OpenRaiser/Intern-Atlas huggingface dataset:
Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation
Venue: arXiv cs.CL 2015
Year: 2015

This paper presents a new method for the discovery of latent domains in diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech Recognition. Our work focuses on transcription of multi-genre broadcast media, which is often only categorised broadly in terms of high level genres such as sports, news, documentary, etc. However, in terms of acoustic modelling these categories are coarse. Instead, it is expected that a mixture of latent domains can better represent the complex and diverse behaviours within a TV show, and therefore lead to better and more robust performance. We propose a new method, whereby these latent domains are discovered with Latent Dirichlet Allocation, in an unsupervised manner. These are used to adapt DNNs using the Unique Binary Code (UBIC) representation for the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more than 2,000 shows for training and 47 shows for testing, show that the use of LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline hybrid DNN models.
Resolving the Geometric Locus Dilemma for Support Vector Learning Machines
Venue: arXiv cs.LG 2015
Year: 2015

Capacity control, the bias/variance dilemma, and learning unknown functions from data, are all concerned with identifying effective and consistent fits of unknown geometric loci to random data points. A geometric locus is a curve or surface formed by points, all of which possess some uniform property. A geometric locus of an algebraic equation is the set of points whose coordinates are solutions of the equation. Any given curve or surface must pass through each point on a specified locus. This paper argues that it is impossible to fit random data points to algebraic equations of partially configured geometric loci that reference arbitrary Cartesian coordinate systems. It also argues that the fundamental curve of a linear decision boundary is actually a principal eigenaxis. It is shown that learning principal eigenaxes of linear decision boundaries involves finding a point of statistical equilibrium for which eigenenergies of principal eigenaxis components are symmetrically balanced with each other. It is demonstrated that learning linear decision boundaries involves strong duality relationships between a statistical eigenlocus of principal eigenaxis components and its algebraic forms, in primal and dual, correlated Hilbert spaces. Locus equations are introduced and developed that describe principal eigen-coordinate systems for lines, planes, and hyperplanes. These equations are used to introduce and develop primal and dual statistical eigenlocus equations of principal eigenaxes of linear decision boundaries. Important generalizations for linear decision boundaries are shown to be encoded within a dual statistical eigenlocus of principal eigenaxis components. Principal eigenaxes of linear decision boundaries are shown to encode Bayes' likelihood ratio for common covariance data and a robust likelihood ratio for all other data.
A Note on Almost Perfect Probabilistically Checkable Proofs of Proximity
Venue: arXiv cs.CC 2015
Year: 2015

Probabilistically checkable proofs of proximity (PCPP) are proof systems where the verifier is given a 3SAT formula, but has only oracle access to an assignment and a proof. The verifier accepts a satisfying assignment with a valid proof, and rejects (with high enough probability) an assignment that is far from all satisfying assignments (for any given proof). In this work, we focus on the type of computation the verifier is allowed to make. Assuming P $\neq$ NP, there can be no PCPP when the verifier is only allowed to answer according to constraints from a set that forms a CSP that is solvable in P. Therefore, the notion of PCPP is relaxed to almost perfect probabilistically checkable proofs of proximity (APPCPP), where the verifier is allowed to reject a satisfying assignment with a valid proof, with arbitrary small probability. We show, unconditionally, a dichotomy of sets of allowable computations: sets that have APPCPPs (which actually follows because they have PCPPs) and sets that do not. This dichotomy turns out to be the same as that of the Dichotomy Theorem, which can be thought of as dividing sets of allowable verifier computations into sets that give rise to NP-hard CSPs, and sets that give rise to CSPs that are solvable in P.
HiFlash: A History Independent Flash Device
Venue: arXiv cs.CR 2015
Year: 2015

Retention regulations require timely and irrecoverable disposal of data, a challenging task, as data and its side effects are stored and maintained at all layers of a computing system. Those side effects can be used as an oracle to derive the past existence of deleted data. Fortunately, history independence can be utilized to eliminate such history-related oracles. HIFS can provide history independence for file storage over mechanical disk drives. However, HIFS cannot provide history independence when deployed on top of flash devices, as flash memory manages its own internal block placement, which is often inherently history dependent. In this work, we initiate research on history independent flash devices. We design HiFlash, which achieves a strong notion of history independence by defending against an adversary allowed access to the flash at multiple different points in time. In addition, we design a simple, yet history independence friendly wear-leveling mechanism that allows HiFlash to smartly and advantageously trade off a tunable small amount of history leakage for a significant increase in the device's lifetime. Our prototype built in an actual flash device as well as extensive simulations validate the effectiveness of HiFlash.
Robust PCA via Nonconvex Rank Approximation
Venue: arXiv cs.CV 2015
Year: 2015

Numerous applications in data mining and machine learning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a general framework for handling this kind of problems. Nuclear norm based convex surrogate of the rank function in RPCA is widely investigated. Under certain assumptions, it can recover the underlying true low rank matrix with high probability. However, those assumptions may not hold in real-world applications. Since the nuclear norm approximates the rank by adding all singular values together, which is essentially a $\ell_1$-norm of the singular values, the resulting approximation error is not trivial and thus the resulting matrix estimator can be significantly biased. To seek a closer approximation and to alleviate the above-mentioned limitations of the nuclear norm, we propose a nonconvex rank approximation. This approximation to the matrix rank is tighter than the nuclear norm. To solve the associated nonconvex minimization problem, we develop an efficient augmented Lagrange multiplier based optimization algorithm. Experimental results demonstrate that our method outperforms current state-of-the-art algorithms in both accuracy and efficiency.
Towards composition of conformant systems
Venue: arXiv cs.SY 2015
Year: 2015

Motivated by the Model-Based Design process for Cyber-Physical Systems, we consider issues in conformance testing of systems. Conformance is a quantitative notion of similarity between the output trajectories of systems, which considers both temporal and spatial aspects of the outputs. Previous work developed algorithms for computing the conformance degree between two systems, and demonstrated how formal verification results for one system can be re-used for a system that is conformant to it. In this paper, we study the relation between conformance and a generalized approximate simulation relation for the class of Open Metric Transition Systems (OMTS). This allows us to prove a small-gain theorem for OMTS, which gives sufficient conditions under which the feedback interconnection of systems respects the conformance relation, thus allowing the building of more complex systems from conformant components.
Systematically Deriving Domain-Specific Transformation Languages
Venue: arXiv cs.SE 2015
Year: 2015

Model transformations are helpful to evolve, refactor, refine and maintain models. While domain-specific languages are normally intuitive for modelers, common model transformation approaches (regardless of whether they transform graphical or textual models) are based on the modeling language's abstract syntax requiring the modeler to learn the internal representation of the model to describe transformations. This paper presents a process that allows to systematically derive a textual domainspecific transformation language from the grammar of a given textual modeling language. As example, we apply this systematic derivation to UML class diagrams to obtain a domain-specific transformation language called CDTrans. CDTrans incorporates the concrete syntax of class diagrams which is already familiar to the modeler and extends it with a few transformation operators. To demonstrate the usefulness of the derived transformation language, we describe several refactoring transformations.
Constant Time EXPected Similarity Estimation using Stochastic Optimization
Venue: arXiv cs.LG 2015
Year: 2015

A new algorithm named EXPected Similarity Estimation (EXPoSE) was recently proposed to solve the problem of large-scale anomaly detection. It is a non-parametric and distribution free kernel method based on the Hilbert space embedding of probability measures. Given a dataset of $n$ samples, EXPoSE needs only $\mathcal{O}(n)$ (linear time) to build a model and $\mathcal{O}(1)$ (constant time) to make a prediction. In this work we improve the linear computational complexity and show that an $ε$-accurate model can be estimated in constant time, which has significant implications for large-scale learning problems. To achieve this goal, we cast the original EXPoSE formulation into a stochastic optimization problem. It is crucial that this approach allows us to determine the number of iteration based on a desired accuracy $ε$, independent of the dataset size $n$. We will show that the proposed stochastic gradient descent algorithm works in general (possible infinite-dimensional) Hilbert spaces, is easy to implement and requires no additional step-size parameters.
Cross-Layer Optimization of Fast Video Delivery in Cache-Enabled Relaying Networks
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates the cross-layer optimization of fast video delivery and caching for minimization of the overall video delivery time in a two-hop relaying network. The half-duplex relay nodes are equipped with both a cache and a buffer which facilitate joint scheduling of fetching and delivery to exploit the channel diversity for improving the overall delivery performance. The fast delivery control is formulated as a two-stage functional non-convex optimization problem. By exploiting the underlying convex and quasi-convex structures, the problem can be solved exactly and efficiently by the developed algorithm. Simulation results show that significant caching and buffering gains can be achieved with the proposed framework, which translates into a reduction of the overall video delivery time. Besides, a trade-off between caching and buffering gains is unveiled.
Topic Model Based Behaviour Modeling and Clustering Analysis for Wireless Network Users
Venue: arXiv cs.SI 2015
Year: 2015

User behaviour analysis based on traffic log in wireless networks can be beneficial to many fields in real life: not only for commercial purposes, but also for improving network service quality and social management. We cluster users into groups marked by the most frequently visited websites to find their preferences. In this paper, we propose a user behaviour model based on Topic Model from document classification problems. We use the logarithmic TF-IDF (term frequency - inverse document frequency) weighing to form a high-dimensional sparse feature matrix. Then we apply LSA (Latent semantic analysis) to deduce the latent topic distribution and generate a low-dimensional dense feature matrix. K-means++, which is a classic clustering algorithm, is then applied to the dense feature matrix and several interpretable user clusters are found. Moreover, by combining the clustering results with additional demographical information, including age, gender, and financial information, we are able to uncover more realistic implications from the clustering results.
Cyclus Archetypes
Venue: arXiv cs.SE 2015
Year: 2015

The current state of nuclear fuel cycle simulation exists in highly customized form. Satisfying a wide range of users requires model modularity within such a tool. Cyclus is a fuel cycle simulator specifically designed to combat the lack of adaptability of previous generations of simulators. This is accomplished through an agent-based infrastructure and treating time discretely. The Cyclus kernel was developed to allow for models, called archetypes, of differing fidelity and function depending on need of the users. To take advantage of this flexibility, a user must write an archetype for their desired simulation if it does not yet exist within the Cyclus ecosystem. At this stage, a user graduates to the title of archetype developer. Without automation, archetype development is difficult for the uninitiated. This paper presents the framework developed for simplifying the writing of archetypes: the Cyclus preprocessor, or cycpp. cycpp addresses the computer science and software development aspects of archetype development that can be addressed algorithmically, allowing the developer to focus on modeling the physics, social policies, and economics. cycpp passes through the code three times to perform the following tasks: normalizing the code via the C preprocessor, accumulation of notations, and code generation. Not only does this reduce the amount of code a developer must write by approximately an order of magnitude, but the archetypes are automatically validated.
Wishart Mechanism for Differentially Private Principal Components Analysis
Venue: arXiv cs.CR 2015
Year: 2015

We propose a new input perturbation mechanism for publishing a covariance matrix to achieve $(ε,0)$-differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, We apply this mechanism to principal component analysis. Our mechanism is able to keep the positive semi-definiteness of the published covariance matrix. Thus, our approach gives rise to a general publishing framework for input perturbation of a symmetric positive semidefinite matrix. Moreover, compared with the classic Laplace mechanism, our method has better utility guarantee. To the best of our knowledge, Wishart mechanism is the best input perturbation approach for $(ε,0)$-differentially private PCA. We also compare our work with previous exponential mechanism algorithms in the literature and provide near optimal bound while having more flexibility and less computational intractability.
HistComp : bibliographic analysis and visualization of 'The Biological Bulletin'
Venue: arXiv cs.DL 2015
Year: 2015

A collection of citation data, the HistComp, is available from the Internet as a database of examples of real life citation networks. The purposes of this approach is the analysis of these citation networks on learned literature by presenting its typical steps and results. We have selected the bibliographic insights into the "The Biological Bulletin", the journal published since 1897 by the Woods Hole Marine Biological Laboratory. Since the bibliographic networks tend to be very scattered, their visualization requires of criteria of convergence. To simplify, the main features in such a structure should include the survey for authoritative sources in the hyperlinked environment and the identification of thematic areas. By avoiding excessive loose connections and too dense clustered layouts to be useful, a smooth presentation is obtained by graphically depicting the citation patterns. HistComp computes 8884 articles published by 'The Biological Bulletin' between 1945-2003. A two-dimensional positioning of these papers that represent the extent of their bibliographic coupling and co-citation is offered as a histograph. The criteria to construct it is the adequateness of the visualization relative to the 8884 data set. The spatial representation obtained optimizes the identification of the clusters or topic areas. The thematic importance of marine science involves its participation in 7 of the 7 presenting clusters. The mainstream subjects were crustaceans and echinoderms, with some 60% of the material presented in the graph. But sea anemone, with about 16% of the total, remains as the best visualized topical area. A perspective of the highly relevant papers is readily confirmed by the visual inspection of width of the glyphs used for nodes representation. For user interaction, HistComp employs mouse-over labels.
Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments
Venue: arXiv cs.CV 2015
Year: 2015

We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, as well as make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.
On LR(k)-parsers of polynomial size
Venue: arXiv cs.FL 2015
Year: 2015

Usually, a parser for an $LR(k)$-grammar $G$ is a deterministic pushdown transducer which produces backwards the unique rightmost derivation for a given input string $x \in L(G)$. The best known upper bound for the size of such a parser is $O(2^{|G||Σ|^k+k\log |Σ| + \log |G|})$ where $|G|$ and $|Σ|$ are the sizes of the grammar $G$ and the terminal alphabet $Σ$, respectively. If we add to a parser the possibility to manipulate a directed graph of size $O(|G|n)$ where $n$ is the length of the input then we obtain an extended parser. The graph is used for an efficient parallel simulation of all potential leftmost derivations of the current right sentential form such that the unique rightmost derivation of the input can be computed. Given an arbitrary $LR(k)$-grammar $G$, we show how to construct an extended parser of $O(|G| + \#LA |N|2^k k \log k)$ size where $|N|$ is the number of nonterminal symbols and $\#LA$ is the number of relevant lookaheads with respect to the grammar $G$. As the usual parser, this extended parser uses only tables as data structure. Using some ingenious data structures and increasing the parsing time by a small constant factor, the size of the extended parser can be reduced to $O(|G| + \#LA|N|k^2)$. The parsing time is $O(ld(input) + k|G|n)$ where $ld(input)$ is the length of the derivation of the input. Moreover, we have constructed a one pass parser.
Applications of Multi-Agent Slime Mould Computing
Venue: arXiv cs.ET 2015
Year: 2015

The giant single-celled slime mould Physarum polycephalum has inspired rapid develop- ments in unconventional computing substrates since the start of this century. This is primarily due to its simple component parts and the distributed nature of the computation which it approximates during its growth, foraging and adaptation to a changing environment. Slime mould functions as a living embodied computational material which can be influenced (or pro- grammed) by the placement of external stimuli. The goal of exploiting this material behaviour for unconventional computation led to the development of a multi-agent approach to the ap- proximation of slime mould behaviour. The basis of the model is a simple dynamical pattern formation mechanism which exhibits self-organised formation and subsequent adaptation of collective transport networks. The system exhibits emergent properties such as relaxation and minimisation and it can be considered as a virtual computing material, influenced by the external application of spatial concentration gradients. In this paper we give an overview of this multi-agent approach to unconventional computing. We describe its computational mechanisms and different generic application domains, together with concrete example ap- plications of material computation. We examine the potential exploitation of the approach for computational geometry, path planning, combinatorial optimisation, data smoothing and statistical applications.
Cache-Conscious Run-time Decomposition of Data Parallel Computations
Venue: arXiv cs.DC 2015
Year: 2015

Multi-core architectures feature an intricate hierarchy of cache memories, with multiple levels and sizes. To adequately decompose an application according to the traits of a particular memory hierarchy is a cumbersome task that may be rewarded with significant performance gains. The current state-of-the-art in memory-hierarchy-aware parallel computing delegates this endeavour on the programmer, demanding from him deep knowledge of both parallel programming and computer architecture. In this paper we propose the shifting of these memory-hierarchy-related concerns to the run-time system, which then takes on the responsibility of distributing the computation's data across the target memory hierarchy. We evaluate our approach from a performance perspective, comparing it against the common cache-neglectful data decomposition strategy.
Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster
Venue: arXiv cs.DL 2015
Year: 2015

We analyze the reaction of academic communities to a particular urgent topic which abruptly arises as a scientific problem. To this end, we have chosen the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. The academic response is evaluated using scientific-publication data concerning the disaster using the Scopus database to present the picture on an international scale and the bibliographic database "Ukrainika naukova" to consider it on a national level. We measured distributions of papers in different scientific fields, their growth rates and properties of co-authorship networks. {The elements of descriptive statistics and the tools of the complex network theory are used to highlight the interdisciplinary as well as international effects.} Our analysis allows to compare contributions of the international community to Chornobyl-related research as well as integration of Ukraine in the international research on this subject. Furthermore, the content analysis of titles and abstracts of the publications allowed to detect the most important terms used for description of Chornobyl-related problems.