OpenRaiser/Intern-Atlas huggingface dataset:
Emerging Security Challenges of Cloud Virtual Infrastructure
Venue: arXiv cs.CR 2016
Year: 2016

The cloud computing model is rapidly transforming the IT landscape. Cloud computing is a new computing paradigm that delivers computing resources as a set of reliable and scalable internet-based services allowing customers to remotely run and manage these services. Infrastructure-as-a-service (IaaS) is one of the popular cloud computing services. IaaS allows customers to increase their computing resources on the fly without investing in new hardware. IaaS adapts virtualization to enable on-demand access to a pool of virtual computing resources. Although there are great benefits to be gained from cloud computing, cloud computing also enables new categories of threats to be introduced. These threats are a result of the cloud virtual infrastructure complexity created by the adoption of the virtualization technology. Breaching the security of any component in the cloud virtual infrastructure significantly impacts on the security of other components and consequently affects the overall system security. This paper explores the security problem of the cloud platform virtual infrastructure identifying the existing security threats and the complexities of this virtual infrastructure. The paper also discusses the existing security approaches to secure the cloud virtual infrastructure and their drawbacks. Finally, we propose and explore some key research challenges of implementing new virtualization-aware security solutions that can provide the pre-emptive protection for complex and ever- dynamic cloud virtual infrastructure.
A Constant Optimization of the Binary Indexed Tree Query Operation
Venue: arXiv cs.DS 2016
Year: 2016

There are several data structures which can calculate the prefix sums of an array efficiently, while handling point updates on the array, such as Segment Trees and Binary Indexed Trees (BIT). Both these data structures can handle the these two operations (query and update) in $O(\log{n})$ time. In this paper, we present a data structure similar to the BIT, but with an even smaller constant. To do this, we use Zeckendorf's Theorem, a property of the Fibonacci sequence of numbers. The new data structure achieves the same complexity of $O(\log{n})$, but requires about $\log_{ϕ^{2}} n$ computations for the Query Operation as opposed to the $\log_{2} n$ computations required for a BIT Query Operation in the worst case.
Weighted information and entropy rates
Venue: arXiv cs.IT 2016
Year: 2016

The weighted entropy $H^{\rm w}_ϕ(X)=H^{\rm w}_ϕ(f)$ of a random variable $X$ with values $x$ and a probability-mass/density function $f$ is defined as the mean value ${\mathbb E} I^{\rm w}_ϕ(X)$ of the weighted information $I^{\rm w}_ϕ(x)=-ϕ(x)\log\,f(x)$. Here $x\mapstoϕ(x)\in{\mathbb R}$ is a given weight function (WF) indicating a 'value' of outcome $x$. For an $n$-component random vector ${\mathbf{X}}_0^{n-1}=(X_0,\ldots ,X_{n-1})$ produced by a random process ${\mathbf{X}}=(X_i,i\in{\mathbb Z})$, the weighted information $I^{\rm w}_{ϕ_n}({\mathbf x}_0^{n-1})$ and weighted entropy $H^{\rm w}_{ϕ_n}({\mathbf{X}}_0^{n-1})$ are defined similarly, with an WF $ϕ_n({\mathbf x}_0^{n-1})$. Two types of WFs $ϕ_n$ are considered, based on additive and a multiplicative forms ($ϕ_n({\mathbf x}_0^{n-1})=\sum\limits_{i=0}^{n-1}φ (x_i)$ and $ϕ_n({\mathbf x}_0^{n-1})=\prod\limits_{i=0}^{n-1}φ (x_i)$, respectively). The focus is upon ${\it rates}$ of the weighted entropy and information, regarded as parameters related to ${\mathbf{X}}$. We show that, in the context of ergodicity, a natural scale for an asymptotically additive/multiplicative WF is $\frac{1}{n^2}H^{\rm w}_{ϕ_n}({\mathbf{X}}_0^{n-1})$ and $\frac{1}{n}\log\;H^{\rm w}_{ϕ_n}({\mathbf{X}}_0^{n-1})$, respectively. This gives rise to ${\it primary}$ ${\it rates}$. The next-order terms can also be identified, leading to ${\it secondary}$ ${\it rates}$. We also consider emerging generalisations of the Shannon-McMillan-Breiman theorem.
A hybrid approach to supervised machine learning for algorithmic melody composition
Venue: arXiv cs.AI 2016
Year: 2016

In this work we present an algorithm for composing monophonic melodies similar in style to those of a given, phrase annotated, sample of melodies. For implementation, a hybrid approach incorporating parametric Markov models of higher order and a contour concept of phrases is used. This work is based on the master thesis of Thayabaran Kathiresan (2015). An online listening test conducted shows that enhancing a pure Markov model with musically relevant context, like count and planed melody contour, improves the result significantly.
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Venue: arXiv cs.RO 2016
Year: 2016

Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational burden which limits its application on many resource-constrained platforms. In this paper, we address the problem of performing real-time localization in large-scale 3D point cloud maps of ever-growing size. While most systems using multi-modal information reduce localization time by employing side-channel information in a coarse manner (eg. WiFi for a rough prior position estimate), we propose to inter-weave the map with rich sensory data. This multi-modal approach achieves two key goals simultaneously. First, it enables us to harness additional sensory data to localise against a map covering a vast area in real-time; and secondly, it also allows us to roughly localise devices which are not equipped with a camera. The key to our approach is a localization policy based on a sequential Monte Carlo estimator. The localiser uses this policy to attempt point-matching only in nodes where it is likely to succeed, significantly increasing the efficiency of the localization process. The proposed multi-modal localization system is evaluated extensively in a large museum building. The results show that our multi-modal approach not only increases the localization accuracy but significantly reduces computational time.
Intelligent information extraction based on artificial neural network
Venue: arXiv cs.CL 2016
Year: 2016

Question Answering System (QAS) is used for information retrieval and natural language processing (NLP) to reduce human effort. There are numerous QAS based on the user documents present today, but they all are limited to providing objective answers and process simple questions only. Complex questions cannot be answered by the existing QAS, as they require interpretation of the current and old data as well as the question asked by the user. The above limitations can be overcome by using deep cases and neural network. Hence we propose a modified QAS in which we create a deep artificial neural network with associative memory from text documents. The modified QAS processes the contents of the text document provided to it and find the answer to even complex questions in the documents.
The Balance Attack Against Proof-Of-Work Blockchains: The R3 Testbed as an Example
Venue: arXiv cs.DC 2016
Year: 2016

In this paper, we identify a new form of attack, called the Balance attack, against proof-of-work blockchain systems. The novelty of this attack consists of delaying network communications between multiple subgroups of nodes with balanced mining power. Our theoretical analysis captures the precise tradeoff between the network delay and the mining power of the attacker needed to double spend in Ethereum with high probability. We quantify our probabilistic analysis with statistics taken from the R3 consortium, and show that a single machine needs 20 minutes to attack the consortium. Finally, we run an Ethereum private chain in a distributed system with similar settings as R3 to demonstrate the feasibility of the approach, and discuss the application of the Balance attack to Bitcoin. Our results clearly confirm that main proof-of-work blockchain protocols can be badly suited for consortium blockchains.
PrASP Report
Venue: arXiv cs.AI 2016
Year: 2016

This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our framework imposes only little restrictions on probabilistic logic programs. In particular, PrASP allows for ASP as well as First-Order Logic syntax, and for the annotation of formulas with point probabilities as well as interval probabilities. A range of widely configurable inference algorithms can be combined in a pipeline-like fashion, in order to cover a variety of use cases.
p-DLA: A Predictive System Model for Onshore Oil and Gas Pipeline Dataset Classification and Monitoring - Part 1
Venue: arXiv cs.CV 2016
Year: 2016

With the rise in militant activity and rogue behaviour in oil and gas regions around the world, oil pipeline disturbances is on the increase leading to huge losses to multinational operators and the countries where such facilities exist. However, this situation can be averted if adequate predictive monitoring schemes are put in place. We propose in the first part of this paper, an artificial intelligence predictive monitoring system capable of predictive classification and pattern recognition of pipeline datasets. The predictive system is based on a highly sparse predictive Deviant Learning Algorithm (p-DLA) designed to synthesize a sequence of memory predictive clusters for eventual monitoring, control and decision making. The DLA (p-DLA) is compared with a popular machine learning algorithm, the Long Short-Term Memory (LSTM) which is based on a temporal version of the standard feed-forward back-propagation trained artificial neural networks (ANNs). The results of simulations study show impressive results and validates the sparse memory predictive approach which favours the sub-synthesis of a highly compressed and low dimensional knowledge discovery and information prediction scheme. It also shows that the proposed new approach is competitive with a well-known and proven AI approach such as the LSTM.
Evaluating Ternary Adders using a hybrid Memristor / CMOS approach
Venue: arXiv cs.ET 2016
Year: 2016

This paper investigates the potentials of using a hybrid memristor CMOS technology, called MeMOS, for the realisation of ternary adders. Ternary adders exploit the qualitative advantage of multi-value storage capability of memristors compared to conventional CMOS flip-flops storing only binary values in one cell. Furthermore they carry out an addition in $O(1)$ and are therefore considered. The MeMOS approach is compared to a CMOS solution for the ternary adders using multi value memristors as registers concerning the achievable latency and the energy consumption. It is shown that using the TEAM, VTEAM model and a model considering commercially available memristors from Known the approach of using CMOS ternary adders using memristors as multi-value register memory is to prefer. MeMOS circuits have advantages for a static operation mode, i.e. if they are operated after a reset.
FogGIS: Fog Computing for Geospatial Big Data Analytics
Venue: arXiv cs.DC 2016
Year: 2016

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.
Fast Stability Scanning for Future Grid Scenario Analysis
Venue: arXiv cs.CY 2016
Year: 2016

Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. To achieve the computational speed-up, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.
Ujian Online Mahasiswa Ilmu Komputer Berbasis Smartphone
Venue: arXiv cs.CY 2016
Year: 2016

Information technology influence higher education in various aspects, including education sector. This article discusses how smartphones facilitate online examination in computer science and information systems students. The research objective to be achieved by the researchers through the research, are as follows: 1) Utilizing smartphone as a media test online exam, 2) How to make use of social technologies in online test, and 3) Identify the features or facilities that could be used for the implementation of an online exam. Observations was conducted with 87 early year students as respondents. Author develop the online questions by using google forms, and facebook to disseminate online examination questions. Research findings show that Android are dominantly gadgets used by students for their online examination. Smartphone based online exam help students concentration in online exam. Social information technology like facebook and google forms have rich features in supporting online examination for computer science students. The use of smartphones, google forms, and facebook can create an atmosphere of exams modern, efficient, and environmentally friendly.
Second-order Cyclostationarity-based Detection of LTE SC-FDMA Signals for Cognitive Radio Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate the detection of long term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, with application to cognitive radio systems. We explore the second-order cyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the cyclic autocorrelation function to signal detection. The proposed detection algorithm provides a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithm is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results.
Power Networks: The Digital Approach
Venue: arXiv cs.NI 2016
Year: 2016

In the current analog grid, power is available at all times, to all users, indiscriminately. This makes the grid vulnerable to demand fluctuations and much effort has been invested to mitigate their effect. The Digital Power Network (DPN) is an energy-on-demand approach to power grids. In the new (digital) approach, the user initiates an energy request. This action alleviates uncertainties in energy demands. The service provider may grant the request fully or partially. Energy is then transmitted in discrete units, analogous to packets of data over a computer network. The packetized energy is routed to the user address. Because energy demands are known ahead of time, the energy provider may optimize the power distribution of the entire power network and isolate pockets of instabilities. For example, under severe energy constraints the energy provider may queue some energy requests and grant these requests later. Alternative energy resources may be seamlessly incorporated into the power network as yet another address in the system and since their energy is coded, they would be connected to specific users directly. In its simplest form, this grid can be realized by overlaying an auxiliary (communication) network on top of an energy delivery network (the current transmission lines) and coupling the two through an array of addressable digital power switches. Optimization of energy requests is the topic of this paper. We investigate the role of the network queue and provide a snapshot of its behavior in time. The DPN with a limited channel capacity and the optimal path for energy flow in a standard IEEE 39 bus are considered, as well.
Optimal Prefix Codes with Fewer Distinct Codeword Lengths are Faster to Construct
Venue: arXiv cs.DS 2016
Year: 2016

A new method for constructing minimum-redundancy binary prefix codes is described. Our method does not explicitly build a Huffman tree; instead it uses a property of optimal prefix codes to compute the codeword lengths corresponding to the input weights. Let $n$ be the number of weights and $k$ be the number of distinct codeword lengths as produced by the algorithm for the optimum codes. The running time of our algorithm is $O(k \cdot n)$. Following our previous work in \cite{be}, no algorithm can possibly construct optimal prefix codes in $o(k \cdot n)$ time. When the given weights are presorted our algorithm performs $O(9^k \cdot \log^{2k}{n})$ comparisons.
An Unsupervised Algorithm For Learning Lie Group Transformations
Venue: arXiv cs.CV 2017
Year: 2017

We present several theoretical contributions which allow Lie groups to be fit to high dimensional datasets. Transformation operators are represented in their eigen-basis, reducing the computational complexity of parameter estimation to that of training a linear transformation model. A transformation specific "blurring" operator is introduced that allows inference to escape local minima via a smoothing of the transformation space. A penalty on traversed manifold distance is added which encourages the discovery of sparse, minimal distance, transformations between states. Both learning and inference are demonstrated using these methods for the full set of affine transformations on natural image patches. Transformation operators are then trained on natural video sequences. It is shown that the learned video transformations provide a better description of inter-frame differences than the standard motion model based on rigid translation.
A New Approximation Technique for Resource-Allocation Problems
Venue: arXiv cs.DS 2017
Year: 2017

We develop a rounding method based on random walks in polytopes, which leads to improved approximation algorithms and integrality gaps for several assignment problems that arise in resource allocation and scheduling. In particular, it generalizes the work of Shmoys and Tardos on the generalized assignment problem to the setting where some jobs can be dropped. New concentration bounds for random bipartite matching are developed as well.
Approximation Algorithms for Optimal Decision Trees and Adaptive TSP Problems
Venue: arXiv cs.DS 2017
Year: 2017

We consider the problem of constructing optimal decision trees: given a collection of tests which can disambiguate between a set of $m$ possible diseases, each test having a cost, and the a-priori likelihood of the patient having any particular disease, what is a good adaptive strategy to perform these tests to minimize the expected cost to identify the disease? We settle the approximability of this problem by giving a tight $O(\log m)$-approximation algorithm. We also consider a more substantial generalization, the Adaptive TSP problem. Given an underlying metric space, a random subset $S$ of cities is drawn from a known distribution, but $S$ is initially unknown to us--we get information about whether any city is in $S$ only when we visit the city in question. What is a good adaptive way of visiting all the cities in the random subset $S$ while minimizing the expected distance traveled? For this problem, we give the first poly-logarithmic approximation, and show that this algorithm is best possible unless we can improve the approximation guarantees for the well-known group Steiner tree problem.
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension
Venue: arXiv cs.LG 2017
Year: 2017

We present a framework for performing efficient regression in general metric spaces. Roughly speaking, our regressor predicts the value at a new point by computing a Lipschitz extension --- the smoothest function consistent with the observed data --- after performing structural risk minimization to avoid overfitting. We obtain finite-sample risk bounds with minimal structural and noise assumptions, and a natural speed-precision tradeoff. The offline (learning) and online (prediction) stages can be solved by convex programming, but this naive approach has runtime complexity $O(n^3)$, which is prohibitive for large datasets. We design instead a regression algorithm whose speed and generalization performance depend on the intrinsic dimension of the data, to which the algorithm adapts. While our main innovation is algorithmic, the statistical results may also be of independent interest.
Fixed and Market Pricing for Cloud Services
Venue: arXiv cs.GT 2017
Year: 2017

We study a model of congestible resources, where pricing and scheduling are intertwined. Motivated by the problem of pricing cloud instances, we model a cloud computing service as linked $GI/GI/\cdot$ queuing systems where the provider chooses to offer a fixed pricing service, a dynamic market based service, or a hybrid of both, where jobs can be preempted in the market-based service. Users (jobs), who are heterogeneous in both the value they place on service and their cost for waiting, then choose between the services offered. Combining insights from auction theory with queuing theory we are able to characterize user equilibrium behavior, and show its insensitivity to the precise market design mechanism used. We then provide theoretical and simulation based evidence suggesting that a fixed price typically, though not always, generates a higher expected revenue than the hybrid system for the provider.
Four Soviets Walk the Dog-Improved Bounds for Computing the Fréchet Distance
Venue: arXiv cs.CG 2017
Year: 2017

Given two polygonal curves in the plane, there are many ways to define a notion of similarity between them. One popular measure is the Fréchet distance. Since it was proposed by Alt and Godau in 1992, many variants and extensions have been studied. Nonetheless, even more than 20 years later, the original $O(n^2 \log n)$ algorithm by Alt and Godau for computing the Fréchet distance remains the state of the art (here, $n$ denotes the number of edges on each curve). This has led Helmut Alt to conjecture that the associated decision problem is 3SUM-hard. In recent work, Agarwal et al. show how to break the quadratic barrier for the discrete version of the Fréchet distance, where one considers sequences of points instead of polygonal curves. Building on their work, we give a randomized algorithm to compute the Fréchet distance between two polygonal curves in time $O(n^2 \sqrt{\log n}(\log\log n)^{3/2})$ on a pointer machine and in time $O(n^2(\log\log n)^2)$ on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the decision problem of depth $O(n^{2-\varepsilon})$, for some $\varepsilon > 0$. We believe that this reveals an intriguing new aspect of this well-studied problem. Finally, we show how to obtain the first subquadratic algorithm for computing the weak Fréchet distance on a word RAM.