OpenRaiser/Intern-Atlas huggingface dataset:
Cognitive Discriminative Mappings for Rapid Learning
Venue: arXiv cs.AI 2016
Year: 2016

Humans can learn concepts or recognize items from just a handful of examples, while machines require many more samples to perform the same task. In this paper, we build a computational model to investigate the possibility of this kind of rapid learning. The proposed method aims to improve the learning task of input from sensory memory by leveraging the information retrieved from long-term memory. We present a simple and intuitive technique called cognitive discriminative mappings (CDM) to explore the cognitive problem. First, CDM separates and clusters the data instances retrieved from long-term memory into distinct classes with a discrimination method in working memory when a sensory input triggers the algorithm. CDM then maps each sensory data instance to be as close as possible to the median point of the data group with the same class. The experimental results demonstrate that the CDM approach is effective for learning the discriminative features of supervised classifications with few training sensory input instances.
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
Venue: arXiv cs.CL 2016
Year: 2016

Acoustic word embeddings --- fixed-dimensional vector representations of variable-length spoken word segments --- have begun to be considered for tasks such as speech recognition and query-by-example search. Such embeddings can be learned discriminatively so that they are similar for speech segments corresponding to the same word, while being dissimilar for segments corresponding to different words. Recent work has found that acoustic word embeddings can outperform dynamic time warping on query-by-example search and related word discrimination tasks. However, the space of embedding models and training approaches is still relatively unexplored. In this paper we present new discriminative embedding models based on recurrent neural networks (RNNs). We consider training losses that have been successful in prior work, in particular a cross entropy loss for word classification and a contrastive loss that explicitly aims to separate same-word and different-word pairs in a "Siamese network" training setting. We find that both classifier-based and Siamese RNN embeddings improve over previously reported results on a word discrimination task, with Siamese RNNs outperforming classification models. In addition, we present analyses of the learned embeddings and the effects of variables such as dimensionality and network structure.
Sparse multivariate factorization by mean of a few bivariate factorizations
Venue: arXiv cs.SC 2016
Year: 2016

We describe an algorithm to factor sparse multivariate polynomials using O(d) bivariate factorizations where d is the number of variables. This algorithm is implemented in the Giac/Xcas computer algebra system.
Contradiction Detection for Rumorous Claims
Venue: arXiv cs.CL 2016
Year: 2016

The utilization of social media material in journalistic workflows is increasing, demanding automated methods for the identification of mis- and disinformation. Since textual contradiction across social media posts can be a signal of rumorousness, we seek to model how claims in Twitter posts are being textually contradicted. We identify two different contexts in which contradiction emerges: its broader form can be observed across independently posted tweets and its more specific form in threaded conversations. We define how the two scenarios differ in terms of central elements of argumentation: claims and conversation structure. We design and evaluate models for the two scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to represent claims and conversation structure implicitly in a generic inference model, while previous studies used explicit or no representation of these properties. To address noisy text, our classifiers use simple similarity features derived from the string and part-of-speech level. Corpus statistics reveal distribution differences for these features in contradictory as opposed to non-contradictory tweet relations, and the classifiers yield state of the art performance.
Path-complete positivity of switching systems
Venue: arXiv cs.SY 2016
Year: 2016

The notion of path-complete positivity is introduced as a way to generalize the property of positivity from one LTI system to a family of switched LTI systems whose switching rule is constrained by a finite automaton. The generalization builds upon the analogy between stability and positivity, the former referring to the contraction of a norm, the latter referring to the contraction of a cone (or, equivalently, a projective norm). We motivate and investigate the potential of path-positivity and we propose an algorithm for the automatic verification of positivity.
A Lightweight, Non-intrusive Approach for Orchestrating Autonomously-managed Network Elements
Venue: arXiv cs.NI 2016
Year: 2016

Software-Defined Networking enables the centralized orchestration of data traffic within a network. However, proposed solutions require a high degree of architectural penetration. The present study targets the orchestration of network elements that do not wish to yield much of their internal operations to an external controller. Backpressure routing principles are used for deriving flow routing rules that optimally stabilize a network, while maximizing its throughput. The elements can then accept in full, partially or reject the proposed routing rule-set. The proposed scheme requires minimal, relatively infrequent interaction with a controller, limiting its imposed workload, promoting scalability. The proposed scheme exhibits attracting network performance gains, as demonstrated by extensive simulations and proven via mathematical analysis.
Protocol for a Systematic Mapping Study on Collaborative Model-Driven Software Engineering
Venue: arXiv cs.SE 2016
Year: 2016

Nowadays, collaborative modeling performed by multiple stakeholders is gaining a growing interest in both academia and practice. However, it poses a set of research challenges, such as large and complex models management, support for multi-user modeling environments, and synchronization mechanisms like models migration and merging, conflicts management, models versioning and rollback support. A body of knowledge in the scientific literature about collaborative model-driven software engineering (MDSE) exists. Still, those studies are scattered across different independent research areas, such as software engineering, model-driven engineering languages and systems, model integrated computing, etc., and a study classifying and comparing the various approaches and methods for collaborative MDSE is still missing. Under this perspective, a systematic mapping study (SMS) can help researchers and practitioners in (i) having a complete, comprehensive and valid picture of the state of the art about collaborative MDSE, and (ii) identifying potential gaps in current research and future research directions.
A Comparative Look into Public IXP Datasets
Venue: arXiv cs.NI 2016
Year: 2016

Internet eXchange Points (IXPs) are core components of the Internet infrastructure where Internet Service Providers (ISPs) meet and exchange traffic. During the last few years, the number and size of IXPs have increased rapidly, driving the flattening and shortening of Internet paths. However, understanding the present status of the IXP ecosystem and its potential role in shaping the future Internet requires rigorous data about IXPs, their presence, status, participants, etc. In this work, we do the first cross-comparison of three well-known publicly available IXP databases, namely of PeeringDB, Euro-IX, and PCH. A key challenge we address is linking IXP identifiers across databases maintained by different organizations. We find different AS-centric versus IXP-centric views provided by the databases as a result of their data collection approaches. In addition, we highlight differences and similarities w.r.t. IXP participants, geographical coverage, and co-location facilities. As a side-product of our linkage heuristics, we make publicly available the union of the three databases, which includes 40.2 % more IXPs and 66.3 % more IXP participants than the commonly-used PeeringDB. We also publish our analysis code to foster reproducibility of our experiments and shed preliminary insights into the accuracy of the union dataset.
Multispectral Deep Neural Networks for Pedestrian Detection
Venue: arXiv cs.CV 2016
Year: 2016

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.
Emulated Inertia and Damping of Converter-Interfaced Power Source
Venue: arXiv cs.SY 2016
Year: 2016

Converter-interfaced power sources (CIPSs), like wind turbine and energy storage, can be switched to the inertia emulation mode when the detected frequency deviation exceeds a pre-designed threshold, i.e. dead band, to support the frequency response of a power grid. This letter proposes an approach to derive the emulated inertia and damping from a CIPS based on the linearized model of the CIPS and the power grid, where the grid is represented by an equivalent single machine. The emulated inertia and damping can be explicitly expressed in time and turn out to be time-dependent.
Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.0)
Venue: arXiv cs.DC 2016
Year: 2016

In this document, we develop a structured approach to the management of HPC resilience based on the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. The catalog of resilience design patterns provides designers with reusable design elements. We define a design framework that enhances our understanding of the important constraints and opportunities for solutions deployed at various layers of the system stack. The framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also enables optimization of the cost-benefit trade-offs among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner in spite of frequent faults, errors, and failures of various types.
Minimum node degree in inhomogeneous random key graphs with unreliable links
Venue: arXiv cs.CR 2016
Year: 2016

We consider wireless sensor networks under a heterogeneous random key predistribution scheme and an on-off channel model. The heterogeneous key predistribution scheme has recently been introduced by Yağan - as an extension to the Eschenauer and Gligor scheme - for the cases when the network consists of sensor nodes with varying level of resources and/or connectivity requirements, e.g., regular nodes vs. cluster heads. The network is modeled by the intersection of the inhomogeneous random key graph (induced by the heterogeneous scheme) with an Erdős-Rényi graph (induced by the on/off channel model). We present conditions (in the form of zero-one laws) on how to scale the parameters of the intersection model so that with high probability all of its nodes are connected to at least $k$ other nodes; i.e., the minimum node degree of the graph is no less than $k$. We also present numerical results to support our results in the finite-node regime. The numerical results suggest that the conditions that ensure $k$-connectivity coincide with those ensuring the minimum node degree being no less than $k$.
A backward pass through a CNN using a generative model of its activations
Venue: arXiv cs.CV 2016
Year: 2016

Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space. For example, a convolutional neural network (CNN) mapping an image into one of a thousand object labels is approaching human performance in this particular task. However the mapping (neural network) does not automatically lend itself to other forms of queries, for example, to detect/reconstruct object instances, to enforce top-down signal on ambiguous inputs, or to recover object instances from occlusion. One way to address these queries is a backward pass through the network that fuses top-down and bottom-up information. In this paper, we show a way of building such a backward pass by defining a generative model of the neural network's activations. Approximate inference of the model would naturally take the form of a backward pass through the CNN layers, and it addresses the aforementioned queries in a unified framework.
Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms
Venue: arXiv cs.CV 2016
Year: 2016

Animal biometrics is an important requirement for monitoring and conservation tasks. The classical animal biometrics risk the animals' integrity, are expensive for numerous animals, and depend on expert criterion. The non-invasive biometrics techniques offer alternatives to manage the aforementioned problems. In this paper we propose an automatic segmentation and identification algorithm based on artificial vision algorithms to recognize Diploglossus millepunctatus. Diploglossus millepunctatus is an endangered lizard species. The algorithm is based on two stages: automatic segmentation to remove the subjective evaluation, and one identification stage to reduce the analysis time. A 82.87% of correct segmentation in average is reached. Meanwhile the identification algorithm is achieved with euclidean distance point algorithms such as Iterative Closest Point and Procrustes Analysis. A performance of 92.99% on the top 1, and a 96.82% on the top 5 is reached. The developed software, and the database used in this paper are publicly available for download from the web page of the project.
An Automated System for Essay Scoring of Online Exams in Arabic based on Stemming Techniques and Levenshtein Edit Operations
Venue: arXiv cs.IR 2016
Year: 2016

In this article, an automated system is proposed for essay scoring in Arabic language for online exams based on stemming techniques and Levenshtein edit operations. An online exam has been developed on the proposed mechanisms, exploiting the capabilities of light and heavy stemming. The implemented online grading system has shown to be an efficient tool for automated scoring of essay questions.
Transforming Hidden Vector Encryption Schemes from Composite to Prime Order Groups
Venue: arXiv cs.CR 2016
Year: 2016

Predicate encryption is a new type of public key encryption that enables searches on encrypted data. By using predicate encryption, we can search keywords or attributes on encrypted data without decrypting ciphertexts. Hidden vector encryption (HVE) is a special kind of predicate encryption. HVE supports the evaluation of conjunctive equality, comparison, and subset operations between attributes in ciphertexts and attributes in tokens. In this paper, we construct efficient HVE schemes in prime order bilinear groups derived from previous HVE schemes in composite order bilinear groups, and prove their selective security under simple assumptions. To achieve this result, we present a conversion method that transforms HVE schemes from composite order bilinear groups into prime order bilinear groups. Our method supports any types of prime order bilinear groups and uses simple assumptions.
Language Support for Reliable Memory Regions
Venue: arXiv cs.DC 2016
Year: 2016

The path to exascale computational capabilities in high-performance computing (HPC) systems is challenged by the inadequacy of present software technologies to adapt to the rapid evolution of architectures of supercomputing systems. The constraints of power have driven system designs to include increasingly heterogeneous architectures and diverse memory technologies and interfaces. Future systems are also expected to experience an increased rate of errors, such that the applications will no longer be able to assume correct behavior of the underlying machine. To enable the scientific community to succeed in scaling their applications, and to harness the capabilities of exascale systems, we need software strategies that provide mechanisms for explicit management of resilience to errors in the system, in addition to locality of reference in the complex memory hierarchies of future HPC systems. In prior work, we introduced the concept of explicitly reliable memory regions, called havens. Memory management using havens supports reliability management through a region-based approach to memory allocations. Havens enable the creation of robust memory regions, whose resilient behavior is guaranteed by software-based protection schemes. In this paper, we propose language support for havens through type annotations that make the structure of a program's havens more explicit and convenient for HPC programmers to use. We describe how the extended haven-based memory management model is implemented, and demonstrate the use of the language-based annotations to affect the resiliency of a conjugate gradient solver application.
Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910
Venue: arXiv cs.CL 2016
Year: 2016

Named Entity Recognition (NER), search, classification and tagging of names and name like frequent informational elements in texts, has become a standard information extraction procedure for textual data. NER has been applied to many types of texts and different types of entities: newspapers, fiction, historical records, persons, locations, chemical compounds, protein families, animals etc. In general a NER system's performance is genre and domain dependent and also used entity categories vary (Nadeau and Sekine, 2007). The most general set of named entities is usually some version of three partite categorization of locations, persons and organizations. In this paper we report first large scale trials and evaluation of NER with data out of a digitized Finnish historical newspaper collection Digi. Experiments, results and discussion of this research serve development of the Web collection of historical Finnish newspapers. Digi collection contains 1,960,921 pages of newspaper material from years 1771-1910 both in Finnish and Swedish. We use only material of Finnish documents in our evaluation. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70-75 % (Kettunen and Pääkkönen, 2016). Our principal NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of limited category semantic tagging with tools of the Semantic Computing Research Group (SeCo) of the Aalto University. Three other tools are also evaluated briefly. This research reports first published large scale results of NER in a historical Finnish OCRed newspaper collection. Results of the research supplement NER results of other languages with similar noisy data.
Towards a Stateful Forwarding Abstraction to Implement Scalable Network Functions in Software and Hardware
Venue: arXiv cs.NI 2016
Year: 2016

An effective packet processing abstraction that leverages software or hardware acceleration techniques can simplify the implementation of high-performance virtual network functions. In this paper, we explore the suitability of SDN switches' stateful forwarding abstractions to model accelerated functions in both software and hardware accelerators, such as optimized software switches and FPGA-based NICs. In particular, we select an Extended Finite State Machine abstraction and demonstrate its suitability by implementing the Linux's iptables interface. By doing so, we provide the acceleration of functions such as stateful firewalls, load balancers and dynamic NATs. We find that supporting a flow-level programming consistency model is an important feature of a programming abstraction in this context. Furthermore, we demonstrate that such a model simplifies the scaling of the system when implemented in software, enabling efficient multi-core processing without harming state consistency.
Coarse mesh partitioning for tree based AMR
Venue: arXiv cs.DC 2016
Year: 2016

In tree based adaptive mesh refinement, elements are partitioned between processes using a space filling curve. The curve establishes an ordering between all elements that derive from the same root element, the tree. When representing more complex geometries by patching together several trees, the roots of these trees form an unstructured coarse mesh. We present an algorithm to partition the elements of the coarse mesh such that (a) the fine mesh can be load-balanced to equal element counts per process regardless of the element-to-tree map and (b) each process that holds fine mesh elements has access to the meta data of all relevant trees. As an additional feature, the algorithm partitions the meta data of relevant ghost (halo) trees as well. We develop in detail how each process computes the communication pattern for the partition routine without handshaking and with minimal data movement. We demonstrate the scalability of this approach on up to 917e3 MPI ranks and .37e12 coarse mesh elements, measuring run times of one second or less.
Channel Estimation for Hybrid Architecture Based Wideband Millimeter Wave Systems
Venue: arXiv cs.IT 2016
Year: 2016

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.