OpenRaiser/Intern-Atlas huggingface dataset:
Association Rule Mining using Maximum Entropy
Venue: arXiv cs.DB 2015
Year: 2015

Recommendations based on behavioral data may be faced with ambiguous statistical evidence. We consider the case of association rules, relevant e.g.~for query and product recommendations. For example: Suppose that a customer belongs to categories A and B, each of which is known to have positive correlation with buying product C, how do we estimate the probability that she will buy product C? For rare terms or products there may not be enough data to directly produce such an estimate --- perhaps we never directly observed a connection between A, B, and C. What can we do when there is no support for estimating the probability by simply computing the observed frequency? In particular, what is the right thing to do when A and B give rise to very different estimates of the probability of C? We consider the use of maximum entropy probability estimates, which give a principled way of extrapolating probabilities of events that do not even occur in the data set! Focusing on the basic case of three variables, our main technical contributions are that (under mild assumptions): 1) There exists a simple, explicit formula that gives a good approximation of maximum entropy estimates, and 2) Maximum entropy estimates based on a small number of samples are provably tightly concentrated around the true maximum entropy frequency that arises if we let the number of samples go to infinity. Our empirical work demonstrates the surprising precision of maximum entropy estimates, across a range of real-life transaction data sets. In particular we observe the average absolute error on maximum entropy estimates is a factor $3$--$14$ less compared to using independence or extrapolation estimates, when the data used to make the estimates has low support. We believe that the same principle can be used to synthesize probability estimates in many settings.
Adaptive and application dependent runtime guided hardware prefetcher reconfiguration on the IBM POWER7
Venue: arXiv cs.DC 2015
Year: 2015

Hardware data prefetcher engines have been extensively used to reduce the impact of memory latency. However, microprocessors' hardware prefetcher engines do not include any automatic hardware control able to dynamically tune their operation. This lacking architectural feature causes systems to operate with prefetchers in a fixed configuration, which in many cases harms performance and energy consumption. In this paper, a piece of software that solves the discussed problem in the context of the IBM POWER7 microprocessor is presented. The proposed solution involves using the runtime software as a bridge that is able to characterize user applications' workload and dynamically reconfigure the prefetcher engine. The proposed mechanisms has been deployed over OmpSs, a state-of-the-art task-based programming model. The paper shows significant performance improvements over a representative set of microbenchmarks and High Performance Computing (HPC) applications.
Optimization of Unequal Error Protection Rateless Codes for Multimedia Multicasting
Venue: arXiv cs.IT 2015
Year: 2015

Rateless codes have been shown to be able to provide greater flexibility and efficiency than fixed-rate codes for multicast applications. In the following, we optimize rateless codes for unequal error protection (UEP) for multimedia multicasting to a set of heterogeneous users. The proposed designs have the objectives of providing either guaranteed or best-effort quality of service (QoS). A randomly interleaved rateless encoder is proposed whereby users only need to decode symbols up to their own QoS level. The proposed coder is optimized based on measured transmission properties of standardized raptor codes over wireless channels. It is shown that a guaranteed QoS problem formulation can be transformed into a convex optimization problem, yielding a globally optimal solution. Numerical results demonstrate that the proposed optimized random interleaved UEP rateless coder's performance compares favorably with that of other recently proposed UEP rateless codes.
CROR: Coding-Aware Opportunistic Routing in Multi-Channel Cognitive Radio Networks
Venue: arXiv cs.NI 2015
Year: 2015

Cognitive radio (CR) is a promising technology to improve spectrum utilization. However, spectrum availability is uncertain which mainly depends on primary user's (PU's) behaviors. This makes it more difficult for most existing CR routing protocols to achieve high throughput in multi-channel cognitive radio networks (CRNs). Inter-session network coding and opportunistic routing can leverage the broadcast nature of the wireless channel to improve the performance for CRNs. In this paper we present a coding aware opportunistic routing protocol for multi-channel CRNs, cognitive radio opportunistic routing (CROR) protocol, which jointly considers the probability of successful spectrum utilization, packet loss rate, and coding opportunities. We evaluate and compare the proposed scheme against three other opportunistic routing protocols with multichannel. It is shown that the CROR, by integrating opportunistic routing with network coding, can obtain much better results, with respect to throughput, the probability of PU-SU packet collision and spectrum utilization efficiency.
Assessing the true role of coauthors in the h-index measure of an author scientific impact
Venue: arXiv cs.DL 2015
Year: 2015

A method based on the classical principal component analysis leads to demonstrate that the role of co-authors should give a h-index measure to a group leader higher than usually accepted. The method rather easily gives what is usually searched for, i.e. an estimate of the role (or "weight") of co-authors, as the additional value to an author papers' popularity. The construction of the co-authorship popularity H-matrix is exemplified and the role of eigenvalues and the main eigenvector component are discussed. An example illustrates the points and serves as the basis for suggesting a generally practical application of the concept.
Modified Trial Division Algorithm Using KNJ-Factorization Method To Factorize RSA Public Key Encryption
Venue: arXiv cs.CR 2015
Year: 2015

The security of RSA algorithm depends upon the positive integer N, which is the multiple of two precise large prime numbers. Factorization of such great numbers is a problematic process. There are many algorithms has been implemented in the past years. The offered KNJ -Factorization algorithm contributes a deterministic way to factorize RSA. The algorithm limits the search by only considering the prime values. Subsequently prime numbers are odd numbers accordingly it also requires smaller number steps to factorize RSA. In this paper, the anticipated algorithm is very simple besides it is very easy to understand and implement. The main concept of this KNJ factorization algorithm is, to check only those factors which are odd and prime. The proposed KNJ- Factorization algorithm works very efficiently on those factors; which are adjoining and close to N. The proposed factorization method can speed up if we can reduce the time for primality testing. It fundamentally decreases the time complexity.
Autonomous Farm Vehicles: Prototype of Power Reaper
Venue: arXiv cs.RO 2015
Year: 2015

Chapter 2 will begin with introduction of Agricultural Robotics. There will be a literature review of the mechanical structure, vision and control algorithms. In chapter 3 we will discuss the methodology in detail using block diagrams and flowcharts. The results of the tested and the proposed algorithms will also be displayed. In chapter 4 we will discuss the results in detail and how they are of significance in our work. In chapter 5 we will conclude our work and discuss some future perspectives. In appendices we will provide some background information necessary regarding this project.
Riemannian Metric Learning for Symmetric Positive Definite Matrices
Venue: arXiv cs.CV 2015
Year: 2015

Over the past few years, symmetric positive definite (SPD) matrices have been receiving considerable attention from computer vision community. Though various distance measures have been proposed in the past for comparing SPD matrices, the two most widely-used measures are affine-invariant distance and log-Euclidean distance. This is because these two measures are true geodesic distances induced by Riemannian geometry. In this work, we focus on the log-Euclidean Riemannian geometry and propose a data-driven approach for learning Riemannian metrics/geodesic distances for SPD matrices. We show that the geodesic distance learned using the proposed approach performs better than various existing distance measures when evaluated on face matching and clustering tasks.
Matching Theory for Backhaul Management in Small Cell Networks with mmWave Capabilities
Venue: arXiv cs.IT 2015
Year: 2015

Designing cost-effective and scalable backhaul solutions is one of the main challenges for emerging wireless small cell networks (SCNs). In this regard, millimeter wave (mmW) communication technologies have recently emerged as an attractive solution to realize the vision of a high-speed and reliable wireless small cell backhaul network (SCBN). In this paper, a novel approach is proposed for managing the spectral resources of a heterogeneous SCBN that can exploit simultaneously mmW and conventional frequency bands via carrier aggregation. In particular, a new SCBN model is proposed in which small cell base stations (SCBSs) equipped with broadband fiber backhaul allocate their frequency resources to SCBSs with wireless backhaul, by using aggregated bands. One unique feature of the studied model is that it jointly accounts for both wireless channel characteristics and economic factors during resource allocation. The problem is then formulated as a one-to-many matching game and a distributed algorithm is proposed to find a stable outcome of the game. The convergence of the algorithm is proven and the properties of the resulting matching are studied. Simulation results show that under the constraints of wireless backhauling, the proposed approach achieves substantial performance gains, reaching up to $30 \%$ compared to a conventional best-effort approach.
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Venue: arXiv cs.LG 2015
Year: 2015

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy guarantees. Crowd-ML endows a crowdsensing system with an ability to learn classifiers or predictors online from crowdsensing data privately with minimal computational overheads on devices and servers, suitable for a practical and large-scale employment of the framework. We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions.
A Novel Clustering Approach Based on Group Quasi-Consensus of Unstable Dynamic Linear High-Order Multi-Agent Systems
Venue: arXiv cs.SY 2015
Year: 2015

This paper introduces a novel approach of clustering, which is based on group consensus of dynamic linear high-order multi-agent systems. The graph topology is associated with a selected multi-agent system, with each agent corresponding to one vertex. In order to reveal the cluster structure, the agents belonging to a similar cluster are expected to aggregate together. As theoretical foundation, a necessary and sufficient condition is given to check the group consensus. Two numerical instances are shown to illustrate the process of approach.
Characterization theorems for PDL and FO(TC)
Venue: arXiv cs.LO 2015
Year: 2015

Our main contributions can be divided in three parts: (1) Fixpoint extensions of first-order logic: we give a precise syntactic and semantic characterization of the relationship between $\mathrm{FO(TC^1)}$ and $\mathrm{FO(LFP)}$; (2) Automata and expressiveness on trees: we introduce a new class of parity automata which, on trees, captures the expressive power of $\mathrm{FO(TC^1)}$ and WCL (weak chain logic). The latter logic is a variant of MSO which quantifies over finite chains; and (3) Expressiveness modulo bisimilarity: we show that PDL is expressively equivalent to the bisimulation-invariant fragment of both $\mathrm{FO(TC^1)}$ and WCL. In particular, point (3) closes the open problems of the bisimulation-invariant characterizations of PDL, $\mathrm{FO(TC^1)}$ and WCL all at once.
Step-Indexed Logical Relations for Probability (long version)
Venue: arXiv cs.LO 2015
Year: 2015

It is well-known that constructing models of higher-order probabilistic programming languages is challenging. We show how to construct step-indexed logical relations for a probabilistic extension of a higher-order programming language with impredicative polymorphism and recursive types. We show that the resulting logical relation is sound and complete with respect to the contextual preorder and, moreover, that it is convenient for reasoning about concrete program equivalences. Finally, we extend the language with dynamically allocated first-order references and show how to extend the logical relation to this language. We show that the resulting relation remains useful for reasoning about examples involving both state and probabilistic choice.
Very Static Enforcement of Dynamic Policies
Venue: arXiv cs.CR 2015
Year: 2015

Security policies are naturally dynamic. Reflecting this, there has been a growing interest in studying information-flow properties which change during program execution, including concepts such as declassification, revocation, and role-change. A static verification of a dynamic information flow policy, from a semantic perspective, should only need to concern itself with two things: 1) the dependencies between data in a program, and 2) whether those dependencies are consistent with the intended flow policies as they change over time. In this paper we provide a formal ground for this intuition. We present a straightforward extension to the principal flow-sensitive type system introduced by Hunt and Sands (POPL '06, ESOP '11) to infer both end-to-end dependencies and dependencies at intermediate points in a program. This allows typings to be applied to verification of both static and dynamic policies. Our extension preserves the principal type system's distinguishing feature, that type inference is independent of the policy to be enforced: a single, generic dependency analysis (typing) can be used to verify many different dynamic policies of a given program, thus achieving a clean separation between (1) and (2). We also make contributions to the foundations of dynamic information flow. Arguably, the most compelling semantic definitions for dynamic security conditions in the literature are phrased in the so-called knowledge-based style. We contribute a new definition of knowledge-based termination insensitive security for dynamic policies. We show that the new definition avoids anomalies of previous definitions and enjoys a simple and useful characterisation as a two-run style property.
Bibliometric-enhanced Information Retrieval: 2nd International BIR Workshop
Venue: arXiv cs.IR 2015
Year: 2015

This workshop brings together experts of communities which often have been perceived as different once: bibliometrics / scientometrics / informetrics on the one side and information retrieval on the other. Our motivation as organizers of the workshop started from the observation that main discourses in both fields are different, that communities are only partly overlapping and from the belief that a knowledge transfer would be profitable for both sides. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. On the other side, more and more information professionals, working in libraries and archives are confronted with applying bibliometric techniques in their services. This way knowledge exchange becomes more urgent. The first workshop set the research agenda, by introducing in each other methods, reporting about current research problems and brainstorming about common interests. This follow-up workshop continues the overall communication, but also puts one problem into the focus. In particular, we will explore how statistical modelling of scholarship can improve retrieval services for specific communities, as well as for large, cross-domain collections like Mendeley or ResearchGate. This second BIR workshop continues to raise awareness of the missing link between Information Retrieval (IR) and bibliometrics and contributes to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the scholarly search engine interface.
Towards Energy-Proportional Computing Using Subsystem-Level Power Management
Venue: arXiv cs.DC 2015
Year: 2015

Massive data centers housing thousands of computing nodes have become commonplace in enterprise computing, and the power consumption of such data centers is growing at an unprecedented rate. Adding to the problem is the inability of the servers to exhibit energy proportionality, i.e., provide energy-efficient execution under all levels of utilization, which diminishes the overall energy efficiency of the data center. It is imperative that we realize effective strategies to control the power consumption of the server and improve the energy efficiency of data centers. With the advent of Intel Sandy Bridge processors, we have the ability to specify a limit on power consumption during runtime, which creates opportunities to design new power-management techniques for enterprise workloads and make the systems that they run on more energy-proportional. In this paper, we investigate whether it is possible to achieve energy proportionality for enterprise-class server workloads, namely SPECpower_ssj2008 and SPECweb2009 benchmarks, by using Intel's Running Average Power Limit (RAPL) interfaces. First, we analyze the average power consumption of the full system as well as the subsystems and describe the energy proportionality of these components. We then characterize the instantaneous power profile of these benchmarks within different subsystems using the on-chip energy meters exposed via the RAPL interfaces. Finally, we present the effects of power limiting on the energy proportionality, performance, power and energy efficiency of enterprise-class server workloads. Our observations and results shed light on the efficacy of the RAPL interfaces and provide guidance for designing power-management techniques for enterprise-class workloads.
On the Energy Proportionality of Scale-Out Workloads
Venue: arXiv cs.DC 2015
Year: 2015

Our increasing reliance on the cloud has led to the emergence of scale-out workloads. These scale-out workloads are latency-sensitive as they are user driven. In order to meet strict latency constraints, they require massive computing infrastructure, which consume significant amount of energy and contribute to operational costs. This cost is further aggravated by the lack of energy proportionality in servers. As Internet services become even more ubiquitous, scale-out workloads will need increasingly larger cluster installations. As such, we desire an investigation into the energy proportionality and the mechanisms to improve the power consumption of scale-out workloads. Therefore, in this paper, we study the energy proportionality and power consumption of clusters in the context of scale-out workloads. Towards this end, we evaluate the potential of power and resource provisioning to improve the energy proportionality for this class of workloads. Using data serving, web searching and data caching as our representative workloads, we first analyze the component-level power distribution on a cluster. Second, we characterize how these workloads utilize the cluster. Third, we analyze the potential of power provisioning techniques (i.e., active low-power, turbo and idle low-power modes) to improve the energy proportionality of scale-out workloads. We then describe the ability of active low-power modes to provide trade-offs in power and latency. Finally, we compare and contrast power provisioning and resource provisioning techniques. Our study reveals various insights which will help improve the energy proportionality and power consumption of scale-out workloads.
Generalized Eilenberg Theorem I: Local Varieties of Languages
Venue: arXiv cs.FL 2015
Year: 2015

We investigate the duality between algebraic and coalgebraic recognition of languages to derive a generalization of the local version of Eilenberg's theorem. This theorem states that the lattice of all boolean algebras of regular languages over an alphabet Σ closed under derivatives is isomorphic to the lattice of all pseudovarieties of Σ-generated monoids. By applying our method to different categories, we obtain three related results: one, due to Gehrke, Grigorieff and Pin, weakens boolean algebras to distributive lattices, one weakens them to join-semilattices, and the last one considers vector spaces over the binary field.
Benchmarking Obfuscators of Functionality
Venue: arXiv cs.CR 2015
Year: 2015

We propose a set of benchmarks for evaluating the practicality of software obfuscators which rely on provably-secure methods for functional obfuscation.
Sensing-Throughput Tradeoff in Cognitive Radio With Random Arrivals and Departures of Multiple Primary Users
Venue: arXiv cs.IT 2015
Year: 2015

This letter analyzes the sensing-throughput tradeoff for a secondary user (SU) under random arrivals and departures of multiple primary users (PUs). We first study the case where PUs change their status only during SU's sensing period. We then generalize to a case where PUs change status anytime during SU frame, and compare the latter case with the former in terms of the optimal sensing time and SU throughput. We also investigate the effects of PU traffic parameters and the number of PUs on the sensing-throughput tradeoff for SU. Results show that, though the increase in the number of PUs reduces the optimal sensing time for SU, the opportunity to find a vacant PU channel reduces simultaneously, in turn, reducing SU throughput. Finally, we validate the analysis by Monte Carlo simulations.
Exploring the efficacy of molecular fragments of different complexity in computational SAR modeling
Venue: arXiv cs.CE 2015
Year: 2015

An important first step in computational SAR modeling is to transform the compounds into a representation that can be processed by predictive modeling techniques. This is typically a feature vector where each feature indicates the presence or absence of a molecular fragment. While the traditional approach to SAR modeling employed size restricted fingerprints derived from path fragments, much research in recent years focussed on mining more complex graph based fragments. Today, there seems to be a growing consensus in the data mining community that these more expressive fragments should be more useful. We question this consensus and show experimentally that fragments of low complexity, i.e. sequences, perform better than equally large sets of more complex ones, an effect we explain by pairwise correlation among fragments and the ability of a fragment set to encode compounds from different classes distinctly. The size restriction on these sets is based on ordering the fragments by class-correlation scores. In addition, we also evaluate the effects of using a significance value instead of a length restriction for path fragments and find a significant reduction in the number of features with little loss in performance.