OpenRaiser/Intern-Atlas huggingface dataset:
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Venue: arXiv cs.CV 2019
Year: 2019

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.
A Framework for Debugging Java Programs in a Bytecode
Venue: arXiv cs.SE 2019
Year: 2019

In the domain of Software Engineering, program analysis and understanding has been considered to be a very challenging task since decade, as it demands dedicated time and efforts. The analysis of source code may occasionally be comparatively easier due to its static nature, however, the back end code (Bytecode), especially in terms of Java programming, is complicated to be analysed. In this paper, we present a methodological approach towards understanding the Bytecode of Java programs. We put forward a framework for the debugging process of Java Bytecode. Furthermore, we discuss the debugging process of Bytecode understanding from simple to multiple statements with regards to data flow analysis. Finally, we present a comparative analysis of Bytecode along with the simulation of the proposed framework for the debugging process
Using Social Network Service to determine the Initial User Requirements for Small Software Businesses
Venue: arXiv cs.SE 2019
Year: 2019

Background/Objectives: Software engineering community has been studied extensively on large-sized software organizations and has provided suitable and interesting solutions. However, small software companies that make a large part of the software industry have been overlooked. Methods/Statistical analysis: The current requirement engineering practices are not suitable for small software companies. We propose a social network-based requirement engineering approach that will complement the traditional requirement engineering approaches and will make it suitable for small software companies. Findings: We have applied our SNS-based requirements determination approach to knowing about its validity. As a result, we concluded that 33.06 % of invited end-users participated in our approach and figured out 156 distinct user requirements. It has been seen that it was not necessary for users to have requirements engineering knowledge to participate in our proposed SNS-based approach that made maximum users to be involved during requirements elicitation process. By investigating the ideas and opinions communicated by users, we were able to figure out a high number of user requirements. It was observed that maximum user-requirements were determined within a short period of time (7days). Our experience with SNS-based approach also says that end-users hardly know about non-functional requirements and express it explicitly. Improvements/Applications: we believe that researchers will consider SNS other than Facebook that would allow applying our SNS-based approach for requirements identification. We have experienced our approach with Facebook but we do not know how our approach would actually work with other SNSs.
X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5D Deep Learning MBIR
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present a deep learning algorithm to rapidly obtain high quality CT reconstructions for AM parts. In particular, we propose to use CAD models of the parts that are to be manufactured, introduce typical defects and simulate XCT measurements. These simulated measurements were processed using FBP (computationally simple but result in noisy images) and the MBIR technique. We then train a 2.5D deep convolutional neural network [4], deemed 2.5D Deep Learning MBIR (2.5D DL-MBIR), on these pairs of noisy and high-quality 3D volumes to learn a fast, non-linear mapping function. The 2.5D DL-MBIR reconstructs a 3D volume in a 2.5D scheme where each slice is reconstructed from multiple inputs slices of the FBP input. Given this trained system, we can take a small set of measurements on an actual part, process it using a combination of FBP followed by 2.5D DL-MBIR. Both steps can be rapidly performed using GPUs, resulting in a real-time algorithm that achieves the high-quality of MBIR as fast as standard techniques. Intuitively, since CAD models are typically available for parts to be manufactured, this provides a strong constraint "prior" which can be leveraged to improve the reconstruction.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
Venue: arXiv cs.IR 2019
Year: 2019

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date.
Beauty Learning and Counterfactual Inference
Venue: arXiv cs.CV 2019
Year: 2019

This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually. We introduce the beauty learning problem as an example, which has been discussed metaphysically for centuries and been proved exists, is quantifiable, and can be learned by deep models in our recent paper, where we utilize a natural image generator coupled with user studies to infer causal effects from facial semantics to beauty outcomes, the results of which also align with existing empirical studies. We expect the proposed framework for a broader application in causal inference.
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms
Venue: arXiv cs.CV 2019
Year: 2019

Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed improvements by dramatically reducing the error rate obtained in a general image classification task from 26.2% to 15.4%. In road safety, CNNs have been applied widely to the detection of traffic signs, obstacle detection, and lane departure checking. In addition, CNNs have been used in data mining systems that monitor driving patterns and recommend rest breaks when appropriate. This paper presents a driver drowsiness detection system and shows that there are potential social challenges regarding the application of these techniques, by highlighting problems in detecting dark-skinned driver's faces. This is a particularly important challenge in African contexts, where there are more dark-skinned drivers. Unfortunately, publicly available datasets are often captured in different cultural contexts, and therefore do not cover all ethnicities, which can lead to false detections or racially biased models. This work evaluates the performance obtained when training convolutional neural network models on commonly used driver drowsiness detection datasets and testing on datasets specifically chosen for broader representation. Results show that models trained using publicly available datasets suffer extensively from over-fitting, and can exhibit racial bias, as shown by testing on a more representative dataset. We propose a novel visualisation technique that can assist in identifying groups of people where there might be the potential of discrimination, using Principal Component Analysis (PCA) to produce a grid of faces sorted by similarity, and combining these with a model accuracy overlay.
Tracking Performance Limitations of MIMO Networked Control Systems with Multiple Communication Constraints
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs is considered as continuous-time linear multi-input multi-output (MIMO) systems with random reference noises. The controlled plants include unstable poles and non-minimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree of freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises (the reference noise power distribution (RNPD) and its directions) and the characteristics of communication constraints. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) always can affects the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channels. Lastly, a instance is given for verifying the effectiveness of the theoretical outcomes.
Exploring Memory Persistency Models for GPUs
Venue: arXiv cs.DC 2019
Year: 2019

Given its high integration density, high speed, byte addressability, and low standby power, non-volatile or persistent memory is expected to supplement/replace DRAM as main memory. Through persistency programming models (which define durability ordering of stores) and durable transaction constructs, the programmer can provide recoverable data structure (RDS) which allows programs to recover to a consistent state after a failure. While persistency models have been well studied for CPUs, they have been neglected for graphics processing units (GPUs). Considering the importance of GPUs as a dominant accelerator for high performance computing, we investigate persistency models for GPUs. GPU applications exhibit substantial differences with CPUs applications, hence in this paper we adapt, re-architect, and optimize CPU persistency models for GPUs. We design a pragma-based compiler scheme to express persistency models for GPUs. We identify that the thread hierarchy in GPUs offers intuitive scopes to form epochs and durable transactions. We find that undo logging produces significant performance overheads. We propose to use idempotency analysis to reduce both logging frequency and the size of logs. Through both real-system and simulation evaluations, we show low overheads of our proposed architecture support.
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
Venue: arXiv cs.CV 2019
Year: 2019

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.
DeLiO: Decoupled LiDAR Odometry
Venue: arXiv cs.CV 2019
Year: 2019

Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which -- for the first time -- decouples the rotation estimation completely from the translation estimation. In particular, the rotation is estimated by extracting the surface normals from the input point clouds and tracking their characteristic pattern on a unit sphere. Using this rotation the point clouds are unrotated so that the underlying transformation is pure translation, which can be easily estimated using a line cloud approach. An evaluation is performed on the KITTI dataset and the results are compared against state-of-the-art algorithms.
Reinforcement Learning Based Orchestration for Elastic Services
Venue: arXiv cs.DC 2019
Year: 2019

Due to the highly variable execution context in which edge services run, adapting their behavior to the execution context is crucial to comply with their requirements. However, adapting service behavior is a challenging task because it is hard to anticipate the execution contexts in which it will be deployed, as well as assessing the impact that each behavior change will produce. In order to provide this adaptation efficiently, we propose a Reinforcement Learning (RL) based Orchestration for Elastic Services. We implement and evaluate this approach by adapting an elastic service in different simulated execution contexts and comparing its performance to a Heuristics based approach. We show that elastic services achieve high precision and requirement satisfaction rates while creating an overhead of less than 0.5% to the overall service. In particular, the RL approach proves to be more efficient than its rule-based counterpart; yielding a 10 to 25% higher precision while being 25% less computationally expensive.
Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches
Venue: arXiv cs.DL 2019
Year: 2019

Adequately disambiguating author names in bibliometric databases is a precondition for conducting reliable analyses at the author level. In the case of bibliometric studies that include many researchers, it is not possible to disambiguate each single researcher manually. Several approaches have been proposed for author name disambiguation but there has not yet been a comparison of them under controlled conditions. In this study, we compare a set of unsupervised disambiguation approaches. Unsupervised approaches specify a model to assess the similarity of author mentions a priori instead of training a model with labelled data. In order to evaluate the approaches, we applied them to a set of author mentions annotated with a ResearcherID, this being an author identifier maintained by the researchers themselves. Apart from comparing the overall performance, we take a more detailed look at the role of the parametrization of the approaches and analyse the dependence of the results on the complexity of the disambiguation task. It could be shown that all of the evaluated approaches produce better results than those that can be obtained by using only author names. In the context of this study, the approach proposed by Caron and van Eck (2014) produced the best results.
The graphs behind Reuleaux polyhedra
Venue: arXiv cs.CG 2019
Year: 2019

This work is about graphs arising from Reuleaux polyhedra. Such graphs must necessarily be planar, $3$-connected and strongly self-dual. We study the question of when these conditions are sufficient. If $G$ is any such a graph with isomorphism $τ: G \to G^*$ (where $G^*$ is the unique dual graph), a metric mapping is a map $η: V(G) \to \mathbb R^3$ such that the diameter of $η(G)$ is $1$ and for every pair of vertices $(u,v)$ such that $u\in τ(v)$ we have dist$(η(u),η(v)) = 1$. If $η$ is injective, it is called a metric embedding. Note that a metric embedding gives rise to a Reuleaux Polyhedra. Our contributions are twofold: Firstly, we prove that any planar, $3$-connected, strongly self-dual graph has a metric mapping by proving that the chromatic number of the diameter graph (whose vertices are $V(G)$ and whose edges are pairs $(u,v)$ such that $u\in τ(v)$) is at most $4$, which means there exists a metric mapping to the tetrahedron. Furthermore, we use the Lovász neighborhood-complex theorem in algebraic topology to prove that the chromatic number of the diameter graph is exactly $4$. Secondly, we develop algorithms that allow us to obtain every such graph with up to $14$ vertices. Furthermore, we numerically construct metric embeddings for every such graph. From the theorem and this computational evidence we conjecture that every such graph is realizable as a Reuleaux polyhedron in $\mathbb R^3$. In previous work the first and last authors described a method to construct a constant-width body from a Reuleaux polyhedron. So in essence, we also construct hundreds of new examples of constant-width bodies. This is related to a problem of Vázsonyi, and also to a problem of Blaschke-Lebesgue.
Routing Networks and the Challenges of Modular and Compositional Computation
Venue: arXiv cs.LG 2019
Year: 2019

Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and offer substantial gains across a variety of domains, including multi-task learning, language modeling, visual question answering, machine comprehension, and others. However, such models present unique challenges during training when both the module parameters and their composition must be learned jointly. In this paper, we identify several of these issues and analyze their underlying causes. Our discussion focuses on routing networks, a general approach to this problem, and examines empirically the interplay of these challenges and a variety of design decisions. In particular, we consider the effect of how the algorithm decides on module composition, how the algorithm updates the modules, and if the algorithm uses regularization.
QRATPre+: Effective QBF Preprocessing via Strong Redundancy Properties
Venue: arXiv cs.LO 2019
Year: 2019

We present version 2.0 of QRATPre+, a preprocessor for quantified Boolean formulas (QBFs) based on the QRAT proof system and its generalization QRAT+. These systems rely on strong redundancy properties of clauses and universal literals. QRATPre+ is the first implementation of these redundancy properties in QRAT and QRAT+ used to simplify QBFs in preprocessing. It is written in C and features an API for easy integration in other QBF tools. We present implementation details and report on experimental results demonstrating that QRATPre+ improves upon the power of state-of-the-art preprocessors and solvers.
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations
Venue: arXiv cs.LG 2019
Year: 2019

Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms. However, the interpretations themselves could contain significant uncertainty that undermines the trust in the outcomes and raises concern about the model's reliability. Focusing on the method "Local Interpretable Model-agnostic Explanations" (LIME), we demonstrate the presence of two sources of uncertainty, namely the randomness in its sampling procedure and the variation of interpretation quality across different input data points. Such uncertainty is present even in models with high training and test accuracy. We apply LIME to synthetic data and two public data sets, text classification in 20 Newsgroup and recidivism risk-scoring in COMPAS, to support our argument.
Realizing Multi-Point Vehicular Positioning via Millimeter-wave Transmission
Venue: arXiv cs.IT 2019
Year: 2019

Multi-point detection of the full-scale environment is an important issue in autonomous driving. The state-of-the-art positioning technologies (such as RADAR and LIDAR) are incapable of real-time detection without line-of-sight. To address this issue, this paper presents a novel multi-point vehicular positioning technology via \emph{millimeter-wave} (mmWave) transmission that exploits multi-path reflection from a \emph{target vehicle} (TV) to a \emph{sensing vehicle} (SV), which enables the SV to fast capture both the shape and location information of the TV in \emph{non-line-of-sight} (NLoS) under the assistance of multi-path reflections. A \emph{phase-difference-of-arrival} (PDoA) based hyperbolic positioning algorithm is designed to achieve the synchronization between the TV and SV. The \emph{stepped-frequency-continuous-wave} (SFCW) is utilized as signals for multi-point detection of the TVs. Transceiver separation enables our approach to work in NLoS conditions and achieve much lower latency compared with conventional positioning techniques.