OpenRaiser/Intern-Atlas huggingface dataset:
CTCModel: a Keras Model for Connectionist Temporal Classification
Venue: arXiv cs.LG 2019
Year: 2019

We report an extension of a Keras Model, called CTCModel, to perform the Connectionist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference method for dealing with unsegmented input sequences, i.e. with data that are a couple of observation and label sequences where each label is related to a subset of observation frames. CTCModel makes use of the CTC implementation in the Tensorflow backend for training and predicting sequences of labels using Keras. It consists of three branches made of Keras models: one for training, computing the CTC loss function; one for predicting, providing sequences of labels; and one for evaluating that returns standard metrics for analyzing sequences of predictions.
Wikipedia Cultural Diversity Dataset: A Complete Cartography for 300 Language Editions
Venue: arXiv cs.CY 2019
Year: 2019

In this paper we present the Wikipedia Cultural Diversity dataset. For each existing Wikipedia language edition, the dataset contains a classification of the articles that represent its associated cultural context, i.e. all concepts and entities related to the language and to the territories where it is spoken. We describe the methodology we employed to classify articles, and the rich set of features that we defined to feed the classifier, and that are released as part of the dataset. We present several purposes for which we envision the use of this dataset, including detecting, measuring and countering content gaps in the Wikipedia project, and encouraging cross-cultural research in the field of digital humanities.
Generalization of Spoofing Countermeasures: a Case Study with ASVspoof 2015 and BTAS 2016 Corpora
Venue: arXiv cs.MM 2019
Year: 2019

Voice-based biometric systems are highly prone to spoofing attacks. Recently, various countermeasures have been developed for detecting different kinds of attacks such as replay, speech synthesis (SS) and voice conversion (VC). Most of the existing studies are conducted with a specific training set defined by the evaluation protocol. However, for realistic scenarios, selecting appropriate training data is an open challenge for the system administrator. Motivated by this practical concern, this work investigates the generalization capability of spoofing countermeasures in restricted training conditions where speech from a broad attack types are left out in the training database. We demonstrate that different spoofing types have considerably different generalization capabilities. For this study, we analyze the performance using two kinds of features, mel-frequency cepstral coefficients (MFCCs) which are considered as baseline and recently proposed constant Q cepstral coefficients (CQCCs). The experiments are conducted with standard Gaussian mixture model - maximum likelihood (GMM-ML) classifier on two recently released spoofing corpora: ASVspoof 2015 and BTAS 2016 that includes cross-corpora performance analysis. Feature-level analysis suggests that static and dynamic coefficients of spectral features, both are important for detecting spoofing attacks in the real-life condition.
A Question-Entailment Approach to Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is to map new questions to formerly answered questions that are `similar'. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare machine learning and deep learning methods for RQE using different kinds of datasets, including textual inference, question similarity and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources, that we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results also support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Venue: arXiv cs.CL 2019
Year: 2019

We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model. Fine-tuning is performed by using a multi-task objective which combines several unsupervised prediction tasks. The resulting fine-tuned model shows strong improvements over the current state-of-the-art end-to-end conversational models like memory augmented seq2seq and information-retrieval models. On the privately held PERSONA-CHAT dataset of the Conversational Intelligence Challenge 2, this approach obtains a new state-of-the-art, with respective perplexity, Hits@1 and F1 metrics of 16.28 (45 % absolute improvement), 80.7 (46 % absolute improvement) and 19.5 (20 % absolute improvement).
Cloud BI: Future of Business Intelligence in the Cloud
Venue: arXiv cs.DC 2019
Year: 2019

Cloud computing is gradually gaining popularity among businesses due to its distinct advantages over self-hosted IT infrastructures. Business Intelligence (BI) is a highly resource intensive system requiring large-scale parallel processing and significant storage capacities to host data warehouses. In self-hosted environments it was feared that BI will eventually face a resource crunch situation because it will not be feasible for companies to keep adding resources to host a neverending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards to taking BI to the cloud. The cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results have reflected that true and extensible parallel processing of database servers on the cloud can efficiently process OLAP application demands on cloud computing. Hence, the BI designer needs to plan for a highly partitioned database running on massively parallel database servers in which, each server hosts at least one partition of the underlying database serving the OLAP demands.
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching
Venue: arXiv cs.DC 2019
Year: 2019

Load balancing is critical for distributed storage to meet strict service-level objectives (SLOs). It has been shown that a fast cache can guarantee load balancing for a clustered storage system. However, when the system scales out to multiple clusters, the fast cache itself would become the bottleneck. Traditional mechanisms like cache partition and cache replication either result in load imbalance between cache nodes or have high overhead for cache coherence. We present DistCache, a new distributed caching mechanism that provides provable load balancing for large-scale storage systems. DistCache co-designs cache allocation with cache topology and query routing. The key idea is to partition the hot objects with independent hash functions between cache nodes in different layers, and to adaptively route queries with the power-of-two-choices. We prove that DistCache enables the cache throughput to increase linearly with the number of cache nodes, by unifying techniques from expander graphs, network flows, and queuing theory. DistCache is a general solution that can be applied to many storage systems. We demonstrate the benefits of DistCache by providing the design, implementation, and evaluation of the use case for emerging switch-based caching.
A PCB Dataset for Defects Detection and Classification
Venue: arXiv cs.CV 2019
Year: 2019

To coupe with the difficulties in the process of inspection and classification of defects in Printed Circuit Board (PCB), other researchers have proposed many methods. However, few of them published their dataset before, which hindered the introduction and comparison of new methods. In this paper, we published a synthesized PCB dataset containing 1386 images with 6 kinds of defects for the use of detection, classification and registration tasks. Besides, we proposed a reference based method to inspect and trained an end-to-end convolutional neural network to classify the defects. Unlike conventional approaches that require pixel-by-pixel processing, our method firstly locate the defects and then classify them by neural networks, which shows superior performance on our dataset.
Semi-Supervised Image-to-Image Translation
Venue: arXiv cs.CV 2019
Year: 2019

Image-to-image translation is a long-established and a difficult problem in computer vision. In this paper we propose an adversarial based model for image-to-image translation. The regular deep neural-network based methods perform the task of image-to-image translation by comparing gram matrices and using image segmentation which requires human intervention. Our generative adversarial network based model works on a conditional probability approach. This approach makes the image translation independent of any local, global and content or style features. In our approach we use a bidirectional reconstruction model appended with the affine transform factor that helps in conserving the content and photorealism as compared to other models. The advantage of using such an approach is that the image-to-image translation is semi-supervised, independant of image segmentation and inherits the properties of generative adversarial networks tending to produce realistic. This method has proven to produce better results than Multimodal Unsupervised Image-to-image translation.
When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?
Venue: arXiv cs.AI 2019
Year: 2019

There is much debate in machine ethics about the most appropriate way to introduce ethical reasoning capabilities into intelligent autonomous machines. Recent incidents involving autonomous vehicles in which humans have been killed or injured have raised questions about how we ensure that such vehicles have an ethical dimension to their behaviour and are therefore trustworthy. The main problem is that hardwiring such machines with rules not to cause harm or damage is not consistent with the notion of autonomy and intelligence. Also, such ethical hardwiring does not leave intelligent autonomous machines with any course of action if they encounter situations or dilemmas for which they are not programmed or where some harm is caused no matter what course of action is taken. Teaching machines so that they learn ethics may also be problematic given recent findings in machine learning that machines pick up the prejudices and biases embedded in their learning algorithms or data. This paper describes a fuzzy reasoning approach to machine ethics. The paper shows how it is possible for an ethics architecture to reason when taking over from a human driver is morally justified. The design behind such an ethical reasoner is also applied to an ethical dilemma resolution case. One major advantage of the approach is that the ethical reasoner can generate its own data for learning moral rules (hence, autometric) and thereby reduce the possibility of picking up human biases and prejudices. The results show that a new type of metric-based ethics appropriate for autonomous intelligent machines is feasible and that our current concept of ethical reasoning being largely qualitative in nature may need revising if want to construct future autonomous machines that have an ethical dimension to their reasoning so that they become moral machines.
Coalition Formation Games Based Sub-Channel Allocation for Device-to-Device Underlay mmWave Small Cells
Venue: arXiv cs.NI 2019
Year: 2019

Small cells in the millimeter wave band densely deployed underlying the macrocell have been regarded as one of promising candidates for the next generation mobile networks. In the user intensive region, device-to-device (D2D) communication in physical proximity can save power and improve spectral efficiency. In this paper, we focus on the optimal sub-channel allocation for access and D2D links in the scenario of densely deployed multiple mmWave small cells. The problem is modeled as a coalitional game to maximize the system sum rate of access and D2D links in the system. Then we propose a coalition formation game based algorithm for sub-channel allocation. Performance evaluation results demonstrate superior performance in terms of the system sum rate compared with other practical schemes.
Glass+Skin: An Empirical Evaluation of the Added Value of Finger Identification to Basic Single-Touch Interaction on Touch Screens
Venue: arXiv cs.HC 2019
Year: 2019

The usability of small devices such as smartphones or interactive watches is often hampered by the limited size of command vocabularies. This paper is an attempt at better understanding how finger identification may help users invoke commands on touch screens, even without recourse to multi-touch input. We describe how finger identification can increase the size of input vocabularies under the constraint of limited real estate, and we discuss some visual cues to communicate this novel modality to novice users. We report a controlled experiment that evaluated, over a large range of input-vocabulary sizes, the efficiency of single-touch command selections with vs. without finger identification. We analyzed the data not only in terms of traditional time and error metrics, but also in terms of a throughput measure based on Shannon's theory, which we show offers a synthetic and parsimonious account of users' performance. The results show that the larger the input vocabulary needed by the designer, the more promising the identification of individual fingers.
A stack-vector routing protocol for automatic tunneling
Venue: arXiv cs.NI 2019
Year: 2019

In a network, a tunnel is a part of a path where a protocol is encapsulated in another one. A tunnel starts with an encapsulation and ends with the corresponding decapsulation. Several tunnels can be nested at some stage, forming a protocol stack. Tunneling is very important nowadays and it is involved in several tasks: IPv4/IPv6 transition, VPNs, security (IPsec, onion routing), etc. However, tunnel establishment is mainly performed manually or by script, which present obvious scalability issues. Some works attempt to automate a part of the process (e.g., TSP, ISATAP, etc.). However, the determination of the tunnel(s) endpoints is not fully automated, especially in the case of an arbitrary number of nested tunnels. The lack of routing protocols performing automatic tunneling is due to the unavailability of path computation algorithms taking into account encapsulations and decapsulations. There is a polynomial centralized algorithm to perform the task. However, to the best of our knowledge, no fully distributed path computation algorithm is known. Here, we propose the first fully distributed algorithm for path computation with automatic tunneling, i.e., taking into account encapsulation, decapsulation and conversion of protocols. Our algorithm is a generalization of the distributed Bellman-Ford algorithm, where the distance vector is replaced by a protocol stack vector. This allows to know how to route a packet with some protocol stack. We prove that the messages size of our algorithm is polynomial, even if the shortest path can be of exponential length. We also prove that the algorithm converges after a polynomial number of steps in a synchronized setting. We adapt our algorithm into a proto-protocol for routing with automatic tunneling and we show its efficiency through simulations.
Can We Prove Time Protection?
Venue: arXiv cs.OS 2019
Year: 2019

Timing channels are a significant and growing security threat in computer systems, with no established solution. We have recently argued that the OS must provide time protection, in analogy to the established memory protection, to protect applications from information leakage through timing channels. Based on a recently-proposed implementation of time protection in the seL4 microkernel, we investigate how such an implementation could be formally proved to prevent timing channels. We postulate that this should be possible by reasoning about a highly abstracted representation of the shared hardware resources that cause timing channels.
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
Venue: arXiv cs.LG 2019
Year: 2019

State-of-the-art neural networks are vulnerable to adversarial examples; they can easily misclassify inputs that are imperceptibly different than their training and test data. In this work, we establish that the use of cross-entropy loss function and the low-rank features of the training data have responsibility for the existence of these inputs. Based on this observation, we suggest that addressing adversarial examples requires rethinking the use of cross-entropy loss function and looking for an alternative that is more suited for minimization with low-rank features. In this direction, we present a training scheme called differential training, which uses a loss function defined on the differences between the features of points from opposite classes. We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset. This larger margin increases the amount of perturbation needed to flip the prediction of the classifier and makes it harder to find an adversarial example with small perturbations. We test differential training on a binary classification task with CIFAR-10 dataset and demonstrate that it radically reduces the ratio of images for which an adversarial example could be found -- not only in the training dataset, but in the test dataset as well.
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm. The usefulness of this approach is evaluated in the setting of retinal fluid segmentation, namely intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a segmentation model on images acquired with a source OCT device. Then we evaluate the model on (1) source, (2) target and (3) transformed versions of the target OCT images. The presented transformation strategy shows an F1 score of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional transformation approaches, this means an F1 score gain of 0.2 (0.12).
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a constant word (RAM model) algorithm for regret minimisation for both finite and infinite Stochastic Multi-Armed Bandit (MAB) instances. Most of the existing regret minimisation algorithms need to remember the statistics of all the arms they encounter. This may become a problem for the cases where the number of available words of memory is limited. Designing an efficient regret minimisation algorithm that uses a constant number of words has long been interesting to the community. Some early attempts consider the number of arms to be infinite, and require the reward distribution of the arms to belong to some particular family. Recently, for finitely many-armed bandits an explore-then-commit based algorithm~\citep{Liau+PSY:2018} seems to escape such assumption. However, due to the underlying PAC-based elimination their method incurs a high regret. We present a conceptually simple, and efficient algorithm that needs to remember statistics of at most $M$ arms, and for any $K$-armed finite bandit instance it enjoys a $O(KM +K^{1.5}\sqrt{T\log (T/MK)}/M)$ upper-bound on regret. We extend it to achieve sub-linear \textit{quantile-regret}~\citep{RoyChaudhuri+K:2018} and empirically verify the efficiency of our algorithm via experiments.
A model for a Lindenmayer reconstruction algorithm
Venue: arXiv cs.FL 2019
Year: 2019

Given an input string s and a specific Lindenmayer system (the so-called Fibonacci grammar), we define an automaton which is capable of (i) determining whether s belongs to the set of strings that the Fibonacci grammar can generate (in other words, if s corresponds to a generation of the grammar) and, if so, (ii) reconstructing the previous generation.
Implementing an ERP System: The Paradigm of a Chemical Company
Venue: arXiv cs.SE 2019
Year: 2019

An application of a methodology present in the previous paper An ERP Implementation Method: Studying a Pharmaceutical Company, is under consideration for a chemical company. Half of the paper as far as the methodology used is similar and only the application is different. In this paper it is tried to resolve problems and drawbacks of the methodology and to prove that it is satisfied enough to add some knowledge in the area of ERP systems Implementation. The modelling tool is again Petri-nets and the effort is to analyze the problem using this mathematical element. Finally, the SAP implementation method is presented, representing all the knowledge captured from this case study.
Spherical sampling methods for the calculation of metamer mismatch volumes
Venue: arXiv cs.CG 2019
Year: 2019

In this paper, we propose two methods of calculating theoretically maximal metamer mismatch volumes. Unlike prior art techniques, our methods do not make any assumptions on the shape of spectra on the boundary of the mismatch volumes. Both methods utilize a spherical sampling approach, but they calculate mismatch volumes in two different ways. The first method uses a linear programming optimization, while the second is a computational geometry approach based on half-space intersection. We show that under certain conditions the theoretically maximal metamer mismatch volume is significantly larger than the one approximated using a prior art method.