OpenRaiser/Intern-Atlas huggingface dataset:
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
Venue: arXiv cs.CV 2018
Year: 2018

This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time -- the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for model adaptation from daytime scenes to nighttime scenes, without using extra human annotation.
AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we propose a self-supervised learning method for affine image registration on 3D medical images. Unlike optimisation-based methods, our affine image registration network (AIRNet) is designed to directly estimate the transformation parameters between two input images without using any metric, which represents the quality of the registration, as the optimising function. But since it is costly to manually identify the transformation parameters between any two images, we leverage the abundance of cheap unlabelled data to generate a synthetic dataset for the training of the model. Additionally, the structure of AIRNet enables us to learn the discriminative features of the images which are useful for registration purpose. Our proposed method was evaluated on magnetic resonance images of the axial view of human brain and compared with the performance of a conventional image registration method. Experiments demonstrate that our approach achieves better overall performance on registration of images from different patients and modalities with 100x speed-up in execution time.
SLIC Based Digital Image Enlargement
Venue: arXiv cs.CV 2018
Year: 2018

Low resolution image enhancement is a classical computer vision problem. Selecting the best method to reconstruct an image to a higher resolution with the limited data available in the low-resolution image is quite a challenge. A major drawback from the existing enlargement techniques is the introduction of color bleeding while interpolating pixels over the edges that separate distinct colors in an image. The color bleeding causes to accentuate the edges with new colors as a result of blending multiple colors over adjacent regions. This paper proposes a novel approach to mitigate the color bleeding by segmenting the homogeneous color regions of the image using Simple Linear Iterative Clustering (SLIC) and applying a higher order interpolation technique separately on the isolated segments. The interpolation at the boundaries of each of the isolated segments is handled by using a morphological operation. The approach is evaluated by comparing against several frequently used image enlargement methods such as bilinear and bicubic interpolation by means of Peak Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the proposed method outperforms the baseline methods by means of PSNR and also mitigates the color bleeding at the edges which improves the overall appearance.
Thinging Ethics for Software Engineers
Venue: arXiv cs.CY 2018
Year: 2018

Ethical systems are usually described as principles for distinguishing right from wrong and forming beliefs about proper conduct. Ethical topics are complex, with excessively verbose accounts of mental models and intensely ingrained philosophical assumptions. From practical experience, in teaching ethics for software engineering students, an explanation of ethics alone often cannot provide insights of behavior and thought for students. Additionally, it seems that there has been no exploration into the development of a conceptual presentation of ethics that appeals to computer engineers. This is particularly clear in the area of software engineering, which focuses on software and associated tools such as algorithms, diagramming, documentation, modeling and design as applied to various types of data and conceptual artifacts. It seems that software engineers look at ethical materials as a collection of ideas and notions that lack systemization and uniformity. Accordingly, this paper explores a thinging schematization for ethical theories that can serve a role similar to that of modeling languages (e.g., UML). In this approach, thinging means actualization (existence, presence, being) of things and mechanisms that define a boundary around some region of ethically related reality, separating it from everything else. The resultant diagrammatic representation then developed to model the process of making ethical decisions in that region.
Automatic Detection of Arousals during Sleep using Multiple Physiological Signals
Venue: arXiv cs.CV 2018
Year: 2018

The visual scoring of arousals during sleep routinely conducted by sleep experts is a challenging task warranting an automatic approach. This paper presents an algorithm for automatic detection of arousals during sleep. Using the Physionet/CinC Challenge dataset, an 80-20% subject-level split was performed to create in-house training and test sets, respectively. The data for each subject in the training set was split to 30-second epochs with no overlap. A total of 428 features from EEG, EMG, EOG, airflow, and SaO2 in each epoch were extracted and used for creating subject-specific models based on an ensemble of bagged classification trees, resulting in 943 models. For marking arousal and non-arousal regions in the test set, the data in the test set was split to 30-second epochs with 50% overlaps. The average of arousal probabilities from different patient-specific models was assigned to each 30-second epoch and then a sample-wise probability vector with the same length as test data was created for model evaluation. Using the PhysioNet/CinC Challenge 2018 scoring criteria, AUPRCs of 0.25 and 0.21 were achieved for the in-house test and blind test sets, respectively.
Demonstration Abstract: A Toolkit for Specifying Service Level Agreements for IoT applications
Venue: arXiv cs.DC 2018
Year: 2018

Today we see the use of the Internet of Things (IoT) in various application domains such as healthcare, smart homes, smart cars, and smart-x applications in smart cities. The number of applications based on IoT and cloud computing is projected to increase rapidly over the next few years. IoT-based services must meet the guaranteed levels of quality of service (QoS) to match users' expectations. Ensuring QoS through specifying the QoS constraints using Service Level Agreements (SLAs) is crucial. Therefore, as a first step toward SLA management, it is essential to provide an SLA specification in a machine-readable format. In this paper, we demonstrate a toolkit for creating SLA specifications for IoT applications. The toolkit is used to simplify the process of capturing the requirements of IoT applications. We present a demonstration of the toolkit using a Remote Health Monitoring Service (RHMS) usecase. The toolkit supports the following: (1) specifying the Service-Level Objectives (SLO) of an IoT application at the application level; (2) specifying the workflow activities of the IoT application; (3) mapping each activity to the required software and hardware resources and specifying the constraints of SLOs and other configuration- related metrics of the required hardware and software; and (4) creating the composed SLA in JSON format.
Autonomous agile teams: Challenges and future directions for research
Venue: arXiv cs.SE 2018
Year: 2018

According to the principles articulated in the agile manifesto, motivated and empowered software developers relying on technical excellence and simple designs, create business value by delivering working software to users at regular short intervals. These principles have spawned many practices. At the core of these practices is the idea of autonomous, self-managing, or self-organizing teams whose members work at a pace that sustains their creativity and productivity. This article summarizes the main challenges faced when implementing autonomous teams and the topics and research questions that future research should address.
Interpretable Convolutional Neural Networks via Feedforward Design
Venue: arXiv cs.CV 2018
Year: 2018

The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any BP as a reference. The FF design adopts a data-centric approach. It derives network parameters of the current layer based on data statistics from the output of the previous layer in a one-pass manner. To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform. It is a variant of the principal component analysis (PCA) with an added bias vector to annihilate activation's nonlinearity. Multiple Saab transforms in cascade yield multiple convolutional layers. As to fully-connected (FC) layers, we construct them using a cascade of multi-stage linear least squared regressors (LSRs). The classification and robustness (against adversarial attacks) performances of BP- and FF-designed CNNs applied to the MNIST and the CIFAR-10 datasets are compared. Finally, we comment on the relationship between BP and FF designs.
ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
Venue: arXiv cs.IR 2018
Year: 2018

Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.
Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence
Venue: arXiv cs.RO 2018
Year: 2018

Efforts are underway at UT Austin to build autonomous robot systems that address the challenges of long-term deployments in office environments and of the more prescribed domestic service tasks of the RoboCup@Home competition. We discuss the contrasts and synergies of these efforts, highlighting how our work to build a RoboCup@Home Domestic Standard Platform League entry led us to identify an integrated software architecture that could support both projects. Further, naturalistic deployments of our office robot platform as part of the Building-Wide Intelligence project have led us to identify and research new problems in a traditional laboratory setting.
The Curses of Blockchain Decentralization
Venue: arXiv cs.CR 2018
Year: 2018

Decentralization, which has backed the hyper growth of many blockchains, comes at the cost of scalability. To understand this fundamental limitation, this paper proposes a quantitative measure of blockchain decentralization, and discusses its implications to various trust models and consensus algorithms. Further, we identify the major challenges in blockchain decentralization. Our key findings are that true decentralization is hard to achieve due to the skewed mining power and that a fully decentralized blockchain inherently limits scalability as it incurs a throughput upper bound and prevents scaling smart contract execution. To address these challenges, we outline three research directions to explore the trade-offs between decentralization and scalability.
Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments
Venue: arXiv cs.DC 2018
Year: 2018

Data centres that use consumer-grade disks drives and distributed peer-to-peer systems are unreliable environments to archive data without enough redundancy. Most redundancy schemes are not completely effective for providing high availability, durability and integrity in the long-term. We propose alpha entanglement codes, a mechanism that creates a virtual layer of highly interconnected storage devices to propagate redundant information across a large scale storage system. Our motivation is to design flexible and practical erasure codes with high fault-tolerance to improve data durability and availability even in catastrophic scenarios. By flexible and practical, we mean code settings that can be adapted to future requirements and practical implementations with reasonable trade-offs between security, resource usage and performance. The codes have three parameters. Alpha increases storage overhead linearly but increases the possible paths to recover data exponentially. Two other parameters increase fault-tolerance even further without the need of additional storage. As a result, an entangled storage system can provide high availability, durability and offer additional integrity: it is more difficult to modify data undetectably. We evaluate how several redundancy schemes perform in unreliable environments and show that alpha entanglement codes are flexible and practical codes. Remarkably, they excel at code locality, hence, they reduce repair costs and become less dependent on storage locations with poor availability. Our solution outperforms Reed-Solomon codes in many disaster recovery scenarios.
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network
Venue: arXiv cs.SI 2018
Year: 2018

Meetup.com is a global online platform which facilitates the organisation of meetups in different parts of the world. A meetup group typically focuses on one specific topic of interest, such as sports, music, language, or technology. However, many users of this platform attend multiple meetups. On this basis, we can construct a co-membership network for a given location. This network encodes how pairs of meetups are connected to one another via common members. In this work we demonstrate that, by applying techniques from social network analysis to this type of representation, we can reveal the underlying meetup community structure, which is not immediately apparent from the platform's website. Specifically, we map the landscape of Dublin's meetup communities, to explore the interests and activities of meetup.com users in the city.
Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes
Venue: arXiv cs.LG 2018
Year: 2018

We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP framework elegantly addresses model uncertainty by incorporating Bayesian belief updates into long-term expected return. However, computing an exact optimal Bayesian policy is intractable. Our key insight is to compute a near-optimal value function by covering the continuous state-belief-action space with a finite set of representative samples and exploiting the Lipschitz continuity of the value function. We prove the near-optimality of our algorithm and analyze a number of schemes that boost the algorithm's efficiency. Finally, we empirically validate our approach on a number of discrete and continuous BAMDPs and show that the learned policy has consistently competitive performance against baseline approaches.
Hierarchical Optimization for Whole-Body Control of Wheeled Inverted Pendulum Humanoids
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present a whole-body control framework for Wheeled Inverted Pendulum (WIP) Humanoids. WIP Humanoids are redundant manipulators dynamically balancing themselves on wheels. Characterized by several degrees of freedom, they have the ability to perform several tasks simultaneously, such as balancing, maintaining a body pose, controlling the gaze, lifting a load or maintaining end-effector configuration in operation space. The problem of whole-body control is to enable simultaneous performance of these tasks with optimal participation of all degrees of freedom at specified priorities for each objective. The control also has to obey constraint of angle and torque limits on each joint. The proposed approach is hierarchical with a low level controller for body joints manipulation and a high-level controller that defines center of mass (CoM) targets for the low-level controller to control zero dynamics of the system driving the wheels. The low-level controller plans for shorter horizons while considering more complete dynamics of the system, while the high-level controller plans for longer horizon based on an approximate model of the robot for computational efficiency.
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection
Venue: arXiv cs.CV 2018
Year: 2018

Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural network (CNN) and compressed sensing (CS) or sparse coding (SC) for end-to-end training. We also derive, for the first time, a backpropagation rule, which is applicable to train any algorithm that implements a sparse code recovery layer. The key observation behind our algorithm is that cell detection task is a point object detection task in computer vision, where the cell centers (i.e., point objects) occupy only a tiny fraction of the total number of pixels in an image. Thus, we can apply compressed sensing (or, equivalently sparse coding) to compactly represent a variable number of cells in a projected space. Then, CNN regresses this compressed vector from the input microscopy image. Thanks to the SC/CS recovery algorithm (L1 optimization) that can recover sparse cell locations from the output of CNN. We train this entire processing pipeline end-to-end and demonstrate that end-to-end training provides accuracy improvements over a training paradigm that treats CNN and CS-recovery layers separately. Our algorithm design also takes into account a form of ensemble average of trained models naturally to further boost accuracy of cell detection. We have validated our algorithm on benchmark datasets and achieved excellent performances.
Counting homomorphisms in plain exponential time
Venue: arXiv cs.DS 2018
Year: 2018

In the counting Graph Homomorphism problem (#GraphHom) the question is: Given graphs G,H, find the number of homomorphisms from G to H. This problem is generally #P-complete, moreover, Cygan et al. proved that unless the ETH is false there is no algorithm that solves this problem in time O(|V(H)|^{o(|V(G)|)}. This, however, does not rule out the possibility that faster algorithms exist for restricted problems of this kind. Wahlstrom proved that #GraphHom can be solved in plain exponential time, that is, in time k^{|V(G)|+V(H)|}\poly(|V(H)|,|V(G)|) provided H has clique width k. We generalize this result to a larger class of graphs, and also identify several other graph classes that admit a plain exponential algorithm for #GraphHom.
Optimal Policies for Status Update Generation in a Wireless System with Heterogeneous Traffic
Venue: arXiv cs.IT 2018
Year: 2018

A large body of applications that involve monitoring, decision making, and forecasting require timely status updates for their efficient operation. Age of Information (AoI) is a newly proposed metric that effectively captures this requirement. Recent research on the subject has derived AoI optimal policies for the generation of status updates and AoI optimal packet queueing disciplines. Unlike previous research we focus on low-end devices that typically support monitoring applications in the context of the Internet of Things. We acknowledge that these devices host a diverse set of applications some of which are AoI sensitive while others are not. Furthermore, due to their limited computational resources they typically utilize a simple First-In First-Out (FIFO) queueing discipline. We consider the problem of optimally controlling the status update generation process for a system with a source-destination pair that communicates via a wireless link, whereby the source node is comprised of a FIFO queue and two applications, one that is AoI sensitive and one that is not. We formulate this problem as a dynamic programming problem and utilize the framework of Markov Decision Processes to derive optimal policies for the generation of status update packets. Due to the lack of comparable methods in the literature, we compare the derived optimal policies against baseline policies, such as the zero-wait policy, and investigate the performance of all policies for a variety of network configurations. Results indicate that existing status update policies fail to capture the trade-off between frequent generation of status updates and queueing delay and thus perform poorly.
Spectral Subspace Sparsification
Venue: arXiv cs.DS 2018
Year: 2018

We introduce a new approach to spectral sparsification that approximates the quadratic form of the pseudoinverse of a graph Laplacian restricted to a subspace. We show that sparsifiers with a near-linear number of edges in the dimension of the subspace exist. Our setting generalizes that of Schur complement sparsifiers. Our approach produces sparsifiers by sampling a uniformly random spanning tree of the input graph and using that tree to guide an edge elimination procedure that contracts, deletes, and reweights edges. In the context of Schur complement sparsifiers, our approach has two benefits over prior work. First, it produces a sparsifier in almost-linear time with no runtime dependence on the desired error. We directly exploit this to compute approximate effective resistances for a small set of vertex pairs in faster time than prior work (Durfee-Kyng-Peebles-Rao-Sachdeva '17). Secondly, it yields sparsifiers that are reweighted minors of the input graph. As a result, we give a near-optimal answer to a variant of the Steiner point removal problem. A key ingredient of our algorithm is a subroutine of independent interest: a near-linear time algorithm that, given a chosen set of vertices, builds a data structure from which we can query a multiplicative approximation to the decrease in the effective resistance between two vertices after identifying all vertices in the chosen set to a single vertex with inverse polynomial additional additive error in near-constant time.