OpenRaiser/Intern-Atlas huggingface dataset:
Convolutional Residual Memory Networks
Venue: arXiv cs.CV 2016
Year: 2016

Very deep convolutional neural networks (CNNs) yield state of the art results on a wide variety of visual recognition problems. A number of state of the the art methods for image recognition are based on networks with well over 100 layers and the performance vs. depth trend is moving towards networks in excess of 1000 layers. In such extremely deep architectures the vanishing or exploding gradient problem becomes a key issue. Recent evidence also indicates that convolutional networks could benefit from an interface to explicitly constructed memory mechanisms interacting with a CNN feature processing hierarchy. Correspondingly, we propose and evaluate a memory mechanism enhanced convolutional neural network architecture based on augmenting convolutional residual networks with a long short term memory mechanism. We refer to this as a convolutional residual memory network. To the best of our knowledge this approach can yield state of the art performance on the CIFAR-100 benchmark and compares well with other state of the art techniques on the CIFAR-10 and SVHN benchmarks. This is achieved using networks with more breadth, much less depth and much less overall computation relative to comparable deep ResNets without the memory mechanism. Our experiments and analysis explore the importance of the memory mechanism, network depth, breadth, and predictive performance.
The Pattern Basis Approach to Circuit Complexity
Venue: arXiv cs.CC 2016
Year: 2016

We describe and motivate a proposed new approach to lowerbounding the circuit complexity of boolean functions, based on a new formalization of "patterns" as elements of a special basis of the vector space of all truth table properties. We prove that a "pattern basis" with certain properties would lead to a useful complexity formula of a specific form, and speculate on how to find such a basis. This formula might take as long to compute on arbitrary functions as a brute-force search among circuits, thus addressing the natural proofs barrier, but has a form amenable to proving lower bounds for well-understood explicit functions.
Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2010-2014)
Venue: arXiv cs.DL 2016
Year: 2016

The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered by Google Scholar Metrics and ranked according to their h-index. Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2010-2014). The searches were conducted between the 8th and 10th of December, 2015. A total of 1501 proceedings have been identified
Parallel Reachability Analysis for Hybrid Systems
Venue: arXiv cs.DC 2016
Year: 2016

We propose two parallel state-space exploration algorithms for hybrid systems with the goal of enhancing performance on multi-core shared memory systems. The first is an adaption of the parallel breadth first search in the SPIN model checker. We show that the adapted algorithm does not provide the desired load balancing for many hybrid systems benchmarks. The second is a task parallel algorithm based on cheaply precomputing cost of post (continuous and discrete) operations for effective load balancing. We illustrate the task parallel algorithm and the cost precomputation of post operators on a support-function-based algorithm for state-space exploration. The performance comparison of the two algorithms displays a better CPU utilization/load-balancing of the second over the first, except for certain cases. The algorithms are implemented in the model checker XSpeed and our experiments show a maximum speed-up of $900\times$ on a navigation benchmark with respect to SpaceEx LGG scenario, comparing on the basis of equal number of post operations evaluated.
An Orthogonal 16-point Approximate DCT for Image and Video Compression
Venue: arXiv cs.IT 2016
Year: 2016

A low-complexity orthogonal multiplierless approximation for the 16-point discrete cosine transform (DCT) was introduced. The proposed method was designed to possess a very low computational cost. A fast algorithm based on matrix factorization was proposed requiring only 60~additions. The proposed architecture outperforms classical and state-of-the-art algorithms when assessed as a tool for image and video compression. Digital VLSI hardware implementations were also proposed being physically realized in FPGA technology and implemented in 45 nm up to synthesis and place-route levels. Additionally, the proposed method was embedded into a high efficiency video coding (HEVC) reference software for actual proof-of-concept. Obtained results show negligible video degradation when compared to Chen DCT algorithm in HEVC.
Computing all Space Curve Solutions of Polynomial Systems by Polyhedral Methods
Venue: arXiv cs.SC 2016
Year: 2016

A polyhedral method to solve a system of polynomial equations exploits its sparse structure via the Newton polytopes of the polynomials. We propose a hybrid symbolic-numeric method to compute a Puiseux series expansion for every space curve that is a solution of a polynomial system. The focus of this paper concerns the difficult case when the leading powers of the Puiseux series of the space curve are contained in the relative interior of a higher dimensional cone of the tropical prevariety. We show that this difficult case does not occur for polynomials with generic coefficients. To resolve this case, we propose to apply polyhedral end games to recover tropisms hidden in the tropical prevariety.
Research on Wikipedia Vandalism: a brief literature review
Venue: arXiv cs.DL 2016
Year: 2016

Research on vandalism in Wikipedia has been of interest for the last decade. This paper performs a literature review on the subject, with the goal of identifying the main research topics and approaches, methods and techniques used. 67 papers have been reviewed. Main topic is the detection of vandalism, although there is a increasing interest about content quality. The most commonly used technique is machine learning, based on feature analysis. It draws attention to the lack of research on information behavior of vandals.
Socially-Informed Timeline Generation for Complex Events
Venue: arXiv cs.CL 2016
Year: 2016

Existing timeline generation systems for complex events consider only information from traditional media, ignoring the rich social context provided by user-generated content that reveals representative public interests or insightful opinions. We instead aim to generate socially-informed timelines that contain both news article summaries and selected user comments. We present an optimization framework designed to balance topical cohesion between the article and comment summaries along with their informativeness and coverage of the event. Automatic evaluations on real-world datasets that cover four complex events show that our system produces more informative timelines than state-of-the-art systems. In human evaluation, the associated comment summaries are furthermore rated more insightful than editor's picks and comments ranked highly by users.
On Reward Function for Survival
Venue: arXiv cs.AI 2016
Year: 2016

Obtaining a survival strategy (policy) is one of the fundamental problems of biological agents. In this paper, we generalize the formulation of previous research related to the survival of an agent and we formulate the survival problem as a maximization of the multi-step survival probability in future time steps. We introduce a method for converting the maximization of multi-step survival probability into a classical reinforcement learning problem. Using this conversion, the reward function (negative temporal cost function) is expressed as the log of the temporal survival probability. And we show that the objective function of the reinforcement learning in this sense is proportional to the variational lower bound of the original problem. Finally, We empirically demonstrate that the agent learns survival behavior by using the reward function introduced in this paper.
Regression-based Online Anomaly Detection for Smart Grid Data
Venue: arXiv cs.DB 2016
Year: 2016

With the widely used smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative detection model refreshment from scalable data sets, but also real-time detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the proposed lambda detection system.
An Efficient Cluster-based Routing Protocol in Cognitive Radio Net-work
Venue: arXiv cs.NI 2016
Year: 2016

Cognitive Radio Networks (CRNs) are being studied intensively and gaining importance as spectrum is the heavily underutilized. CRN has the capability to exploit smartly the unutilized frequency spectrum. Recently, the research community started to work in the area of cognitive radio routing. In a flat topology, all nodes are of the same level and functionality, thus making it simple and efficient for smaller networks. However, when the network is large with sparse nodes, the routing information becomes more complex making cluster-based techniques really relevant to tackle such situations. In a cluster-based routing, all nodes in the network are dynamically organized into partitions called groups or clusters. In each cluster, a cluster head is chosen to help in the data transmission management and to maintain cluster membership information. This paper proposes a novel routing protocol for cognitive radio ad hoc networks (CRAHNs) based on clustering model which amends swiftly to the topological changes and establishes the routing efficiently. Our proposed approach is thoroughly evaluated through simulation study. The results state the suitability of the proposed protocol for cognitive radio ad hoc networks and demonstrate that it has better performance in terms of finding the source-destination route, reducing the amount of messages that are transmitted all over the network and minimizing the routing delay.
You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors
Venue: arXiv cs.SI 2016
Year: 2016

We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users, via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.
Preserving Color in Neural Artistic Style Transfer
Venue: arXiv cs.CV 2016
Year: 2016

This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.). The original algorithm transforms an image to have the style of another given image. For example, a photograph can be transformed to have the style of a famous painting. Here we address a potential shortcoming of the original method: the algorithm transfers the colors of the original painting, which can alter the appearance of the scene in undesirable ways. We describe simple linear methods for transferring style while preserving colors.
Digital Calibration Method for High Resolution in Analog/RF Designs
Venue: arXiv cs.ET 2016
Year: 2016

Transistor random mismatch continuously poses challenges for analog/RF circuit design for achieving high accuracy and high yield as the process technology advances. Existing statistical element selection (SES) design method can improve transistor matching property, but it falls short of being a general calibration method due to its limited calibration range. In this dissertation, we propose a high resolution digital calibration method, called extended statistical element selection (ESES). As compared to the SES method, the ESES method not only provides wider calibration range, but also it results in higher calibration yield with same calibration resolution target. Two types of ESES based calibration application in analog/RF circuits are also proposed. One is current source calibration and the other is phase/delay calibration. To verify this proposed digital calibration method in circuit implementation, we designed, fabricated and tested a wideband harmonic rejection receiver design. The receiver utilizes ESES-based gain and phase error calibration for improving harmonic rejection ratios. With the high calibration resolution provided by the ESES method, after calibration, we achieved best-in-class harmonic rejection ratios. To extend the application of the proposed method, we further designed a current-steering D/A data converter (CS-DAC). The CS-DAC utilizes ESES-based amplitude and timing error calibration for improving linearity performance. Simulation results showed that we can achieve more than one order of magnitude linearity improvement after performing ESES-based calibration in the CS-DAC.
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
Venue: arXiv cs.NE 2016
Year: 2016

Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling layers and fully connected layers. In this paper, we propose to apply a novel method, namely Hybrid Orthogonal Projection and Estimation (HOPE), to CNNs in order to introduce orthogonality into the CNN structure. The HOPE model can be viewed as a hybrid model to combine feature extraction using orthogonal linear projection with mixture models. It is an effective model to extract useful information from the original high-dimension feature vectors and meanwhile filter out irrelevant noises. In this work, we present three different ways to apply the HOPE models to CNNs, i.e., {\em HOPE-Input}, {\em single-HOPE-Block} and {\em multi-HOPE-Blocks}. For {\em HOPE-Input} CNNs, a HOPE layer is directly used right after the input to de-correlate high-dimension input feature vectors. Alternatively, in {\em single-HOPE-Block} and {\em multi-HOPE-Blocks} CNNs, we consider to use HOPE layers to replace one or more blocks in the CNNs, where one block may include several convolutional layers and one pooling layer. The experimental results on both Cifar-10 and Cifar-100 data sets have shown that the orthogonal constraints imposed by the HOPE layers can significantly improve the performance of CNNs in these image classification tasks (we have achieved one of the best performance when image augmentation has not been applied, and top 5 performance with image augmentation).
Reversible Sessions Using Monitors
Venue: arXiv cs.LO 2016
Year: 2016

Much research has studied foundations for correct and reliable communication-centric systems. A salient approach to correctness uses session types to enforce structured communications; a recent approach to reliability uses reversible actions as a way of reacting to unanticipated events or failures. This note develops a simple observation: the machinery required to define asynchronous semantics and monitoring can also support reversible protocols. We propose a process framework of session communication in which monitors support reversibility. A key novelty in our approach are session types with present and past, which allow us to streamline the semantics of reversible actions.
Future-based Static Analysis of Message Passing Programs
Venue: arXiv cs.LO 2016
Year: 2016

Message passing is widely used in industry to develop programs consisting of several distributed communicating components. Developing functionally correct message passing software is very challenging due to the concurrent nature of message exchanges. Nonetheless, many safety-critical applications rely on the message passing paradigm, including air traffic control systems and emergency services, which makes proving their correctness crucial. We focus on the modular verification of MPI programs by statically verifying concrete Java code. We use separation logic to reason about local correctness and define abstractions of the communication protocol in the process algebra used by mCRL2. We call these abstractions futures as they predict how components will interact during program execution. We establish a provable link between futures and program code and analyse the abstract futures via model checking to prove global correctness. Finally, we verify a leader election protocol to demonstrate our approach.
Debugging OpenStack Problems Using a State Graph Approach
Venue: arXiv cs.DC 2016
Year: 2016

It is hard to operate and debug systems like OpenStack that integrate many independently developed modules with multiple levels of abstractions. A major challenge is to navigate through the complex dependencies and relationships of the states in different modules or subsystems, to ensure the correctness and consistency of these states. We present a system that captures the runtime states and events from the entire OpenStack-Ceph stack, and automatically organizes these data into a graph that we call system operation state graph (SOSG).With SOSG we can use intuitive graph traversal techniques to solve problems like reasoning about the state of a virtual machine. Also, using graph-based anomaly detection, we can automatically discover hidden problems in OpenStack. We have a scalable implementation of SOSG, and evaluate the approach on a 125-node production OpenStack cluster, finding a number of interesting problems.
A New Parallel Algorithm for Two-Pass Connected Component Labeling
Venue: arXiv cs.DS 2016
Year: 2016

Connected Component Labeling (CCL) is an important step in pattern recognition and image processing. It assigns labels to the pixels such that adjacent pixels sharing the same features are assigned the same label. Typically, CCL requires several passes over the data. We focus on two-pass technique where each pixel is given a provisional label in the first pass whereas an actual label is assigned in the second pass. We present a scalable parallel two-pass CCL algorithm, called PAREMSP, which employs a scan strategy and the best union-find technique called REMSP, which uses REM's algorithm for storing label equivalence information of pixels in a 2-D image. In the first pass, we divide the image among threads and each thread runs the scan phase along with REMSP simultaneously. In the second phase, we assign the final labels to the pixels. As REMSP is easily parallelizable, we use the parallel version of REMSP for merging the pixels on the boundary. Our experiments show the scalability of PAREMSP achieving speedups up to $20.1$ using $24$ cores on shared memory architecture using OpenMP for an image of size $465.20$ MB. We find that our proposed parallel algorithm achieves linear scaling for a large resolution fixed problem size as the number of processing elements are increased. Additionally, the parallel algorithm does not make use of any hardware specific routines, and thus is highly portable.
Nonlinear Interference Alignment in a One-dimensional Space
Venue: arXiv cs.IT 2016
Year: 2016

Real interference alignment is efficient in breaking-up a one-dimensional space over time-invariant channels into fractional dimensions. As such, multiple symbols can be simultaneously transmitted with fractional degrees-of-freedom (DoF). Of particular interest is when the one dimensional space is partitioned into two fractional dimensions. In such scenario, the interfering signals are confined to one sub-space and the intended signal is confined to the other sub-space. Existing real interference alignment schemes achieve near-capacity performance at high SNR for time-invariant channels. However, such techniques yield poor achievable rate at finite SNR, which is of interest from a practical point of view. In this paper, we propose a radically novel nonlinear interference alignment technique, which we refer to as Interference Dissolution (ID). ID allows to break-up a one-dimensional space into two fractional dimensions while achieving near-capacity performance for the entire SNR range. This is achieved by aligning signals by signals, as opposed to aligning signals by the channel. We introduce ID by considering a time-invariant MISO channel. This channel has a one-dimensional space and offers one DoF. We show that, by breaking-up the one dimensional space into two sub-spaces, ID achieves a rate of two symbols per channel use while providing $\frac{1}{2}$ DoF for each symbol. We analyze the performance of the proposed ID scheme in terms of the achievable rate and the symbol error rate. In characterizing the achievable rate of ID for the entire SNR range, we prove that, assuming Gaussian signals, the sum achievable rate is at most one bit away from the capacity. We present numerical examples to validate the theoretical analysis. We also compare the performance of ID in terms of the achievable rate performance to that of existing schemes and demonstrate ID's superiority.