OpenRaiser/Intern-Atlas huggingface dataset:
JSClassFinder: A Tool to Detect Class-like Structures in JavaScript
Venue: arXiv cs.SE 2016
Year: 2016

With the increasing usage of JavaScript in web applications, there is a great demand to write JavaScript code that is reliable and maintainable. To achieve these goals, classes can be emulated in the current JavaScript standard version. In this paper, we propose a reengineering tool to identify such class-like structures and to create an object-oriented model based on JavaScript source code. The tool has a parser that loads the AST (Abstract Syntax Tree) of a JavaScript application to model its structure. It is also integrated with the Moose platform to provide powerful visualization, e.g., UML diagram and Distribution Maps, and well-known metric values for software analysis. We also provide some examples with real JavaScript applications to evaluate the tool.
Reachability in Networks of Register Protocols under Stochastic Schedulers
Venue: arXiv cs.LO 2016
Year: 2016

We study the almost-sure reachability problem in a distributed system obtained as the asynchronous composition of N copies (called processes) of the same automaton (called protocol), that can communicate via a shared register with finite domain. The automaton has two types of transitions: write-transitions update the value of the register, while read-transitions move to a new state depending on the content of the register. Non-determinism is resolved by a stochastic scheduler. Given a protocol, we focus on almost-sure reachability of a target state by one of the processes. The answer to this problem naturally depends on the number N of processes. However, we prove that our setting has a cut-off property: the answer to the almost-sure reachability problem is constant when N is large enough; we then develop an EXPSPACE algorithm deciding whether this constant answer is positive or negative.
Multi-resolution Compressive Sensing Reconstruction
Venue: arXiv cs.CV 2016
Year: 2016

We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple regions, each one with a different resolution. This problem arises in situations where the image to reconstruct contains a certain region of interest (RoI) that is more important than the rest. Through a theoretical analysis and simulation experiments we show that the multi-resolution reconstruction provides a higher quality of the RoI compared to the traditional single-resolution approach.
On the Nonexistence of the Ding-Helleseth-Martinsens Constructions of Almost Difference Set for Cyclotomic Classes of Order 6
Venue: arXiv cs.CR 2016
Year: 2016

Pseudorandom sequences with optimal three-level autocorrelation have important applications in CDMA communication systems. Constructing the sequences with three-level autocorrelation is equivalent to finding cyclic almost difference sets as their supports. In a paper of Ding, Helleseth, and Martinsen, the authors developed a new method known as the Ding-Helleseth-Martinsens Constructions in literature to construct the almost difference set using product set between GF(2) and union sets of cyclotomic classes of order 4. In this correspondence, we show that there do not exist such constructions for cyclotomic classes of order 6.
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Venue: arXiv cs.CL 2016
Year: 2016

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.
Automatic Generation of High-Coverage Tests for RTL Designs using Software Techniques and Tools
Venue: arXiv cs.SE 2016
Year: 2016

Register Transfer Level (RTL) design validation is a crucial stage in the hardware design process. We present a new approach to enhancing RTL design validation using available software techniques and tools. Our approach converts the source code of a RTL design into a C++ software program. Then a powerful symbolic execution engine is employed to execute the converted C++ program symbolically to generate test cases. To better generate efficient test cases, we limit the number of cycles to guide symbolic execution. Moreover, we add bit-level symbolic variable support into the symbolic execution engine. Generated test cases are further evaluated by simulating the RTL design to get accurate coverage. We have evaluated the approach on a floating point unit (FPU) design. The preliminary results show that our approach can deliver high-quality tests to achieve high coverage.
Uniresolution representations of white-matter data from CoCoMac
Venue: arXiv cs.NE 2016
Year: 2016

Tracing data as collated by CoCoMac, a seminal neuroinformatics database, is at multiple resolutions -- white matter tracts were studied for areas and their subdivisions by different reports. Network theoretic analysis of this multi-resolution data often assumes that the data at various resolutions is equivalent, which may not be correct. In this paper we propose three methods to resolve the multi-resolution issue such that the resultant networks have connectivity data at only one resolution. The different resultant networks are compared in terms of their network analysis metrics and degree distributions.
Competitive Path Computation and Function Placement in SDNs
Venue: arXiv cs.DS 2016
Year: 2016

We consider a task of serving requests that arrive in an online fashion in Software-Defined Networks (SDNs) with network function virtualization (NFV). Each request specifies an abstract routing and processing "plan" for a flow. Each processing function can be performed by a specified subset of servers in the system. The algorithm needs to either reject the request or admit it and return detailed routing (a.k.a. "path computation") and processing assignment ("function placement"). Each request also specifies the communication bandwidth and the processing load it requires. Components in the system (links and processors) have bounded capacity; a feasible solution may not violate the capacity constraints. Requests have benefits and the goal is to maximize the total benefit of accepted requests. In this paper we first formalize the problem, and propose a new service model that allows us to cope with requests with unknown duration. The new service model augments the traditional accept/reject schemes with a new possible response of "stand by." Our main result is an online algorithm for path computation and function placement that guarantees, in each time step, throughput of at least $Ω\left(\frac{\text{OPT}^*}{\log n}\right)$, where $n$ is the system size and $\text{OPT}^*$ is an upper bound on the maximal possible throughput. The guarantee holds assuming that requests ask for at most an $O\left(1/{\log n}\right)$-fraction of the capacity of any component in the system. Furthermore, the guarantee holds even though our algorithm serves requests in an all-or-nothing fashion using a single path and never preempts accepted flows, while $\text{OPT}^*$ may serve fractional requests, may split the allocation over multiple paths, and may arbitrarily preempt and resume service of requests.
Context-guided diffusion for label propagation on graphs
Venue: arXiv cs.CV 2016
Year: 2016

Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regularizer. Inspired by the success of diffusivity tensors for anisotropic diffusion in image processing, we presents anisotropic diffusion on graphs and the corresponding label propagation algorithm. We develop positive definite diffusivity operators on the vector bundles of Riemannian manifolds, and discretize them to diffusivity operators on graphs. This enables us to easily define new robust diffusivity operators which significantly improve semi-supervised learning performance over existing diffusion algorithms.
Robust Recognition of Simultaneous Speech By a Mobile Robot
Venue: arXiv cs.RO 2016
Year: 2016

This paper describes a system that gives a mobile robot the ability to perform automatic speech recognition with simultaneous speakers. A microphone array is used along with a real-time implementation of Geometric Source Separation and a post-filter that gives a further reduction of interference from other sources. The post-filter is also used to estimate the reliability of spectral features and compute a missing feature mask. The mask is used in a missing feature theory-based speech recognition system to recognize the speech from simultaneous Japanese speakers in the context of a humanoid robot. Recognition rates are presented for three simultaneous speakers located at 2 meters from the robot. The system was evaluated on a 200 word vocabulary at different azimuths between sources, ranging from 10 to 90 degrees. Compared to the use of the microphone array source separation alone, we demonstrate an average reduction in relative recognition error rate of 24% with the post-filter and of 42% when the missing features approach is combined with the post-filter. We demonstrate the effectiveness of our multi-source microphone array post-filter and the improvement it provides when used in conjunction with the missing features theory.
Developing a Repeating Model Using the Structured Spreadsheet Modelling and Implementation Methodology
Venue: arXiv cs.SE 2016
Year: 2016

Spreadsheets often have variables and formulas that are similar, differing only by the fact that they refer to different instances of an entity. For example, the calculation of the sales revenues of the South and East regions are Revenues South = Price * Quantity Sold South and Revenues East = Price * Quantity Sold East. In this paper, we present a conceptual modelling approach that takes advantage of these similarities and leads the spreadsheet developer to the formula Revenues = Price * Quantity. We then present simple but strict rules to implement the spreadsheet.
Undermining and Strengthening Social Networks through Network Modification
Venue: arXiv cs.SI 2016
Year: 2016

Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.
The Singularity May Never Be Near
Venue: arXiv cs.AI 2016
Year: 2016

There is both much optimism and pessimism around artificial intelligence (AI) today. The optimists are investing millions of dollars, and even in some cases billions of dollars into AI. The pessimists, on the other hand, predict that AI will end many things: jobs, warfare, and even the human race. Both the optimists and the pessimists often appeal to the idea of a technological singularity, a point in time where machine intelligence starts to run away, and a new, more intelligent species starts to inhabit the earth. If the optimists are right, this will be a moment that fundamentally changes our economy and our society. If the pessimists are right, this will be a moment that also fundamentally changes our economy and our society. It is therefore very worthwhile spending some time deciding if either of them might be right.
Fuzzy Opinion Networks: A Mathematical Framework for the Evolution of Opinions and Their Uncertainties Across Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

We propose a new mathematical framework for the evolution and propagation of opinions, called Fuzzy Opinion Network, which is the connection of a number of Gaussian Nodes, possibly through some weighted average, time-delay or logic operators, where a Gaussian Node is a Gaussian fuzzy set with the center and the standard deviation being the node inputs and the fuzzy set itself being the node output. In this framework an opinion is modeled as a Gaussian fuzzy set with the center representing the opinion itself and the standard deviation characterizing the uncertainty about the opinion. We study the basic connections of Fuzzy Opinion Networks, including basic center, basic standard deviation (sdv), basic center-sdv, chain-in-center and chain-in-sdv connections, and we analyze a number of dynamic connections to show how opinions and their uncertainties propagate and evolve across different network structures and scenarios. We explain what insights we might gain from these mathematical results about the formation and evolution of human opinions.
Robust Resource Allocation for Full-Duplex Cognitive Radio Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate resource allocation algorithm design for full-duplex (FD) cognitive radio systems. The secondary network employs a FD base station for serving multiple half-duplex downlink and uplink users simultaneously. We study the resource allocation design for minimizing the maximum interference leakage to primary users while providing quality of service for secondary users. The imperfectness of the channel state information of the primary users is taken into account for robust resource allocation algorithm design. The algorithm design is formulated as a non-convex optimization problem and solved optimally by applying semidefinite programming (SDP) relaxation. Simulation results not only show the significant reduction in interference leakage compared to baseline schemes, but also confirm the robustness of the proposed algorithm.
Automatic Building Extraction in Aerial Scenes Using Convolutional Networks
Venue: arXiv cs.CV 2016
Year: 2016

Automatic building extraction from aerial and satellite imagery is highly challenging due to extremely large variations of building appearances. To attack this problem, we design a convolutional network with a final stage that integrates activations from multiple preceding stages for pixel-wise prediction, and introduce the signed distance function of building boundaries as the output representation, which has an enhanced representation power. We leverage abundant building footprint data available from geographic information systems (GIS) to compile training data. The trained network achieves superior performance on datasets that are significantly larger and more complex than those used in prior work, demonstrating that the proposed method provides a promising and scalable solution for automating this labor-intensive task.
Towards Unified Combinatorial Interaction Testing
Venue: arXiv cs.SE 2016
Year: 2016

We believe that we can exploit the benefits of combinatorial interaction testing (CIT) on many "non-traditional" combinatorial spaces using many "non-traditional" coverage criteria. However, this requires truly flexible CIT approaches. To this end, we introduce Unified Combinatorial Interaction Testing (U-CIT), which enables practitioners to define their own combinatorial spaces and coverage criteria for testing, and present a unified construction approach to compute specific instances of U-CIT objects. We, furthermore, argue that most (if not all) existing CIT objects are a special case of U-CIT and demonstrate the flexibility of U-CIT on a simple, yet realistic scenario.
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
Venue: arXiv cs.DC 2016
Year: 2016

We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior. We perform a detailed analysis of scaling, and identify optimal design points for different networks. We demonstrate scaling of CNNs on 100s of nodes, and present what we believe to be record training throughputs. A 512 minibatch VGG-A CNN training run is scaled 90X on 128 nodes. Also 256 minibatch VGG-A and OverFeat-FAST networks are scaled 53X and 42X respectively on a 64 node cluster. We also demonstrate the generality of our approach via best-in-class 6.5X scaling for a 7-layer DNN on 16 nodes. Thereafter we attempt to democratize deep-learning by training on an Ethernet based AWS cluster and show ~14X scaling on 16 nodes.
Sensing Throughput Optimization in Fading Cognitive Multiple Access Channels With Energy Harvesting Secondary Transmitters
Venue: arXiv cs.IT 2016
Year: 2016

The paper investigates the problem of maximizing expected sum throughput in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of sum-capacity of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer non-linear program with respect to two decision variables namely spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU \textit{transmission power} and \textit{sensing time}, which is non-convex in nature. We solve the resulting optimization problem as an alternating convex optimization problem for both non-causal and causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC). We present an analytic solution for the non-causal scenario with infinite battery capacity for a general finite horizon problem.We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.
Fast Online k-nn Graph Building
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we propose an online approximate k-nn graph building algorithm, which is able to quickly update a k-nn graph using a flow of data points. One very important step of the algorithm consists in using the current distributed graph to search for the neighbors of a new node. Hence we also propose a distributed partitioning method based on balanced k-medoids clustering, that we use to optimize the distributed search process. Finally, we present the improved sequential search procedure that is used inside each partition. We also perform an experimental evaluation of the different algorithms, where we study the influence of the parameters and compare the result of our algorithms to existing state of the art. This experimental evaluation confirms that the fast online k-nn graph building algorithm produces a graph that is highly similar to the graph produced by an offline exhaustive algorithm, while it requires less similarity computations.
Understanding Visual Concepts with Continuation Learning
Venue: arXiv cs.LG 2016
Year: 2016

We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations. We propose to learn these concepts by observing consecutive frames, letting all the components of the hidden representation except a small discrete set (gating units) be predicted from the previous frame, and let the factors of variation in the next frame be represented entirely by these discrete gated units (corresponding to symbolic representations). We demonstrate the efficacy of our approach on datasets of faces undergoing 3D transformations and Atari 2600 games.
Higher-Order Low-Rank Regression
Venue: arXiv cs.LG 2016
Year: 2016

This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate the regression problem as the minimization of a least square criterion under a multilinear rank constraint, a difficult non convex problem. HOLRR computes efficiently an approximate solution of this problem, with solid theoretical guarantees. A kernel extension is also presented. Experiments on synthetic and real data show that HOLRR outperforms multivariate and multilinear regression methods and is considerably faster than existing tensor methods.
Blind score normalization method for PLDA based speaker recognition
Venue: arXiv cs.CL 2016
Year: 2016

Probabilistic Linear Discriminant Analysis (PLDA) has become state-of-the-art method for modeling $i$-vector space in speaker recognition task. However the performance degradation is observed if enrollment data size differs from one speaker to another. This paper presents a solution to such problem by introducing new PLDA scoring normalization technique. Normalization parameters are derived in a blind way, so that, unlike traditional \textit{ZT-norm}, no extra development data is required. Moreover, proposed method has shown to be optimal in terms of detection cost function. The experiments conducted on NIST SRE 2014 database demonstrate an improved accuracy in a mixed enrollment number condition.
Empath: Understanding Topic Signals in Large-Scale Text
Venue: arXiv cs.CL 2016
Year: 2016

Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by deep learning a neural embedding across more than 1.8 billion words of modern fiction. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated from common topics in our web dataset, like neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.