OpenRaiser/Intern-Atlas huggingface dataset:
Anti-spoofing Methods for Automatic SpeakerVerification System
Venue: arXiv cs.SD 2017
Year: 2017

Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them. Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still vulnerable to spoofing attacks. Inthis work we overview different acoustic feature spaces and classifiersto determine reliable and robust countermeasures against spoofing at-tacks. We compared several spoofing detection systems, presented so far,on the development and evaluation datasets of the Automatic SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge 2015.Experimental results presented in this paper demonstrate that the useof magnitude and phase information combination provides a substantialinput into the efficiency of the spoofing detection systems. Also wavelet-based features show impressive results in terms of equal error rate. Inour overview we compare spoofing performance for systems based on dif-ferent classifiers. Comparison results demonstrate that the linear SVMclassifier outperforms the conventional GMM approach. However, manyresearchers inspired by the great success of deep neural networks (DNN)approaches in the automatic speech recognition, applied DNN in thespoofing detection task and obtained quite low EER for known and un-known type of spoofing attacks.
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accelerators such as TPU. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as well as the prediction accuracy. Coarse-grained pruning creates regular sparsity patterns, making it more amenable for hardware acceleration but more challenging to maintain the same accuracy. In this paper we quantitatively measure the trade-off between sparsity regularity and prediction accuracy, providing insights in how to maintain accuracy while having more a more structured sparsity pattern. Our experimental results show that coarse-grained pruning can achieve a sparsity ratio similar to unstructured pruning without loss of accuracy. Moreover, due to the index saving effect, coarse-grained pruning is able to obtain a better compression ratio than fine-grained sparsity at the same accuracy threshold. Based on the recent sparse convolutional neural network accelerator (SCNN), our experiments further demonstrate that coarse-grained sparsity saves about 2x the memory references compared to fine-grained sparsity. Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.
Transport Protocols for Large Bandwidth-Delay Product Networks - TCP Extensions and Alternative Transport Protocols
Venue: arXiv cs.NI 2017
Year: 2017

TCP's poor performance is identified as the bottleneck of high-speed networks. Extensions to TCP have been proposed and implemented. Some authors abandon TCP at all and suggest new transport protocols to overcome TCP limitations, at the expense of compatibility. This paper reports a research on the most significant TCP extensions and transport alternatives, and comparison between them. The majority of the solutions pointed out are difficult to compare because they are tailored to specific configurations. Still there is no specific criteria to evaluate performance metrics and comparison is done on the most evident issues.
The tessellation problem of quantum walks
Venue: arXiv cs.DM 2017
Year: 2017

Quantum walks have received a great deal of attention recently because they can be used to develop new quantum algorithms and to simulate interesting quantum systems. In this work, we focus on a model called staggered quantum walk, which employs advanced ideas of graph theory and has the advantage of including the most important instances of other discrete-time models. The evolution operator of the staggered model is obtained from a tessellation cover, which is defined in terms of a set of partitions of the graph into cliques. It is important to establish the minimum number of tessellations required in a tessellation cover, and what classes of graphs admit a small number of tessellations. We describe two main results: (1) infinite classes of graphs where we relate the chromatic number of the clique graph to the minimum number of tessellations required in a tessellation cover, and (2) the problem of deciding whether a graph is $k$-tessellable for $k\ge 3$ is NP-complete.
Automating the Proofs of Strengthening Lemmas in the Abella Proof Assistant
Venue: arXiv cs.LO 2017
Year: 2017

In logical reasoning, it is often the case that only some of a collection of assumptions are needed to reach a conclusion. A strengthening lemma is an assertion that a given conclusion is independent in this sense of a particular assumption. Strengthening lemmas underlie many useful techniques for simplifying proofs in automated and interactive theorem-provers. For example, they underlie a mechanism called subordination that is useful in determining that expressions of a particular type cannot contain objects of another type and in thereby reducing the number of cases to be considered in proving universally quantified statements. This thesis concerns the automation of the proofs of strengthening lemmas in a specification logic called the logic of hereditary Harrop formulas (HOHH). The Abella Proof Assistant embeds this logic in a way that allows it to prove properties of both the logic itself and of specifications written in it. Previous research has articulated a (conservative) algorithm for checking if a claimed strengthening lemma is, in fact, true. We provide here an implementation of this algorithm within the setting of Abella. Moreover, we show how to generate an actual proof of the strengthening lemma in Abella from the information computed by the algorithm; such a proof serves as a more trustworthy certificate of the correctness of the lemma than the algorithm itself. The results of this work have been incorporated into the Abella system in the form of a "tactic command" that can be invoked within the interactive theorem-prover and that will result in an elaboration of a proof of the lemma and its incorporation into the collection of proven facts about a given specification.
Data-Driven Program Completion
Venue: arXiv cs.PL 2017
Year: 2017

We introduce program splicing, a programming methodology that aims to automate the commonly used workflow of copying, pasting, and modifying code available online. Here, the programmer starts by writing a "draft" that mixes unfinished code, natural language comments, and correctness requirements in the form of test cases or API call sequence constraints. A program synthesizer that interacts with a large, searchable database of program snippets is used to automatically complete the draft into a program that meets the requirements. The synthesis process happens in two stages. First, the synthesizer identifies a small number of programs in the database that are relevant to the synthesis task. Next it uses an enumerative search to systematically fill the draft with expressions and statements from these relevant programs. The resulting program is returned to the programmer, who can modify it and possibly invoke additional rounds of synthesis. We present an implementation of program splicing for the Java programming language. The implementation uses a corpus of over 3.5 million procedures from an open-source software repository. Our evaluation uses the system in a suite of everyday programming tasks, and includes a comparison with a state-of-the-art competing approach as well as a user study. The results point to the broad scope and scalability of program splicing and indicate that the approach can significantly boost programmer productivity.
Detecting Malignant TLS Servers Using Machine Learning Techniques
Venue: arXiv cs.CR 2017
Year: 2017

TLS uses X.509 certificates for server authentication. A X.509 certificate is a complex document and various innocent errors may occur while creating/ using it. Also, many certificates belong to malicious websites and should be rejected by the client and those web servers should not be visited. Usually, when a client finds a certificate that is doubtful using the traditional tests, it asks for human intervention. But, looking at certificates, most people can't differentiate between malicious and non-malicious websites. Thus, once traditional certificate validation has failed, instead of asking for human intervention, we use machine learning techniques to enable a web browser to decide whether the server to which the certificate belongs to is malignant or not ie, whether the website should be visited or not. Once a certificate has been accepted in the above phase, we observe that the website may still turn out to be malicious. So, in the second phase, we download a part of the website in a sandbox without decrypting it and observe the TLS encrypted traffic (encrypted malicious data captured in a sandbox cannot harm the system). As the traffic is encrypted after Handshake is completed, traditional pattern-matching techniques cannot be employed. Thus we use flow features of the traffic along with the features used in the above first phase. We couple these features with the unencrypted TLS header information obtained during TLS Handshake and use these in a machine learning classifier to identify whether the traffic is malicious or not.
Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation. In contrast, web images and their image-level labels are much easier and cheaper to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. The method utilizes the internet to retrieve a large number of images and uses a large scale co-segmentation framework to generate masks for the retrieved images. We first retrieve images from search engines, e.g. Flickr and Google, using semantic class names as queries, e.g. class names in the dataset PASCAL VOC 2012. We then use high quality masks produced by co-segmentation on the retrieved images as well as the target dataset images with image level labels to train segmentation networks. We obtain an IoU score of 56.9 on test set of PASCAL VOC 2012, which reaches the state-of-the-art performance.
The cost of fairness in classification
Venue: arXiv cs.LG 2017
Year: 2017

We study the problem of learning classifiers with a fairness constraint, with three main contributions towards the goal of quantifying the problem's inherent tradeoffs. First, we relate two existing fairness measures to cost-sensitive risks. Second, we show that for cost-sensitive classification and fairness measures, the optimal classifier is an instance-dependent thresholding of the class-probability function. Third, we show how the tradeoff between accuracy and fairness is determined by the alignment between the class-probabilities for the target and sensitive features. Underpinning our analysis is a general framework that casts the problem of learning with a fairness requirement as one of minimising the difference of two statistical risks.
MagNet: a Two-Pronged Defense against Adversarial Examples
Venue: arXiv cs.CR 2017
Year: 2017

Deep learning has shown promising results on hard perceptual problems in recent years. However, deep learning systems are found to be vulnerable to small adversarial perturbations that are nearly imperceptible to human. Such specially crafted perturbations cause deep learning systems to output incorrect decisions, with potentially disastrous consequences. These vulnerabilities hinder the deployment of deep learning systems where safety or security is important. Attempts to secure deep learning systems either target specific attacks or have been shown to be ineffective. In this paper, we propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet does not modify the protected classifier or know the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. Different from previous work, MagNet learns to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since it does not rely on any process for generating adversarial examples, it has substantial generalization power. Moreover, MagNet reconstructs adversarial examples by moving them towards the manifold, which is effective for helping classify adversarial examples with small perturbation correctly. We discuss the intrinsic difficulty in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we propose to use diversity to strengthen MagNet. We show empirically that MagNet is effective against most advanced state-of-the-art attacks in blackbox and graybox scenarios while keeping false positive rate on normal examples very low.
Load Balancing for Skewed Streams on Heterogeneous Cluster
Venue: arXiv cs.DC 2017
Year: 2017

Streaming applications frequently encounter skewed workloads and execute on heterogeneous clusters. Optimal resource utilization in such adverse conditions becomes a challenge, as it requires inferring the resource capacities and input distribution at run time. In this paper, we tackle the aforementioned challenges by modeling them as a load balancing problem. We propose a novel partitioning strategy called Consistent Grouping (CG), which enables each processing element instance (PEI) to process the workload according to its capacity. The main idea behind CG is the notion of small, equal-sized virtual workers at the sources, which are assigned to workers based on their capacities. We provide a theoretical analysis of the proposed algorithm and show via extensive empirical evaluation that our proposed scheme outperforms the state-of-the-art approaches, like key grouping. In particular, CG achieves 3.44x better performance in terms of latency compared to key grouping.
Performance Optimization of Co-Existing Underlay Secondary Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we analyze the throughput performance of two co-existing downlink multiuser underlay secondary networks that use fixed-rate transmissions. We assume that the interference temperature limit (ITL) is apportioned to accommodate two concurrent transmissions using an interference temperature apportioning parameter so as to ensure that the overall interference to the primary receiver does not exceed the ITL. Using the derived analytical expressions for throughput, when there is only one secondary user in each network, or when the secondary networks do not employ opportunistic user selection (use round robin scheduling for example), there exists a critical fixed-rate below which sum throughput with co-existing secondary networks is higher than the throughput with a single secondary network. We derive an expression for this critical fixed-rate. Below this critical rate, we show that careful apportioning of the ITL is critical to maximizing sum throughput of the co-existing networks. We derive an expression for this apportioning parameter. Throughput is seen to increase with increase in number of users in each of the secondary networks. Computer simulations demonstrate accuracy of the derived expressions.
Deep image representations using caption generators
Venue: arXiv cs.CV 2017
Year: 2017

Deep learning exploits large volumes of labeled data to learn powerful models. When the target dataset is small, it is a common practice to perform transfer learning using pre-trained models to learn new task specific representations. However, pre-trained CNNs for image recognition are provided with limited information about the image during training, which is label alone. Tasks such as scene retrieval suffer from features learned from this weak supervision and require stronger supervision to better understand the contents of the image. In this paper, we exploit the features learned from caption generating models to learn novel task specific image representations. In particular, we consider the state-of-the art captioning system Show and Tell~\cite{SnT-pami-2016} and the dense region description model DenseCap~\cite{densecap-cvpr-2016}. We demonstrate that, owing to richer supervision provided during the process of training, the features learned by the captioning system perform better than those of CNNs. Further, we train a siamese network with a modified pair-wise loss to fuse the features learned by~\cite{SnT-pami-2016} and~\cite{densecap-cvpr-2016} and learn image representations suitable for retrieval. Experiments show that the proposed fusion exploits the complementary nature of the individual features and yields state-of-the art retrieval results on benchmark datasets.
Geometric Methods for Robust Data Analysis in High Dimension
Venue: arXiv cs.LG 2017
Year: 2017

Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quantities of data gathered from automated sensors and user input. Furthermore, the dimensionality of many datasets is extreme: more details are being gathered about single user interactions or sensor readings. All of these applications encounter problems with a common theme: use observed data to make inferences about the world. Our work obtains the first provably efficient algorithms for Independent Component Analysis (ICA) in the presence of heavy-tailed data. The main tool in this result is the centroid body (a well-known topic in convex geometry), along with optimization and random walks for sampling from a convex body. This is the first algorithmic use of the centroid body and it is of independent theoretical interest, since it effectively replaces the estimation of covariance from samples, and is more generally accessible. This reduction relies on a non-linear transformation of samples from such an intersection of halfspaces (i.e. a simplex) to samples which are approximately from a linearly transformed product distribution. Through this transformation of samples, which can be done efficiently, one can then use an ICA algorithm to recover the vertices of the intersection of halfspaces. Finally, we again use ICA as an algorithmic primitive to construct an efficient solution to the widely-studied problem of learning the parameters of a Gaussian mixture model. Our algorithm again transforms samples from a Gaussian mixture model into samples which fit into the ICA model and, when processed by an ICA algorithm, result in recovery of the mixture parameters. Our algorithm is effective even when the number of Gaussians in the mixture grows polynomially with the ambient dimension
Synthesizing Mapping Relationships Using Table Corpus
Venue: arXiv cs.DB 2017
Year: 2017

Mapping relationships, such as (country, country-code) or (company, stock-ticker), are versatile data assets for an array of applications in data cleaning and data integration like auto-correction and auto-join. However, today there are no good repositories of mapping tables that can enable these intelligent applications. Given a corpus of tables such as web tables or spreadsheet tables, we observe that values of these mappings often exist in pairs of columns in same tables. Motivated by their broad applicability, we study the problem of synthesizing mapping relationships using a large table corpus. Our synthesis process leverages compatibility of tables based on co-occurrence statistics, as well as constraints such as functional dependency. Experiment results using web tables and enterprise spreadsheets suggest that the proposed approach can produce high quality mappings.
Overcommitment in Cloud Services -- Bin packing with Chance Constraints
Venue: arXiv cs.DS 2017
Year: 2017

This paper considers a traditional problem of resource allocation, scheduling jobs on machines. One such recent application is cloud computing, where jobs arrive in an online fashion with capacity requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an overcommitment policy, i.e., selling resources beyond capacity. Setting the right overcommitment level can induce a significant cost reduction for the cloud provider, while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as a bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint into a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data, and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services, and suggest a cost reduction of 1.5% to 17% depending on the provider's risk tolerance.
Learnable Programming: Blocks and Beyond
Venue: arXiv cs.PL 2017
Year: 2017

Blocks-based programming has become the lingua franca for introductory coding. Studies have found that experience with blocks-based programming can help beginners learn more traditional text-based languages. We explore how blocks environments improve learnability for novices by 1) favoring recognition over recall, 2) reducing cognitive load, and 3) preventing errors. Increased usability of blocks programming has led to widespread adoption within introductory programming contexts across a range of ages. Ongoing work explores further reducing barriers to programming, supporting novice programmers in expanding their programming skills, and transitioning to textual programming. New blocks frameworks are making it easier to access a variety of APIs through blocks environments, opening the doors to a greater diversity of programming domains and supporting greater experimentation for novices and professionals alike.
Equivalences Between Network Codes With Link Errors and Index Codes With Side Information Errors
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, new equivalence relationships between a network code with link errors (NCLE) and an index code with side information errors (ICSIE) are studied. First, for a given network coding instance, the equivalent index coding instance is derived, where an NCLE is converted to the corresponding ICSIE and vice versa. Next, for a given index coding instance, the equivalent network coding instance is also derived, where an ICSIE is converted to the corresponding NCLE and vice versa if a pair of encoding functions of an original link and the duplicated link are functionally related in the network code. Finally, several properties of an NCLE are derived from those of the equivalent ICSIE using the fact that the NCLE and the ICSIE are equivalent.