OpenRaiser/Intern-Atlas huggingface dataset:
Expression Recognition Using the Periocular Region: A Feasibility Study
Venue: arXiv cs.CV 2018
Year: 2018

This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0%/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.
Improving Automated Latent Fingerprint Identification using Extended Minutia Types
Venue: arXiv cs.CV 2018
Year: 2018

Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.
Nonequispaced Fast Fourier Transform (NFFT) Interface for Julia
Venue: arXiv cs.MS 2018
Year: 2018

This report describes the newly added Julia interface to the NFFT3 library. We explain the multidimensional NFFT algorithm and basics of the interface. Furthermore, we go into detail about the different parameters and how to adjust them properly.
GhostVLAD for set-based face recognition
Venue: arXiv cs.CV 2018
Year: 2018

The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes {\em ghost clusters}, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network's ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.
Using Deep Learning for price prediction by exploiting stationary limit order book features
Venue: arXiv cs.LG 2018
Year: 2018

The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested.
A Systematic Framework and Characterization of Influence-Based Network Centrality
Venue: arXiv cs.SI 2018
Year: 2018

In this paper, we present a framework for studying the following fundamental question in network analysis: How should one assess the centralities of nodes in an information/influence propagation process over a social network? Our framework systematically extends a family of classical graph-theoretical centrality formulations, including degree centrality, harmonic centrality, and their "sphere-of-influence" generalizations, to influence-based network centralities. We further extend natural group centralities from graph models to influence models, since group cooperation is essential in social influences. This in turn enables us to assess individuals' centralities in group influence settings by applying the concept of Shapley value from cooperative game theory. Mathematically, using the property that these centrality formulations are Bayesian, we prove the following characterization theorem: Every influence-based centrality formulation in this family is the unique Bayesian centrality that conforms with its corresponding graph-theoretical centrality formulation. Moreover, the uniqueness is fully determined by the centrality formulation on the class of layered graphs, which is derived from a beautiful algebraic structure of influence instances modeled by cascading sequences. Our main mathematical result that layered graphs in fact form a basis for the space of influence-cascading-sequence profiles could also be useful in other studies of network influences. We further provide an algorithmic framework for efficient approximation of these influence-based centrality measures. Our study provides a systematic road map for comparative analyses of different influence-based centrality formulations, as well as for transferring graph-theoretical concepts to influence models.
Automated Reasoning in Normative Detachment Structures with Ideal Conditions
Venue: arXiv cs.AI 2018
Year: 2018

Systems of deontic logic suffer either from being too expressive and therefore hard to mechanize, or from being too simple to capture relevant aspects of normative reasoning. In this article we look for a suitable way in between: the automation of a simple logic of normative ideality and sub-ideality that is not affected by many deontic paradoxes and that is expressive enough to capture contrary-to-duty reason- ing. We show that this logic is very useful to reason on normative scenarios from which one can extract a certain kind of argumentative structure, called a Normative Detachment Structure with Ideal Conditions. The theoretical analysis of the logic is accompanied by examples of automated reasoning on a concrete legal text.
Analysis of Strategy and Spread of Russia-sponsored Content in the US in 2017
Venue: arXiv cs.SI 2018
Year: 2018

The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. We investigate the scope of IRA activities in 2017, joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine. The studies demonstrate both the ease with which malicious actors can harness social media and search engines for propaganda campaigns, and the ability to track and understand such activities by fusing content and activity resources from multiple internet services. We show how cross-platform analyses can provide an unprecedented lens on attempts to manipulate opinions and elections in democracies.
A new approach of contextual recommendation based on the method of Hierarchical Analysis of Processes
Venue: arXiv cs.IR 2018
Year: 2018

Recommender systems are able to estimate the user's interest for resource given from some relative information to others similar users and to propriety of the resource. In this Memory, we introduced a new contextual recommendation approach based on the AHP Process Hierarchical Analysis method. This work consisted in making a bibliographic study on the works having proposed systems of recommendation based on the context of the users in the field of films. The goal is to design and develop a new approach to recommending movies based on user context. And we relied on methods of multi-criteria decision making (MCDM) and more precisely the method of Hierarchical Process Analysis (AHP) for context integration in the recommendation process.
A Statistical Approach to Adult Census Income Level Prediction
Venue: arXiv cs.LG 2018
Year: 2018

The prominent inequality of wealth and income is a huge concern especially in the United States. The likelihood of diminishing poverty is one valid reason to reduce the world's surging level of economic inequality. The principle of universal moral equality ensures sustainable development and improve the economic stability of a nation. Governments in different countries have been trying their best to address this problem and provide an optimal solution. This study aims to show the usage of machine learning and data mining techniques in providing a solution to the income equality problem. The UCI Adult Dataset has been used for the purpose. Classification has been done to predict whether a person's yearly income in US falls in the income category of either greater than 50K Dollars or less equal to 50K Dollars category based on a certain set of attributes. The Gradient Boosting Classifier Model was deployed which clocked the highest accuracy of 88.16%, eventually breaking the benchmark accuracy of existing works.
A Deep-Learning-Based Fashion Attributes Detection Model
Venue: arXiv cs.CV 2018
Year: 2018

Analyzing fashion attributes is essential in the fashion design process. Current fashion forecasting firms, such as WGSN utilizes information from all around the world (from fashion shows, visual merchandising, blogs, etc). They gather information by experience, by observation, by media scan, by interviews, and by exposed to new things. Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections. In fact, such abstraction ability is useful in many fashion careers with different purposes. Fashion forecasters abstract across design collections and across time to identify fashion change and directions; designers, product developers and buyers abstract across a group of garments and collections to develop a cohesive and visually appeal lines; sales and marketing executives abstract across product line each season to recognize selling points; fashion journalist and bloggers abstract across runway photos to recognize symbolic core concepts that can be translated into editorial features. Fashion attributes analysis for such fashion insiders requires much detailed and in-depth attributes annotation than that for consumers, and requires inference on multiple domains. In this project, we propose a data-driven approach for recognizing fashion attributes. Specifically, a modified version of Faster R-CNN model is trained on images from a large-scale localization dataset with 594 fine-grained attributes under different scenarios, for example in online stores and street snapshots. This model will then be used to detect garment items and classify clothing attributes for runway photos and fashion illustrations.
Automatic Identification of Indicators of Compromise using Neural-Based Sequence Labelling
Venue: arXiv cs.AI 2018
Year: 2018

Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage. Thus, they exert an important role in the field of cybersecurity. However, state-of-the-art IOCs detection systems rely heavily on hand-crafted features with expert knowledge of cybersecurity, and require a large amount of supervised training corpora to train an IOC classifier. In this paper, we propose using a neural-based sequence labelling model to identify IOCs automatically from reports on cybersecurity without expert knowledge of cybersecurity. Our work is the first to apply an end-to-end sequence labelling to the task in IOCs identification. By using an attention mechanism and several token spelling features, we find that the proposed model is capable of identifying the low frequency IOCs from long sentences contained in cybersecurity reports. Experiments show that the proposed model outperforms other sequence labelling models, achieving over 88% average F1-score.
Fault Area Detection in Leaf Diseases using k-means Clustering
Venue: arXiv cs.CV 2018
Year: 2018

With increasing population the crisis of food is getting bigger day by day.In this time of crisis,the leaf disease of crops is the biggest problem in the food industry.In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease.Leaf diseases can be detected from sample images of the leaf with the help of image processing and segmentation.Using k-means clustering and Otsu's method the faulty region in a leaf is detected which helps to determine proper course of action to be taken.Further the ratio of normal and faulty region if calculated would be able to predict if the leaf can be cured at all.
Niji: Bitcoin Bridge Utilizing Payment Channels
Venue: arXiv cs.DC 2018
Year: 2018

Bitcoin's enormous success has inspired the development of alternative blockchains, such as consortium chains. Several cross-chain protocols have been proposed as ways of connecting these universes of individual blockchains in a distributed and secure manner. In this paper, we present Niji, a new cross-chain protocol that allows parties to perform virtual Bitcoin payment securely on a consortium chain, without any trusted third-party or mediators. Our work focuses on the issue that it is difficult for a consortium chain's token to hold a stable market value, and Niji makes it possible for smart contract services to acquire means of payment in the consortium chain. With the Bitcoin payment channel built on the consortium chain, the process from payment to service provision runs autonomously without any interaction between parties. Niji introduces the concept of a transaction template to validate Bitcoin payments efficiently on different blockchains, and it allows a service provider to delegate all of its tasks for verifying state updates to a smart contract on the consortium chain. We also propose a novel bi-directional payment channel adapted for design of the Niji protocol, which can update payments non-interactively between parties. We implemented a prototype of the Niji protocol and conducted an experiment measuring the computational cost and latency that demonstrates the protocol's feasibility on practical platforms.
Faster approximation algorithms for computing shortest cycles on weighted graphs
Venue: arXiv cs.DS 2018
Year: 2018

Given an $n$-vertex $m$-edge graph $G$ with non negative edge-weights, the girth of $G$ is the weight of a shortest cycle in $G$. For any graph $G$ with polynomially bounded integer weights, we present a deterministic algorithm that computes, in $\tilde{\cal O}(n^{5/3}+m)$-time, a cycle of weight at most twice the girth of $G$. Our approach combines some new insights on the previous approximation algorithms for this problem (Lingas and Lundell, IPL'09; Roditty and Tov, TALG'13) with Hitting Set based methods that are used for approximate distance oracles and date back from (Thorup and Zwick, JACM'05). Then, we turn our algorithm into a deterministic $(2+\varepsilon)$-approximation for graphs with arbitrary non negative edge-weights, at the price of a slightly worse running-time in $\tilde{\cal O}(n^{5/3}\log^{{\cal O}(1)}{(1/\varepsilon)}+m)$. Finally, if we insist in removing the dependency in the number $m$ of edges, we can transform our algorithms into an $\tilde{\cal O}(n^{5/3})$-time randomized $4$-approximation for the graphs with non negative edge-weights -- assuming the adjacency lists are sorted. Combined with the aforementioned Hitting Set based methods, this algorithm can be derandomized, thereby yielding an $\tilde{\cal O}(n^{5/3})$-time deterministic $4$-approximation for the graphs with polynomially bounded integer weights, and an $\tilde{\cal O}(n^{5/3}\log^{{\cal O}(1)}{(1/\varepsilon)})$-time deterministic $(4+\varepsilon)$-approximation for the graphs with non negative edge-weights. To the best of our knowledge, these are the first known subquadratic-time approximation algorithms for computing the girth of weighted graphs.
A general proof certification framework for modal logic
Venue: arXiv cs.LO 2018
Year: 2018

One of the main issues in proof certification is that different theorem provers, even when designed for the same logic, tend to use different proof formalisms and produce outputs in different formats. The project ProofCert promotes the usage of a common specification language and of a small and trusted kernel in order to check proofs coming from different sources and for different logics. By relying on that idea and by using a classical focused sequent calculus as a kernel, we propose here a general framework for checking modal proofs. We present the implementation of the framework in a Prolog-like language and show how it is possible to specialize it in a simple and modular way in order to cover different proof formalisms, such as labeled systems, tableaux, sequent calculi and nested sequent calculi. We illustrate the method for the logic K by providing several examples and discuss how to further extend the approach.
A Maximum Edge-Weight Clique Extraction Algorithm Based on Branch-and-Bound
Venue: arXiv cs.DS 2018
Year: 2018

The maximum edge-weight clique problem is to find a clique whose sum of edge-weight is the maximum for a given edge-weighted undirected graph. The problem is NP-hard and some branch-and-bound algorithms have been proposed. In this paper, we propose a new exact algorithm based on branch-and-bound. It assigns edge-weights to vertices and calculates upper bounds using vertex coloring. By some computational experiments, we confirmed our algorithm is faster than previous algorithms.
A Proof-Theoretic Approach to Scope Ambiguity in Compositional Vector Space Models
Venue: arXiv cs.CL 2018
Year: 2018

We investigate the extent to which compositional vector space models can be used to account for scope ambiguity in quantified sentences (of the form "Every man loves some woman"). Such sentences containing two quantifiers introduce two readings, a direct scope reading and an inverse scope reading. This ambiguity has been treated in a vector space model using bialgebras by (Hedges and Sadrzadeh, 2016) and (Sadrzadeh, 2016), though without an explanation of the mechanism by which the ambiguity arises. We combine a polarised focussed sequent calculus for the non-associative Lambek calculus NL, as described in (Moortgat and Moot, 2011), with the vector based approach to quantifier scope ambiguity. In particular, we establish a procedure for obtaining a vector space model for quantifier scope ambiguity in a derivational way.
QuanFuzz: Fuzz Testing of Quantum Program
Venue: arXiv cs.SE 2018
Year: 2018

Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from traditional program make this issue hard to resolve. In this paper, we present QuanFuzz, a search-based test input generator for quantum program. We define the quantum sensitive information to evaluate test input for quantum program and use matrix generator to generate test cases with higher coverage. First, we extract quantum sensitive information -- measurement operations on those quantum registers and the sensitive branches associated with those measurement results, from the quantum source code. Then, we use the sensitive information guided algorithm to mutate the initial input matrix and select those matrices which improve the probability weight for a value of the quantum register to trigger the sensitive branch. The process keeps iterating until the sensitive branch triggered. We tested QuanFuzz on benchmarks and acquired 20% - 60% more coverage compared to traditional testing input generation.
Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8 diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma, benign keratosis, actinic keratosis and intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the ability of deep learning with the performance of highly trained dermatologists. Overall, the mean results show that all deep learning models outperformed dermatologists (at least 11%). The best ROC AUC values for melanoma and basal cell carcinoma are 94.40% (ResNet 152) and 99.30% (DenseNet 201) versus 82.26% and 88.82% of dermatologists, respectively. Also, DenseNet 201 had the highest macro and micro averaged AUC values for overall classification (98.16%, 98.79%, respectively).
Proportional fairness in wireless powered CSMA/CA based IoT networks
Venue: arXiv cs.NI 2018
Year: 2018

This paper considers the deployment of a hybrid wireless data/power access point in an 802.11-based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA/CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.