OpenRaiser/Intern-Atlas huggingface dataset:
Radar Interferometry using Two Images with Different Resolutions
Venue: arXiv cs.IT 2018
Year: 2018

Radar interferometry usually exploits two complex-valued radar images with the same resolution to extract terrain elevation information. This paper considers the interferometry using two radar images with different resolutions, which we refer to as dual-resolution radar interferometry. We find that it is feasible to recover a high-resolution interferogram from a high-resolution image and a low-resolution one. We formulate the dual-resolution interferometry into a compressive sensing problem, and exploit the wavelet-domain sparsity of the interferogram to solve it. Due to the speckle effect in coherent radar imaging, the sensing matrix of our model is expected to have small mutual coherence, which guarantees the performance of our method. In comparison with the conventional radar interferometry methods, the proposed method reduces the resolution requirement of radar image acquisition. It therefore can promote wide coverage, low sampling/data rate and storage cost. Numerical experiments on Sentinel-1 data are made to validate our method.
Dynamic Feature Generation Network for Answer Selection
Venue: arXiv cs.CL 2018
Year: 2018

Extracting appropriate features to represent a corpus is an important task for textual mining. Previous attention based work usually enhance feature at the lexical level, which lacks the exploration of feature augmentation at the sentence level. In this paper, we exploit a Dynamic Feature Generation Network (DFGN) to solve this problem. Specifically, DFGN generates features based on a variety of attention mechanisms and attaches features to sentence representation. Then a thresholder is designed to filter the mined features automatically. DFGN extracts the most significant characteristics from datasets to keep its practicability and robustness. Experimental results on multiple well-known answer selection datasets show that our proposed approach significantly outperforms state-of-the-art baselines. We give a detailed analysis of the experiments to illustrate why DFGN provides excellent retrieval and interpretative ability.
Conceptualizing Business Process Maps
Venue: arXiv cs.SE 2018
Year: 2018

Process maps provide a high-level overview of an organisation's business processes. While used for many years in different shapes and forms, there is little shared understanding of the concept and its relationship to enterprise architecture. In this report we position the concept of business process map within the domain of enterprise architecture. Based on literature, we provide a conceptualisation of the process map as a business process architecture model that can be integrated with the broader enterprise architecture model. From our conceptualisation we derive requirements for designing a meta-model of a modelling language for process maps. The design of this meta-model is the subject of a research paper, entitled Architecting Business Process Maps, for which this report acts as a complement that details the underlying process map conceptualisation.
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
Venue: arXiv cs.CL 2018
Year: 2018

Natural language understanding is a challenging problem that covers a wide range of tasks. While previous methods generally train each task separately, we consider combining the cross-task features to enhance the task performance. In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on SCT considered various semantic information, such as sentiment and topic, but lack the logic information between sentences which is an essential element of stories. Thus we propose to extract the logic information during the course of the story to improve the understanding of the whole story. The logic information is modeled with the help of the NLI task. Experimental results prove the strength of the logic information.
Sidechains: Decoupled Consensus Between Chains
Venue: arXiv cs.CR 2018
Year: 2018

We propose a novel sidechain construction tailored to be compatible with the Horizen blockchain and designed for conducting secure and decentralized cross-chain transfers without requiring the mainchain nodes to track sidechains to verify them. The proposed scheme can also be adopted for other similar blockchain systems. We show that our cross-ledger transfer mechanism is secure under certain plausible assumptions.
A practical approach to detection of distributed denial-of-service attacks using a hybrid detection method
Venue: arXiv cs.CR 2018
Year: 2018

This paper presents a hybrid method for the detection of distributed denial-of-service (DDoS) attacks that combines feature-based and volume-based detection. Our approach is based on an exponential moving average algorithm for decision-making, applied to both entropy and packet number time series. The approach has been tested by performing a controlled DDoS experiment in a real academic network. The network setup and test scenarios including both high-rate and low-rate attacks are described in the paper. The performance of the proposed method is compared to the performance of two methods that are already known in the literature. One is based on the counting of SYN packets and is used for detection of SYN flood attacks, while the other is based on a CUSUM algorithm applied to the entropy time series. The results show the advantage of our approach compared to methods that are based on either entropy or number of packets only.
A Survey on Blockchain Technology and Its Potential Applications in Distributed Control and Cooperative Robots
Venue: arXiv cs.CR 2018
Year: 2018

As a disruptive technology, blockchain, particularly its original form of bitcoin as a type of digital currency, has attracted great attentions. The innovative distributed decision making and security mechanism lay the technical foundation for its success, making us consider to penetrate the power of blockchain technology to distributed control and cooperative robotics, in which the distributed and secure mechanism is also highly demanded. Actually, security and distributed communication have long been unsolved problems in the field of distributed control and cooperative robotics. It has been reported on the network failure and intruder attacks of distributed control and multi-robotic systems. Blockchain technology provides promise to remedy this situation thoroughly. This work is intended to create a global picture of blockchain technology on its working principle and key elements in the language of control and robotics, to provide a shortcut for beginners to step into this research field.
Graph Comparison via the Non-backtracking Spectrum
Venue: arXiv cs.SI 2018
Year: 2018

The comparison of graphs is a vitally important, yet difficult task which arises across a number of diverse research areas including biological and social networks. There have been a number of approaches to define graph distance however often these are not metrics (rendering standard data-mining techniques infeasible), or are computationally infeasible for large graphs. In this work we define a new metric based on the spectrum of the non-backtracking graph operator and show that it can not only be used to compare graphs generated through different mechanisms, but can reliably compare graphs of varying size. We observe that the family of Watts-Strogatz graphs lie on a manifold in the non-backtracking spectral embedding and show how this metric can be used in a standard classification problem of empirical graphs.
Deep Face Image Retrieval: a Comparative Study with Dictionary Learning
Venue: arXiv cs.CV 2018
Year: 2018

Facial image retrieval is a challenging task since faces have many similar features (areas), which makes it difficult for the retrieval systems to distinguish faces of different people. With the advent of deep learning, deep networks are often applied to extract powerful features that are used in many areas of computer vision. This paper investigates the application of different deep learning models for face image retrieval, namely, Alexlayer6, Alexlayer7, VGG16layer6, VGG16layer7, VGG19layer6, and VGG19layer7, with two types of dictionary learning techniques, namely $K$-means and $K$-SVD. We also investigate some coefficient learning techniques such as the Homotopy, Lasso, Elastic Net and SSF and their effect on the face retrieval system. The comparative results of the experiments conducted on three standard face image datasets show that the best performers for face image retrieval are Alexlayer7 with $K$-means and SSF, Alexlayer6 with $K$-SVD and SSF, and Alexlayer6 with $K$-means and SSF. The APR and ARR of these methods were further compared to some of the state of the art methods based on local descriptors. The experimental results show that deep learning outperforms most of those methods and therefore can be recommended for use in practice of face image retrieval
FA-RPN: Floating Region Proposals for Face Detection
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feature map, we adopt a pooling-based approach for generating region proposals. However, pooling hundreds of thousands of anchors which are evaluated for generating proposals becomes a computational bottleneck during inference. To this end, an efficient anchor placement strategy for reducing the number of anchor-boxes is proposed. We then show that proposals generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are better than RPN for generating region proposals for face detection. We discuss several beneficial features of FA-RPN proposals like iterative refinement, placement of fractional anchors and changing anchors which can be enabled without making any changes to the trained model. Our face detector based on FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.
Pay Voice: Point of Sale Recognition for Visually Impaired People
Venue: arXiv cs.CV 2018
Year: 2018

Millions of visually impaired people depend on relatives and friends to perform their everyday tasks. One relevant step towards self-sufficiency is to provide them with means to verify the value and operation presented in payment machines. In this work, we developed and released a smartphone application, named Pay Voice, that uses image processing, optical character recognition (OCR) and voice synthesis to recognize the value and operation presented in POS and PIN pad machines, and thus informing the user with auditive and visual feedback. The proposed approach presented significant results for value and operation recognition, especially for POS, due to the higher display quality. Importantly, we achieved the key performance indicators, namely, more than 80% of accuracy in a real-world scenario, and less than $5$ seconds of processing time for recognition. Pay Voice is publicly available on Google Play and App Store for free.
An Incentive-Aware Job Offloading Control Framework for Mobile Edge Computing
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a scenario in which an access point (AP) is equipped with a mobile edge server of finite computing power, and serves multiple resource-hungry mobile users by charging users a price. Pricing provides users with incentives in offloading. However, existing works on pricing are based on abstract concave utility functions (e.g, the logarithm function), giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individual's interest and the other maximizing the overall system's interest. We analyze the structural property of the games and admit in closed form the Nash Equilibrium and the Social Equilibrium, respectively. The proposed expressions are functions of the user parameters such as the weights of computational time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal pricing-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.
Data Security and Privacy Protection Data Security and Privacy Protection in Public Cloud
Venue: arXiv cs.CR 2018
Year: 2018

This paper discusses about the challenges, advantages and shortcomings of existing solutions in data security and privacy in public cloud computing. As in cloud computing, oceans of data will be stored. Data stored in public cloud would face both outside attacks and inside attacks since public cloud provider themselves are untrusted. Conventional encryption could be used for storage, however most data in cloud needs further computation. Decryption before computation will cause large overheads for data operation and lots of inconvenience. Thus, efficient methods to protect data security as well as privacy for large amount of data in cloud are necessary. In the paper, different mechanisms to protect data security and privacy in public cloud are discussed. A data security and privacy enabled multi-cloud architecture is proposed.
On Stacked Denoising Autoencoder based Pre-training of ANN for Isolated Handwritten Bengali Numerals Dataset Recognition
Venue: arXiv cs.LG 2018
Year: 2018

This work attempts to find the most optimal parameter setting of a deep artificial neural network (ANN) for Bengali digit dataset by pre-training it using stacked denoising autoencoder (SDA). Although SDA based recognition is hugely popular in image, speech and language processing related tasks among the researchers, it was never tried in Bengali dataset recognition. For this work, a dataset of 70000 handwritten samples were used from (Chowdhury and Rahman, 2016) and was recognized using several settings of network architecture. Among all these settings, the most optimal setting being found to be five or more deeper hidden layers with sigmoid activation and one output layer with softmax activation. We proposed the optimal number of neurons that can be used in the hidden layer is 1500 or more. The minimum validation error found from this work is 2.34% which is the lowest error rate on handwritten Bengali dataset proposed till date.
Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this paper, we propose a new paradigm based on machine translation. In our approach, we translate a product's natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph (DAG). We discuss how the resultant taxonomy DAG promotes user-friendly navigation, and how it is more adaptable to new products.
Low-rank Matrix Completion in a General Non-orthogonal Basis
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers theoretical analysis of recovering a low rank matrix given a few expansion coefficients with respect to any basis. The current approach generalizes the existing analysis for the low-rank matrix completion problem with sampling under entry sensing or with respect to a symmetric orthonormal basis. The analysis is based on dual certificates using a dual basis approach and does not assume the restricted isometry property (RIP). We introduce a condition on the basis called the correlation condition. This condition can be computed in time $O(n^3)$ and holds for many cases of deterministic basis where RIP might not hold or is NP hard to verify. If the correlation condition holds and the underlying low rank matrix obeys the coherence condition with parameter $ν$, under additional mild assumptions, our main result shows that the true matrix can be recovered with very high probability from $O(nrν\log^2n)$ uniformly random expansion coefficients.
Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring
Venue: arXiv cs.CV 2018
Year: 2018

Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we investigate the combination of deep learning based methods and depth maps as input images to tackle the problem of driver attention monitoring. Moreover, we assume the concept of attention as Head Pose Estimation and Facial Landmark Detection tasks. Differently from other proposals in the literature, the proposed systems are able to work directly and based only on raw depth data. All presented methods are trained and tested on two new public datasets, namely Pandora and MotorMark, achieving state-of-art results and running with real time performance.
The Coherent Point Drift for Clustered Point Sets
Venue: arXiv cs.CV 2018
Year: 2018

The problem of non-rigid point set registration is a key problem for many computer vision tasks. In many cases the nature of the data or capabilities of the point detection algorithms can give us some prior information on point sets distribution. In non-rigid case this information is able to drastically improve registration results by limiting number of possible solutions. In this paper we explore use of prior information about point sets clustering, such information can be obtained with preliminary segmentation. We extend existing probabilistic framework for fitting two level Gaussian mixture model and derive closed form solution for maximization step of the EM algorithm. This enables us to improve method accuracy with almost no performance loss. We evaluate our approach and compare the Cluster Coherent Point Drift with other existing non-rigid point set registration methods and show it's advantages for digital medicine tasks, especially for heart template model personalization using patient's medical data.
Evaluation of an open-source implementation of the SRP-PHAT algorithm within the 2018 LOCATA challenge
Venue: arXiv cs.SD 2018
Year: 2018

This short paper presents an efficient, flexible implementation of the SRP-PHAT multichannel sound source localization method. The method is evaluated on the single-source tasks of the LOCATA 2018 development dataset, and an associated Matlab toolbox is made available online.
Expected Emergence of Algorithmic Information from a Lower Bound for Stationary Prevalence
Venue: arXiv cs.SI 2018
Year: 2018

We study emergent information in populations of randomly generated networked computable systems that follow a Susceptible-Infected-Susceptible contagion (or infection) model of imitation of the fittest neighbor. These networks have a scale-free degree distribution in the form of a power-law following the Barabási-Albert model. We show that there is a lower bound for the stationary prevalence (or average density of infected nodes) that triggers an unlimited increase of the expected emergent algorithmic complexity (or information) of a node as the population size grows.
Block encryption of quantum messages
Venue: arXiv cs.CR 2018
Year: 2018

In modern cryptography, block encryption is a fundamental cryptographic primitive. However, it is impossible for block encryption to achieve the same security as one-time pad. Quantum mechanics has changed the modern cryptography, and lots of researches have shown that quantum cryptography can outperform the limitation of traditional cryptography. This article proposes a new constructive mode for private quantum encryption, named $\mathcal{EHE}$, which is a very simple method to construct quantum encryption from classical primitive. Based on $\mathcal{EHE}$ mode, we construct a quantum block encryption (QBE) scheme from pseudorandom functions. If the pseudorandom functions are standard secure, our scheme is indistinguishable encryption under chosen plaintext attack. If the pseudorandom functions are permutation on the key space, our scheme can achieve perfect security. In our scheme, the key can be reused and the randomness cannot, so a $2n$-bit key can be used in an exponential number of encryptions, where the randomness will be refreshed in each time of encryption. Thus $2n$-bit key can perfectly encrypt $O(n2^n)$ qubits, and the perfect secrecy would not be broken if the $2n$-bit key is reused for only exponential times. Comparing with quantum one-time pad (QOTP), our scheme can be the same secure as QOTP, and the secret key can be reused (no matter whether the eavesdropping exists or not). Thus, the limitation of perfectly secure encryption (Shannon's theory) is broken in the quantum setting. Moreover, our scheme can be viewed as a positive answer to the open problem in quantum cryptography "how to unconditionally reuse or recycle the whole key of private-key quantum encryption". In order to physically implement the QBE scheme, we only need to implement two kinds of single-qubit gates (Pauli $X$ gate and Hadamard gate), so it is within reach of current quantum technology.
Modeling Temporal Evidence from External Collections
Venue: arXiv cs.IR 2018
Year: 2018

Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.
Bias Mitigation Post-processing for Individual and Group Fairness
Venue: arXiv cs.LG 2018
Year: 2018

Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation algorithm aiming to improve the group fairness measure of disparate impact. We show superior performance to previous work in the combination of classification accuracy, individual fairness and group fairness on several real-world datasets in applications such as credit, employment, and criminal justice.