OpenRaiser/Intern-Atlas huggingface dataset:
Capturing spatial interdependence in image features: the counting grid, an epitomic representation for bags of features
Venue: arXiv cs.CV 2014
Year: 2014

In recent scene recognition research images or large image regions are often represented as disorganized "bags" of features which can then be analyzed using models originally developed to capture co-variation of word counts in text. However, image feature counts are likely to be constrained in different ways than word counts in text. For example, as a camera pans upwards from a building entrance over its first few floors and then further up into the sky Fig. 1, some feature counts in the image drop while others rise -- only to drop again giving way to features found more often at higher elevations. The space of all possible feature count combinations is constrained both by the properties of the larger scene and the size and the location of the window into it. To capture such variation, in this paper we propose the use of the counting grid model. This generative model is based on a grid of feature counts, considerably larger than any of the modeled images, and considerably smaller than the real estate needed to tile the images next to each other tightly. Each modeled image is assumed to have a representative window in the grid in which the feature counts mimic the feature distribution in the image. We provide a learning procedure that jointly maps all images in the training set to the counting grid and estimates the appropriate local counts in it. Experimentally, we demonstrate that the resulting representation captures the space of feature count combinations more accurately than the traditional models, not only when the input images come from a panning camera, but even when modeling images of different scenes from the same category.
Enhanced TKIP Michael Attacks
Venue: arXiv cs.CR 2014
Year: 2014

This paper presents new attacks against TKIP within IEEE 802.11 based networks. Using the known Beck-Tews attack, we define schemas to con- tinuously generate new keystreams, which allow more and longer arbitrary packets to be injected into the network. We further describe an attack against the Michael message integrity code, that allows an attacker to concatenate a known with an unknown valid TKIP packet such that the unknown MIC at the end is still valid for the new entire packet. Based on this, a schema to decrypt all traffic that flows towards the client is described.
A Parallel and Efficient Algorithm for Learning to Match
Venue: arXiv cs.LG 2014
Year: 2014

Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.
QoE-Based Scheduling Algorithm in WiMAX Network using Manhattan Grid Mobility Model
Venue: arXiv cs.NI 2014
Year: 2014

WiMAX (acronym for Worldwide Interoperability for Microwave Access) is a family of technical standards based on IEEE 802.16 standard that defines the high speed connection through radio waves unlike DSL (Digital Subscriber Line) or other wired technology. It can provide coverage to remote rural areas of several kilometers in radius, it's an adequate response to some rural or inaccessible areas. WiMAX can provide point-to-point (P2P) and point-to-multipoint (PMP) modes. In parallel, it was observed that, unlike the traditional assessment methods for quality, nowadays, current research focuses on the user perceived quality, the existing scheduling approaches take into account the quality of service (QoS) and many technical parameters, but does not take into account the quality of experience (QoE). In this paper, we present a scheduling algorithm to provide QoE in WiMAX network under Manhattan Mobility. A new approach is proposed, particularly for the Best Effort (BE) service class WiMAX, in this approach, if a packet loss occurs on a link connection, the system then reduces the transmission rate of this connection to obtain its minimum allowable transmission rate. The NS-2 simulation results show that the QoE provided to users is enhanced in terms of throughput, jitter, packet loss rate and delay.
Quantum Clouds: A future perspective
Venue: arXiv cs.DC 2014
Year: 2014

Quantum computing and cloud computing are two giants for futuristic computing. Both technologies complement each other. Quantum clouds, therefore, is deploying the resources of quantum computation in a cloud environment to provide solution to the challenges and problems faced by present model of classical cloud computation. State of the art challenges faced by the cloud such as VM migration, data security, traffic management can be addressed by the quantum principles. But the merging of these two technologies have challenges of their own which need to be addressed before moving forward. What are those challenges and how does a quantum computer solve the cloud problems? The relation among quantum parallelism, superposition and flash crowd effect; Laundauer's principle and energy management; photon polarization principle and data security; these fascinating queries are addressed in the paper.
Outsource Photo Sharing and Searching for Mobile Devices With Privacy Protection
Venue: arXiv cs.CR 2014
Year: 2014

With the proliferation of mobile devices, cloud-based photo sharing and searching services are becoming common due to the mobile devices' resource constrains. Meanwhile, there is also increasing concern about privacy in photos. In this work, we present a framework \ourprotocolNSP, which enables cloud servers to provide privacy-preserving photo sharing and search as a service to mobile device users. Privacy-seeking users can share their photos via our framework to allow only their authorized friends to browse and search their photos using resource-bounded mobile devices. This is achieved by our carefully designed architecture and novel outsourced privacy-preserving computation protocols, through which no information about the outsourced photos or even the search contents (including the results) would be revealed to the cloud servers. Our framework is compatible with most of the existing image search technologies, and it requires few changes to the existing cloud systems. The evaluation of our prototype system with 31,772 real-life images shows the communication and computation efficiency of our system.
Some Combinatorial Problems on Halin Graphs
Venue: arXiv cs.DS 2014
Year: 2014

Let $T$ be a tree with no degree 2 vertices and $L(T)=\{l_1,\ldots,l_r\}, r \geq 2$ denote the set of leaves in $T$. An Halin graph $G$ is a graph obtained from $T$ such that $V(G)=V(T)$ and $E(G)=E(T) \cup \{\{l_i,l_{i+1}\} ~|~ 1 \leq i \leq r-1\} \cup \{l_1,l_r\}$. In this paper, we investigate combinatorial problems such as, testing whether a given graph is Halin or not, chromatic bounds, an algorithm to color Halin graphs with the minimum number of colors. Further, we present polynomial-time algorithms for testing and coloring problems.
That Ain't You: Detecting Spearphishing Emails Before They Are Sent
Venue: arXiv cs.CR 2014
Year: 2014

One of the ways in which attackers try to steal sensitive information from corporations is by sending spearphishing emails. This type of emails typically appear to be sent by one of the victim's coworkers, but have instead been crafted by an attacker. A particularly insidious type of spearphishing emails are the ones that do not only claim to come from a trusted party, but were actually sent from that party's legitimate email account that was compromised in the first place. In this paper, we propose a radical change of focus in the techniques used for detecting such malicious emails: instead of looking for particular features that are indicative of attack emails, we look for possible indicators of impersonation of the legitimate owners. We present IdentityMailer, a system that validates the authorship of emails by learning the typical email-sending behavior of users over time, and comparing any subsequent email sent from their accounts against this model. Our experiments on real world e-mail datasets demonstrate that our system can effectively block advanced email attacks sent from genuine email accounts, which traditional protection systems are unable to detect. Moreover, we show that it is resilient to an attacker willing to evade the system. To the best of our knowledge, IdentityMailer is the first system able to identify spearphishing emails that are sent from within an organization, by a skilled attacker having access to a compromised email account.
ASTOR: Evolutionary Automatic Software Repair for Java
Venue: arXiv cs.SE 2014
Year: 2014

Context: During last years, many automatic software repair approaches have been presented by the software engineering research community. According to the corresponding papers, these approaches are able to repair real defects from open source projects. Problematic: Some previous publications in the automatic repair field do not provide the implementation of theirs approaches. Consequently, it is not possible for the research community to re-execute the original evaluation, to set up new evaluations (for example, to evaluate the performance against new defects) or to compare approaches against each others. Solution: We propose a publicly available automatic software repair tool called Astor. It implements three state-of-the-art automatic software repair approaches in the context of Java programs (including GenProg and a subset of PAR's templates). The source code of Astor is licensed under the GNU General Public Licence (GPL v2).
On the Complexity of Optimization Problems based on Compiled NNF Representations
Venue: arXiv cs.AI 2014
Year: 2014

Optimization is a key task in a number of applications. When the set of feasible solutions under consideration is of combinatorial nature and described in an implicit way as a set of constraints, optimization is typically NP-hard. Fortunately, in many problems, the set of feasible solutions does not often change and is independent from the user's request. In such cases, compiling the set of constraints describing the set of feasible solutions during an off-line phase makes sense, if this compilation step renders computationally easier the generation of a non-dominated, yet feasible solution matching the user's requirements and preferences (which are only known at the on-line step). In this article, we focus on propositional constraints. The subsets L of the NNF language analyzed in Darwiche and Marquis' knowledge compilation map are considered. A number of families F of representations of objective functions over propositional variables, including linear pseudo-Boolean functions and more sophisticated ones, are considered. For each language L and each family F, the complexity of generating an optimal solution when the constraints are compiled into L and optimality is to be considered w.r.t. a function from F is identified.
Gröbner Bases and Nullstellensätze for Graph-Coloring Ideals
Venue: arXiv cs.SC 2014
Year: 2014

We revisit a well-known family of polynomial ideals encoding the problem of graph-$k$-colorability. Our paper describes how the inherent combinatorial structure of the ideals implies several interesting algebraic properties. Specifically, we provide lower bounds on the difficulty of computing Gröbner bases and Nullstellensatz certificates for the coloring ideals of general graphs. For chordal graphs, however, we explicitly describe a Gröbner basis for the coloring ideal, and provide a polynomial-time algorithm.
Clustering Words by Projection Entropy
Venue: arXiv cs.CL 2014
Year: 2014

We apply entropy agglomeration (EA), a recently introduced algorithm, to cluster the words of a literary text. EA is a greedy agglomerative procedure that minimizes projection entropy (PE), a function that can quantify the segmentedness of an element set. To apply it, the text is reduced to a feature allocation, a combinatorial object to represent the word occurences in the text's paragraphs. The experiment results demonstrate that EA, despite its reduction and simplicity, is useful in capturing significant relationships among the words in the text. This procedure was implemented in Python and published as a free software: REBUS.
The Quantum Nature of Identity in Human Thought: Bose-Einstein Statistics for Conceptual Indistinguishability
Venue: arXiv cs.AI 2014
Year: 2014

Increasing experimental evidence shows that humans combine concepts in a way that violates the rules of classical logic and probability theory. On the other hand, mathematical models inspired by the formalism of quantum theory are in accordance with data on concepts and their combinations. In this paper, we investigate a novel type of concept combination were a number is combined with a noun, e.g., `Eleven Animals. Our aim is to study 'conceptual identity' and the effects of 'indistinguishability' - in the combination 'Eleven Animals', the 'animals' are identical and indistinguishable - on the mechanisms of conceptual combination. We perform experiments on human subjects and find significant evidence of deviation from the predictions of classical statistical theories, more specifically deviations with respect to Maxwell-Boltzmann statistics. This deviation is of the 'same type' of the deviation of quantum mechanical from classical mechanical statistics, due to indistinguishability of microscopic quantum particles, i.e we find convincing evidence of the presence of Bose-Einstein statistics. We also present preliminary promising evidence of this phenomenon in a web-based study.
Choice of Mel Filter Bank in Computing MFCC of a Resampled Speech
Venue: arXiv cs.SD 2014
Year: 2014

Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications. In this paper, we study the effect of resampling a speech signal on these speech features. We first derive a relationship between the MFCC param- eters of the resampled speech and the MFCC parameters of the original speech. We propose six methods of calculating the MFCC parameters of downsampled speech by transforming the Mel filter bank used to com- pute MFCC of the original speech. We then experimentally compute the MFCC parameters of the down sampled speech using the proposed meth- ods and compute the Pearson coefficient between the MFCC parameters of the downsampled speech and that of the original speech to identify the most effective choice of Mel-filter band that enables the computed MFCC of the resampled speech to be as close as possible to the original speech sample MFCC.
QoE Modelling, Measurement and Prediction: A Review
Venue: arXiv cs.NI 2014
Year: 2014

In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. These devices connect to the Internet via network or telecommunications operators. Users usually have some expectations about the services provided to them by different operators. Users' expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. Thus, QoE measurement and prediction techniques may benefit users in availing personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switchover. This paper presents a review of the state-the-art research in the area of QoE modelling, measurement and prediction. In particular, we investigate and discuss the strengths and shortcomings of existing techniques. Finally, we present future research directions for developing novel QoE measurement and prediction techniques
Success and Failure of Adaptation-Diffusion Algorithms for Consensus in Multi-Agent Networks
Venue: arXiv cs.MA 2014
Year: 2014

This paper investigates the problem of distributed stochastic approximation in multi-agent systems. The algorithm under study consists of two steps: a local stochastic approximation step and a diffusion step which drives the network to a consensus. The diffusion step uses row-stochastic matrices to weight the network exchanges. As opposed to previous works, exchange matrices are not supposed to be doubly stochastic, and may also depend on the past estimate. We prove that non-doubly stochastic matrices generally influence the limit points of the algorithm. Nevertheless, the limit points are not affected by the choice of the matrices provided that the latter are doubly-stochastic in expectation. This conclusion legitimates the use of broadcast-like diffusion protocols, which are easier to implement. Next, by means of a central limit theorem, we prove that doubly stochastic protocols perform asymptotically as well as centralized algorithms and we quantify the degradation caused by the use of non doubly stochastic matrices. Throughout the paper, a special emphasis is put on the special case of distributed non-convex optimization as an illustration of our results.
Parameterizing the semantics of fuzzy attribute implications by systems of isotone Galois connections
Venue: arXiv cs.AI 2014
Year: 2014

We study the semantics of fuzzy if-then rules called fuzzy attribute implications parameterized by systems of isotone Galois connections. The rules express dependencies between fuzzy attributes in object-attribute incidence data. The proposed parameterizations are general and include as special cases the parameterizations by linguistic hedges used in earlier approaches. We formalize the general parameterizations, propose bivalent and graded notions of semantic entailment of fuzzy attribute implications, show their characterization in terms of least models and complete axiomatization, and provide characterization of bases of fuzzy attribute implications derived from data.
Sparse Distributed Learning via Heterogeneous Diffusion Adaptive Networks
Venue: arXiv cs.LG 2014
Year: 2014

In-network distributed estimation of sparse parameter vectors via diffusion LMS strategies has been studied and investigated in recent years. In all the existing works, some convex regularization approach has been used at each node of the network in order to achieve an overall network performance superior to that of the simple diffusion LMS, albeit at the cost of increased computational overhead. In this paper, we provide analytical as well as experimental results which show that the convex regularization can be selectively applied only to some chosen nodes keeping rest of the nodes sparsity agnostic, while still enjoying the same optimum behavior as can be realized by deploying the convex regularization at all the nodes. Due to the incorporation of unregularized learning at a subset of nodes, less computational cost is needed in the proposed approach. We also provide a guideline for selection of the sparsity aware nodes and a closed form expression for the optimum regularization parameter.
Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain
Venue: arXiv cs.AI 2014
Year: 2014

Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool for localizing and analyzing brain activity. This study focuses on one very important aspect of the functional properties of human brain, specifically the estimation of the level of parallelism when performing complex cognitive tasks. Using fMRI as the main modality, the human brain activity is investigated through a purely data-driven signal processing and dimensionality analysis approach. Specifically, the fMRI signal is treated as a multi-dimensional data space and its intrinsic `complexity' is studied via dataset fractal analysis and blind-source separation (BSS) methods. One simulated and two real fMRI datasets are used in combination with Independent Component Analysis (ICA) and fractal analysis for estimating the intrinsic (true) dimensionality, in order to provide data-driven experimental evidence on the number of independent brain processes that run in parallel when visual or visuo-motor tasks are performed. Although this number is can not be defined as a strict threshold but rather as a continuous range, when a specific activation level is defined, a corresponding number of parallel processes or the casual equivalent of `cpu cores' can be detected in normal human brain activity.
An Unsupervised Ensemble-based Markov Random Field Approach to Microscope Cell Image Segmentation
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, we propose an approach to the unsupervised segmentation of images using Markov Random Field. The proposed approach is based on the idea of Bit Plane Slicing. We use the planes as initial labellings for an ensemble of segmentations. With pixelwise voting, a robust segmentation approach can be achieved, which we demonstrate on microscope cell images. We tested our approach on a publicly available database, where it proven to be competitive with other methods and manual segmentation.
In-Network Distributed Solar Current Prediction
Venue: arXiv cs.DC 2014
Year: 2014

Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this paper, we present a model and algorithms for distributed solar current prediction, based on multiple linear regression to predict future solar current based on local, in-situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-week-long experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10^{-7}% of the harvested energy) to gain a prediction improvement of 39.7%.