OpenRaiser/Intern-Atlas huggingface dataset:
Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
Venue: arXiv cs.CL 2018
Year: 2018

We introduce the first system towards the novel task of answering complex multisentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules: question understanding and answering. For question understanding, we define an SQL-like query language that captures the semantic intent of a question; it supports operators like subset, negation, preference and similarity, which are often found in recommendation questions. We train and compare traditional CRFs as well as bidirectional LSTM-based models for converting a question to its semantic representation. We extend these models to a semisupervised setting with partially labeled sequences gathered through crowdsourcing. We find that our best model performs semi-supervised training of BiDiLSTM+CRF with hand-designed features and CCM(Chang et al., 2007) constraints. Finally, in an end to end QA system, our answering component converts our question representation into queries fired on underlying knowledge sources. Our experiments on two different answer corpora demonstrate that our system can significantly outperform baselines with up to 20 pt higher accuracy and 17 pt higher recall.
Shielding Google's language toxicity model against adversarial attacks
Venue: arXiv cs.CL 2018
Year: 2018

Lack of moderation in online communities enables participants to incur in personal aggression, harassment or cyberbullying, issues that have been accentuated by extremist radicalisation in the contemporary post-truth politics scenario. This kind of hostility is usually expressed by means of toxic language, profanity or abusive statements. Recently Google has developed a machine-learning-based toxicity model in an attempt to assess the hostility of a comment; unfortunately, it has been suggested that said model can be deceived by adversarial attacks that manipulate the text sequence of the comment. In this paper we firstly characterise such adversarial attacks as using obfuscation and polarity transformations. The former deceives by corrupting toxic trigger content with typographic edits, whereas the latter deceives by grammatical negation of the toxic content. Then, we propose a two--stage approach to counter--attack these anomalies, bulding upon a recently proposed text deobfuscation method and the toxicity scoring model. Lastly, we conducted an experiment with approximately 24000 distorted comments, showing how in this way it is feasible to restore toxicity of the adversarial variants, while incurring roughly on a twofold increase in processing time. Even though novel adversary challenges would keep coming up derived from the versatile nature of written language, we anticipate that techniques combining machine learning and text pattern recognition methods, each one targeting different layers of linguistic features, would be needed to achieve robust detection of toxic language, thus fostering aggression--free digital interaction.
Verifying and Synthesizing Constant-Resource Implementations with Types
Venue: arXiv cs.PL 2018
Year: 2018

We propose a novel type system for verifying that programs correctly implement constant-resource behavior. Our type system extends recent work on automatic amortized resource analysis (AARA), a set of techniques that automatically derive provable upper bounds on the resource consumption of programs. We devise new techniques that build on the potential method to achieve compositionality, precision, and automation. A strict global requirement that a program always maintains constant resource usage is too restrictive for most practical applications. It is sufficient to require that the program's resource behavior remain constant with respect to an attacker who is only allowed to observe part of the program's state and behavior. To account for this, our type system incorporates information flow tracking into its resource analysis. This allows our system to certify programs that need to violate the constant-time requirement in certain cases, as long as doing so does not leak confidential information to attackers. We formalize this guarantee by defining a new notion of resource-aware noninterference, and prove that our system enforces it. Finally, we show how our type inference algorithm can be used to synthesize a constant-time implementation from one that cannot be verified as secure, effectively repairing insecure programs automatically. We also show how a second novel AARA system that computes lower bounds on resource usage can be used to derive quantitative bounds on the amount of information that a program leaks through its resource use. We implemented each of these systems in Resource Aware ML, and show that it can be applied to verify constant-time behavior in a number of applications including encryption and decryption routines, database queries, and other resource-aware functionality.
A Comprehensive Survey of Ontology Summarization: Measures and Methods
Venue: arXiv cs.IR 2018
Year: 2018

The Semantic Web is becoming a large scale framework that enables data to be published, shared, and reused in the form of ontologies. The ontology which is considered as basic building block of semantic web consists of two layers including data and schema layer. With the current exponential development of ontologies in both data size and complexity of schemas, ontology understanding which is playing an important role in different tasks such as ontology engineering, ontology learning, etc., is becoming more difficult. Ontology summarization as a way to distill knowledge from an ontology and generate an abridge version to facilitate a better understanding is getting more attention recently. There are various approaches available for ontology summarization which are focusing on different measures in order to produce a proper summary for a given ontology. In this paper, we mainly focus on the common metrics which are using for ontology summarization and meet the state-of-the-art in ontology summarization.
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Venue: arXiv cs.LG 2018
Year: 2018

We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.
Trainable ISTA for Sparse Signal Recovery
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a novel sparse signal recovery algorithm called Trainable ISTA (TISTA). The proposed algorithm consists of two estimation units such as a linear estimation unit and a minimum mean squared error (MMSE) estimator-based shrinkage unit. The estimated error variance required in the MMSE shrinkage unit is precisely estimated from a tentative estimate of the original signal. The remarkable feature of the proposed scheme is that TISTA includes adjustable variables controlling a step size and the error variance for the MMSE shrinkage. The variables are adjusted by standard deep learning techniques. The number of trainable variables of TISTA is equal to the number of iteration rounds and it is much smaller than that of known learnable sparse signal recovery algorithms. This feature leads to highly stable and fast training processes of TISTA. Computer experiments show that TISTA is applicable to various classes of sensing matrices such as Gaussian matrices, binary matrices and matrices with large condition numbers. Numerical results also demonstrate that TISTA shows significantly faster convergence than those of AMP and LISTA in many cases.
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures
Venue: arXiv cs.ET 2018
Year: 2018

Deep Learning and its applications have gained tremendous interest recently in both academia and industry. Restricted Boltzmann Machines (RBMs) offer a key methodology to implement deep learning paradigms. This paper presents a novel approach for realizing hybrid CMOS-OxRAM based deep generative models (DGM). In our proposed hybrid DGM architectures, HfOx based (filamentary-type switching) OxRAM devices are extensively used for realizing multiple computational and non-computational functions such as: (i) Synapses (weights), (ii) internal neuron-state storage, (iii) stochastic neuron activation and (iv) programmable signal normalization. To validate the proposed scheme we have simulated two different architectures: (i) Deep Belief Network (DBN) and (ii) Stacked Denoising Autoencoder for classification and reconstruction of hand-written digits from a reduced MNIST dataset of 6000 images. Contrastive-divergence (CD) specially optimized for OxRAM devices was used to drive the synaptic weight update mechanism of each layer in the network. Overall learning rule was based on greedy-layer wise learning with no back propagation which allows the network to be trained to a good pre-training stage. Performance of the simulated hybrid CMOS-RRAM DGM model matches closely with software based model for a 2-layers deep network. Top-3 test accuracy achieved by the DBN was 95.5%. MSE of the SDA network was 0.003, lower than software based approach. Endurance analysis of the simulated architectures show that for 200 epochs of training (single RBM layer), maximum switching events/per OxRAM device was ~ 7000 cycles.
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural Networks (RNN), which have fewer parameters than other deep neural networks, e.g. Convolutional Neural Networks (CNN). First, we learn RNN parameters to discriminate between the target object and background in the first frame of a test sequence. Tree structure over local patches of an exemplar region is randomly generated by using a bottom-up greedy search strategy. Given the learned RNN parameters, we create two dictionaries regarding target regions and corresponding local patches based on the learned hierarchical features from both top and leaf nodes of multiple random trees. In each of the subsequent frames, we conduct sparse dictionary coding on all candidates to select the best candidate as the new target location. In addition, we online update two dictionaries to handle appearance changes of target objects. Experimental results demonstrate that our feature learning algorithm can significantly improve tracking performance on benchmark datasets.
Complexity Theory for Discrete Black-Box Optimization Heuristics
Venue: arXiv cs.NE 2018
Year: 2018

A predominant topic in the theory of evolutionary algorithms and, more generally, theory of randomized black-box optimization techniques is running time analysis. Running time analysis aims at understanding the performance of a given heuristic on a given problem by bounding the number of function evaluations that are needed by the heuristic to identify a solution of a desired quality. As in general algorithms theory, this running time perspective is most useful when it is complemented by a meaningful complexity theory that studies the limits of algorithmic solutions. In the context of discrete black-box optimization, several black-box complexity models have been developed to analyze the best possible performance that a black-box optimization algorithm can achieve on a given problem. The models differ in the classes of algorithms to which these lower bounds apply. This way, black-box complexity contributes to a better understanding of how certain algorithmic choices (such as the amount of memory used by a heuristic, its selective pressure, or properties of the strategies that it uses to create new solution candidates) influences performance. In this chapter we review the different black-box complexity models that have been proposed in the literature, survey the bounds that have been obtained for these models, and discuss how the interplay of running time analysis and black-box complexity can inspire new algorithmic solutions to well-researched problems in evolutionary computation. We also discuss in this chapter several interesting open questions for future work.
Optimal Error Correcting Delivery Scheme for an Optimal Coded Caching Scheme with Small Buffers
Venue: arXiv cs.IT 2018
Year: 2018

Optimal delivery scheme for coded caching problems with small buffer sizes and the number of users no less than the amount of files in the server was proposed by Chen, Fan and Letaief ["Fundamental limits of caching: improved bounds for users with small buffers," (IET Communications), 2016]. This scheme is referred to as the CFL scheme. In this paper, the link between the server and the users is assumed to be error prone only during the delivery phase. Closed form expressions for average rate and peak rate of error correcting delivery scheme for CFL prefetching scheme is obtained. An optimal error correcting delivery scheme for caching problems employing CFL prefetching is proposed.
Resource Optimization with Flexible Numerology and Frame Structure for Heterogeneous Services
Venue: arXiv cs.IT 2018
Year: 2018

We explore the potential of optimizing resource allocation with flexible numerology in frequency domain and variable frame structure in time domain, in presence of services with different types of requirements. We analyze the computational complexity and propose a scalable optimization algorithm based on searching in both the primal space and dual space that are complementary to each other. Numerical results show significant advantages of adopting flexibility in both time and frequency domains for capacity enhancement and meeting the requirements of mission critical services.
Privacy-Preserving Aggregate Queries for Optimal Location Selection
Venue: arXiv cs.CR 2018
Year: 2018

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.
Network Coding Implementation Details: A Guidance Document
Venue: arXiv cs.NI 2018
Year: 2018

In recent years, network coding has become one of the most interesting fields and has attracted considerable attention from both industry and academia. The idea of network coding is based on the concept of allowing intermediate nodes to encode and combine incoming packets instead of only copy and forward them. This approach, by augmenting the multicast and broadcast efficiency of multi-hop wireless networks, increases the capacity of the network and improves its throughput and robustness. While a wide variety of papers described applications of network coding in different types of networks such as delay tolerant networks, peer to peer networks and wireless sensor networks, the detailed practical implementation of network coding has not been noted in most papers. Since applying network coding in real scenarios requires an acceptable understanding of mathematics and algebra, especially linear equations, reduced row echelon matrices, field and its operations, this paper provides a comprehensive guidance for the implementation of almost all required concepts in network coding. The paper explains the implementation details of network coding in real scenarios and describes the effect of the field size on network coding.
FlexONC: Joint Cooperative Forwarding and Network Coding with Precise Encoding Conditions
Venue: arXiv cs.NI 2018
Year: 2018

In recent years, network coding has emerged as an innovative method that helps a wireless network approach its maximum capacity, by combining multiple unicasts in one broadcast. However, the majority of research conducted in this area is yet to fully utilize the broadcasting nature of wireless networks, and still assumes fixed route between the source and destination that every packet should travel through. This assumption not only limits coding opportunities, but can also cause buffer overflow in some specific intermediate nodes. Although some studies considered scattering of the flows dynamically in the network, they still face some limitations. This paper explains pros and cons of some prominent research in network coding and proposes a Flexible and Opportunistic Network Coding scheme (FlexONC) as a solution to such issues. Furthermore, this research discovers that the conditions used in previous studies to combine packets of different flows are overly optimistic and would affect the network performance adversarially. Therefore, we provide a more accurate set of rules for packet encoding. The experimental results show that FlexONC outperforms previous methods especially in networks with high bit error rate, by better utilizing redundant packets spread in the network.
Binning based algorithm for Pitch Detection in Hindustani Classical Music
Venue: arXiv cs.SD 2018
Year: 2018

Speech coding forms a crucial element in speech communications. An important area concerning it lies in feature extraction which can be used for analyzing Hindustani Classical Music. An important feature in this respect is the fundamental frequency often referred to as the pitch. In this work, the terms pitch and its acoustical sensation, the frequency is used interchangeably. There exists numerous pitch detection algorithms which detect the main/ fundamental frequency in a given musical piece, but we have come up with a unique algorithm for pitch detection using the binning method as described in the paper using appropriate bin size. Previous work on this subject throws light on pitch identification for Hindustani Classical Music. Pitch Class Distribution has been employed in this work. It can be used to identify pitches in Hindustani Classical Music which is based on suitable intonations and swaras. It follows a particular ratio pattern which is a tuning for diatonic scale proposed by Ptolemy and confirmed by Zarlino is explored in this paper. We have also given our estimated of these ratios and compared the error with the above. The error produced by varying the bin size in our algorithm is investigated and an estimate for an appropriate bin size is suggested and tested. The binning algorithm thus helps to segregate the important pitches in a given musical piece.
EdgeFlow: Open-Source Multi-layer Data Flow Processing in Edge Computing for 5G and Beyond
Venue: arXiv cs.NI 2018
Year: 2018

Edge computing has evolved to be a promising avenue to enhance the system computing capability by offloading processing tasks from the cloud to edge devices. In this paper, we propose a multi-layer edge computing framework called EdgeFlow. In this framework, different nodes ranging from edge devices to cloud data centers are categorized into corresponding layers and cooperate together for data processing. With the help of EdgeFlow, one can balance the trade-off between computing and communication capability so that the tasks are assigned to each layer optimally. At the same time, resources are carefully allocated throughout the whole network to mitigate performance fluctuation. The proposed open-source data flow processing framework is implemented on a platform that can emulate various computing nodes in multiple layers and corresponding network connections. Evaluated on the face recognition scenario, EdgeFlow can significantly reduce task finish time and perform more tolerance to run-time variation, compared with the pure cloud computing, the pure edge computing and Cloudlet. Potential applications of EdgeFlow, including network function visualization, Internet of Things, and vehicular networks, are also discussed in the end of this work.
Arrows for Parallel Computation
Venue: arXiv cs.PL 2018
Year: 2018

Arrows are a general interface for computation and an alternative to Monads for API design. In contrast to Monad-based parallelism, we explore the use of Arrows for specifying generalised parallelism. Specifically, we define an Arrow-based language and implement it using multiple parallel Haskells. As each parallel computation is an Arrow, such parallel Arrows (PArrows) can be readily composed and transformed as such. To allow for more sophisticated communication schemes between computation nodes in distributed systems, we utilise the concept of Futures to wrap direct communication. To show that PArrows have similar expressive power as existing parallel languages, we implement several algorithmic skeletons and four benchmarks. Benchmarks show that our framework does not induce any notable performance overhead. We conclude that Arrows have considerable potential for composing parallel programs and for producing programs that can execute on multiple parallel language implementations.
Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding
Venue: arXiv cs.CV 2018
Year: 2018

A common approach for moving objects segmentation in a scene is to perform a background subtraction. Several methods have been proposed in this domain. However, they lack the ability of handling various difficult scenarios such as illumination changes, background or camera motion, camouflage effect, shadow etc. To address these issues, we propose a robust and flexible encoder-decoder type neural network based approach. We adapt a pre-trained convolutional network, i.e. VGG-16 Net, under a triplet framework in the encoder part to embed an image in multiple scales into the feature space and use a transposed convolutional network in the decoder part to learn a mapping from feature space to image space. We train this network end-to-end by using only a few training samples. Our network takes an RGB image in three different scales and produces a foreground segmentation probability mask for the corresponding image. In order to evaluate our model, we entered the Change Detection 2014 Challenge (changedetection.net) and our method outperformed all the existing state-of-the-art methods by an average F-Measure of 0.9770. Our source code will be made publicly available at https://github.com/lim-anggun/FgSegNet.