OpenRaiser/Intern-Atlas huggingface dataset:
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents an empirical study of two widely-used sequence prediction models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs), on two fundamental tasks for Vietnamese text processing, including part-of-speech tagging and named entity recognition. We show that a strong lower bound for labeling accuracy can be obtained by relying only on simple word-based features with minimal hand-crafted feature engineering, of 90.65\% and 86.03\% performance scores on the standard test sets for the two tasks respectively. In particular, we demonstrate empirically the surprising efficiency of word embeddings in both of the two tasks, with both of the two models. We point out that the state-of-the-art LSTMs model does not always outperform significantly the traditional CRFs model, especially on moderate-sized data sets. Finally, we give some suggestions and discussions for efficient use of sequence labeling models in practical applications.
Improvements on the k-center problem for uncertain data
Venue: arXiv cs.CG 2017
Year: 2017

In real applications, there are situations where we need to model some problems based on uncertain data. This leads us to define an uncertain model for some classical geometric optimization problems and propose algorithms to solve them. In this paper, we study the $k$-center problem, for uncertain input. In our setting, each uncertain point $P_i$ is located independently from other points in one of several possible locations $\{P_{i,1},\dots, P_{i,z_i}\}$ in a metric space with metric $d$, with specified probabilities and the goal is to compute $k$-centers $\{c_1,\dots, c_k\}$ that minimize the following expected cost $$Ecost(c_1,\dots, c_k)=\sum_{R\in Ω} prob(R)\max_{i=1,\dots, n}\min_{j=1,\dots k} d(\hat{P}_i,c_j)$$ here $Ω$ is the probability space of all realizations $$R=\{\hat{P}_1,\dots, \hat{P}_n\}$$ of given uncertain points and $$prob(R)=\prod_{i=1}^n prob(\hat{P}_i).$$ In restricted assigned version of this problem, an assignment $A:\{P_1,\dots, P_n\}\rightarrow \{c_1,\dots, c_k\}$ is given for any choice of centers and the goal is to minimize $$Ecost_A(c_1,\dots, c_k)=\sum_{R\in Ω} prob(R)\max_{i=1,\dots, n} d(\hat{P}_i,A(P_i)).$$ In unrestricted version, the assignment is not specified and the goal is to compute $k$ centers $\{c_1,\dots, c_k\}$ and an assignment $A$ that minimize the above expected cost. We give several improved constant approximation factor algorithms for the assigned versions of this problem in a Euclidean space and in a general metric space. Our results significantly improve the results of \cite{guh} and generalize the results of \cite{wang} to any dimension. Our approach is to replace a certain center point for each uncertain point and study the properties of these certain points. The proposed algorithms are efficient and simple to implement.
Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet
Venue: arXiv cs.LG 2017
Year: 2017

We propose a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) formed by replacing the first few layers of a CNN network with a parametric log based DTCWT ScatterNet. The ScatterNet extracts edge based invariant representations that are used by the later layers of the CNN to learn high-level features. This improves the training of the network as the later layers can learn more complex patterns from the start of learning because the edge representations are already present. The efficient learning of the DTSCNN network is demonstrated on CIFAR-10 and Caltech-101 datasets. The generic nature of the ScatterNet front-end is shown by an equivalent performance to pre-trained CNN front-ends. A comparison with the state-of-the-art on CIFAR-10 and Caltech-101 datasets is also presented.
Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Automation of brain matter segmentation from MR images is a challenging task due to the irregular boundaries between the grey and white matter regions. In addition, the presence of intensity inhomogeneity in the MR images further complicates the problem. In this paper, we propose a texture and vesselness incorporated version of the ScatterNet Hybrid Deep Learning Network (TS-SHDL) that extracts hierarchical invariant mid-level features, used by fisher vector encoding and a conditional random field (CRF) to perform the desired segmentation. The performance of the proposed network is evaluated by extensive experimentation and comparison with the state-of-the-art methods on several 2D MRI scans taken from the synthetic McGill Brain Web as well as on the MRBrainS dataset of real 3D MRI scans. The advantages of the TS-SHDL network over supervised deep learning networks is also presented in addition to its superior performance over the state-of-the-art.
An algorithm to simulate alternating Turing machine in signal machine
Venue: arXiv cs.CG 2017
Year: 2017

Geometrical Computation as a new model of computation is the counterpart of Cellular Automata that has Turing computing ability. In this paper we provide an algorithm to simulate Alternating Turing Machine in the context of Signal Machine using techniques adopted from the features of Signal Machine to set up and manage the copies/branches of Alternating Turing Machine. We show that our algorithm can simulate Alternating Turing Machine in Signal Machine as same functionality as classic family of Turing Machines. Time complexity of the algorithm is linear as ordinary simulated Turing Machines. Depending on the computation tree space complexity is exponential order of d, where d is the depth of the computation tree.
VADER: Visualization and Analytics for Distributed Energy Resources
Venue: arXiv cs.CY 2017
Year: 2017

Enabling deep penetration of distributed energy resources (DERs) requires comprehensive monitoring and control of the distribution network. Increasing observability beyond the substation and extending it to the edge of the grid is required to achieve this goal. The growing availability of data from measurements from inverters, smart meters, EV chargers, smart thermostats and other devices provides an opportunity to address this problem. Integration of these new data poses many challenges since not all devices are connected to the traditional supervisory control and data acquisition (SCADA) networks and can be novel types of information, collected at various sampling rates and with potentially missing values. Visualization and analytics for distributed energy resources (VADER) system and workflow is introduced as an approach and platform to fuse these different streams of data from utilities and third parties to enable comprehensive situational awareness, including scenario analysis and system state estimation. The system leverages modern large scale computing platforms, machine learning and data analytics and can be used alongside traditional advanced distribution management system (ADMS) systems to provide improved insights for distribution system management in the presence of DERs.
Learning Invariant Riemannian Geometric Representations Using Deep Nets
Venue: arXiv cs.CV 2017
Year: 2017

Non-Euclidean constraints are inherent in many kinds of data in computer vision and machine learning, typically as a result of specific invariance requirements that need to be respected during high-level inference. Often, these geometric constraints can be expressed in the language of Riemannian geometry, where conventional vector space machine learning does not apply directly. The central question this paper deals with is: How does one train deep neural nets whose final outputs are elements on a Riemannian manifold? To answer this, we propose a general framework for manifold-aware training of deep neural networks -- we utilize tangent spaces and exponential maps in order to convert the proposed problem into a form that allows us to bring current advances in deep learning to bear upon this problem. We describe two specific applications to demonstrate this approach: prediction of probability distributions for multi-class image classification, and prediction of illumination-invariant subspaces from a single face-image via regression on the Grassmannian. These applications show the generality of the proposed framework, and result in improved performance over baselines that ignore the geometry of the output space. In addition to solving this specific problem, we believe this paper opens new lines of enquiry centered on the implications of Riemannian geometry on deep architectures.
Automatically Generating Commit Messages from Diffs using Neural Machine Translation
Venue: arXiv cs.SE 2017
Year: 2017

Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high level rationale. In this paper, we adapt Neural Machine Translation (NMT) to automatically "translate" diffs into commit messages. We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects. We designed a filter to help ensure that we only trained the algorithm on higher-quality commit messages. Our evaluation uncovered a pattern in which the messages we generate tend to be either very high or very low quality. Therefore, we created a quality-assurance filter to detect cases in which we are unable to produce good messages, and return a warning instead.
Integer sorting on multicores: some (experiments and) observations
Venue: arXiv cs.DC 2017
Year: 2017

There have been many proposals for sorting integers on multicores/GPUs that include radix-sort and its variants or other approaches that exploit specialized hardware features of a particular multicore architecture. Comparison-based algorithms have also been used. Network-based algorithms have also been used with primary example Batcher's bitonic sorting algorithm. Although such a latter approach is theoretically "inefficient", if there are few keys to sort, it can lead to better running times as it has low overhead and is simple to implement. In this work we perform an experimental study of integer sorting on multicore processors using not only multithreading but also multiprocessing parallel programming approaches. Our implementations work under Open MPI, MulticoreBSP, and BSPlib. We have implemented serial and parallel radix-sort for various radixes and also some previously little explored or unexplored variants of bitonic-sort and odd-even transposition sort. We offer our observations on a performance evaluation using the MBSP model of such algorithm implementations on multiple platforms and architectures and multiple programming libraries. If we can conclude anything is that modeling their performance by taking into consideration architecture dependent features such as the structure and characteristics of multiple memory hierarchies is difficult and more often than not unsuccessful or unreliable. However we can still draw some very simple conclusions using traditional architecture independent parallel modeling.
SINR-Threshold Scheduling with Binary Power Control for D2D Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider a device-to-device communication network in which $K$ transmitter-receiver pairs are sharing spectrum with each other. We propose a novel but simple binary scheduling scheme for this network to maximize the average sum rate of the pairs. According to the scheme, each receiver predicts its Signal-to-Interference-plus-Noise Ratio (SINR), assuming \emph{all} other user pairs are active, and compares it to a preassigned threshold to decide whether its corresponding transmitter to be activated or not. For our proposed scheme, the optimal threshold that maximizes the expected sum rate is obtained analytically for the two user-pair case and empirically in the general $K$ user-pair case. Simulation results reveal that our proposed SINR-threshold scheduling scheme outperforms ITLinQ \cite{navid}, FlashLinQ \cite{flash} and the method presented in \cite{G} in terms of the expected sum rate (network throughput). In addition, the computational complexity of the proposed scheme is $O(K)$, outperforming both ITLinQ and FlashLinQ that have $O(K^2)$ complexity requirements. Moreover, we also discuss the application of our proposed new scheme into an operator-assisted cellular D2D heterogeneous network.
PolarBear: A 28-nm FD-SOI ASIC for Decoding of Polar Codes
Venue: arXiv cs.AR 2017
Year: 2017

Polar codes are a recently proposed class of block codes that provably achieve the capacity of various communication channels. They received a lot of attention as they can do so with low-complexity encoding and decoding algorithms, and they have an explicit construction. Their recent inclusion in a 5G communication standard will only spur more research. However, only a couple of ASICs featuring decoders for polar codes were fabricated, and none of them implements a list-based decoding algorithm. In this paper, we present ASIC measurement results for a fabricated 28 nm CMOS chip that implements two different decoders: the first decoder is tailored toward error-correction performance and flexibility. It supports any code rate as well as three different decoding algorithms: successive cancellation (SC), SC flip and SC list (SCL). The flexible decoder can also decode both non-systematic and systematic polar codes. The second decoder targets speed and energy efficiency. We present measurement results for the first silicon-proven SCL decoder, where its coded throughput is shown to be of 306.8 Mbps with a latency of 3.34 us and an energy per bit of 418.3 pJ/bit at a clock frequency of 721 MHz for a supply of 1.3 V. The energy per bit drops down to 178.1 pJ/bit with a more modest clock frequency of 308 MHz, lower throughput of 130.9 Mbps and a reduced supply voltage of 0.9 V. For the other two operating modes, the energy per bit is shown to be of approximately 95 pJ/bit. The less flexible high-throughput unrolled decoder can achieve a coded throughput of 9.2 Gbps and a latency of 628 ns for a measured energy per bit of 1.15 pJ/bit at 451 MHz.
Neural Class-Specific Regression for face verification
Venue: arXiv cs.CV 2017
Year: 2017

Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques. While it has been shown that kernel-based Class-Specific Discriminant Analysis is able to provide excellent performance in small- and medium-scale face verification problems, its application in today's large-scale problems is difficult due to its training space and computational requirements. In this paper, generalizing our previous work on kernel-based class-specific discriminant analysis, we show that class-specific subspace learning can be cast as a regression problem. This allows us to derive linear, (reduced) kernel and neural network-based class-specific discriminant analysis methods using efficient batch and/or iterative training schemes, suited for large-scale learning problems. We test the performance of these methods in two datasets describing medium- and large-scale face verification problems.
Büchi VASS recognise w-languages that are Sigma^1_1 - complete
Venue: arXiv cs.FL 2017
Year: 2017

This short note exhibits an example of a Sigma^1_1-complete language that can be recognised by a one blind counter Büchi automaton (or equivalently a Büchi VASS with only one place).
Quality Enhancement by Weighted Rank Aggregation of Crowd Opinion
Venue: arXiv cs.HC 2017
Year: 2017

Expertise of annotators has a major role in crowdsourcing based opinion aggregation models. In such frameworks, accuracy and biasness of annotators are occasionally taken as important features and based on them priority of the annotators are assigned. But instead of relying on a single feature, multiple features can be considered and separate rankings can be produced to judge the annotators properly. Finally, the aggregation of those rankings with perfect weightage can be done with an aim to produce better ground truth prediction. Here, we propose a novel weighted rank aggregation method and its efficacy with respect to other existing approaches is shown on artificial dataset. The effectiveness of weighted rank aggregation to enhance quality prediction is also shown by applying it on an Amazon Mechanical Turk (AMT) dataset.
On Security and Sparsity of Linear Classifiers for Adversarial Settings
Venue: arXiv cs.LG 2017
Year: 2017

Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection.
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Venue: arXiv cs.CV 2017
Year: 2017

Thanks to recent advances in CNNs, solid improvements have been made in semantic segmentation of high resolution remote sensing imagery. However, most of the previous works have not fully taken into account the specific difficulties that exist in remote sensing tasks. One of such difficulties is that objects are small and crowded in remote sensing imagery. To tackle with this challenging task we have proposed a novel architecture called local feature extraction (LFE) module attached on top of dilated front-end module. The LFE module is based on our findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects. The proposed LFE module solves this problem by aggregating local features with decreasing dilation factor. We tested our network on three remote sensing datasets and acquired remarkably good results for all datasets especially for small objects.
Semantic Composition via Probabilistic Model Theory
Venue: arXiv cs.CL 2017
Year: 2017

Semantic composition remains an open problem for vector space models of semantics. In this paper, we explain how the probabilistic graphical model used in the framework of Functional Distributional Semantics can be interpreted as a probabilistic version of model theory. Building on this, we explain how various semantic phenomena can be recast in terms of conditional probabilities in the graphical model. This connection between formal semantics and machine learning is helpful in both directions: it gives us an explicit mechanism for modelling context-dependent meanings (a challenge for formal semantics), and also gives us well-motivated techniques for composing distributed representations (a challenge for distributional semantics). We present results on two datasets that go beyond word similarity, showing how these semantically-motivated techniques improve on the performance of vector models.
Adversarial Task Allocation
Venue: arXiv cs.GT 2017
Year: 2017

The problem of allocating tasks to workers is of long standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, as well as the more recent problem of crowdsourcing where a broad array of tasks are slated to be completed by human workers. Extensive research into this problem generally addresses important issues such as uncertainty and, in crowdsourcing, incentives. However, the problem of adversarial tampering with the task allocation process has not received as much attention. We are concerned with a particular adversarial setting in task allocation where an attacker may target a specific worker in order to prevent the tasks assigned to this worker from being completed. We consider two attack models: one in which the adversary observes only the allocation policy (which may be randomized), and the second in which the attacker observes the actual allocation decision. For the case when all tasks are homogeneous, we provide polynomial-time algorithms for both settings. When tasks are heterogeneous, however, we show the adversarial allocation problem to be NP-Hard, and present algorithms for solving it when the defender is restricted to assign only a single worker per task. Our experiments show, surprisingly, that the difference between the two attack models is minimal: deterministic allocation can achieve nearly as much utility as randomized.
On Reliability-Aware Server Consolidation in Cloud Datacenters
Venue: arXiv cs.DC 2017
Year: 2017

In the past few years, datacenter (DC) energy consumption has become an important issue in technology world. Server consolidation using virtualization and virtual machine (VM) live migration allows cloud DCs to improve resource utilization and hence energy efficiency. In order to save energy, consolidation techniques try to turn off the idle servers, while because of workload fluctuations, these offline servers should be turned on to support the increased resource demands. These repeated on-off cycles could affect the hardware reliability and wear-and-tear of servers and as a result, increase the maintenance and replacement costs. In this paper we propose a holistic mathematical model for reliability-aware server consolidation with the objective of minimizing total DC costs including energy and reliability costs. In fact, we try to minimize the number of active PMs and racks, in a reliability-aware manner. We formulate the problem as a Mixed Integer Linear Programming (MILP) model which is in form of NP-complete. Finally, we evaluate the performance of our approach in different scenarios using extensive numerical MATLAB simulations.
Mobile Edge Computing Empowers Internet of Things
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods are validated via extensive simulations.