OpenRaiser/Intern-Atlas huggingface dataset:
Flexible Byzantine Fault Tolerance
Venue: arXiv cs.CR 2019
Year: 2019

This paper introduces Flexible BFT, a new approach for BFT consensus solution design revolving around two pillars, stronger resilience and diversity. The first pillar, stronger resilience, involves a new fault model called alive-but-corrupt faults. Alive-but-corrupt replicas may arbitrarily deviate from the protocol in an attempt to break safety of the protocol. However, if they cannot break safety, they will not try to prevent liveness of the protocol. Combining alive-but-corrupt faults into the model, Flexible BFT is resilient to higher corruption levels than possible in a pure Byzantine fault model. The second pillar, diversity, designs consensus solutions whose protocol transcript is used to draw different commit decisions under diverse beliefs. With this separation, the same Flexible BFT solution supports synchronous and asynchronous beliefs, as well as varying resilience threshold combinations of Byzantine and alive-but-corrupt faults. At a technical level, Flexible BFT achieves the above results using two new ideas. First, it introduces a synchronous BFT protocol in which only the commit step requires to know the network delay bound and thus replicas execute the protocol without any synchrony assumption. Second, it introduces a notion called Flexible Byzantine Quorums by dissecting the roles of different quorums in existing consensus protocols.
Intersection Subtyping with Constructors
Venue: arXiv cs.LO 2019
Year: 2019

We study the question of extending the BCD intersection type system with additional type constructors. On the typing side, we focus on adding the usual rules for product types. On the subtyping side, we consider a generic way of defining a subtyping relation on families of types which include intersection types. We find back the BCD subtyping relation by considering the particular case where the type constructors are intersection, omega and arrow. We obtain an extension of BCD subtyping to product types as another instance. We show how the preservation of typing by both reduction and expansion is satisfied in all the considered cases. Our approach takes benefits from a "subformula property" of the proposed presentation of the subtyping relation.
Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention
Venue: arXiv cs.LG 2019
Year: 2019

Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classifying land use change without high resolution imagery offers the potential to monitor vital aspects of global development agenda including climate smart agriculture, drought resistant crops, and sustainable land management. Utilizing a combination of capsule layers and long-short term memory layers with distributed attention, the present paper achieves state-of-the-art accuracy on temporal crop type classification at a 30x30m resolution with Sentinel 2 imagery.
Android Malicious Application Classification Using Clustering
Venue: arXiv cs.CR 2019
Year: 2019

Android malware have been growing at an exponential pace and becomes a serious threat to mobile users. It appears that most of the anti-malware still relies on the signature-based detection system which is generally slow and often not able to detect advanced obfuscated malware. Hence time-to-time various authors have proposed different machine learning solutions to identify sophisticated malware. However, it appears that detection accuracy can be improved by using the clustering method. Therefore in this paper, we propose a novel scalable and effective clustering method to improve the detection accuracy of the malicious android application and obtained a better overall accuracy (98.34%) by random forest classifier compared to regular method, i.e., taking the data altogether to detect the malware. However, as far as true positive and true negative are concerned, by clustering method, true positive is best obtained by decision tree (97.59%) and true negative by support vector machine (99.96%) which is the almost same result obtained by the random forest true positive (97.30%) and true negative (99.38%) respectively. The reason that overall accuracy of random forest is high because the true positive of support vector machine and true negative of the decision tree is significantly less than the random forest.
Dynamic evaluation of exponential polynomial curves and surfaces via basis transformation
Venue: arXiv cs.NA 2019
Year: 2019

It is shown in "SIAM J. Sci. Comput. 39 (2017):B424-B441" that free-form curves used in computer aided geometric design can usually be represented as the solutions of linear differential systems and points and derivatives on the curves can be evaluated dynamically by solving the differential systems numerically. In this paper we present an even more robust and efficient algorithm for dynamic evaluation of exponential polynomial curves and surfaces. Based on properties that spaces spanned by general exponential polynomials are translation invariant and polynomial spaces are invariant with respect to a linear transform of the parameter, the transformation matrices between bases with or without translated or linearly transformed parameters are explicitly computed. Points on curves or surfaces with equal or changing parameter steps can then be evaluated dynamically from a start point using a pre-computed matrix. Like former dynamic evaluation algorithms, the newly proposed approach needs only arithmetic operations for evaluating exponential polynomial curves and surfaces. Unlike conventional numerical methods that solve a linear differential system, the new method can give robust and accurate evaluation results for any chosen parameter steps. Basis transformation technique also enables dynamic evaluation of polynomial curves with changing parameter steps using a constant matrix, which reduces time costs significantly than computing each point individually by classical algorithms.
Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion
Venue: arXiv cs.DB 2019
Year: 2019

Existing works for truth discovery in categorical data usually assume that claimed values are mutually exclusive and only one among them is correct. However, many claimed values are not mutually exclusive even for functional predicates due to their hierarchical structures. Thus, we need to consider the hierarchical structure to effectively estimate the trustworthiness of the sources and infer the truths. We propose a probabilistic model to utilize the hierarchical structures and an inference algorithm to find the truths. In addition, in the knowledge fusion, the step of automatically extracting information from unstructured data (e.g., text) generates a lot of false claims. To take advantages of the human cognitive abilities in understanding unstructured data, we utilize crowdsourcing to refine the result of the truth discovery. We propose a task assignment algorithm to maximize the accuracy of the inferred truths. The performance study with real-life datasets confirms the effectiveness of our truth inference and task assignment algorithms.
Two variable fragment of Term Modal Logic
Venue: arXiv cs.LO 2019
Year: 2019

Term modal logics (TML) are modal logics with unboundedly many modalities, with quantification over modal indices, so that we can have formulas of the form $\exists y. \forall x. (\Box_x P(x,y) \supset\Diamond_y P(y,x))$. Like First order modal logic, TML is also "notoriously" undecidable, in the sense that even very simple fragments are undecidable. In this paper, we show the decidability of one interesting fragment, that of two variable TML. This is in contrast to two-variable First order modal logic, which is undecidable.
Improving benchmarks for autonomous vehicles testing using synthetically generated images
Venue: arXiv cs.LG 2019
Year: 2019

Nowadays autonomous technologies are a very heavily explored area and particularly computer vision as the main component of vehicle perception. The quality of the whole vision system based on neural networks relies on the dataset it was trained on. It is extremely difficult to find traffic sign datasets from most of the counties of the world. Meaning autonomous vehicle from the USA will not be able to drive though Lithuania recognizing all road signs on the way. In this paper, we propose a solution on how to update model using a small dataset from the country vehicle will be used in. It is important to mention that is not panacea, rather small upgrade which can boost autonomous car development in countries with limited data access. We achieved about 10 percent quality raise and expect even better results during future experiments.
PowerDrive: Accurate De-Obfuscation and Analysis of PowerShell Malware
Venue: arXiv cs.CR 2019
Year: 2019

PowerShell is nowadays a widely-used technology to administrate and manage Windows-based operating systems. However, it is also extensively used by malware vectors to execute payloads or drop additional malicious contents. Similarly to other scripting languages used by malware, PowerShell attacks are challenging to analyze due to the extensive use of multiple obfuscation layers, which make the real malicious code hard to be unveiled. To the best of our knowledge, a comprehensive solution for properly de-obfuscating such attacks is currently missing. In this paper, we present PowerDrive, an open-source, static and dynamic multi-stage de-obfuscator for PowerShell attacks. PowerDrive instruments the PowerShell code to progressively de-obfuscate it by showing the analyst the employed obfuscation steps. We used PowerDrive to successfully analyze thousands of PowerShell attacks extracted from various malware vectors and executables. The attained results show interesting patterns used by attackers to devise their malicious scripts. Moreover, we provide a taxonomy of behavioral models adopted by the analyzed codes and a comprehensive list of the malicious domains contacted during the analysis.
Modified Covariance Intersection for Data Fusion in Distributed Non-homogeneous Monitoring Systems Network
Venue: arXiv cs.SY 2019
Year: 2019

Monitoring networks contain monitoring nodes which observe an area of interest to detect any possible existing object and estimate its states. Each node has characteristics such as probability of detection and clutter density which may have different values for distinct nodes in non-homogeneous monitoring networks. This paper proposes a modified covariance intersection method for data fusion in such networks. It is derived by formulating a mixed game model between neighbor monitoring nodes as players and considering inverse of the trace of fused covariance matrix as players' utility function. Monitoring nodes estimate the states of any possible existing object by applying joint target detection and tracking filter on their own observations. Processing nodes fuse the estimated states received from neighbor monitoring nodes by the proposed modified covariance intersection. It is validated by simulating target detection and tracking problem in two situations: one-target and unknown number of targets.
Privacy Preserving Group Membership Verification and Identification
Venue: arXiv cs.CV 2019
Year: 2019

When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly, group membership identification states which group the individual belongs to, without knowing his/her identity. A recent contribution provides privacy and security for group membership protocols through the joint use of two mechanisms: quantizing biometric templates into discrete embeddings and aggregating several templates into one group representation. This paper significantly improves that contribution because it jointly learns how to embed and aggregate instead of imposing fixed and hard coded rules. This is demonstrated by exposing the mathematical underpinnings of the learning stage before showing the improvements through an extensive series of experiments targeting face recognition. Overall, experiments show that learning yields an excellent trade-off between security /privacy and verification /identification performances.
Drishtikon: An advanced navigational aid system for visually impaired people
Venue: arXiv cs.HC 2019
Year: 2019

Today, many of the aid systems deployed for visually impaired people are mostly made for a single purpose. Be it navigation, object detection, or distance perceiving. Also, most of the deployed aid systems use indoor navigation which requires a pre-knowledge of the environment. These aid systems often fail to help visually impaired people in the unfamiliar scenario. In this paper, we propose an aid system developed using object detection and depth perceivement to navigate a person without dashing into an object. The prototype developed detects 90 different types of objects and compute their distances from the user. We also, implemented a navigation feature to get input from the user about the target destination and hence, navigate the impaired person to his/her destination using Google Directions API. With this system, we built a multi-feature, high accuracy navigational aid system which can be deployed in the wild and help the visually impaired people in their daily life by navigating them effortlessly to their desired destination.
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Venue: arXiv cs.IR 2019
Year: 2019

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a contextual hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty. Keywords: Artificial Neural Networks, Context-Aware Recommender Systems, Hybrid Recommender Systems, News Recommender Systems, Session-based Recommendation
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping
Venue: arXiv cs.CV 2019
Year: 2019

Neural networks are now actively being used for computer vision tasks in security critical areas such as robotics, face recognition, autonomous vehicles yet their safety is under question after the discovery of adversarial attacks. In this paper we develop simplified adversarial attack algorithms based on a scoping idea, which enables execution of fast adversarial attacks that minimize structural image quality (SSIM) loss, allows performing efficient transfer attacks with low target inference network call count and opens a possibility of an attack using pen-only drawings on a paper for the MNIST handwritten digit dataset. The presented adversarial attack analysis and the idea of attack scoping can be easily expanded to different datasets, thus making the paper's results applicable to a wide range of practical tasks.
Efficient single input-output layer spiking neural classifier with time-varying weight model
Venue: arXiv cs.NE 2019
Year: 2019

This paper presents a supervised learning algorithm, namely, the Synaptic Efficacy Function with Meta-neuron based learning algorithm (SEF-M) for a spiking neural network with a time-varying weight model. For a given pattern, SEF-M uses the learning algorithm derived from meta-neuron based learning algorithm to determine the change in weights corresponding to each presynaptic spike times. The changes in weights modulate the amplitude of a Gaussian function centred at the same presynaptic spike times. The sum of amplitude modulated Gaussian functions represents the synaptic efficacy functions (or time-varying weight models). The performance of SEF-M is evaluated against state-of-the-art spiking neural network learning algorithms on 10 benchmark datasets from UCI machine learning repository. Performance studies show superior generalization ability of SEF-M. An ablation study on time-varying weight model is conducted using JAFFE dataset. The results of the ablation study indicate that using a time-varying weight model instead of single weight model improves the classification accuracy by 14%. Thus, it can be inferred that a single input-output layer spiking neural network with time-varying weight model is computationally more efficient than a multi-layer spiking neural network with long-term or short-term weight model.
The Theorem Prover Museum -- Conserving the System Heritage of Automated Reasoning
Venue: arXiv cs.LO 2019
Year: 2019

We present the Theorem Prover Museum, and initiative to conserve -- and make publicly available -- the sources and source-related artefacts of automated reasoning systems. Theorem provers have been at the forefront of Artificial Intelligence, stretching the limits of computation, and incubating many innovations we take for granted today. Without the systems themselves as preserved cultural artefacts, future historians will have difficulties to study the history of science and engineering in our discipline.
Power Consumption and Energy-Efficiency for In-Band Full-Duplex Wireless Systems
Venue: arXiv cs.NI 2019
Year: 2019

This paper presents an analytical model of power consumption for In-Band Full-Duplex (IBFD) Wireless Local-Area Networks (WLANs). Energy-efficiency is compared for both Half-Duplex (HD) and IBFD networks. The presented analytical model closely matches the results generated by simulation. For a given traffic scenario, IBFD systems exhibit higher power consumption, however at improved energy efficiency when compared to equivalent HD WLANs.
Generated Loss, Augmented Training, and Multiscale VAE
Venue: arXiv cs.LG 2019
Year: 2019

The variational autoencoder (VAE) framework remains a popular option for training unsupervised generative models, especially for discrete data where generative adversarial networks (GANs) require workaround to create gradient for the generator. In our work modeling US postal addresses, we show that our discrete VAE with tree recursive architecture demonstrates limited capability of capturing field correlations within structured data, even after overcoming the challenge of posterior collapse with scheduled sampling and tuning of the KL-divergence weight $β$. Worse, VAE seems to have difficulty mapping its generated samples to the latent space, as their VAE loss lags behind or even increases during the training process. Motivated by this observation, we show that augmenting training data with generated variants (augmented training) and training a VAE with multiple values of $β$ simultaneously (multiscale VAE) both improve the generation quality of VAE. Despite their differences in motivation and emphasis, we show that augmented training and multiscale VAE are actually connected and have similar effects on the model.
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
Venue: arXiv cs.LG 2019
Year: 2019

Multi-label classification is an approach which allows a datapoint to be labelled with more than one class at the same time. A common but trivial approach is to train individual binary classifiers per label, but the performance can be improved by considering associations within the labels. Like with any machine learning algorithm, hyperparameter tuning is important to train a good multi-label classifier model. The task of selecting the best hyperparameter settings for an algorithm is an optimisation problem. Very limited work has been done on automatic hyperparameter tuning and AutoML in the multi-label domain. This paper attempts to fill this gap by proposing a neural network algorithm, CascadeML, to train multi-label neural network based on cascade neural networks. This method requires minimal or no hyperparameter tuning and also considers pairwise label associations. The cascade algorithm grows the network architecture incrementally in a two phase process as it learns the weights using adaptive first order gradient algorithm, therefore omitting the requirement of preselecting the number of hidden layers, nodes and the learning rate. The method was tested on 10 multi-label datasets and compared with other multi-label classification algorithms. Results show that CascadeML performs very well without hyperparameter tuning.
Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
Venue: arXiv cs.LG 2019
Year: 2019

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum annealer, the D-Wave 2000Q, is used to sample from this model. This algorithm joins a growing family of algorithms that use a quantum annealing subroutine in deep learning, and provides a framework to test the advantages of quantum-assisted learning in GANs. Fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum annealing sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies, and is also applied to the LSUN dataset bedrooms class for the Chimera topology. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset.