OpenRaiser/Intern-Atlas huggingface dataset:
Benchmarking State-of-the-Art Deep Learning Software Tools
Venue: arXiv cs.DC 2017
Year: 2017

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. However, different tools exhibit different features and running performance when training different types of deep networks on different hardware platforms, which makes it difficult for end users to select an appropriate pair of software and hardware. In this paper, we aim to make a comparative study of the state-of-the-art GPU-accelerated deep learning software tools, including Caffe, CNTK, MXNet, TensorFlow, and Torch. We first benchmark the running performance of these tools with three popular types of neural networks on two CPU platforms and three GPU platforms. We then benchmark some distributed versions on multiple GPUs. Our contribution is two-fold. First, for end users of deep learning tools, our benchmarking results can serve as a guide to selecting appropriate hardware platforms and software tools. Second, for software developers of deep learning tools, our in-depth analysis points out possible future directions to further optimize the running performance.
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Venue: arXiv cs.CL 2017
Year: 2017

Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a result, accessing large collections of the former is much more difficult and time-consuming than the latter for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content and summarize the key information for humans to browse over. In this endeavor, a new task of machine comprehension of spoken content was proposed recently. The initial goal was defined as the listening comprehension test of TOEFL, a challenging academic English examination for English learners whose native languages are not English. An Attention-based Multi-hop Recurrent Neural Network (AMRNN) architecture was also proposed for this task, which considered only the sequential relationship within the speech utterances. In this paper, we propose a new Hierarchical Attention Model (HAM), which constructs multi-hopped attention mechanism over tree-structured rather than sequential representations for the utterances. Improved comprehension performance robust with respect to ASR errors were obtained.
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.
On Constructing Persistent Identifiers with Persistent Resolution Targets
Venue: arXiv cs.NI 2017
Year: 2017

Persistent Identifiers (PID) are the foundation referencing digital assets in scientific publications, books, and digital repositories. In its realization, PIDs contain metadata and resolving targets in form of URLs that point to data sets located on the network. In contrast to PIDs, the target URLs are typically changing over time; thus, PIDs need continuous maintenance -- an effort that is increasing tremendously with the advancement of e-Science and the advent of the Internet-of-Things (IoT). Nowadays, billions of sensors and data sets are subject of PID assignment. This paper presents a new approach of embedding location independent targets into PIDs that allows the creation of maintenance-free PIDs using content-centric network technology and overlay networks. For proving the validity of the presented approach, the Handle PID System is used in conjunction with Magnet Link access information encoding, state-of-the-art decentralized data distribution with BitTorrent, and Named Data Networking (NDN) as location-independent data access technology for networks. Contrasting existing approaches, no green-field implementation of PID or major modifications of the Handle System is required to enable location-independent data dissemination with maintenance-free PIDs.
LiRa: A New Likelihood-Based Similarity Score for Collaborative Filtering
Venue: arXiv cs.IR 2017
Year: 2017

Recommender system data presents unique challenges to the data mining, machine learning, and algorithms communities. The high missing data rate, in combination with the large scale and high dimensionality that is typical of recommender systems data, requires new tools and methods for efficient data analysis. Here, we address the challenge of evaluating similarity between two users in a recommender system, where for each user only a small set of ratings is available. We present a new similarity score, that we call LiRa, based on a statistical model of user similarity, for large-scale, discrete valued data with many missing values. We show that this score, based on a ratio of likelihoods, is more effective at identifying similar users than traditional similarity scores in user-based collaborative filtering, such as the Pearson correlation coefficient. We argue that our approach has significant potential to improve both accuracy and scalability in collaborative filtering.
Several Proofs of Security for a Tokenization Algorithm
Venue: arXiv cs.CR 2017
Year: 2017

In this paper we propose a tokenization algorithm of Reversible Hybrid type, as defined in PCI DSS guidelines for designing a tokenization solution, based on a block cipher with a secret key and (possibly public) additional input. We provide some formal proofs of security for it, which imply our algorithm satisfies the most significant security requirements described in PCI DSS tokenization guidelines. Finally, we give an instantiation with concrete cryptographic primitives and fixed length of the PAN, and we analyze its efficiency and security.
Large-Scale Antenna-Assisted Grant-free Non-Orthogonal Multiple Access via Compressed Sensing
Venue: arXiv cs.IT 2017
Year: 2017

Support massive connectivity is an important requirement in 5G wireless communication system. For massive Machine Type Communication (MTC) scenario, since the network is expected to accommodate a massive number of MTC devices with sparse short message, the multiple access scheme like current LTE uplink would not be suitable. In order to reduce the signaling overhead, we consider an grant-free multiple access system, which requires the receiver facilitate activity detection, channel estimation, and data decoding in "one shot" and without the knowledge of active user's pilots. However, most of the "one shot" communication research haven't considered the massive MIMO scenario. In this work we propose a Multiple Measurement Model (MMV) model based Massive MIMO and exploit the covariance matrix of the measurements to confirm a high activity detection rate.
Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework
Venue: arXiv cs.SY 2017
Year: 2017

A Learning Model Predictive Controller (LMPC) for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and non-increasing performance at each iteration. The paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
Energy Transparency for Deeply Embedded Programs
Venue: arXiv cs.OH 2017
Year: 2017

Energy transparency is a concept that makes a program's energy consumption visible, from hardware up to software, through the different system layers. Such transparency can enable energy optimizations at each layer and between layers, and help both programmers and operating systems make energy-aware decisions. In this paper, we focus on deeply embedded devices, typically used for Internet of Things (IoT) applications, and demonstrate how to enable energy transparency through existing Static Resource Analysis (SRA) techniques and a new target-agnostic profiling technique, without hardware energy measurements. Our novel mapping technique enables software energy consumption estimations at a higher level than the Instruction Set Architecture (ISA), namely the LLVM Intermediate Representation (IR) level, and therefore introduces energy transparency directly to the LLVM optimizer. We apply our energy estimation techniques to a comprehensive set of benchmarks, including single- and also multi-threaded embedded programs from two commonly used concurrency patterns, task farms and pipelines. Using SRA, our LLVM IR results demonstrate a high accuracy with a deviation in the range of 1% from the ISA SRA. Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of 3%.
Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging
Venue: arXiv cs.AI 2017
Year: 2017

Many AI applications rely on knowledge encoded in a locigal knowledge base (KB). The most essential benefit of such logical KBs is the opportunity to perform automatic reasoning which however requires a KB to meet some minimal quality criteria such as consistency. Without adequate tool assistance, the task of resolving such violated quality criteria in a KB can be extremely hard, especially when the problematic KB is large and complex. To this end, interactive KB debuggers have been introduced which ask a user queries whether certain statements must or must not hold in the intended domain. The given answers help to gradually restrict the search space for KB repairs. Existing interactive debuggers often rely on a pool-based strategy for query computation. A pool of query candidates is precomputed, from which the best candidate according to some query quality criterion is selected to be shown to the user. This often leads to the generation of many unnecessary query candidates and thus to a high number of expensive calls to logical reasoning services. We tackle this issue by an in-depth mathematical analysis of diverse real-valued active learning query selection measures in order to determine qualitative criteria that make a query favorable. These criteria are the key to devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation for interactive KB debugging while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability for the user, of the generated query that existing methods cannot realize. Further, we study different relations between active learning measures. The obtained picture gives a hint about which measures are more favorable in which situation or which measures always lead to the same outcomes, based on given types of queries.
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
Venue: arXiv cs.SE 2017
Year: 2017

Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners. Objective: We want to evaluate which method can find better parameters in terms of performance score and runtime cost. Methods: This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. Results: We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. When repeated 20 times to check for conclusion validity, DE was over 210 times faster than grid search to tune Random Forests on 17 testing data sets with F-Measure Conclusions: These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality-- an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.
A Threshold-based Scheme for Reinforcement Learning in Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

A generic and scalable Reinforcement Learning scheme for Artificial Neural Networks is presented, providing a general purpose learning machine. By reference to a node threshold three features are described 1) A mechanism for Primary Reinforcement, capable of solving linearly inseparable problems 2) The learning scheme is extended to include a mechanism for Conditioned Reinforcement, capable of forming long term strategy 3) The learning scheme is modified to use a threshold-based deep learning algorithm, providing a robust and biologically inspired alternative to backpropagation. The model may be used for supervised as well as unsupervised training regimes.
Feedback Motion Planning for Liquid Transfer using Supervised Learning
Venue: arXiv cs.RO 2017
Year: 2017

We present a novel motion planning algorithm for transferring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that takes into account fluid constraints and avoids collisions. In order to efficiently handle the high-dimensional configuration space of a liquid body, we use system identification to learn its dynamics characteristics using a neural network. We generate the training dataset using stochastic optimization in a transfer-problem-specific search space. The runtime feedback motion planner is used for real-time planning and we observe high success rate in our simulated 2D and 3D fluid transfer benchmarks.
Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing
Venue: arXiv cs.CL 2017
Year: 2017

We present a dependency parser implemented as a single deep neural network that reads orthographic representations of words and directly generates dependencies and their labels. Unlike typical approaches to parsing, the model doesn't require part-of-speech (POS) tagging of the sentences. With proper regularization and additional supervision achieved with multitask learning we reach state-of-the-art performance on Slavic languages from the Universal Dependencies treebank: with no linguistic features other than characters, our parser is as accurate as a transition- based system trained on perfect POS tags.
Complexity of Universality and Related Problems for Partially Ordered NFAs
Venue: arXiv cs.FL 2017
Year: 2017

Partially ordered nondeterminsitic finite automata (poNFAs) are NFAs whose transition relation induces a partial order on states, that is, for which cycles occur only in the form of self-loops on a single state. A poNFA is universal if it accepts all words over its input alphabet. Deciding universality is PSPACE-complete for poNFAs, and we show that this remains true even when restricting to a fixed alphabet. This is nontrivial since standard encodings of alphabet symbols in, e.g., binary can turn self-loops into longer cycles. A lower coNP-complete complexity bound can be obtained if we require that all self-loops in the poNFA are deterministic, in the sense that the symbol read in the loop cannot occur in any other transition from that state. We find that such restricted poNFAs (rpoNFAs) characterise the class of $\mathcal{R}$-trivial languages, and we establish the complexity of deciding if the language of an NFA is $\mathcal{R}$-trivial. Nevertheless, the limitation to fixed alphabets turns out to be essential even in the restricted case: deciding universality of rpoNFAs with unbounded alphabets is PSPACE-complete. Based on a close relation between universality and the problems of inclusion and equivalence, we also obtain the complexity results for these two problems. Finaly, we show that the languages of rpoNFAs are definable by deterministic (one-unambiguous) regular expressions, which makes them interesting in schema languages for XML data.
DOA estimation in structured phase-noisy environments: technical report
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we focus on the problem of estimating the directions of arrival (DOA) of a set of incident plane waves. Unlike many previous works, which assume that the received observations are only affected by additive noise, we consider the setup where some phase noise also corrupts the data (as for example observed in atmospheric sound propagation or underwater acoustics). We propose a new methodology to solve this problem in a Bayesian framework by resorting to a variational mean-field approximation. Our simulation results illustrate the benefits of carefully accounting for the phase noise in the DOA estimation process.
When to make a step? Tackling the timing problem in multi-contact locomotion by TOPP-MPC
Venue: arXiv cs.RO 2017
Year: 2017

We present a model predictive controller (MPC) for multi-contact locomotion where predictive optimizations are realized by time-optimal path parameterization (TOPP). A key feature of this solution is that, contrary to existing planners where step timings are provided as inputs, here the timing between contact switches is computed as output of a fast nonlinear optimization. This is particularly appealing to multi-contact locomotion, where proper timings depend on terrain topology and suitable heuristics are unknown. We show how to formulate legged locomotion as a TOPP problem and demonstrate the behavior of the resulting TOPP-MPC controller in simulations with a model of the HRP-4 humanoid robot.
Optimal Control of Large-Scale Networks using Clustering Based Projections
Venue: arXiv cs.SY 2017
Year: 2017

In this paper we present a set of projection-based designs for constructing simplified linear quadratic regulator (LQR) controllers for large-scale network systems. When such systems have tens of thousands of states, the design of conventional LQR controllers becomes numerically challenging, and their implementation requires a large number of communication links. Our proposed algorithms bypass these difficulties by clustering the system states using structural properties of its closed-loop transfer matrix. The assignment of clusters is defined through a structured projection matrix P, which leads to a significantly lower-dimensional LQR design. The reduced-order controller is finally projected back to the original coordinates via an inverse projection. The problem is, therefore, posed as a model matching problem of finding the optimal set of clusters or P that minimizes the H2-norm of the error between the transfer matrix of the full-order network with the full-order LQR and that with the projected LQR. We derive a tractable relaxation for this model matching problem, and design a P that solves the relaxation. The design is shown to be implementable by a convenient, hierarchical two-layer control architecture, requiring far less number of communication links than full-order LQR.
Improved Lower Bounds on the Size of Balls over Permutations with the Infinity Metric
Venue: arXiv cs.IT 2017
Year: 2017

We study the size (or volume) of balls in the metric space of permutations, $S_n$, under the infinity metric. We focus on the regime of balls with radius $r = ρ\cdot (n\!-\!1)$, $ρ\in [0,1]$, i.e., a radius that is a constant fraction of the maximum possible distance. We provide new lower bounds on the size of such balls. These new lower bounds reduce the asymptotic gap to the known upper bounds to at most $0.029$ bits per symbol. Additionally, they imply an improved ball-packing bound for error-correcting codes, and an improved upper bound on the size of optimal covering codes.
Citation Analysis with Microsoft Academic
Venue: arXiv cs.DL 2017
Year: 2017

We explore if and how Microsoft Academic (MA) could be used for bibliometric analyses. First, we examine the Academic Knowledge API (AK API), an interface to access MA data, and compare it to Google Scholar (GS). Second, we perform a comparative citation analysis of researchers by normalizing data from MA and Scopus. We find that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing. In addition, the AK API allows retrieving frequency distributions of citations. We consider these features to be a major advantage of MA over GS. However, we identify four main limitations regarding the available metadata. First, MA does not provide the document type of a publication. Second, the 'fields of study' are dynamic, too specific and field hierarchies are incoherent. Third, some publications are assigned to incorrect years. Fourth, the metadata of some publications did not include all authors. Nevertheless, we show that an average-based indicator (i.e. the journal normalized citation score; JNCS) as well as a distribution-based indicator (i.e. percentile rank classes; PR classes) can be calculated with relative ease using MA. Hence, normalization of citation counts is feasible with MA. The citation analyses in MA and Scopus yield uniform results. The JNCS and the PR classes are similar in both databases, and, as a consequence, the evaluation of the researchers' publication impact is congruent in MA and Scopus. Given the fast development in the last year, we postulate that MA has the potential to be used for full-fledged bibliometric analyses.