than 50000 for the least popular). We recorded over 400 brute-force login attempts to the web-interface of our devices using a total of 1826 distinct credentials, from which 11 attempts were successful. Moreover, we noted login attempts to Telnet and SSH ports some of which used credentials found in the recently disclosed Mirai malware.
Vandermonde Matrices with Nodes in the Unit Disk and the Large Sieve
Venue: arXiv cs.IT 2017
Year: 2017

We derive bounds on the extremal singular values and the condition number of NxK, with N>=K, Vandermonde matrices with nodes in the unit disk. The mathematical techniques we develop to prove our main results are inspired by a link---first established by by Selberg [1] and later extended by Moitra [2]---between the extremal singular values of Vandermonde matrices with nodes on the unit circle and large sieve inequalities. Our main conceptual contribution lies in establishing a connection between the extremal singular values of Vandermonde matrices with nodes in the unit disk and a novel large sieve inequality involving polynomials in z \in C with |z|<=1. Compared to Bazán's upper bound on the condition number [3], which, to the best of our knowledge, constitutes the only analytical result---available in the literature---on the condition number of Vandermonde matrices with nodes in the unit disk, our bound not only takes a much simpler form, but is also sharper for certain node configurations. Moreover, the bound we obtain can be evaluated consistently in a numerically stable fashion, whereas the evaluation of Bazán's bound requires the solution of a linear system of equations which has the same condition number as the Vandermonde matrix under consideration and can therefore lead to numerical instability in practice. As a byproduct, our result---when particularized to the case of nodes on the unit circle---slightly improves upon the Selberg-Moitra bound.
MDPs with Energy-Parity Objectives
Venue: arXiv cs.LO 2017
Year: 2017

Energy-parity objectives combine $ω$-regular with quantitative objectives of reward MDPs. The controller needs to avoid to run out of energy while satisfying a parity objective. We refute the common belief that, if an energy-parity objective holds almost-surely, then this can be realised by some finite memory strategy. We provide a surprisingly simple counterexample that only uses coBüchi conditions. We introduce the new class of bounded (energy) storage objectives that, when combined with parity objectives, preserve the finite memory property. Based on these, we show that almost-sure and limit-sure energy-parity objectives, as well as almost-sure and limit-sure storage parity objectives, are in $\mathit{NP}\cap \mathit{coNP}$ and can be solved in pseudo-polynomial time for energy-parity MDPs.
List Decoding of Insertions and Deletions
Venue: arXiv cs.IT 2017
Year: 2017

List decoding of insertions and deletions in the Levenshtein metric is considered. The Levenshtein distance between two sequences is the minimum number of insertions and deletions needed to turn one of the sequences into the other. In this paper, a Johnson-like upper bound on the maximum list size when list decoding in the Levenshtein metric is derived. This bound depends only on the length and minimum Levenshtein distance of the code, the length of the received word, and the alphabet size. It shows that polynomial-time list decoding beyond half the Levenshtein distance is possible for many parameters. Further, we also prove a lower bound on list decoding of deletions with with the well-known binary Varshamov-Tenengolts (VT) codes which shows that the maximum list size grows exponentially with the number of deletions. Finally, an efficient list decoding algorithm for two insertions/deletions with VT codes is given. This decoder can be modified to a polynomial-time list decoder of any constant number of insertions/deletions.
Distributed Algorithm for Collision Avoidance at Road Intersections in the Presence of Communication Failures
Venue: arXiv cs.NI 2017
Year: 2017

Vehicle-to-vehicle (V2V) communication is a crucial component of the future autonomous driving systems since it enables improved awareness of the surrounding environment, even without extensive processing of sensory information. However, V2V communication is prone to failures and delays, so a distributed fault-tolerant approach is required for safe and efficient transportation. In this paper, we focus on the intersection crossing (IC) problem with autonomous vehicles that cooperate via V2V communications, and propose a novel distributed IC algorithm that can handle an unknown number of communication failures. Our analysis shows that both safety and liveness requirements are satisfied in all realistic situations. We also found, based on a real data set, that the crossing delay is only slightly increased even in the presence of highly correlated failures.
Spectral Projector-Based Graph Fourier Transforms
Venue: arXiv cs.SI 2017
Year: 2017

The paper presents the graph Fourier transform (GFT) of a signal in terms of its spectral decomposition over the Jordan subspaces of the graph adjacency matrix $A$. This representation is unique and coordinate free, and it leads to unambiguous definition of the spectral components ("harmonics") of a graph signal. This is particularly meaningful when $A$ has repeated eigenvalues, and it is very useful when $A$ is defective or not diagonalizable (as it may be the case with directed graphs). Many real world large sparse graphs have defective adjacency matrices. We present properties of the GFT and show it to satisfy a generalized Parseval inequality and to admit a total variation ordering of the spectral components. We express the GFT in terms of spectral projectors and present an illustrative example for a real world large urban traffic dataset.
Universal Joint Image Clustering and Registration using Partition Information
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of universal joint clustering and registration of images and define algorithms using multivariate information functionals. We first study registering two images using maximum mutual information and prove its asymptotic optimality. We then show the shortcomings of pairwise registration in multi-image registration, and design an asymptotically optimal algorithm based on multiinformation. Further, we define a novel multivariate information functional to perform joint clustering and registration of images, and prove consistency of the algorithm. Finally, we consider registration and clustering of numerous limited-resolution images, defining algorithms that are order-optimal in scaling of number of pixels in each image with the number of images.
Bidirectional American Sign Language to English Translation
Venue: arXiv cs.CL 2017
Year: 2017

We outline a bidirectional translation system that converts sentences from American Sign Language (ASL) to English, and vice versa. To perform machine translation between ASL and English, we utilize a generative approach. Specifically, we employ an adjustment to the IBM word-alignment model 1 (IBM WAM1), where we define language models for English and ASL, as well as a translation model, and attempt to generate a translation that maximizes the posterior distribution defined by these models. Then, using these models, we are able to quantify the concepts of fluency and faithfulness of a translation between languages.
Full-reference image quality assessment-based B-mode ultrasound image similarity measure
Venue: arXiv cs.CV 2017
Year: 2017

During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically. Yet, despite of the remarkable progress that has been made, the medical ultrasound image similarity measurement remains largely unsolved due to a high level of speckle noise contamination. Potential applications of the ultrasound image similarity measurement seem evident in several aspects. To name a few, ultrasound imaging quality assessment, abnormal function region detection, etc. In this paper, a comparative study was made on full-reference image quality assessment methods for ultrasound image visual structural similarity measure. Moreover, based on the image similarity index, a generic ultrasound motion tracking re-initialization framework is given in this work. The experiments are conducted on synthetic data and real-ultrasound liver data and the results demonstrate that, with proposed similarity-based tracking re-initialization, the mean error of landmarks tracking can be decreased from 2 mm to about 1.5 mm in the ultrasound liver sequence.
DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems
Venue: arXiv cs.NI 2017
Year: 2017

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs.
Stochastic Generative Hashing
Venue: arXiv cs.LG 2017
Year: 2017

Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash functions, learning such functions is known to be very challenging. In addition, the objective functions adopted by existing hashing techniques are mostly chosen heuristically. In this paper, we propose a novel generative approach to learn hash functions through Minimum Description Length principle such that the learned hash codes maximally compress the dataset and can also be used to regenerate the inputs. We also develop an efficient learning algorithm based on the stochastic distributional gradient, which avoids the notorious difficulty caused by binary output constraints, to jointly optimize the parameters of the hash function and the associated generative model. Extensive experiments on a variety of large-scale datasets show that the proposed method achieves better retrieval results than the existing state-of-the-art methods.
Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines
Venue: arXiv cs.CV 2017
Year: 2017

The retina is a complex nervous system which encodes visual stimuli before higher order processing occurs in the visual cortex. In this study we evaluated whether information about the stimuli received by the retina can be retrieved from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting High-Density 64x64 MEA technology. To this end, we modeled the RGC population activity using mean-covariance Restricted Boltzmann Machines, latent variable models capable of learning the joint distribution of a set of continuous observed random variables and a set of binary unobserved random units. The idea was to figure out if binary latent states encode the regularities associated to different visual stimuli, as modes in the joint distribution. We measured the goodness of mcRBM encoding by calculating the Mutual Information between the latent states and the stimuli shown to the retina. Results show that binary states can encode the regularities associated to different stimuli, using both gratings and natural scenes as stimuli. We also discovered that hidden variables encode interesting properties of retinal activity, interpreted as population receptive fields. We further investigated the ability of the model to learn different modes in population activity by comparing results associated to a retina in normal conditions and after pharmacologically blocking GABA receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual Information tends to decrease if we pharmacologically block receptors. We finally stress that the computational method described in this work could potentially be applied to any kind of neural data obtained through MEA technology, though different techniques should be applied to interpret the results.
Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree
Venue: arXiv cs.IT 2017
Year: 2017

The reconstruction of sparse signals requires the solution of an $\ell_0$-norm minimization problem in Compressed Sensing. Previous research has focused on the investigation of a single candidate to identify the support (index of nonzero elements) of a sparse signal. To ensure that the optimal candidate can be obtained in each iteration, we propose here an iterative greedy reconstruction algorithm (GSRA). First, the intersection of the support sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP) is set as the initial support set. Then, a hope-tree is built to expand the set. Finally, a developed decreasing subspace pursuit method is used to rectify the candidate set. Detailed simulation results demonstrate that GSRA is more accurate than other typical methods in recovering Gaussian signals, 0--1 sparse signals, and synthetic signals.