OpenRaiser/Intern-Atlas huggingface dataset:
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning
Venue: arXiv cs.IT 2018
Year: 2018

In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e.g., race or gender). In this paper, we propose an information-theoretic framework to analyze the disparate impact of a binary classification model. We view the model as a fixed channel, and quantify disparate impact as the divergence in output distributions over two groups. Our aim is to find a correction function that can perturb the input distributions of each group to align their output distributions. We present an optimization problem that can be solved to obtain a correction function that will make the output distributions statistically indistinguishable. We derive closed-form expressions to efficiently compute the correction function, and demonstrate the benefits of our framework on a recidivism prediction problem based on the ProPublica COMPAS dataset.
Expectation Propagation for Approximate Inference: Free Probability Framework
Venue: arXiv cs.IT 2018
Year: 2018

We study asymptotic properties of expectation propagation (EP) -- a method for approximate inference originally developed in the field of machine learning. Applied to generalized linear models, EP iteratively computes a multivariate Gaussian approximation to the exact posterior distribution. The computational complexity of the repeated update of covariance matrices severely limits the application of EP to large problem sizes. In this study, we present a rigorous analysis by means of free probability theory that allows us to overcome this computational bottleneck if specific data matrices in the problem fulfill certain properties of asymptotic freeness. We demonstrate the relevance of our approach on the gene selection problem of a microarray dataset.
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Venue: arXiv cs.CV 2018
Year: 2018

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.
Considerations regarding security issues impact on systems availability
Venue: arXiv cs.CR 2018
Year: 2018

Control systems behavior can be analyzed taking into account a large number of parameters: performances, reliability, availability, security. Each control system presents various security vulnerabilities that affect in lower or higher measure its functioning. In this paper the authors present a method to assess the impact of security issues on the systems availability. A fuzzy model for estimating the availability of the system based on the security level and achieved availability coefficient (depending on MTBF and MTR) is developed and described. The results of the fuzzy inference system (FIS) are presented in the last section of the paper.
Blind De-anonymization Attacks using Social Networks
Venue: arXiv cs.SI 2018
Year: 2018

It is important to study the risks of publishing privacy-sensitive data. Even if sensitive identities (e.g., name, social security number) were removed and advanced data perturbation techniques were applied, several de-anonymization attacks have been proposed to re-identify individuals. However, existing attacks have some limitations: 1) they are limited in de-anonymization accuracy; 2) they require prior seed knowledge and suffer from the imprecision of such seed information. We propose a novel structure-based de-anonymization attack, which does not require the attacker to have prior information (e.g., seeds). Our attack is based on two key insights: using multi-hop neighborhood information, and optimizing the process of de-anonymization by exploiting enhanced machine learning techniques. The experimental results demonstrate that our method is robust to data perturbations and significantly outperforms the state-of-the-art de-anonymization techniques by up to $10\times$ improvement.
Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices
Venue: arXiv cs.CR 2018
Year: 2018

Purpose: Used extensively in the diagnosis, treatment, and prevention of disease, Medical Imaging Devices (MIDs), such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) machines, play an important role in medicine today. MIDs are increasingly connected to hospital networks, making them vulnerable to sophisticated cyber-attacks targeting the devices' infrastructure and components, which can disrupt digital patient records, and potentially jeopardize patients' health. Attacks on MIDs are likely to increase, as attackers' skills improve and the number of unpatched devices with known vulnerabilities that can be easily exploited grows. Attackers may also block access to MIDs or disable them, as part of ransomware attacks, which have been shown to be successful against hospitals. Method and Materials: We conducted a comprehensive risk analysis survey at the Malware-Lab, based on the Confidentiality, Integrity, and Availability (CIA) model, in collaboration with our country's largest health maintenance organization, to define the characteristics of cyber-attacks on MIDs. The survey includes a range of vulnerabilities and potential attacks aimed at MIDs, medical and imaging information systems, and medical protocols and standards such as DICOM and HL7. Results: Based on our survey, we found that CT devices face the greatest risk of cyber-attack, due to their pivotal role in acute care imaging. Thus, we identified several possible attack vectors that target the infrastructure and functionality of CT devices, which can cause: 1. Disruption of the parameters' values used in the scanning protocols within the CT devices (e.g., tampering with the radiation exposure levels); 2. Mechanical disruption of the CT device (e.g., changing the pitch); 3. Disruption of the tomography scan signals constructing the digital images; and 4. Denial-of-Service attacks against the CT device.
Multi-View Stereo 3D Edge Reconstruction
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a novel method for the reconstruction of 3D edges in multi-view stereo scenarios. Previous research in the field typically relied on video sequences and limited the reconstruction process to either straight line-segments, or edge-points, i.e., 3D points that correspond to image edges. We instead propose a system, denoted as EdgeGraph3D, able to recover both straight and curved 3D edges from an unordered image sequence. A second contribution of this work is a graph-based representation for 2D edges that allows the identification of the most structurally significant edges detected in an image. We integrate EdgeGraph3D in a multi-view stereo reconstruction pipeline and analyze the benefits provided by 3D edges to the accuracy of the recovered surfaces. We evaluate the effectiveness of our approach on multiple datasets from two different collections in the multi-view stereo literature. Experimental results demonstrate the ability of EdgeGraph3D to work in presence of strong illumination changes and reflections, which are usually detrimental to the effectiveness of classical photometric reconstruction systems.
The Socket Store: An App Model for the Application-Network Interaction
Venue: arXiv cs.OH 2018
Year: 2018

A developer of mobile or desktop applications is responsible for implementing the network logic of his software. Nonetheless: i) Developers are not network specialists, while pressure for emphasis on the visible application parts places the network logic out of the coding focus. Moreover, computer networks undergo evolution at paces that developers may not follow. ii) From the network resource provider point of view, marketing novel services and involving a broad audience is also challenge for the same reason. Moreover, the objectives of end-user networking logic are neither clear nor uniform. This constitutes the central optimization of network resources an additional challenge. As a solution to these problems, we propose the Socket Store. The Store is a marketplace containing end-user network logic in modular form. The Store modules act as intelligent mediators between the end-user and the network resources. Each module has a clear, specialized objective, such as connecting two clients over the Internet while avoiding transit networks suspicious for eavesdropping. The Store is populated and peer-reviewed by network specialists, whose motive is the visibility, practical applicability and monetization potential of their work. A developer first purchases access to a given socket module. Subsequently, he incorporates it to his applications under development, obtaining state-of-the-art performance with trivial coding burden. A full Store prototype is implemented and a critical data streaming module is evaluated as a driving case.
Mixed Delay Constraints in Wyner's Soft-Handoff Network
Venue: arXiv cs.IT 2018
Year: 2018

Wyner's soft-handoff network with mixed delay constraints is considered when neighbouring receivers can cooperate over rate-limited links. Each source message is a combination of independent "fast" and "slow" bits, where the former are subject to a stringent decoding delay. Inner and outer bounds on the capacity region are derived, and the multiplexing gain region is characterized when only transmitters or only receivers cooperate.
Real Time Impact Control on Charging Energy Storage For Shipboard Power Systems
Venue: arXiv cs.SY 2018
Year: 2018

Medium voltage direct-current based integrated power system is projected as one of the solutions for powering the all-electric ship. It faces significant challenges for accurately energizing advanced loads, especially the pulsed power load, which can be rail gun, high power radar, and other state of art equipment. Energy storage based on supercapacitors is proposed as a technique for buffering the direct impact of pulsed power load on the power systems. However, the high magnitude of charging current of the energy storage can pose as a disturbance to both distribution and generation systems. This paper presents a fast switching device based real time control system that can achieve a desired balance between maintaining the required power quality and fast charging the energy storage in required time. Test results are shown to verify the performance of the proposed control algorithm.
Network Representation Learning: A Survey
Venue: arXiv cs.SI 2018
Year: 2018

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.
Sparse Activity Detection for Massive Connectivity
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers the massive connectivity application in which a large number of potential devices communicate with a base-station (BS) in a sporadic fashion. The detection of device activity pattern together with the estimation of the channel are central problems in such a scenario. Due to the large number of potential devices in the network, the devices need to be assigned non-orthogonal signature sequences. The main objective of this paper is to show that by using random signature sequences and by exploiting sparsity in the user activity pattern, the joint user detection and channel estimation problem can be formulated as a compressed sensing single measurement vector (SMV) problem or multiple measurement vector (MMV) problem, depending on whether the BS has a single antenna or multiple antennas, and be efficiently solved using an approximate message passing (AMP) algorithm. This paper proposes an AMP algorithm design that exploits the statistics of the wireless channel and provides an analytical characterization of the probabilities of false alarm and missed detection by using the state evolution. We consider two cases depending on whether the large-scale component of the channel fading is known at the BS and design the minimum mean squared error (MMSE) denoiser for AMP according to the channel statistics. Simulation results demonstrate the substantial advantage of exploiting the statistical channel information in AMP design; however, knowing the large-scale fading component does not offer tangible benefits. For the multiple-antenna case, we employ two different AMP algorithms, namely the AMP with vector denoiser and the parallel AMP-MMV, and quantify the benefit of deploying multiple antennas at the BS.
A Large-Scale Empirical Comparison of Static and Dynamic Test Case Prioritization Techniques
Venue: arXiv cs.SE 2018
Year: 2018

The large body of existing research in Test Case Prioritization (TCP) techniques, can be broadly classified into two categories: dynamic techniques (that rely on run-time execution information) and static techniques (that operate directly on source and test code). Absent from this current body of work is a comprehensive study aimed at understanding and evaluating the static approaches and comparing them to dynamic approaches on a large set of projects. In this work, we perform the first extensive study aimed at empirically evaluating four static TCP techniques comparing them with state-of-research dynamic TCP techniques at different test-case granularities (e.g., method and class-level) in terms of effectiveness, efficiency and similarity of faults detected. This study was performed on 30 real-word Java programs encompassing 431 KLoC. In terms of effectiveness, we find that the static call-graph-based technique outperforms the other static techniques at test-class level, but the topic-model-based technique performs better at test-method level. In terms of efficiency, the static call-graph-based technique is also the most efficient when compared to other static techniques. When examining the similarity of faults detected for the four static techniques compared to the four dynamic ones, we find that on average, the faults uncovered by these two groups of techniques are quite dissimilar, with the top 10% of test cases agreeing on only 25% - 30% of detected faults. This prompts further research into the severity/importance of faults uncovered by these techniques, and into the potential for combining static and dynamic information for more effective approaches.
WiLAD: Wireless Localisation through Anomaly Detection
Venue: arXiv cs.NI 2018
Year: 2018

We propose a new approach towards RSS (Received Signal Strength) based wireless localisation for scenarios where, instead of absolute positioning of an object, only the information whether an object is inside or outside of a specific area is required. This is motivated through a number of applications including, but not limited to, a) security: detecting whether an object is removed from a secure location, b) wireless sensor networks: detecting sensor movements outside of a network area, and c) computational behaviour analytics: detecting customers leaving a retail store. The result of such detection systems can naturally be utilised in building a higher level contextual understanding of a system or user behaviours. We use a supervised learning method to overcome issues related to RSS based localisation systems including multipath fading, shadowing, and incorrect model parameters (as in unsupervised methods). Moreover, to reduce the cost of collecting training data, we employ a detection method called One-Class SVM (OC-SVM) which requires only one class of data (positive data, or target class data) for training. We derive a mathematical approximation of accuracy which utilises the characteristics of wireless signals as well as OC-SVM. Based on this we then propose a novel mathematical formula to find optimal placement of devices. This enables us to optimize the placement without performing any costly experiments or simulations. We validate our proposed mathematical framework based on simulated and real experiments.
FUSION: A Tool for Facilitating and Augmenting Android Bug Reporting
Venue: arXiv cs.SE 2018
Year: 2018

As the popularity of mobile smart devices continues to climb the complexity of "apps" continues to increase, making the development and maintenance process challenging. Current bug tracking systems lack key features to effectively support construction of reports with actionable information that directly lead to a bug's resolution. In this demo we present the implementation of a novel bug reporting system, called Fusion, that facilitates users including reproduction steps in bug reports for mobile apps. Fusion links user-provided information to program artifacts extracted through static and dynamic analysis performed before testing or release. Results of preliminary studies demonstrate that Fusion both effectively facilitates reporting and allows for more reliable reproduction of bugs from reports compared to traditional issue tracking systems by presenting more detailed contextual app information. Tool website: www.fusion-android. com Video url: https://youtu.be/AND9h0ElxRg
Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors
Venue: arXiv cs.HC 2018
Year: 2018

Visual attention is highly fragmented during mobile interactions, but the erratic nature of attention shifts currently limits attentive user interfaces to adapting after the fact, i.e. after shifts have already happened. We instead study attention forecasting -- the challenging task of predicting users' gaze behaviour (overt visual attention) in the near future. We present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). We propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate that our method can forecast bidirectional attention shifts and predict whether the primary attentional focus is on the handheld mobile device. We study the impact of different feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions.
Latitude: A Model for Mixed Linear-Tropical Matrix Factorization
Venue: arXiv cs.LG 2018
Year: 2018

Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.
A Formalization for Specifying and Implementing Correct Pull-Stream Modules
Venue: arXiv cs.PL 2018
Year: 2018

Pull-stream is a JavaScript demand-driven functional design pattern based on callback functions that enables the creation and easy composition of independent modules that are used to create streaming applications. It is used in popular open source projects and the community around it has created over a hundred compatible modules. While the description of the pull-stream design pattern may seem simple, it does exhibit complicated termination cases. Despite the popularity and large uptake of the pull-stream design pattern, there was no existing formal specification that could help programmers reason about the correctness of their implementations. Thus, the main contribution of this paper is to provide a formalization for specifying and implementing correct pull-stream modules based on the following: (1) we show the pull-stream design pattern is a form of declarative concurrent programming; (2) we present an event-based protocol language that supports our formalization, independently of JavaScript; (3) we provide the first precise and explicit definition of the expected sequences of events that happen at the interface of two modules, which we call the pull-stream protocol; (4) we specify reference modules that exhibit the full range of behaviors of the pull-stream protocol; (5) we validate our definitions against the community expectations by testing the existing core pull-stream modules against them and identify unspecified behaviors in existing modules. Our approach helps to better understand the pull-stream protocol, to ensure interoperability of community modules, and to concisely and precisely specify new pull-stream abstractions in papers and documentation.