OpenRaiser/Intern-Atlas huggingface dataset:
Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertainty
Venue: arXiv cs.SE 2020
Year: 2020

The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.
Low-Congestion Shortcuts for Graphs Excluding Dense Minors
Venue: arXiv cs.DS 2020
Year: 2020

We prove that any $n$-node graph $G$ with diameter $D$ admits shortcuts with congestion $O(δD \log n)$ and dilation $O(δD)$, where $δ$ is the maximum edge-density of any minor of $G$. Our proof is simple, elementary, and constructive - featuring a $\tildeΘ(δD)$-round distributed construction algorithm. Our results are tight up to $\tilde{O}(1)$ factors and generalize, simplify, unify, and strengthen several prior results. For example, for graphs excluding a fixed minor, i.e., graphs with constant $δ$, only a $\tilde{O}(D^2)$ bound was known based on a very technical proof that relies on the Robertson-Seymour Graph Structure Theorem. A direct consequence of our result is that many graph families, including any minor-excluded ones, have near-optimal $\tildeΘ(D)$-round distributed algorithms for many fundamental communication primitives and optimization problems including minimum spanning tree, minimum cut, and shortest-path approximations.
Associative Partial Domain Adaptation
Venue: arXiv cs.CV 2020
Year: 2020

Partial Adaptation (PDA) addresses a practical scenario in which the target domain contains only a subset of classes in the source domain. While PDA should take into account both class-level and sample-level to mitigate negative transfer, current approaches mostly rely on only one of them. In this paper, we propose a novel approach to fully exploit multi-level associations that can arise in PDA. Our Associative Partial Domain Adaptation (APDA) utilizes intra-domain association to actively select out non-trivial anomaly samples in each source-private class that sample-level weighting cannot handle. Additionally, our method considers inter-domain association to encourage positive transfer by mapping between nearby target samples and source samples with high label-commonness. For this, we exploit feature propagation in a proposed label space consisting of source ground-truth labels and target probabilistic labels. We further propose a geometric guidance loss based on the label commonness of each source class to encourage positive transfer. Our APDA consistently achieves state-of-the-art performance across public datasets.
A $2^{O(k)}n$ algorithm for $k$-cycle in minor-closed graph families
Venue: arXiv cs.DS 2020
Year: 2020

Let ${\mathcal C}$ be a proper minor-closed family of graphs. We present a randomized algorithm that given a graph $G \in {\mathcal C}$ with $n$ vertices, finds a simple cycle of size $k$ in $G$ (if exists) in $2^{O(k)}n$ time. The algorithm applies to both directed and undirected graphs. In previous linear time algorithms for this problem, the runtime dependence on $k$ is super-exponential. The algorithm can be derandomized yielding a $2^{O(k)}n\log n$ time algorithm.
Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
Venue: arXiv cs.CV 2020
Year: 2020

Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET. Methods: The CNN (3D U-Net) was trained on [68Ga]PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: [68Ga]PSMA-PET, n=18 and cohort 2: [18F]PSMA-PET, n=19) and one external (cohort 3: [68Ga]PSMA-PET, n=20) test-datasets. Accordance between manual contours and GTV-CNN was assessed with Dice-Sørensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal test-datasets by using whole-mount histology. Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion: The application of a CNN for automated contouring of intraprostatic GTV in [68Ga]PSMA- and [18F]PSMA-PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.
Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube
Venue: arXiv cs.HC 2020
Year: 2020

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, practitioners lack relevant knowledge to improve the user experience of such systems. To address this problem, we conducted semi-structured interviews with middle-aged YouTube video consumers to analyze their user beliefs about the video recommendation system. Our analysis revealed different factors that users believe influence their recommendations. Based on these factors, we identified four groups of user beliefs: Previous Actions, Social Media, Recommender System, and Company Policy. Additionally, we propose a framework to distinguish the four main actors that users believe influence their video recommendations: the current user, other users, the algorithm, and the organization. This framework provides a new lens to explore design suggestions based on the agency of these four actors. It also exposes a novel aspect previously unexplored: the effect of corporate decisions on the interaction with algorithmic recommendations. While we found that users are aware of the existence of the recommendation system on YouTube, we show that their understanding of this system is limited.
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
Venue: arXiv cs.LG 2020
Year: 2020

Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging. In this paper, we propose ESPRESSO (Entropy and ShaPe awaRe timE-Series SegmentatiOn), a hybrid segmentation model for multi-dimensional time-series that is formulated to exploit the entropy and temporal shape properties of time-series. ESPRESSO differs from existing methods that focus upon particular statistical or temporal properties of time-series exclusively. As part of model development, a novel temporal representation of time-series $WCAC$ was introduced along with a greedy search approach that estimate segments based upon the entropy metric. ESPRESSO was shown to offer superior performance to four state-of-the-art methods across seven public datasets of wearable and wear-free sensing. In addition, we undertake a deeper investigation of these datasets to understand how ESPRESSO and its constituent methods perform with respect to different dataset characteristics. Finally, we provide two interesting case-studies to show how applying ESPRESSO can assist in inferring daily activity routines and the emotional state of humans.
A Novel Tampering Attack on AES Cores with Hardware Trojans
Venue: arXiv cs.CR 2020
Year: 2020

The implementation of cryptographic primitives in integrated circuits (ICs) continues to increase over the years due to the recent advancement of semiconductor manufacturing and reduction of cost per transistors. The hardware implementation makes cryptographic operations faster and more energy-efficient. However, various hardware attacks have been proposed aiming to extract the secret key in order to undermine the security of these primitives. In this paper, we focus on the widely used advanced encryption standard (AES) block cipher and demonstrate its vulnerability against tampering attack. Our proposed attack relies on implanting a hardware Trojan in the netlist by an untrusted foundry, which can design and implement such a Trojan as it has access to the design layout and mask information. The hardware Trojan's activation modifies a particular round's input data by preventing the effect of all previous rounds' key-dependent computation. We propose to use a sequential hardware Trojan to deliver the payload at the input of an internal round for achieving this modification of data. All the internal subkeys, and finally, the secret key can be computed from the observed ciphertext once the Trojan is activated. We implement our proposed tampering attack with a sequential hardware Trojan inserted into a 128-bit AES design from OpenCores benchmark suite and report the area overhead to demonstrate the feasibility of the proposed tampering attack.
A $4/3$-Approximation Algorithm for the Minimum $2$-Edge Connected Multisubgraph Problem in the Half-Integral Case
Venue: arXiv cs.DS 2020
Year: 2020

Given a connected undirected graph $\bar{G}$ on $n$ vertices, and non-negative edge costs $c$, the 2ECM problem is that of finding a $2$-edge~connected spanning multisubgraph of $\bar{G}$ of minimum cost. The natural linear program (LP) for 2ECM, which coincides with the subtour LP for the Traveling Salesman Problem on the metric closure of $\bar{G}$, gives a lower bound on the optimal cost. For instances where this LP is optimized by a half-integral solution $x$, Carr and Ravi (1998) showed that the integrality gap is at most $\frac43$: they show that the vector $\frac43 x$ dominates a convex combination of incidence vectors of $2$-edge connected spanning multisubgraphs of $\bar{G}$. We present a simpler proof of the result due to Carr and Ravi by applying an extension of Lovász's splitting-off theorem. Our proof naturally leads to a $\frac43$-approximation algorithm for half-integral instances. Given a half-integral solution $x$ to the LP for 2ECM, we give an $O(n^2)$-time algorithm to obtain a $2$-edge connected spanning multisubgraph of $\bar{G}$ whose cost is at most $\frac43 c^T x$.
Hybrid Score- and Rank-level Fusion for Person Identification using Face and ECG Data
Venue: arXiv cs.CV 2020
Year: 2020

Uni-modal identification systems are vulnerable to errors in sensor data collection and are therefore more likely to misidentify subjects. For instance, relying on data solely from an RGB face camera can cause problems in poorly lit environments or if subjects do not face the camera. Other identification methods such as electrocardiograms (ECG) have issues with improper lead connections to the skin. Errors in identification are minimized through the fusion of information gathered from both of these models. This paper proposes a methodology for combining the identification results of face and ECG data using Part A of the BioVid Heat Pain Database containing synchronized RGB-video and ECG data on 87 subjects. Using 10-fold cross-validation, face identification was 98.8% accurate, while the ECG identification was 96.1% accurate. By using a fusion approach the identification accuracy improved to 99.8%. Our proposed methodology allows for identification accuracies to be significantly improved by using disparate face and ECG models that have non-overlapping modalities.
Security Design Patterns in Distributed Microservice Architecture
Venue: arXiv cs.CR 2020
Year: 2020

Micro service architecture has revolutionized the landscape for the development of web and mobile applications alike. Due to the stateless nature and loose coupling involved in the design of micro services, native mobile applications can be developed by utilizing the same backend services which feed the inputs to the web application front ends. Extending the same concept, a plethora of automated devices, thanks to the advancements in the field of IOT, have come into existence which can feed on the same set of micro services. This concept of build once and utilize for many use cases has become a new norm in the enterprise design patterns. To handle the horizontal scalability needs of so many calling clients, significant advancements have been made on the containerization and their orchestration strategies on the public cloud platforms. However, scalable design techniques have led to the increased exposure of backend services to unwanted entities. This broadened the attack surface and also the risk. On top of it the mix of heterogeneous technologies in MSA, their distinct logging strategies, makes the central logging difficult, which in turn loosens the security. Additionally, the complexity around building the resilience for fault tolerance across the decentralized networks, adds to the security loop holes. The simple security designs which were once used with traditional web applications cannot be used for Microservice based applications. This paper articulates the innovative approaches of handling the security needs involved in protection of distributed services in Microservice architecture.
Point or Generate Dialogue State Tracker
Venue: arXiv cs.CL 2020
Year: 2020

Dialogue state tracking is a key part of a task-oriented dialogue system, which estimates the user's goal at each turn of the dialogue. In this paper, we propose the Point-Or-Generate Dialogue State Tracker (POGD). POGD solves the dialogue state tracking task in two perspectives: 1) point out explicitly expressed slot values from the user's utterance, and 2) generate implicitly expressed ones based on slot-specific contexts. It also shares parameters across all slots, which achieves knowledge sharing and gains scalability to large-scale across-domain dialogues. Moreover, the training process of its submodules is formulated as a multi-task learning procedure to further promote its capability of generalization. Experiments show that POGD not only obtains state-of-the-art results on both WoZ 2.0 and MultiWoZ 2.0 datasets but also has good generalization on unseen values and new slots.
Learning-Based Safety-Stability-Driven Control for Safety-Critical Systems under Model Uncertainties
Venue: arXiv cs.RO 2020
Year: 2020

Safety and tracking stability are crucial for safety-critical systems such as self-driving cars, autonomous mobile robots, industrial manipulators. To efficiently control safety-critical systems to ensure their safety and achieve tracking stability, accurate system dynamic models are usually required. However, accurate system models are not always available in practice. In this paper, a learning-based safety-stability-driven control (LBSC) algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties. Gaussian Processes (GPs) are employed to learn the model error between the nominal model and the actual system dynamics, and the estimated mean and variance of the model error are used to quantify a high-confidence uncertainty bound. Using this estimated uncertainty bound, a safety barrier constraint is devised to ensure safety, and a stability constraint is developed to achieve rapid and accurate tracking. Then the proposed LBSC method is formulated as a quadratic program incorporating the safety barrier, the stability constraint, and the control constraints. The effectiveness of the LBSC method is illustrated on the safety-critical connected cruise control (CCC) system simulator under model uncertainties.
Learning (Re-)Starting Solutions for Vehicle Routing Problems
Venue: arXiv cs.AI 2020
Year: 2020

A key challenge in solving a combinatorial optimization problem is how to guide the agent (i.e., solver) to efficiently explore the enormous search space. Conventional approaches often rely on enumeration (e.g., exhaustive, random, or tabu search) or have to restrict the exploration to rather limited regions (e.g., a single path as in iterative algorithms). In this paper, we show it is possible to use machine learning to speedup the exploration. In particular, a value network is trained to evaluate solution candidates, which provides a useful structure (i.e., an approximate value surface) over the search space; this value network is then used to screen solutions to help a black-box optimization agent to initialize or restart so as to navigate through the search space towards desirable solutions. Experiments demonstrate that the proposed ``Learn to Restart'' algorithm achieves promising results in solving Capacitated Vehicle Routing Problems (CVRPs).
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Venue: arXiv cs.SI 2020
Year: 2020

The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes, has become a pressing problem on social media. Moreover, information sharing on social media facilitates diffusion of viral information cascades. In this work, we focus on understanding and leveraging diffusion dynamics of false and legitimate contents in order to facilitate network interventions for fake news mitigation. We analyze real-world Twitter datasets comprising fake and true news cascades, to understand differences in diffusion dynamics and user behaviours with regards to fake and true contents. Based on the analysis, we model the diffusion as a mixture of Independent Cascade models (MIC) with parameters $θ_T, θ_F$ over the social network graph; and derive unsupervised inference techniques for parameter estimation of the diffusion mixture model from observed, unlabeled cascades. Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics. Characteristics of the identified influential users reveal positive correlation between influential users identified for fake news and their relative appearance in fake news cascades. Identified influential users tend to be related to topics of more viral information cascades than less viral ones; and identified fake news influential users have relatively fewer counts of direct followers, compared to the true news influential users. Intervention analysis on nodes and edges demonstrates capacity of the inferred diffusion dynamics in supporting network interventions for mitigation.
Why to "grow" and "harvest" deep learning models?
Venue: arXiv cs.LG 2020
Year: 2020

Current expectations from training deep learning models with gradient-based methods include: 1) transparency; 2) high convergence rates; 3) high inductive biases. While the state-of-art methods with adaptive learning rate schedules are fast, they still fail to meet the other two requirements. We suggest reconsidering neural network models in terms of single-species population dynamics where adaptation comes naturally from open-ended processes of "growth" and "harvesting". We show that the stochastic gradient descent (SGD) with two balanced pre-defined values of per capita growth and harvesting rates outperform the most common adaptive gradient methods in all of the three requirements.
The perceptions of social and information privacy risks of Inflammatory Bowel Disease patients using social media platforms for health-related support
Venue: arXiv cs.CY 2020
Year: 2020

With hundreds of thousands of individuals using social media to discuss health concerns, sensitive personal data is self-disclosed on these platforms every day. Previous research indicates an understanding of social privacy concerns by patients with chronic illnesses, but there is a lack of understanding in the perception of information privacy concerns. Qualitative interviews were conducted with 38 patients with inflammatory bowel disease (IBD) using social media platforms to engage with online communities. Using thematic analysis, we identified that patients typically demonstrate the privacy and risk dual calculus for perceived social privacy concerns. Patients demonstrate mixed knowledge of what data is collected and how it is used by social media platforms and often described their platform use as a trade-off between the unknown information privacy risks and the therapeutic affordances of engaging with the online community (the privacy calculus). Our findings illustrate the different levels of understanding between social and information privacy and the impacts on how individuals take agency over their personal data. We conclude with the suggestion for future research to further understand the relationship between knowledge, information privacy concerns and mitigating actions in the online health community context.
Cross-modal Center Loss
Venue: arXiv cs.CV 2020
Year: 2020

Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we propose an approach to jointly train the components of cross-modal retrieval framework with metadata, and enable the network to find optimal features. The proposed end-to-end framework is updated with three loss functions: 1) a novel cross-modal center loss to eliminate cross-modal discrepancy, 2) cross-entropy loss to maximize inter-class variations, and 3) mean-square-error loss to reduce modality variations. In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities, including 2D image, 3D point cloud, and mesh data. The proposed framework significantly outperforms the state-of-the-art methods on the ModelNet40 dataset.