OpenRaiser/Intern-Atlas huggingface dataset:
Telemedicine as a special case of Machine Translation
Venue: arXiv cs.CL 2015
Year: 2015

Machine translation is evolving quite rapidly in terms of quality. Nowadays, we have several machine translation systems available in the web, which provide reasonable translations. However, these systems are not perfect, and their quality may decrease in some specific domains. This paper examines the effects of different training methods when it comes to Polish - English Statistical Machine Translation system used for the medical data. Numerous elements of the EMEA parallel text corpora and not related OPUS Open Subtitles project were used as the ground for creation of phrase tables and different language models including the development, tuning and testing of these translation systems. The BLEU, NIST, METEOR, and TER metrics have been used in order to evaluate the results of various systems. Our experiments deal with the systems that include POS tagging, factored phrase models, hierarchical models, syntactic taggers, and other alignment methods. We also executed a deep analysis of Polish data as preparatory work before automatized data processing such as true casing or punctuation normalization phase. Normalized metrics was used to compare results. Scores lower than 15% mean that Machine Translation engine is unable to provide satisfying quality, scores greater than 30% mean that translations should be understandable without problems and scores over 50 reflect adequate translations. The average results of Polish to English translations scores for BLEU, NIST, METEOR, and TER were relatively high and ranged from 70,58 to 82,72. The lowest score was 64,38. The average results ranges for English to Polish translations were little lower (67,58 - 78,97). The real-life implementations of presented high quality Machine Translation Systems are anticipated in general medical practice and telemedicine.
SAFE: A Declarative Trust Management System with Linked Credentials
Venue: arXiv cs.CR 2015
Year: 2015

We present SAFE, an integrated system for managing trust using a logic-based declarative language. Logical trust systems authorize each request by constructing a proof from a context---a set of authenticated logic statements representing credentials and policies issued by various principals in a networked system. A key barrier to practical use of logical trust systems is the problem of managing proof contexts: identifying, validating, and assembling the credentials and policies that are relevant to each trust decision. This paper describes a new approach to managing proof contexts using context linking and caching. Credentials and policies are stored as certified logic sets named by secure identifiers in a shared key-value store. SAFE offers language constructs to build and modify logic sets, link sets to form unions, pass them by reference, and add them to proof contexts. SAFE fetches and validates credential sets on demand and caches them in the authorizer. We evaluate and discuss our experience using SAFE to build secure services based on case studies drawn from practice: a secure name service resolver, a secure proxy shim for a key value store, and an authorization module for a networked infrastructure-as-a-service system with a federated trust structure.
Linear Software Models: Key Ideas
Venue: arXiv cs.SE 2015
Year: 2015

Linear Software Models is a systematic effort to formulate a theory of software systems neatly based upon standard mathematics, viz. linear algebra. It has appeared in a series of papers dealing with various aspects of the theory. But one was lacking a single source for its key ideas. This paper concisely distills foundational ideas and results obtained within Linear Software Models. First and foremost we claim that one must have a deep comprehension of the theory of software - the aim of this effort - before embarking into theory and practice of software engineering. The Modularity Matrix is the central algebraic structure of the software theory: it is the source of quantitative modularity criteria; it displays high cohesion, i.e. high sparsity; a Standard Modularity Matrix is defined - square and block-diagonal - enabling designs comparison for any software systems in a Software Design Laboratory. It triggers formulation of novel open questions waiting for resolution.
NEMO5: Achieving High-end Internode Communication for Performance Projection Beyond Moore's Law
Venue: arXiv cs.DC 2015
Year: 2015

Electronic performance predictions of modern nanotransistors require nonequilibrium Green's functions including incoherent scattering on phonons as well as inclusion of random alloy disorder and surface roughness effects. The solution of all these effects is numerically extremely expensive and has to be done on the world's largest supercomputers due to the large memory requirement and the high performance demands on the communication network between the compute nodes. In this work, it is shown that NEMO5 covers all required physical effects and their combination. Furthermore, it is also shown that NEMO5's implementation of the algorithm scales very well up to about 178176CPUs with a sustained performance of about 857 TFLOPS. Therefore, NEMO5 is ready to simulate future nanotransistors.
Exploring Social Ties for Enhanced Device-to-Device Communications in Wireless Networks
Venue: arXiv cs.SI 2015
Year: 2015

Device-to-device (D2D) communications is seen as a major technology to overcome the imminent wireless capacity crunch and to enable novel application services. In this paper, we propose a novel, social-aware approach for optimizing D2D communications by exploiting two network layers: the social network and the physical, wireless network. First we formulate the physical layer D2D network according to users' encounter histories. Subsequently, we propose a novel approach, based on the so-called Indian Buffet Process, so as to model the distribution of contents in users' online social networks. Given the online and offline social relations collected by the Evolved Node B, we jointly optimize the traffic offload process in D2D communication. Simulation results show that the proposed approach offload the traffic of Evolved Node B successfully.
A Graph Theoretic Characterization of Perfect Attackability and Detection in Distributed Control Systems
Venue: arXiv cs.SY 2015
Year: 2015

This paper is concerned with the analysis and design of secure Distributed Control Systems in the face of integrity attacks on sensors and controllers by external attackers or insiders. In general a DCS consists of many heterogenous components and agents including sensors, actuators, controllers. Due to its distributed nature, some agents may start misbehaving to disrupt the system. This paper first reviews necessary and sufficient conditions for deterministic detection of integrity attacks carried out by any number of malicious agents, based on the concept of left invertibility of structural control systems. It then develops a notion equivalent to structural left invertibility in terms of vertex separators of a graph. This tool is then leveraged to design minimal communication networks for DCSs, which ensure that an adversary cannot generate undetectable attacks. Numerical examples are included to illustrate these results.
A Method for Modeling Co-Occurrence Propensity of Clinical Codes with Application to ICD-10-PCS Auto-Coding
Venue: arXiv cs.CL 2015
Year: 2015

Objective. Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely related procedures or diagnoses to the same document, even when they do not tend to occur together in practice, simply because the right choice can be difficult to infer from the clinical narrative. Materials and Methods. We propose a method that injects awareness of the propensities for code co-occurrence into this process. First, a model is trained to estimate the conditional probability that one code is assigned by a human coder, given than another code is known to have been assigned to the same document. Then, at runtime, an iterative algorithm is used to apply this model to the output of an existing statistical auto-coder to modify the confidence scores of the codes. Results. We tested this method in combination with a primary auto-coder for ICD-10 procedure codes, achieving a 12% relative improvement in F-score over the primary auto-coder baseline. Discussion. The proposed method can be used, with appropriate features, in combination with any auto-coder that generates codes with different levels of confidence. Conclusion. The promising results obtained for ICD-10 procedure codes suggest that the proposed method may have wider applications in auto-coding.
Formalization of the pumping lemma for context-free languages
Venue: arXiv cs.FL 2015
Year: 2015

Context-free languages (CFLs) are highly important in computer language processing technology as well as in formal language theory. The Pumping Lemma is a property that is valid for all context-free languages, and is used to show the existence of non context-free languages. This paper presents a formalization, using the Coq proof assistant, of the Pumping Lemma for context-free languages.
The Vampire and the FOOL
Venue: arXiv cs.LO 2015
Year: 2015

This paper presents new features recently implemented in the theorem prover Vampire, namely support for first-order logic with a first class boolean sort (FOOL) and polymorphic arrays. In addition to having a first class boolean sort, FOOL also contains if-then-else and let-in expressions. We argue that presented extensions facilitate reasoning-based program analysis, both by increasing the expressivity of first-order reasoners and by gains in efficiency.
Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography
Venue: arXiv cs.CV 2015
Year: 2015

We propose a de-identification pipeline that protects the privacy of humans in video sequences by replacing them with rendered 3D human models, hence concealing their identity while retaining the naturalness of the scene. The original images of humans are steganographically encoded in the carrier image, i.e. the image containing the original scene and the rendered 3D human models. We qualitatively explore the feasibility of our approach, utilizing the Kinect sensor and its libraries to detect and localize human joints. A 3D avatar is rendered into the scene using the obtained joint positions, and the original human image is steganographically encoded in the new scene. Our qualitative evaluation shows reasonably good results that merit further exploration.
Multi-dimensional intra-tile parallelization for memory-starved stencil computations
Venue: arXiv cs.DC 2015
Year: 2015

Optimizing the performance of stencil algorithms has been the subject of intense research over the last two decades. Since many stencil schemes have low arithmetic intensity, most optimizations focus on increasing the temporal data access locality, thus reducing the data traffic through the main memory interface with the ultimate goal of decoupling from this bottleneck. There are, however, only few approaches that explicitly leverage the shared cache feature of modern multicore chips. If every thread works on its private, separate cache block, the available cache space can become too small, and sufficient temporal locality may not be achieved. We propose a flexible multi-dimensional intra-tile parallelization method for stencil algorithms on multicore CPUs with a shared outer-level cache. This method leads to a significant reduction in the required cache space without adverse effects from hardware prefetching or TLB shortage. Our \emph{Girih} framework includes an auto-tuner to select optimal parameter configurations on the target hardware. We conduct performance experiments on two contemporary Intel processors and compare with the state-of-the-art stencil frameworks PLUTO and Pochoir, using four corner-case stencil schemes and a wide range of problem sizes. \emph{Girih} shows substantial performance advantages and best arithmetic intensity at almost all problem sizes, especially on low-intensity stencils with variable coefficients. We study in detail the performance behavior at varying grid size using phenomenological performance modeling. Our analysis of energy consumption reveals that our method can save energy by reduced DRAM bandwidth usage even at marginal performance gain. It is thus well suited for future architectures that will be strongly challenged by the cost of data movement, be it in terms of performance or energy consumption.
Faster algorithms to enumerate hypergraph transversals
Venue: arXiv cs.DS 2015
Year: 2015

A transversal of a hypergraph is a set of vertices intersecting each hyperedge. We design and analyze new exponential-time algorithms to enumerate all inclusion-minimal transversals of a hypergraph. For each fixed k>2, our algorithms for hypergraphs of rank k, where the rank is the maximum size of a hyperedge, outperform the previous best. This also implies improved upper bounds on the maximum number of minimal transversals in n-vertex hypergraphs of rank k>2. Our main algorithm is a branching algorithm whose running time is analyzed with Measure and Conquer. It enumerates all minimal transversals of hypergraphs of rank 3 on n vertices in time O(1.6755^n). Our algorithm for hypergraphs of rank 4 is based on iterative compression. Our enumeration algorithms improve upon the best known algorithms for counting minimum transversals in hypergraphs of rank k for k>2 and for computing a minimum transversal in hypergraphs of rank k for k>5.
Bibliometric-Enhanced Information Retrieval: 3rd International BIR Workshop
Venue: arXiv cs.IR 2015
Year: 2015

The BIR workshop brings together experts in Bibliometrics and Information Retrieval. While sometimes perceived as rather loosely related, these research areas share various interests and face similar challenges. Our motivation as organizers of the BIR workshop stemmed from a twofold observation. First, both communities only partly overlap, albeit sharing various interests. Second, it will be profitable for both sides to tackle some of the emerging problems that scholars face today when they have to identify relevant and high quality literature in the fast growing number of electronic publications available worldwide. Bibliometric techniques are not yet used widely to enhance retrieval processes in digital libraries, although they offer value-added effects for users. Information professionals working in libraries and archives, however, are increasingly confronted with applying bibliometric techniques in their services. The first BIR workshop in 2014 set the research agenda by introducing each group to the other, illustrating state-of-the-art methods, reporting on current research problems, and brainstorming about common interests. The second workshop in 2015 further elaborated these themes. This third BIR workshop aims to foster a common ground for the incorporation of bibliometric-enhanced services into scholarly search engine interfaces. In particular we will address specific communities, as well as studies on large, cross-domain collections like Mendeley and ResearchGate. This third BIR workshop addresses explicitly both scholarly and industrial researchers.
Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems
Venue: arXiv cs.CV 2015
Year: 2015

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.
Optimizing the Convergence Rate of the Quantum Consensus: A Discrete Time Model
Venue: arXiv cs.SY 2015
Year: 2015

Motivated by the recent advances in the field of quantum computing, quantum systems are modelled and analyzed as networks of decentralized quantum nodes which employ distributed quantum consensus algorithms for coordination. In the literature, both continuous and discrete time models have been proposed for analyzing these algorithms. This paper aims at optimizing the convergence rate of the discrete time quantum consensus algorithm over a quantum network with $N$ qudits. The induced graphs are categorized in terms of the partitions of integer $N$ by arranging them as the Schreier graphs. It is shown that the original optimization problem reduces to optimizing the Second Largest Eigenvalue Modulus (SLEM) of the weight matrix. Exploiting the Specht module representation of partitions of $N$, the Aldous' conjecture is generalized to all partitions (except ($N$)) in the Hasse diagram of integer $N$. Based on this result, it is shown that the spectral gap of Laplacian of all induced graphs corresponding to partitions (other than ($N$)) of $N$ are the same, while the spectral radius of the Laplacian is obtained from the feasible least dominant partition in the Hasse diagram of integer $N$. The semidefinite programming formulation of the problem is addressed analytically for $N \leq d^2 + 1$ and a wide range of topologies where closed-form expressions for the optimal results are provided. For a quantum network with complete graph topology, solution of the optimization problem based on group association schemes is provided for all values of $N$.
Temporal Matrix Factorization for Tracking Concept Drift in Individual User Preferences
Venue: arXiv cs.SI 2015
Year: 2015

The matrix factorization (MF) technique has been widely adopted for solving the rating prediction problem in recommender systems. The MF technique utilizes the latent factor model to obtain static user preferences (user latent vectors) and item characteristics (item latent vectors) based on historical rating data. However, in the real world user preferences are not static but full of dynamics. Though there are several previous works that addressed this time varying issue of user preferences, it seems (to the best of our knowledge) that none of them is specifically designed for tracking concept drift in individual user preferences. Motivated by this, we develop a Temporal Matrix Factorization approach (TMF) for tracking concept drift in each individual user latent vector. There are two key innovative steps in our approach: (i) we develop a modified stochastic gradient descent method to learn an individual user latent vector at each time step, and (ii) by the Lasso regression we learn a linear model for the transition of the individual user latent vectors. We test our method on a synthetic dataset and several real datasets. In comparison with the original MF, our experimental results show that our temporal method is able to achieve lower root mean square errors (RMSE) for both the synthetic and real datasets. One interesting finding is that the performance gain in RMSE is mostly from those users who indeed have concept drift in their user latent vectors at the time of prediction. In particular, for the synthetic dataset and the Ciao dataset, there are quite a few users with that property and the performance gains for these two datasets are roughly 20% and 5%, respectively.
Real-time Tracking Based on Neuromrophic Vision
Venue: arXiv cs.CV 2015
Year: 2015

Real-time tracking is an important problem in computer vision in which most methods are based on the conventional cameras. Neuromorphic vision is a concept defined by incorporating neuromorphic vision sensors such as silicon retinas in vision processing system. With the development of the silicon technology, asynchronous event-based silicon retinas that mimic neuro-biological architectures has been developed in recent years. In this work, we combine the vision tracking algorithm of computer vision with the information encoding mechanism of event-based sensors which is inspired from the neural rate coding mechanism. The real-time tracking of single object with the advantage of high speed of 100 time bins per second is successfully realized. Our method demonstrates that the computer vision methods could be used for the neuromorphic vision processing and we can realize fast real-time tracking using neuromorphic vision sensors compare to the conventional camera.
Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation
Venue: arXiv cs.SY 2015
Year: 2015

This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search--based least--mean--squares(LMS)/recursive least squares(RLS) link selection algorithms and sparsity--inspired LMS/RLS link selection algorithms that can exploit the topology of networks with poor--quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady--state and tracking performance, and computational complexity. In comparison with existing centralized or distributed estimation strategies, key features of the proposed algorithms are: 1) more accurate estimates and faster convergence speed can be obtained; and 2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS
Venue: arXiv cs.LG 2015
Year: 2015

As part of daily monitoring of human activities, wearable sensors and devices are becoming increasingly popular sources of data. With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently. In this paper, we propose a fast inference method for an unsupervised non-parametric time series model namely variational inference for sticky HDP-SLDS(Hierarchical Dirichlet Process Switching Linear Dynamical System). We show that the proposed algorithm can differentiate various indoor activities such as sitting, walking, turning, going up/down the stairs and taking the elevator using only the acceloremeter of an Android smartphone Samsung Galaxy S4. We used the front camera of the smartphone to annotate activity types precisely. We compared the proposed method with Hidden Markov Models with Gaussian emission probabilities on a dataset of 10 subjects. We showed that the efficacy of the stickiness property. We further compared the variational inference to the Gibbs sampler on the same model and show that variational inference is faster in one order of magnitude.