OpenRaiser/Intern-Atlas huggingface dataset:
On Low-Space Differentially Private Low-rank Factorization in the Spectral Norm
Venue: arXiv cs.DS 2016
Year: 2016

Low-rank factorization is used in many areas of computer science where one performs spectral analysis on large sensitive data stored in the form of matrices. In this paper, we study differentially private low-rank factorization of a matrix with respect to the spectral norm in the turnstile update model. In this problem, given an input matrix $\mathbf{A} \in \mathbb{R}^{m \times n}$ updated in the turnstile manner and a target rank $k$, the goal is to find two rank-$k$ orthogonal matrices $\mathbf{U}_k \in \mathbb{R}^{m \times k}$ and $\mathbf{V}_k \in \mathbb{R}^{n \times k}$, and one positive semidefinite diagonal matrix $\textbfΣ_k \in \mathbb{R}^{k \times k}$ such that $\mathbf{A} \approx \mathbf{U}_k \textbfΣ_k \mathbf{V}_k^\mathsf{T}$ with respect to the spectral norm. Our main contributions are two computationally efficient and sub-linear space algorithms for computing a differentially private low-rank factorization. We consider two levels of privacy. In the first level of privacy, we consider two matrices neighboring if their difference has a Frobenius norm at most $1$. In the second level of privacy, we consider two matrices as neighboring if their difference can be represented as an outer product of two unit vectors. Both these privacy levels are stronger than those studied in the earlier papers such as Dwork {\it et al.} (STOC 2014), Hardt and Roth (STOC 2013), and Hardt and Price (NIPS 2014). As a corollary to our results, we get non-private algorithms that compute low-rank factorization in the turnstile update model with respect to the spectral norm. We note that, prior to this work, no algorithm that outputs low-rank factorization with respect to the spectral norm in the turnstile update model was known; i.e., our algorithm gives the first non-private low-rank factorization with respect to the spectral norm in the turnstile update mode.
Range Loss for Deep Face Recognition with Long-tail
Venue: arXiv cs.CV 2016
Year: 2016

Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities. To train such a well-designed deep network, tremendous amounts of data is indispensable. Long tail distribution specifically refers to the fact that a small number of generic entities appear frequently while other objects far less existing. Considering the existence of long tail distribution of the real world data, large but uniform distributed data are usually hard to retrieve. Empirical experiences and analysis show that classes with more samples will pose greater impact on the feature learning process and inversely cripple the whole models feature extracting ability on tail part data. Contrary to most of the existing works that alleviate this problem by simply cutting the tailed data for uniform distributions across the classes, this paper proposes a new loss function called range loss to effectively utilize the whole long tailed data in training process. More specifically, range loss is designed to reduce overall intra-personal variations while enlarging inter-personal differences within one mini-batch simultaneously when facing even extremely unbalanced data. The optimization objective of range loss is the $k$ greatest range's harmonic mean values in one class and the shortest inter-class distance within one batch. Extensive experiments on two famous and challenging face recognition benchmarks (Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) not only demonstrate the effectiveness of the proposed approach in overcoming the long tail effect but also show the good generalization ability of the proposed approach.
Object Detection Free Instance Segmentation With Labeling Transformations
Venue: arXiv cs.CV 2016
Year: 2016

Instance segmentation has attracted recent attention in computer vision and existing methods in this domain mostly have an object detection stage. In this paper, we study the intrinsic challenge of the instance segmentation problem, the presence of a quotient space (swapping the labels of different instances leads to the same result), and propose new methods that are object proposal- and object detection- free. We propose three alternative methods, namely pixel-based affinity mapping, superpixel-based affinity learning, and boundary-based component segmentation, all focusing on performing labeling transformations to cope with the quotient space problem. By adopting fully convolutional neural networks (FCN) like models, our framework attains competitive results on both the PASCAL dataset (object-centric) and the Gland dataset (texture-centric), which the existing methods are not able to do. Our work also has the advantages in its transparency, simplicity, and being all segmentation based.
A vertex ordering characterization of simple-triangle graphs
Venue: arXiv cs.DM 2016
Year: 2016

Consider two horizontal lines in the plane. A pair of a point on the top line and an interval on the bottom line defines a triangle between two lines. The intersection graph of such triangles is called a simple-triangle graph. This paper shows a vertex ordering characterization of simple-triangle graphs as follows: a graph is a simple-triangle graph if and only if there is a linear ordering of the vertices that contains both an alternating orientation of the graph and a transitive orientation of the complement of the graph.
A Self-Authentication and Deniable Efficient Group Key Agreement Protocol for VANET
Venue: arXiv cs.CR 2016
Year: 2016

With the rapid development of vehicular ad hoc Network (VANET), it is gaining significant popularity and receiving increasing attentions from academics and industry in security and efficiency. To address security and efficiency issues, a self-authentication and deniable efficient group key agreement protocol is proposed in this paper. This scheme establishes a group between road-side unit (RSU) and vehicles by using self-authentication without certification authority, and enhances certification efficiency by using group key (GK) transmission method. At the same time, to avoid the attacker to attack the legal vehicle by RSU, we adopt deniable group key agreement method to negotiation session key (sk) and use it to transmit GK between RSU. In addition, vehicles not only broadcast messages to other vehicles, but also communicate with other members in the same group. So group communication is necessary in VANET. Finally, the security and performance analysis shown that our scheme is security, meanwhile the verification delay, transmission overheard and message delay are more efficient than other related schemes in authentication, transmission and communication.
Simulation of clustering algorithms in OODBs in order to evaluate their performances
Venue: arXiv cs.DB 2016
Year: 2016

A good object clustering is critical to the performance of object-oriented databases. However, it always involves some kind of overhead for the system. The aim of this paper is to propose a modelling methodology in order to evaluate the performances of different clustering policies. This methodology has been used to compare the performances of three clustering algorithms found in the literature (Cactis, CK and ORION) that we considered representative of the current research in the field of object clustering. The actual performance evaluation was performed using simulation. Simulation experiments showed that the Cactis algorithm is better than the ORION algorithm and that the CK algorithm totally outperforms both other algorithms in terms of response time and clustering overhead.
AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech
Venue: arXiv cs.CL 2016
Year: 2016

Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech. We demonstrate that we can model human raters' mean opinion scores (MOS) of synthesized speech using a deep recurrent neural network whose inputs consist solely of a raw waveform. Our best models provide utterance-level estimates of MOS only moderately inferior to sampled human ratings, as shown by Pearson and Spearman correlations. When multiple utterances are scored and averaged, a scenario common in synthesizer quality assessment, AutoMOS achieves correlations approaching those of human raters. The AutoMOS model has a number of applications, such as the ability to explore the parameter space of a speech synthesizer without requiring a human-in-the-loop.
Joint Copying and Restricted Generation for Paraphrase
Venue: arXiv cs.CL 2016
Year: 2016

Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main writing modes. Most previous sequence-to-sequence (Seq2Seq) models use a single decoder and neglect this fact. In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder. The copying decoder finds the position to be copied based on a typical attention model. The generative decoder produces words limited in the source-specific vocabulary. To combine the two decoders and determine the final output, we develop a predictor to predict the mode of copying or rewriting. This predictor can be guided by the actual writing mode in the training data. We conduct extensive experiments on two different paraphrase datasets. The result shows that our model outperforms the state-of-the-art approaches in terms of both informativeness and language quality.
Locality-Sensitive Hashing without False Negatives for l_p
Venue: arXiv cs.DS 2016
Year: 2016

In this paper, we show a construction of locality-sensitive hash functions without false negatives, i.e., which ensure collision for every pair of points within a given radius $R$ in $d$ dimensional space equipped with $l_p$ norm when $p \in [1,\infty]$. Furthermore, we show how to use these hash functions to solve the $c$-approximate nearest neighbor search problem without false negatives. Namely, if there is a point at distance $R$, we will certainly report it and points at distance greater than $cR$ will not be reported for $c=Ω(\sqrt{d},d^{1-\frac{1}{p}})$. The constructed algorithms work: - with preprocessing time $\mathcal{O}(n \log(n))$ and sublinear expected query time, - with preprocessing time $\mathcal{O}(\mathrm{poly}(n))$ and expected query time $\mathcal{O}(\log(n))$. Our paper reports progress on answering the open problem presented by Pagh [8] who considered the nearest neighbor search without false negatives for the Hamming distance.
Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors
Venue: arXiv cs.RO 2016
Year: 2016

The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two components: prior knowledge acquired through simulation with an intact robot, and Bayesian optimization (BO) that operates on-line, on the damaged robot. While IT&E leads to fast damage recovery, it does not incorporate any safety constraints that prevent the robot from attempting harmful behaviors. In this work, we address this limitation by replacing the BO component with a constrained BO procedure. We evaluate our approach on a simulated damaged humanoid robot that needs to crawl as fast as possible, while performing as few unsafe trials as possible. We compare our new "safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in which the safety constraints are dealt as separate objectives. Our results show that our algorithm outperforms the other approaches, both in crawling speed within the safe regions and number of unsafe trials.
Localization of a unicycle-like mobile robot using LRF and omni-directional camera
Venue: arXiv cs.RO 2016
Year: 2016

This paper addresses the localization problem. The extended Kalman filter (EKF) is employed to localize a unicycle-like mobile robot equipped with a laser range finder (LRF) sensor and an omni-directional camera. The LRF is used to scan the environment which is described with line segments. The segments are extracted by a modified least square quadratic method in which a dynamic threshold is injected. The camera is employed to determine the robot's orientation. The prediction step of the EKF is performed by extracting parameters from the kinematic model and input signal of the robot. The correction step is conducted with the implementation of a line matching algorithm and the comparison between line's parameters of the local and global maps. In the line matching algorithm, a conversion matrix is introduced to reduce the computation cost. Experiments have been carried out in a real mobile robot system and the results prove the applicability of the method for the purpose of localization.
A novel platform for internet-based mobile robot systems
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, we introduce a software and hardware structure for on-line mobile robotic systems. The hardware mainly consists of a Multi-Sensor Smart Robot connected to the Internet through 3G mobile network. The system employs a client-server software architecture in which the exchanged data between the client and the server is transmitted through different transport protocols. Autonomous mechanisms such as obstacle avoidance and safe-point achievement are implemented to ensure the robot safety. This architecture is put into operation on the real Internet and the preliminary result is promising. By adopting this structure, it will be very easy to construct an experimental platform for the research on diverse tele-operation topics such as remote control algorithms, interface designs, network protocols and applications etc.
Proposal of algorithms for navigation and obstacles avoidance of autonomous mobile robot
Venue: arXiv cs.RO 2016
Year: 2016

This paper presents algorithms to navigate and avoid obstacles for an in-door autonomous mobile robot. A laser range finder is used to obtain 3D images of the environment. A new algorithm, namely 3D-to-2D image pressure and barriers detection (IPaBD), is proposed to create a 2D global map from the 3D images. This map is basic to design the trajectory. A tracking controller is developed to control the robot to follow the trajectory. The obstacle avoidance is addressed with the use of sonar sensors. An improved vector field histogram (Improved-VFH) algorithm is presented with improvements to overcome some limitations of the original VFH. Experiments have been conducted and the result is encouraged.
FPGA Based Implementation of Distributed Minority and Majority Voting Based Redundancy for Mission and Safety-Critical Applications
Venue: arXiv cs.AR 2016
Year: 2016

Electronic circuits and systems used in mission and safety-critical applications usually employ redundancy in the design to overcome arbitrary fault(s) or failure(s) and guarantee the correct operation. In this context, the distributed minority and majority voting based redundancy (DMMR) scheme forms an efficient alternative to the conventional N-modular redundancy (NMR) scheme for implementing mission and safety-critical circuits and systems by significantly minimizing their weight and design cost and also their design metrics whilst providing a similar degree of fault tolerance. This article presents the first FPGAs based implementation of example DMMR circuits and compares it with counterpart NMR circuits on the basis of area occupancy and critical path delay viz. area-delay product (ADP). The example DMMR circuits and counterpart NMR circuits are able to accommodate the faulty or failure states of 2, 3 and 4 function modules. For physical synthesis, two commercial Xilinx FPGAs viz. Spartan 3E and Virtex 5 corresponding to 90nm and 65nm CMOS processes, and two radiation-tolerant and military grade Xilinx FPGAs viz. QPro Virtex 2 and QPro Virtex E corresponding to 150nm and 180nm CMOS processes were considered for the NMR and DMMR circuit realizations which employ the 4-by-4 array multiplier as a representative function module. To achieve a fault tolerance of 2 function modules, both the DMMR and the NMR schemes provide near similar mean ADPs across all the four FPGAs. But while achieving a fault tolerance of 3 function modules the DMMR features reduced ADP by 44.5% on average compared to the NMR, and in achieving a fault tolerance of 4 function modules the DMMR reports reduced ADP by 56.5% on average compared to the NMR with respect to all the four FPGAs considered.
Proust: A Nano Proof Assistant
Venue: arXiv cs.PL 2016
Year: 2016

Proust is a small Racket program offering rudimentary interactive assistance in the development of verified proofs for propositional and predicate logic. It is constructed in stages, some of which are done by students before using it to complete proof exercises, and in parallel with the study of its theoretical underpinnings, including elements of Martin-Lof type theory. The goal is twofold: to demystify some of the machinery behind full-featured proof assistants such as Coq and Agda, and to better integrate the study of formal logic with other core elements of an undergraduate computer science curriculum.
Wearable Assistive Devices for the Blind
Venue: arXiv cs.RO 2016
Year: 2016

Assistive devices are a key aspect in wearable systems for biomedical applications, as they represent potential aids for people with physical and sensory disabilities that might lead to improvements in the quality of life. This chapter focuses on wearable assistive devices for the blind. It intends to review the most significant work done in this area, to present the latest approaches for assisting this population and to understand universal design concepts for the development of wearable assistive devices and systems for the blind.
Transmit design for MIMO wiretap channel with a malicious jammer
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the transmit design for multi-input multi-output (MIMO) wiretap channel including a malicious jammer. We first transform the system model into the traditional three-node wiretap channel by whitening the interference at the legitimate user. Additionally, the eavesdropper channel state information (ECSI) may be fully or statistically known, even unknown to the transmitter. Hence, some strategies are proposed in terms of different levels of ECSI available to the transmitter in our paper. For the case of unknown ECSI, a target rate for the legitimate user is first specified. And then an inverse water-filling algorithm is put forward to find the optimal power allocation for each information symbol, with a stepwise search being used to adjust the spatial dimension allocated to artificial noise (AN) such that the target rate is achievable. As for the case of statistical ECSI, several simulated channels are randomly generated according to the distribution of ECSI. We show that the ergodic secrecy capacity can be approximated as the average secrecy capacity of these simulated channels. Through maximizing this average secrecy capacity, we can obtain a feasible power and spatial dimension allocation scheme by using one dimension search. Finally, numerical results reveal the effectiveness and computational efficiency of our algorithms.
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model
Venue: arXiv cs.CV 2016
Year: 2016

Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner. To incorporate FV encoding strategy into deep generative models, we introduce Variational Auto-Encoder model, which steers a variational inference and learning in a neural network which can be straightforwardly optimized using standard stochastic gradient method. Different from the FV characterized by conventional generative models (e.g., Gaussian Mixture Model) which parsimoniously fit a discrete mixture model to data distribution, the proposed FV-VAE is more flexible to represent the natural property of data for better generalization. Extensive experiments are conducted on three public datasets, i.e., UCF101, ActivityNet, and CUB-200-2011 in the context of video action recognition and fine-grained image classification, respectively. Superior results are reported when compared to state-of-the-art representations. Most remarkably, our proposed FV-VAE achieves to-date the best published accuracy of 94.2% on UCF101.
Graph-Based Manifold Frequency Analysis for Denoising
Venue: arXiv cs.LG 2016
Year: 2016

We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to per- form non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods. We theoretically justify our approach, based on the fact that for smooth manifolds the coordinate information energy is localized in the low spectral graph wavelet sub-bands, while the noise affects all frequency bands in a similar way. Experimental results show that our proposed manifold frequency denoising (MFD) approach significantly outperforms the state of the art denoising meth- ods, and is robust to a wide range of parameter selections, e.g., the choice of k nearest neighbor connectivity of the graph.
Proposal of Optimum Application Deployment Technology for Heterogeneous IaaS Cloud
Venue: arXiv cs.DC 2016
Year: 2016

Recently, cloud systems composed of heterogeneous hardware have been increased to utilize progressed hardware power. However, to program applications for heterogeneous hardware to achieve high performance needs much technical skill and is difficult for users. Therefore, to achieve high performance easily, this paper proposes a PaaS which analyzes application logics and offloads computations to GPU and FPGA automatically when users deploy applications to clouds.
Data Partitioning View of Mining Big Data
Venue: arXiv cs.DB 2016
Year: 2016

There are two main approximations of mining big data in memory. One is to partition a big dataset to several subsets, so as to mine each subset in memory. By this way, global patterns can be obtained by synthesizing all local patterns discovered from these subsets. Another is the statistical sampling method. This indicates that data partitioning should be an important strategy for mining big data. This paper recalls our work on mining big data with a data partitioning and shows some interesting findings among the local patterns discovered from subsets of a dataset.
Information Rates and post-FEC BER Prediction in Optical Fiber Communications
Venue: arXiv cs.IT 2016
Year: 2016

Information-theoretic metrics to analyze optical fiber communications systems with binary and nonbinary soft-decision FEC are reviewed. The numerical evaluation of these metrics in both simulations and experiments is also discussed. Ready-to-use closed-form approximations are presented.
Geometry of Compositionality
Venue: arXiv cs.CL 2016
Year: 2016

This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.