OpenRaiser/Intern-Atlas huggingface dataset:
SCOUT: Prefetching for Latent Feature Following Queries
Venue: arXiv cs.DB 2012
Year: 2012

Today's scientists are quickly moving from in vitro to in silico experimentation: they no longer analyze natural phenomena in a petri dish, but instead they build models and simulate them. Managing and analyzing the massive amounts of data involved in simulations is a major task. Yet, they lack the tools to efficiently work with data of this size. One problem many scientists share is the analysis of the massive spatial models they build. For several types of analysis they need to interactively follow the structures in the spatial model, e.g., the arterial tree, neuron fibers, etc., and issue range queries along the way. Each query takes long to execute, and the total time for executing a sequence of queries significantly delays data analysis. Prefetching the spatial data reduces the response time considerably, but known approaches do not prefetch with high accuracy. We develop SCOUT, a structure-aware method for prefetching data along interactive spatial query sequences. SCOUT uses an approximate graph model of the structures involved in past queries and attempts to identify what particular structure the user follows. Our experiments with neuroscience data show that SCOUT prefetches with an accuracy from 71% to 92%, which translates to a speedup of 4x-15x. SCOUT also improves the prefetching accuracy on datasets from other scientific domains, such as medicine and biology.
Multiple Location Profiling for Users and Relationships from Social Network and Content
Venue: arXiv cs.DB 2012
Year: 2012

Users' locations are important for many applications such as personalized search and localized content delivery. In this paper, we study the problem of profiling Twitter users' locations with their following network and tweets. We propose a multiple location profiling model (MLP), which has three key features: 1) it formally models how likely a user follows another user given their locations and how likely a user tweets a venue given his location, 2) it fundamentally captures that a user has multiple locations and his following relationships and tweeted venues can be related to any of his locations, and some of them are even noisy, and 3) it novelly utilizes the home locations of some users as partial supervision. As a result, MLP not only discovers users' locations accurately and completely, but also "explains" each following relationship by revealing users' true locations in the relationship. Experiments on a large-scale data set demonstrate those advantages. Particularly, 1) for predicting users' home locations, MLP successfully places 62% users and outperforms two state-of-the-art methods by 10% in accuracy, 2) for discovering users' multiple locations, MLP improves the baseline methods by 14% in recall, and 3) for explaining following relationships, MLP achieves 57% accuracy.
Don't Thrash: How to Cache Your Hash on Flash
Venue: arXiv cs.DB 2012
Year: 2012

This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter, a compact data structure supporting set insertion and membership queries, has found wide application in databases, storage systems, and networks. Because the Bloom filter performs frequent random reads and writes, it is used almost exclusively in RAM, limiting the size of the sets it can represent. This paper first describes the quotient filter, which supports the basic operations of the Bloom filter, achieving roughly comparable performance in terms of space and time, but with better data locality. Operations on the quotient filter require only a small number of contiguous accesses. The quotient filter has other advantages over the Bloom filter: it supports deletions, it can be dynamically resized, and two quotient filters can be efficiently merged. The paper then gives two data structures, the buffered quotient filter and the cascade filter, which exploit the quotient filter advantages and thus serve as SSD-optimized alternatives to the Bloom filter. The cascade filter has better asymptotic I/O performance than the buffered quotient filter, but the buffered quotient filter outperforms the cascade filter on small to medium data sets. Both data structures significantly outperform recently-proposed SSD-optimized Bloom filter variants, such as the elevator Bloom filter, buffered Bloom filter, and forest-structured Bloom filter. In experiments, the cascade filter and buffered quotient filter performed insertions 8.6-11 times faster than the fastest Bloom filter variant and performed lookups 0.94-2.56 times faster.
Learning Expressive Linkage Rules using Genetic Programming
Venue: arXiv cs.DB 2012
Year: 2012

A central problem in data integration and data cleansing is to find entities in different data sources that describe the same real-world object. Many existing methods for identifying such entities rely on explicit linkage rules which specify the conditions that entities must fulfill in order to be considered to describe the same real-world object. In this paper, we present the GenLink algorithm for learning expressive linkage rules from a set of existing reference links using genetic programming. The algorithm is capable of generating linkage rules which select discriminative properties for comparison, apply chains of data transformations to normalize property values, choose appropriate distance measures and thresholds and combine the results of multiple comparisons using non-linear aggregation functions. Our experiments show that the GenLink algorithm outperforms the state-of-the-art genetic programming approach to learning linkage rules recently presented by Carvalho et. al. and is capable of learning linkage rules which achieve a similar accuracy as human written rules for the same problem.
Multidimensional Membership Mixture Models
Venue: arXiv cs.LG 2012
Year: 2012

We present the multidimensional membership mixture (M3) models where every dimension of the membership represents an independent mixture model and each data point is generated from the selected mixture components jointly. This is helpful when the data has a certain shared structure. For example, three unique means and three unique variances can effectively form a Gaussian mixture model with nine components, while requiring only six parameters to fully describe it. In this paper, we present three instantiations of M3 models (together with the learning and inference algorithms): infinite, finite, and hybrid, depending on whether the number of mixtures is fixed or not. They are built upon Dirichlet process mixture models, latent Dirichlet allocation, and a combination respectively. We then consider two applications: topic modeling and learning 3D object arrangements. Our experiments show that our M3 models achieve better performance using fewer topics than many classic topic models. We also observe that topics from the different dimensions of M3 models are meaningful and orthogonal to each other.
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Venue: arXiv cs.NE 2012
Year: 2012

Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer systems over a network. Two broad approaches exist to tackle this problem: anomaly detection and misuse detection. An anomaly detection system is trained only on examples of normal connections, and thus has the potential to detect novel attacks. However, many anomaly detection systems simply report the anomalous activity, rather than analysing it further in order to report higher-level information that is of more use to a security officer. On the other hand, misuse detection systems recognise known attack patterns, thereby allowing them to provide more detailed information about an intrusion. However, such systems cannot detect novel attacks. A hybrid system is presented in this paper with the aim of combining the advantages of both approaches. Specifically, anomalous network connections are initially detected using an artificial immune system. Connections that are flagged as anomalous are then categorised using a Kohonen Self Organising Map, allowing higher-level information, in the form of cluster membership, to be extracted. Experimental results on the KDD 1999 Cup dataset show a low false positive rate and a detection and classification rate for Denial-of-Service and User-to-Root attacks that is higher than those in a sample of other works.
Green Telecom Metrics in Perspective
Venue: arXiv cs.NI 2012
Year: 2012

A fast and parallel evolution of ways to measure and assess energy efficiency in telecom has resulted in an entangled web of drafts and recommendations originating from government, research, and standards organizations. This paper focuses primarily on so-called "large network equipment" metrics and intends to capture state-of-the-art in this area of green communications. Competing approaches towards efficiency assessment are studied for their applicability and completeness, with special emphasis on topics relevant to future subject studies
Optimal Degree of Optical Circuit Switching in IP-over-WDM Networks
Venue: arXiv cs.NI 2012
Year: 2012

In this paper, we study the influence of technology, traffic properties and price trends on optimized design of a reference IP-over-WDM network with rich underlying fiber topology. In each network node, we investigate the optimal degree of traffic switching in an optical (lambda) domain versus an electrical (packet) domain, also known as measure of node transparency. This measure is studied in connection to changes in traffic volume, demand affinity, optical circuit speeds and equipment cost. By applying variable design constraints, we assess the relative roles of the two distinct equipment groups, IP routers and optical cross-connects, with respect to resulting changes in cost-sensitive network architectures.
Strategies for Distributed Sensor Selection Using Convex Optimization
Venue: arXiv cs.IT 2012
Year: 2012

Consider the estimation of an unknown parameter vector in a linear measurement model. Centralized sensor selection consists in selecting a set of k_s sensor measurements, from a total number of m potential measurements. The performance of the corresponding selection is measured by the volume of an estimation error covariance matrix. In this work, we consider the problem of selecting these sensors in a distributed or decentralized fashion. In particular, we study the case of two leader nodes that perform naive decentralized selections. We demonstrate that this can degrade the performance severely. Therefore, two heuristics based on convex optimization methods are introduced, where we first allow one leader to make a selection, and then to share a modest amount of information about his selection with the remaining node. We will show that both heuristics clearly outperform the naive decentralized selection, and achieve a performance close to the centralized selection.
Payment Rules through Discriminant-Based Classifiers
Venue: arXiv cs.GT 2012
Year: 2012

In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex post regret, we are able to adapt statistical machine learning techniques to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi-dimensional types and situations where computational efficiency is a concern. Specifically, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex post regret, and that penalizing classification errors is effective in preventing failures of ex post individual rationality.
An efficient classification in IBE Provide with an improvement of BB2 to an efficient Commutative Blinding scheme
Venue: arXiv cs.CR 2012
Year: 2012

Because of the revolution and the success of the technique IBE (Identification Based Encryption) in the recent years. The need is growing to have a standardization to this technology to streamline communication based on it. But this requires a thorough study to extract the strength and weakness of the most recognized cryptosystems. Our first goal in this work is to approach to this standardization, by applying a study which permit to extract the best cryptosystems. As we will see in this work and as Boneh and Boyen said in 2011 (Journal of Cryptology) the BB1 and BB2 are the most efficient schemes in the model selective ID and without random oracle (they are the only schemes traced in this model). This is right as those schemes are secure (under this model), efficient and useful for some applications. Our second goal behind this work is to make an approvement in BB2 to admit a more efficient schemes. We will study the security of our schemes, which is basing on an efficient strong Diffie-Hellman problem compared to BB1 and BB2. More than that our HIBE support s+ID-HIBE compared to BBG (Boneh Boyen Goh). Additionally the ID in our scheme will be in Zp instead of Zp* as with BBG. We will cite more clearly all these statements in in this article.
A conservation-law-based modular fluid-flow model for network congestion modeling
Venue: arXiv cs.NI 2012
Year: 2012

A modular fluid-flow model for network congestion analysis and control is proposed. The model is derived from an information conservation law stating that the information is either in transit, lost or received. Mathematical models of network elements such as queues, users, and transmission channels, and network description variables, including sending/acknowledgement rates and delays, are inferred from this law and obtained by applying this principle locally. The modularity of the devised model makes it sufficiently generic to describe any network topology, and appealing for building simulators. Previous models in the literature are often not capable of capturing the transient behavior of the network precisely, making the resulting analysis inaccurate in practice. Those models can be recovered from exact reduction or approximation of this new model. An important aspect of this particular modeling approach is the introduction of new tight building blocks that implement mechanisms ignored by the existing ones, notably at the queue and user levels. Comparisons with packet-level simulations corroborate the proposed model.
Numerical Issues Affecting LDPC Error Floors
Venue: arXiv cs.IT 2012
Year: 2012

Numerical issues related to the occurrence of error floors in floating-point simulations of belief propagation (BP) decoders are examined. Careful processing of messages corresponding to highly-certain bit values can sometimes reduce error floors by several orders of magnitude. Computational solutions for properly handling such messages are provided for the sum-product algorithm (SPA) and several variants.
Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees
Venue: arXiv cs.SD 2012
Year: 2012

This paper proposes a voice morphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A primitive method of achieving voice morphing is by extracting the source's vocal coefficients and then converting them into the target speaker's vocal parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping the coefficients from source to destination. However, the use of the traditional/conventional GMM-based mapping approach results in the problem of over-smoothening of the converted voice. Thus, we hereby propose a unique method to perform efficient voice morphing and conversion based on GMM,which overcomes the traditional-method effects of over-smoothening. It uses a technique of glottal waveform separation and prediction of excitations and hence the result shows that not only over-smoothening is eliminated but also the transformed vocal tract parameters match with the target. Moreover, the synthesized speech thus obtained is found to be of a sufficiently high quality. Thus, voice morphing based on a unique GMM approach has been proposed and also critically evaluated based on various subjective and objective evaluation parameters. Further, an application of voice morphing for Laryngectomees which deploys this unique approach has been recommended by this paper.
Deploying Health Monitoring ECU Towards Enhancing the Performance of In-Vehicle Network
Venue: arXiv cs.OH 2012
Year: 2012

Electronic Control Units (ECUs) are the fundamental electronic building blocks of any automotive system. They are multi-purpose, multi-chip and multicore computer systems where more functionality is delivered in software rather than hardware. ECUs are valuable assets for the vehicles as critical time bounded messages are communicated through. Looking into the safety criticality, already developed mission critical systems such as ABS, ESP etc, rely fully on electronic components leading to increasing requirements of more reliable and dependable electronic systems in vehicles. Hence it is inevitable to maintain and monitor the health of an ECU which will enable the ECUs to be followed, assessed and improved throughout their life-cycle starting from their inception into the vehicle. In this paper, we propose a Health monitoring ECU that enables the early trouble shooting and servicing of the vehicle prior to any catastrophic failure.
Certification extends Termination Techniques
Venue: arXiv cs.LO 2012
Year: 2012

There are termination proofs that are produced by termination tools for which certifiers are not powerful enough. However, a similar situation also occurs in the other direction. We have formalized termination techniques in a more general setting as they have been introduced. Hence, we can certify proofs using techniques that no termination tool supports so far. In this paper we shortly present two of these formalizations: Polynomial orders with negative constants and Arctic termination.
A Relative Dependency Pair Framework
Venue: arXiv cs.LO 2012
Year: 2012

In this paper we generalize the DP framework to a relative DP framework, where a so called split is possible.
Color Assessment and Transfer for Web Pages
Venue: arXiv cs.HC 2012
Year: 2012

Colors play a particularly important role in both designing and accessing Web pages. A well-designed color scheme improves Web pages' visual aesthetic and facilitates user interactions. As far as we know, existing color assessment studies focus on images; studies on color assessment and editing for Web pages are rare. This paper investigates color assessment for Web pages based on existing online color theme-rating data sets and applies this assessment to Web color edit. This study consists of three parts. First, we study the extraction of a Web page's color theme. Second, we construct color assessment models that score the color compatibility of a Web page by leveraging machine learning techniques. Third, we incorporate the learned color assessment model into a new application, namely, color transfer for Web pages. Our study combines techniques from computer graphics, Web mining, computer vision, and machine learning. Experimental results suggest that our constructed color assessment models are effective, and useful in the color transfer for Web pages, which has received little attention in both Web mining and computer graphics communities.
Information-Theoretical Security for Several Models of Multiple-Access Channel
Venue: arXiv cs.IT 2012
Year: 2012

Several security models of multiple-access channel (MAC) are investigated. First, we study the degraded MAC with confidential messages, where two users transmit their confidential messages (no common message) to a destination, and each user obtains a degraded version of the output of the MAC. Each user views the other user as a eavesdropper, and wishes to keep its confidential message as secret as possible from the other user. Measuring each user's uncertainty about the other user's confidential message by equivocation, the inner and outer bounds on the capacity-equivocation region for this model have been provided. The result is further explained via the binary and Gaussian examples. Second, the discrete memoryless multiple-access wiretap channel (MAC-WT) is studied, where two users transmit their corresponding confidential messages (no common message) to a legitimate receiver, while an additional wiretapper wishes to obtain the messages via a wiretap channel. This new model is considered into two cases: the general MAC-WT with cooperative encoders, and the degraded MAC-WT with non-cooperative encoders. The capacity-equivocation region is totally determined for the cooperative case, and inner and outer bounds on the capacity-equivocation region are provided for the non-cooperative case. For both cases, the results are further explained via the binary examples.
Ownership Authentication Transfer Protocol for Ubiquitous Computing Devices
Venue: arXiv cs.CR 2012
Year: 2012

In ubiquitous computing devices, users tend to store some valuable information in their device. Even though the device can be borrowed by the other user temporarily, it is not safe for any user to borrow or lend the device as it may result the private data of the user to be public. To safeguard the user data and also to preserve user privacy we propose the technique of ownership authentication transfer. The user who is willing to sell the device has to transfer the ownership of the device under sale. Once the device is sold and the ownership has been transferred, the old owner will not be able to use that device at any cost. Either of the users will not be able to use the device if the process of ownership has not been carried out properly. This also takes care of the scenario when the device has been stolen or lost, avoiding the impersonation attack. The proposed protocol has been modeled and verified using Automated Validation of Internet Security Protocols and Applications (AVISPA) and is found to be safe.
Margin Distribution Controlled Boosting
Venue: arXiv cs.LG 2012
Year: 2012

Schapire's margin theory provides a theoretical explanation to the success of boosting-type methods and manifests that a good margin distribution (MD) of training samples is essential for generalization. However the statement that a MD is good is vague, consequently, many recently developed algorithms try to generate a MD in their goodness senses for boosting generalization. Unlike their indirect control over MD, in this paper, we propose an alternative boosting algorithm termed Margin distribution Controlled Boosting (MCBoost) which directly controls the MD by introducing and optimizing a key adjustable margin parameter. MCBoost's optimization implementation adopts the column generation technique to ensure fast convergence and small number of weak classifiers involved in the final MCBooster. We empirically demonstrate: 1) AdaBoost is actually also a MD controlled algorithm and its iteration number acts as a parameter controlling the distribution and 2) the generalization performance of MCBoost evaluated on UCI benchmark datasets is validated better than those of AdaBoost, L2Boost, LPBoost, AdaBoost-CG and MDBoost.