OpenRaiser/Intern-Atlas huggingface dataset:
Fast sequential forensic camera identification
Venue: arXiv cs.CR 2015
Year: 2015

Two sequential camera source identification methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of error probabilities and average number of test observations, is based on treating the alternative hypothesis as a doubly stochastic model. We also discuss how the standard sequential test can be corrected to account for the event of weak fingerprints. Finally, we validate the goodness of our methods with experiments.
Towards Trainable Media: Using Waves for Neural Network-Style Training
Venue: arXiv cs.NE 2015
Year: 2015

In this paper we study the concept of using the interaction between waves and a trainable medium in order to construct a matrix-vector multiplier. In particular we study such a device in the context of the backpropagation algorithm, which is commonly used for training neural networks. Here, the weights of the connections between neurons are trained by multiplying a `forward' signal with a backwards propagating `error' signal. We show that this concept can be extended to trainable media, where the gradient for the local wave number is given by multiplying signal waves and error waves. We provide a numerical example of such a system with waves traveling freely in a trainable medium, and we discuss a potential way to build such a device in an integrated photonics chip.
Nonlinear memory capacity of parallel time-delay reservoir computers in the processing of multidimensional signals
Venue: arXiv cs.NE 2015
Year: 2015

This paper addresses the reservoir design problem in the context of delay-based reservoir computers for multidimensional input signals, parallel architectures, and real-time multitasking. First, an approximating reservoir model is presented in those frameworks that provides an explicit functional link between the reservoir parameters and architecture and its performance in the execution of a specific task. Second, the inference properties of the ridge regression estimator in the multivariate context is used to assess the impact of finite sample training on the decrease of the reservoir capacity. Finally, an empirical study is conducted that shows the adequacy of the theoretical results with the empirical performances exhibited by various reservoir architectures in the execution of several nonlinear tasks with multidimensional inputs. Our results confirm the robustness properties of the parallel reservoir architecture with respect to task misspecification and parameter choice that had already been documented in the literature.
The equivalence of the torus and the product of two circles in homotopy type theory
Venue: arXiv cs.LO 2015
Year: 2015

Homotopy type theory is a new branch of mathematics which merges insights from abstract homotopy theory and higher category theory with those of logic and type theory. It allows us to represent a variety of mathematical objects as basic type-theoretic constructions, higher inductive types. We present a proof that in homotopy type theory, the torus is equivalent to the product of two circles. This result indicates that the synthetic definition of torus as a higher inductive type is indeed correct.
Multi-head Watson-Crick automata
Venue: arXiv cs.FL 2015
Year: 2015

Inspired by multi-head finite automata and Watson-Crick automata in this paper, we introduce new structure namely multi-head Watson-Crick automata where we replace the single tape of multi-head finite automaton by a DNA double strand. The content of the second tape is determined using a complementarity relation similar to Watson-Crick complementarity relation. We establish the superiority of our model over multi-head finite automata and also show that both the deterministic and non-deterministic variant of the model can accept non-regular unary languages. We also compare our model with parallel communicating Watson-Crick automata systems and prove that both of them have the same computational power.
Collaboration between SAML Federations and OpenStack Clouds
Venue: arXiv cs.SE 2015
Year: 2015

In this paper, we present the design process of a novel solution for enabling the collaboration between OpenStack cloud systems in SAML federations with standalone attribute authorities, such as national research and education federations or eduGAIN. The software solution that realizes the integration of systems serves as a case study to show how abstract desirable engineering properties fixed at the beginning of the design process can be implemented during the development phase. An analysis of earlier generations of OpenStack-related developments trying to tackle the same problem is given. Many aspects of this software integration can be generalized to serve as a template for federative cloud access.
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
Venue: arXiv cs.NI 2015
Year: 2015

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.
Automatic Transcription of Flamenco Singing from Polyphonic Music Recordings
Venue: arXiv cs.SD 2015
Year: 2015

Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection and overall performance when evaluated on flamenco singing datasets.
Fine-Grained Product Class Recognition for Assisted Shopping
Venue: arXiv cs.CV 2015
Year: 2015

Assistive solutions for a better shopping experience can improve the quality of life of people, in particular also of visually impaired shoppers. We present a system that visually recognizes the fine-grained product classes of items on a shopping list, in shelves images taken with a smartphone in a grocery store. Our system consists of three components: (a) We automatically recognize useful text on product packaging, e.g., product name and brand, and build a mapping of words to product classes based on the large-scale GroceryProducts dataset. When the user populates the shopping list, we automatically infer the product class of each entered word. (b) We perform fine-grained product class recognition when the user is facing a shelf. We discover discriminative patches on product packaging to differentiate between visually similar product classes and to increase the robustness against continuous changes in product design. (c) We continuously improve the recognition accuracy through active learning. Our experiments show the robustness of the proposed method against cross-domain challenges, and the scalability to an increasing number of products with minimal re-training.
Exploiters-Based Knowledge Extraction in Object-Oriented Knowledge Representation
Venue: arXiv cs.AI 2015
Year: 2015

This paper contains the consideration of knowledge extraction mechanisms of such object-oriented knowledge representation models as frames, object-oriented programming and object-oriented dynamic networks. In addition, conception of universal exploiters within object-oriented dynamic networks is also discussed. The main result of the paper is introduction of new exploiters-based knowledge extraction approach, which provides generation of a finite set of new classes of objects, based on the basic set of classes. The methods for calculation of quantity of new classes, which can be obtained using proposed approach, and of quantity of types, which each of them describes, are proposed. Proof that basic set of classes, extended according to proposed approach, together with union exploiter create upper semilattice is given. The approach always allows generating of finitely defined set of new classes of objects for any object-oriented dynamic network. A quantity of these classes can be precisely calculated before the generation. It allows saving of only basic set of classes in the knowledge base.
Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step
Venue: arXiv cs.HC 2015
Year: 2015

Increase in workload across many organisations and consequent increase in occupational stress is negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of self- reporting and variability between and within individuals. With the advent of smartphones it is now possible to monitor diverse aspects of human behaviour, including objectively measured behaviour related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behaviour that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (in comparison to location, video or audio recording, for example) and because its low power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. 30 subjects from two different organizations were provided with smartphones. The study lasted for 8 weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify self reported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.
Arabesque: A System for Distributed Graph Mining - Extended version
Venue: arXiv cs.DC 2015
Year: 2015

Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs and finding patterns that match some "interestingness" criteria desired by the user. These algorithms are very important for areas such as social net- works, semantic web, and bioinformatics. In this paper, we present Arabesque, the first distributed data processing platform for implementing graph mining algorithms. Arabesque automates the process of exploring a very large number of subgraphs. It defines a high-level filter-process computational model that simplifies the development of scalable graph mining algorithms: Arabesque explores subgraphs and passes them to the application, which must simply compute outputs and decide whether the subgraph should be further extended. We use Arabesque's API to produce distributed solutions to three fundamental graph mining problems: frequent subgraph mining, counting motifs, and finding cliques. Our implementations require a handful of lines of code, scale to trillions of subgraphs, and represent in some cases the first available distributed solutions.
Probabilistic Opacity in Refinement-Based Modeling
Venue: arXiv cs.CR 2015
Year: 2015

Given a probabilistic transition system (PTS) $\cal A$ partially observed by an attacker, and an $ω$-regular predicate $φ$over the traces of $\cal A$, measuring the disclosure of the secret $φ$ in $\cal A$ means computing the probability that an attacker who observes a run of $\cal A$ can ascertain that its trace belongs to $φ$. In the context of refinement, we consider specifications given as Interval-valued Discrete Time Markov Chains (IDTMCs), which are underspecified Markov chains where probabilities on edges are only required to belong to intervals. Scheduling an IDTMC $\cal S$ produces a concrete implementation as a PTS and we define the worst case disclosure of secret $φ$ in ${\cal S}$ as the maximal disclosure of $φ$ over all PTSs thus produced. We compute this value for a subclass of IDTMCs and we prove that refinement can only improve the opacity of implementations.
Group-Invariant Subspace Clustering
Venue: arXiv cs.IT 2015
Year: 2015

In this paper we consider the problem of group invariant subspace clustering where the data is assumed to come from a union of group-invariant subspaces of a vector space, i.e. subspaces which are invariant with respect to action of a given group. Algebraically, such group-invariant subspaces are also referred to as submodules. Similar to the well known Sparse Subspace Clustering approach where the data is assumed to come from a union of subspaces, we analyze an algorithm which, following a recent work [1], we refer to as Sparse Sub-module Clustering (SSmC). The method is based on finding group-sparse self-representation of data points. In this paper we primarily derive general conditions under which such a group-invariant subspace identification is possible. In particular we extend the geometric analysis in [2] and in the process we identify a related problem in geometric functional analysis.
Towards Disruption Tolerant ICN
Venue: arXiv cs.NI 2015
Year: 2015

Information-Centric Networking (ICN) is a promi- nent topic in current networking research. ICN design signifi- cantly considers the increased demand of scalable and efficient content distribution for Future Internet. However, intermittently connected mobile environments or disruptive networks present a significant challenge to ICN deployment. In this context, delay tolerant networking (DTN) architecture is an initiative that effec- tively deals with network disruptions. Among all ICN proposals, Content Centric Networking (CCN) is gaining more and more interest for its architectural design, but still has the limitation in highly disruptive environment. In this paper, we design a protocol stack referred as CCNDTN which integrates DTN architecture in the native CCN to deal with network disruption. We also present the implementation details of the proposed CCNDTN. We extend CCN routing strategies by integrating Bundle protocol of DTN architecture. The integration of CCN and DTN enriches the connectivity options of CCN architecture in fragmented networks. Furthermore, CCNDTN can be beneficial through the simultaneous use of all available connectivities and opportunistic networking of DTN for the dissemination of larger data items. This paper also highlights the potential use cases of CCNDTN architecture and crucial questions about integrating CCN and DTN
Adaptive Streaming in P2P Live Video Systems: A Distributed Rate Control Approach
Venue: arXiv cs.NI 2015
Year: 2015

Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows to leverage the cooperation among peers, guaranteeing to serve every video request with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms, and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to warrant its users a very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, both in terms of robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.
SINR Model with Best Server Association for High Availability Studies of Wireless Networks
Venue: arXiv cs.NI 2015
Year: 2015

The signal-to-interference-and-noise ratio (SINR) is of key importance for the analysis and design of wireless networks. For addressing new requirements imposed on wireless communication, in particular high availability, a highly accurate modeling of the SINR is needed. We propose a stochastic model of the SINR distribution where shadow fading is characterized by random variables. Therein, the impact of shadow fading on the user association is incorporated by modification of the distributions involved. The SINR model is capable to describe all parts of the SINR distribution in detail, especially the left tail which is of interest for studies of high availability.
An Improved Randomized Data Structure for Dynamic Graph Connectivity
Venue: arXiv cs.DS 2015
Year: 2015

We present a randomized algorithm for dynamic graph connectivity. With failure probability less than $1/n^c$ (for any constant $c$ we choose), our solution has worst case running time $O(\log^3 n)$ per edge insertion, $O(\log^4 n)$ per edge deletion, and $O(\log n/\log\log n)$ per query, where $n$ is the number of vertices. The previous best algorithm has worst case running time $O(\log^4 n)$ per edge insertion and $O(\log^5 n)$ per edge deletion. The improvement is made by reducing the randomness used in the previous result, so that we save a $\log n$ factor in update time. Specifically, \cite{kapron2013dynamic} uses $\log n$ copies of a data structure in order to boost a success probability from $1/2$ to $1-n^{-c}$. We show that, in fact though, because of the special structure of their algorithm, this boosting via repetition is unnecessary. Rather, we can still obtain the same correctness guarantee with high probability by arguing via a new invariant, without repetition.
Multilingual Image Description with Neural Sequence Models
Venue: arXiv cs.CL 2015
Year: 2015

In this paper we present an approach to multi-language image description bringing together insights from neural machine translation and neural image description. To create a description of an image for a given target language, our sequence generation models condition on feature vectors from the image, the description from the source language, and/or a multimodal vector computed over the image and a description in the source language. In image description experiments on the IAPR-TC12 dataset of images aligned with English and German sentences, we find significant and substantial improvements in BLEU4 and Meteor scores for models trained over multiple languages, compared to a monolingual baseline.
Analysis of Path Loss mitigation through Dynamic Spectrum Access: Software Defined Radio
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, an analysis is carried out for a method to mitigate the path loss through the dynamic spectrum access (DSA) method. The path loss is a major component which determines the QoS of a wireless link. Its effect is complemented by the presence of obstruction between the transmitter and receiver. The future cellular network (5G) focuses on operating with the millimeter-wave (mmW). In higher frequency, path loss can play a significant role in degrading the link quality due to higher attenuation. In a scenario, where the operating environment is changing dynamically, sudden degradation of operating conditions or arrival of obstruction between transmitter and receiver may result in link failure. The method analyzed here includes dynamically allocating spectrum at a lower frequency band for a link suffering from high path loss. For the analysis, a wireless link was set up using Universal Software Radio Peripherals (USRPs). The received power is observed to increase by dynamically changing the operating frequency from 1.9 GHz to 830 MHz. Finally the utility of software defined radio (SDR) in the RF front end, to combat the path loss in the future cellular networks, is studied.