OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Feature Scaling for Online Learning of Binary Classifiers
Venue: arXiv cs.LG 2014
Year: 2014

Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scaling is often required in order to learn an accurate classifier. However, feature scaling is conducted as a preprocessing task prior to learning. This is problematic in an online setting because of two reasons. First, it might not be possible to accurately determine the value range of a feature at the initial stages of learning when we have observed only a few number of training instances. Second, the distribution of data can change over the time, which render obsolete any feature scaling that we perform in a pre-processing step. We propose a simple but an effective method to dynamically scale features at train time, thereby quickly adapting to any changes in the data stream. We compare the proposed dynamic feature scaling method against more complex methods for estimating scaling parameters using several benchmark datasets for binary classification. Our proposed feature scaling method consistently outperforms more complex methods on all of the benchmark datasets and improves classification accuracy of a state-of-the-art online binary classifier algorithm.
Double Counting in $2^t$-ary RSA Precomputation Reveals the Secret Exponent
Venue: arXiv cs.CR 2014
Year: 2014

A new fault attack, double counting attack (DCA), on the precomputation of $2^t$-ary modular exponentiation for a classical RSA digital signature (i.e., RSA without the Chinese remainder theorem) is proposed. The $2^t$-ary method is the most popular and widely used algorithm to speed up the RSA signature process. Developers can realize the fastest signature process by choosing optimum $t$. For example, $t=6$ is optimum for a 1536-bit classical RSA implementation. The $2^t$-ary method requires precomputation to generate small exponentials of message. Conventional fault attack research has paid little attention to precomputation, even though precomputation could be a target of a fault attack. The proposed DCA induces faults in precomputation by using instruction skip technique, which is equivalent to replacing an instruction with a no operation in assembly language. This paper also presents a useful "position checker" tool to determine the position of the $2^t$-ary coefficients of the secret exponent from signatures based on faulted precomputations. The DCA is demonstrated to be an effective attack method for some widely used parameters. DCA can reconstruct an entire secret exponent using the position checker with $63(=2^6-1)$ faulted signatures in a short time for a 1536-bit RSA implementation using the $2^6$-ary method. The DCA process can be accelerated for a small public exponent (e.g., 65537). The the best of our knowledge, the proposed DCA is the first fault attack against classical RSA precomputation.
Resource Competition on Integral Polymatroids
Venue: arXiv cs.GT 2014
Year: 2014

We study competitive resource allocation problems in which players distribute their demands integrally on a set of resources subject to player-specific submodular capacity constraints. Each player has to pay for each unit of demand a cost that is a nondecreasing and convex function of the total allocation of that resource. This general model of resource allocation generalizes both singleton congestion games with integer-splittable demands and matroid congestion games with player-specific costs. As our main result, we show that in such general resource allocation problems a pure Nash equilibrium is guaranteed to exist by giving a pseudo-polynomial algorithm computing a pure Nash equilibrium.
Distributed Maximum Matching in Bounded Degree Graphs
Venue: arXiv cs.DC 2014
Year: 2014

We present deterministic distributed algorithms for computing approximate maximum cardinality matchings and approximate maximum weight matchings. Our algorithm for the unweighted case computes a matching whose size is at least $(1-\eps)$ times the optimal in $Δ^{O(1/\eps)} + O\left(\frac{1}{\eps^2}\right) \cdot\log^*(n)$ rounds where $n$ is the number of vertices in the graph and $Δ$ is the maximum degree. Our algorithm for the edge-weighted case computes a matching whose weight is at least $(1-\eps)$ times the optimal in $\log(\min\{1/\wmin,n/\eps\})^{O(1/\eps)}\cdot(Δ^{O(1/\eps)}+\log^*(n))$ rounds for edge-weights in $[\wmin,1]$. The best previous algorithms for both the unweighted case and the weighted case are by Lotker, Patt-Shamir, and Pettie~(SPAA 2008). For the unweighted case they give a randomized $(1-\eps)$-approximation algorithm that runs in $O((\log(n)) /\eps^3)$ rounds. For the weighted case they give a randomized $(1/2-\eps)$-approximation algorithm that runs in $O(\log(\eps^{-1}) \cdot \log(n))$ rounds. Hence, our results improve on the previous ones when the parameters $Δ$, $\eps$ and $\wmin$ are constants (where we reduce the number of runs from $O(\log(n))$ to $O(\log^*(n))$), and more generally when $Δ$, $1/\eps$ and $1/\wmin$ are sufficiently slowly increasing functions of $n$. Moreover, our algorithms are deterministic rather than randomized.
Entity-Linking via Graph-Distance Minimization
Venue: arXiv cs.DS 2014
Year: 2014

Entity-linking is a natural-language-processing task that consists in identifying the entities mentioned in a piece of text, linking each to an appropriate item in some knowledge base; when the knowledge base is Wikipedia, the problem comes to be known as wikification (in this case, items are wikipedia articles). One instance of entity-linking can be formalized as an optimization problem on the underlying concept graph, where the quantity to be optimized is the average distance between chosen items. Inspired by this application, we define a new graph problem which is a natural variant of the Maximum Capacity Representative Set. We prove that our problem is NP-hard for general graphs; nonetheless, under some restrictive assumptions, it turns out to be solvable in linear time. For the general case, we propose two heuristics: one tries to enforce the above assumptions and another one is based on the notion of hitting distance; we show experimentally how these approaches perform with respect to some baselines on a real-world dataset.
The Gaussian Channel with Noisy Feedback: Near-Capacity Performance via Simple Interaction
Venue: arXiv cs.IT 2014
Year: 2014

Consider a pair of terminals connected by two independent additive white Gaussian noise channels, and limited by individual power constraints. The first terminal would like to reliably send information to the second terminal, within a given error probability. We construct an explicit interactive scheme consisting of only (non-linear) scalar operations, by endowing the Schalkwijk-Kailath noiseless feedback scheme with modulo arithmetic. Our scheme achieves a communication rate close to the Shannon limit, in a small number of rounds. For example, for an error probability of $10^{-6}$, if the Signal to Noise Ratio ($\mathrm{SNR}$) of the feedback channel exceeds the $\mathrm{SNR}$ of the forward channel by $20\mathrm{dB}$, our scheme operates $0.8\mathrm{dB}$ from the Shannon limit with only $19$ rounds of interaction. In comparison, attaining the same performance using state of the art Forward Error Correction (FEC) codes requires two orders of magnitude increase in delay and complexity. On the other extreme, a minimal delay uncoded system with the same error probability is bounded away by $9\mathrm{dB}$ from the Shannon limit.
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
Venue: arXiv cs.LG 2014
Year: 2014

\textit{Drosophila melanogaster} has been established as a model organism for investigating the fundamental principles of developmental gene interactions. The gene expression patterns of \textit{Drosophila melanogaster} can be documented as digital images, which are annotated with anatomical ontology terms to facilitate pattern discovery and comparison. The automated annotation of gene expression pattern images has received increasing attention due to the recent expansion of the image database. The effectiveness of gene expression pattern annotation relies on the quality of feature representation. Previous studies have demonstrated that sparse coding is effective for extracting features from gene expression images. However, solving sparse coding remains a computationally challenging problem, especially when dealing with large-scale data sets and learning large size dictionaries. In this paper, we propose a novel algorithm to solve the sparse coding problem, called Stochastic Coordinate Coding (SCC). The proposed algorithm alternatively updates the sparse codes via just a few steps of coordinate descent and updates the dictionary via second order stochastic gradient descent. The computational cost is further reduced by focusing on the non-zero components of the sparse codes and the corresponding columns of the dictionary only in the updating procedure. Thus, the proposed algorithm significantly improves the efficiency and the scalability, making sparse coding applicable for large-scale data sets and large dictionary sizes. Our experiments on Drosophila gene expression data sets demonstrate the efficiency and the effectiveness of the proposed algorithm.
Consistent transformations of belief functions
Venue: arXiv cs.AI 2014
Year: 2014

Consistent belief functions represent collections of coherent or non-contradictory pieces of evidence, but most of all they are the counterparts of consistent knowledge bases in belief calculus. The use of consistent transformations cs[.] in a reasoning process to guarantee coherence can therefore be desirable, and generalizes similar techniques in classical logic. Transformations can be obtained by minimizing an appropriate distance measure between the original belief function and the collection of consistent ones. We focus here on the case in which distances are measured using classical Lp norms, in both the "mass space" and the "belief space" representation of belief functions. While mass consistent approximations reassign the mass not focussed on a chosen element of the frame either to the whole frame or to all supersets of the element on an equal basis, approximations in the belief space do distinguish these focal elements according to the "focussed consistent transformation" principle. The different approximations are interpreted and compared, with the help of examples.
Quantifying the Effect of Matrix Structure on Multithreaded Performance of the SpMV Kernel
Venue: arXiv cs.DC 2014
Year: 2014

Sparse matrix-vector multiplication (SpMV) is the core operation in many common network and graph analytics, but poor performance of the SpMV kernel handicaps these applications. This work quantifies the effect of matrix structure on SpMV performance, using Intel's VTune tool for the Sandy Bridge architecture. Two types of sparse matrices are considered: finite difference (FD) matrices, which are structured, and R-MAT matrices, which are unstructured. Analysis of cache behavior and prefetcher activity reveals that the SpMV kernel performs far worse with R-MAT matrices than with FD matrices, due to the difference in matrix structure. To address the problems caused by unstructured matrices, novel architecture improvements are proposed.
Social-aware Forwarding in Opportunistic Wireless Networks: Content Awareness or Obliviousness?
Venue: arXiv cs.NI 2014
Year: 2014

With the current host-based Internet architecture, networking faces limitations in dynamic scenarios, due mostly to host mobility. The ICN paradigm mitigates such problems by releasing the need to have an end-to-end transport session established during the life time of the data transfer. Moreover, the ICN concept solves the mismatch between the Internet architecture and the way users would like to use it: currently a user needs to know the topological location of the hosts involved in the communication when he/she just wants to get the data, independently of its location. Most of the research efforts aim to come up with a stable ICN architecture in fixed networks, with few examples in ad-hoc and vehicular networks. However, the Internet is becoming more pervasive with powerful personal mobile devices that allow users to form dynamic networks in which content may be exchanged at all times and with low cost. Such pervasive wireless networks suffer with different levels of disruption given user mobility, physical obstacles, lack of cooperation, intermittent connectivity, among others. This paper discusses the combination of content knowledge (e.g., type and interested parties) and social awareness within opportunistic networking as to drive the deployment of ICN solutions in disruptive networking scenarios. With this goal in mind, we go over few examples of social-aware content-based opportunistic networking proposals that consider social awareness to allow content dissemination independently of the level of network disruption. To show how much content knowledge can improve social-based solutions, we illustrate by means of simulation some content-oblivious/oriented proposals in scenarios based on synthetic mobility patterns and real human traces.
SLI, a New Metric to determine Success of a Software Project
Venue: arXiv cs.SE 2014
Year: 2014

Project Management process plays a critical role in managing factors such as cost, time, technology and personnel towards achieving the success of a project and henceforth the sustainability of the company in the industrial market. This paper emphasizes empirical study of several projects developed over a period of time in a product and service based CMMI Level 5 Software Company. The investigation shows impact analysis of resources such as cost, time, and number of developers towards the successful completion of the project as allocated by the project manager during the developmental process. The analysis has further led to the introduction of a new qualitative metric, Success Level Index Metric (SLI) whose index value varies from 0 to 1. SLI acts as a maturity indicator that indicates the degree of maturity of the company in terms of success of their projects based on which the company can choose their desired level of success for their projects.
MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning
Venue: arXiv cs.AI 2014
Year: 2014

This manuscript uses machine learning techniques to exploit baseball pitchers' decision making, so-called "Baseball IQ," by modeling the at-bat information, pitch selection and counts, as a Markov Decision Process (MDP). Each state of the MDP models the pitcher's current pitch selection in a Markovian fashion, conditional on the information immediately prior to making the current pitch. This includes the count prior to the previous pitch, his ensuing pitch selection, the batter's ensuing action and the result of the pitch.
Joint Design of Optimal Cooperative Jamming and Power Allocation for Linear Precoding
Venue: arXiv cs.IT 2014
Year: 2014

Linear precoding and cooperative jamming for multiuser broadcast channel is studied to enhance the physical layer security. We consider the system where multiple independent data streams are transmitted from the base station to multiple legitimate users with the help of a friendly jammer. It is assumed that a normalized linear precoding matrix is given at the base station, whereas the power allocated to each user is to be determined. The problem is to jointly design the power allocation across different users for linear precoding and the cooperative jamming at the friendly jammer. The goal is to maximize a lower bound of the secrecy rate, provided that a minimum communication rate to the users is guaranteed. The optimal solution is obtained when the number of antennas at the friendly jammer is no less than the total number of antennas at the users and eavesdropper. Moreover, a suboptimal algorithm is proposed, which can be applied for all the scenarios. Numerical results demonstrate that the proposed schemes are effective for secure communications.
Adaptive Attacker Strategy Development Against Moving Target Cyber Defenses
Venue: arXiv cs.CR 2014
Year: 2014

A model of strategy formulation is used to study how an adaptive attacker learns to overcome a moving target cyber defense. The attacker-defender interaction is modeled as a game in which a defender deploys a temporal platform migration defense. Against this defense, a population of attackers develop strategies specifying the temporal ordering of resource investments that bring targeted zero-day exploits into existence. Attacker response to two defender temporal platform migration scheduling policies are examined. In the first defender scheduling policy, the defender selects the active platform in each match uniformly at random from a pool of available platforms. In the second policy the defender schedules each successive platform to maximize the diversity of the source code presented to the attacker. Adaptive attacker response strategies are modeled by finite state machine (FSM) constructs that evolve during simulated play against defender strategies via an evolutionary algorithm. It is demonstrated that the attacker learns to invest heavily in exploit creation for the platform with the least similarity to other platforms when faced with a diversity defense, while avoiding investment in exploits for this least similar platform when facing a randomization defense. Additionally, it is demonstrated that the diversity-maximizing defense is superior for shorter duration attacker-defender engagements, but performs sub-optimally in extended attacker-defender interactions.
A Framework for learning multi-agent dynamic formation strategy in real-time applications
Venue: arXiv cs.RO 2014
Year: 2014

Formation strategy is one of the most important parts of many multi-agent systems with many applications in real world problems. In this paper, a framework for learning this task in a limited domain (restricted environment) is proposed. In this framework, agents learn either directly by observing an expert behavior or indirectly by observing other agents or objects behavior. First, a group of algorithms for learning formation strategy based on limited features will be presented. Due to distributed and complex nature of many multi-agent systems, it is impossible to include all features directly in the learning process; thus, a modular scheme is proposed in order to reduce the number of features. In this method, some important features have indirect influence in learning instead of directly involving them as input features. This framework has the ability to dynamically assign a group of positions to a group of agents to improve system performance. In addition, it can change the formation strategy when the context changes. Finally, this framework is able to automatically produce many complex and flexible formation strategy algorithms without directly involving an expert to present and implement such complex algorithms.
Energy Efficiency Scheme with Cellular Partition Zooming for Massive MIMO Systems
Venue: arXiv cs.IT 2014
Year: 2014

Massive multiple-input multiple-output (Massive MIMO) has been realized as a promising technology for next generation wireless mobile communications, in which Spectral efficiency (SE) and energy efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy of the cellular system need to be supplied by the operator, especially to feed the base station (BS). While varies scheduling studies concerned on the user equipment (UE) to reduce the total energy consumption instead of BS. Fewer literatures address EE issues from a BS perspective. In this paper, an EE scheme is proposed by reducing the energy consumption of BS. The transmission model and parameters related to EE is formulated first. Afterwards, an cellular partition zooming (CPZ) scheme is proposed where the BS can zoom in to maintain the coverage area. Specifically, if no user exists in the rare area of the coverage, BS will zoom out to sleep mode to save energy. Comprehensive simulation results demonstrate that CPZ has better EE performance with negligible impact on transmission rate.
The Pricing War Continues: On Competitive Multi-Item Pricing
Venue: arXiv cs.GT 2014
Year: 2014

We study a game with \emph{strategic} vendors who own multiple items and a single buyer with a submodular valuation function. The goal of the vendors is to maximize their revenue via pricing of the items, given that the buyer will buy the set of items that maximizes his net payoff. We show this game may not always have a pure Nash equilibrium, in contrast to previous results for the special case where each vendor owns a single item. We do so by relating our game to an intermediate, discrete game in which the vendors only choose the available items, and their prices are set exogenously afterwards. We further make use of the intermediate game to provide tight bounds on the price of anarchy for the subset games that have pure Nash equilibria; we find that the optimal PoA reached in the previous special cases does not hold, but only a logarithmic one. Finally, we show that for a special case of submodular functions, efficient pure Nash equilibria always exist.
Stochastic Shortest Paths and Risk Measures
Venue: arXiv cs.DS 2014
Year: 2014

We consider three shortest path problems in directed graphs with random arc lengths. For the first and the second problems, a risk measure is involved. While the first problem consists in finding a path minimizing this risk measure, the second one consists in finding a path minimizing a deterministic cost, while satisfying a constraint on the risk measure. We propose algorithms solving these problems for a wide range of risk measures, which includes among several others the $CVaR$ and the probability of being late. Their performances are evaluated through experiments. One of the key elements in these algorithms is the use of stochastic lower bounds that allow to discard partial solutions. Good stochastic lower bounds are provided by the so-called Stochastic Ontime Arrival Problem. This latter problem is the third one studied in this paper and we propose a new and very efficient algorithm solving it. Complementary discussions on the complexity of the problems are also provided.
Normal variation for adaptive feature size
Venue: arXiv cs.CG 2014
Year: 2014

The change in the normal between any two nearby points on a closed, smooth surface is bounded with respect to the local feature size (distance to the medial axis). An incorrect proof of this lemma appeared as part of the analysis of the "crust" algorithm of Amenta and Bern.
HSkip+: A Self-Stabilizing Overlay Network for Nodes with Heterogeneous Bandwidths
Venue: arXiv cs.DC 2014
Year: 2014

In this paper we present and analyze HSkip+, a self-stabilizing overlay network for nodes with arbitrary heterogeneous bandwidths. HSkip+ has the same topology as the Skip+ graph proposed by Jacob et al. [PODC 2009] but its self-stabilization mechanism significantly outperforms the self-stabilization mechanism proposed for Skip+. Also, the nodes are now ordered according to their bandwidths and not according to their identifiers. Various other solutions have already been proposed for overlay networks with heterogeneous bandwidths, but they are not self-stabilizing. In addition to HSkip+ being self-stabilizing, its performance is on par with the best previous bounds on the time and work for joining or leaving a network of peers of logarithmic diameter and degree and arbitrary bandwidths. Also, the dilation and congestion for routing messages is on par with the best previous bounds for such networks, so that HSkip+ combines the advantages of both worlds. Our theoretical investigations are backed by simulations demonstrating that HSkip+ is indeed performing much better than Skip+ and working correctly under high churn rates.