OpenRaiser/Intern-Atlas huggingface dataset:
Parallel Transport Unfolding: A Connection-based Manifold Learning Approach
Venue: arXiv cs.LG 2018
Year: 2018

Manifold learning offers nonlinear dimensionality reduction of high-dimensional datasets. In this paper, we bring geometry processing to bear on manifold learning by introducing a new approach based on metric connection for generating a quasi-isometric, low-dimensional mapping from a sparse and irregular sampling of an arbitrary manifold embedded in a high-dimensional space. Geodesic distances of discrete paths over the input pointset are evaluated through "parallel transport unfolding" (PTU) to offer robustness to poor sampling and arbitrary topology. Our new geometric procedure exhibits the same strong resilience to noise as one of the staples of manifold learning, the Isomap algorithm, as it also exploits all pairwise geodesic distances to compute a low-dimensional embedding. While Isomap is limited to geodesically-convex sampled domains, parallel transport unfolding does not suffer from this crippling limitation, resulting in an improved robustness to irregularity and voids in the sampling. Moreover, it involves only simple linear algebra, significantly improves the accuracy of all pairwise geodesic distance approximations, and has the same computational complexity as Isomap. Finally, we show that our connection-based distance estimation can be used for faster variants of Isomap such as L-Isomap.
Bené: On Demand Cost-Effective Scaling at the Edge
Venue: arXiv cs.DC 2018
Year: 2018

Edge computing has become increasingly popular across many domains and enterprises. However, given the locality constraint of edges (i.e., only close-by edges are useful), multiplexing diverse workloads becomes challenging. This results in poor resource utilization in edge resources that are provisioned for peak demand. A simple way to allow multiplexing is through micro-data centers, that bring computation close to the users while supporting diverse workloads throughout the data, along with edges. In this paper, we argue for a hybrid approach of dedicated edge resources within an enterprise and on demand resources in micro-data centers that are shared across entities. We show that this hybrid approach is an effective and cost-efficient way for scaling workloads and removes the need for over-provisioning dedicated resources per enterprise. Moreover, compared to a scaling approach that uses only the edges across enterprises, micro-data centers also form a trusted third party that can maintain important quality of service guarantees such as data privacy, security, and availability.
A new benchmark set for Traveling salesman problem and Hamiltonian cycle problem
Venue: arXiv cs.DS 2018
Year: 2018

We present a benchmark set for Traveling salesman problem (TSP) with characteristics that are different from the existing benchmark sets. In particular, we focus on small instances which prove to be challenging for one or more state-of-the-art TSP algorithms. These instances are based on difficult instances of Hamiltonian cycle problem (HCP). This includes instances from literature, specially modified randomly generated instances, and instances arising from the conversion of other difficult problems to HCP. We demonstrate that such benchmark instances are helpful in understanding the weaknesses and strengths of algorithms. In particular, we conduct a benchmarking exercise for this new benchmark set totalling over five years of CPU time, comparing the TSP algorithms Concorde, Chained Lin-Kernighan, and LKH. We also include the HCP heuristic SLH in the benchmarking exercise. A discussion about the benefits of specifically considering outlying instances, and in particular instances which are unusually difficult relative to size, is also included.
Request for Comments: Proposal of a Blockchain for the Automatic Management and Acceptance of Student Achievements
Venue: arXiv cs.CY 2018
Year: 2018

Staying abroad during their studies is increasingly popular for students. However, there are various challenges for both students and universities. One important question for students is whether or not achievements performed at different universities can be taken into account for either enrolling at a foreign university or for completing the studies at their home university. In addition to university achievements, an increasing proportion of the 195 million students worldwide increasingly receive certificates from MOOCs or other social media services. The integration of such services into university teaching is still in the initial stages and presents some challenges. In this paper we describe the idea to manage all these study achievements worldwide in a blockchain, which might solve the national and international challenges regarding the recognition of student achievements. The aim of this paper is to encourage discussion in the global community instead of presenting a finished concept. Some of the open research questions are: How to ensure student data protection, how to deal with fraud and how to deal with the possibility that students can analytically calculate the easiest way through their studies?
Beamforming Design and Power Allocation for Secure Transmission with NOMA
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we propose a novel beamforming design to enhance physical layer security of a non-orthogonal multiple access (NOMA) system with the aid of artificial noise (AN). The proposed design uses two scalars to balance the useful signal strength and interference at the strong and weak users, which is a generalized version of the existing beamforming designs in the context of physical layer security for NOMA. We determine the optimal power allocation among useful signals and AN together with the two optimal beamforming scalars in order to maximize the secrecy sum rate (SSR). Our asymptotic analysis in the high signal-to-noise ratio regime provides an efficient and near-optimal solution to optimizing the beamforming scalars and power allocation coefficients. Our analysis indicates that it is not optimal to form a beam towards either the strong user or the weak user in NOMA systems for security enhancement. In addition, the asymptotically optimal power allocation informs that, as the transmit power increases, more power should be allocated to the weak user or AN signals, while the power allocated to the strong user keeps constant. Our examination shows that the proposed novel beamforming design can significantly outperform two benchmark schemes.
Prior Attention for Style-aware Sequence-to-Sequence Models
Venue: arXiv cs.CL 2018
Year: 2018

We extend sequence-to-sequence models with the possibility to control the characteristics or style of the generated output, via attention that is generated a priori (before decoding) from a latent code vector. After training an initial attention-based sequence-to-sequence model, we use a variational auto-encoder conditioned on representations of input sequences and a latent code vector space to generate attention matrices. By sampling the code vector from specific regions of this latent space during decoding and imposing prior attention generated from it in the seq2seq model, output can be steered towards having certain attributes. This is demonstrated for the task of sentence simplification, where the latent code vector allows control over output length and lexical simplification, and enables fine-tuning to optimize for different evaluation metrics.
Optimized Video Streaming over Cloud: A Stall-Quality Trade-off
Venue: arXiv cs.NI 2018
Year: 2018

As video-streaming services have expanded and improved, cloud-based video has evolved into a necessary feature of any successful business for reaching internal and external audiences. In this paper, video streaming over distributed storage is considered where the video segments are encoded using an erasure code for better reliability. There are multiple parallel streams between each server and the edge router. For each client request, we need to determine the subset of servers to get the data, as well as one of the parallel stream from each chosen server. In order to have this scheduling, this paper proposes a two-stage probabilistic scheduling. The selection of video quality is also chosen with a certain probability distribution. With these parameters, the playback time of video segments is determined by characterizing the download time of each coded chunk for each video segment. Using the playback times, a bound on the moment generating function of the stall duration is used to bound the mean stall duration. Based on this, we formulate an optimization problem to jointly optimize the convex combination of mean stall duration and average video quality for all requests, where the two-stage probabilistic scheduling, probabilistic video quality selection, bandwidth split among parallel streams, and auxiliary bound parameters can be chosen. This non-convex problem is solved using an efficient iterative algorithm. Evaluation results show significant improvement in QoE metrics for cloud-based video as compared to the considered baselines.
Building Correct SDN-Based Components from a Global Formal Mode
Venue: arXiv cs.SE 2018
Year: 2018

Software Defined Networking (SDN) brings flexibility in the construction and managment of distributed applications by reducing the constraints imposed by physical networks and by moving the control of networks closer to the applications. However mastering SDN still poses numerous challenges among which the design of correct SDN components (more specifically controller and switches). In this work we use a formal stepwise approach to model and reason on SDN. Although formal approaches have already been used in this area, this contribution is the first state-based approach; it is based on the Event-B formal method, and it enables a correct-by-construction of SDN components. We provide the steps to build, using several refinements, a global formal model of a SDN system; correct SDN components are then systematically built from the global formal model satisfying the desired properties. Event-B is used to experiment the approach.
Finding Optimal Solutions to Token Swapping by Conflict-based Search and Reduction to SAT
Venue: arXiv cs.AI 2018
Year: 2018

We study practical approaches to solving the token swapping (TSWAP) problem optimally in this short paper. In TSWAP, we are given an undirected graph with colored vertices. A colored token is placed in each vertex. A pair of tokens can be swapped between adjacent vertices. The goal is to perform a sequence of swaps so that token and vertex colors agree across the graph. The minimum number of swaps is required in the optimization variant of the problem. We observed similarities between the TSWAP problem and multi-agent path finding (MAPF) where instead of tokens we have multiple agents that need to be moved from their current vertices to given unique target vertices. The difference between both problems consists in local conditions that state transitions (swaps/moves) must satisfy. We developed two algorithms for solving TSWAP optimally by adapting two different approaches to MAPF - CBS and MDD- SAT. This constitutes the first attempt to design optimal solving algorithms for TSWAP. Experimental evaluation on various types of graphs shows that the reduction to SAT scales better than CBS in optimal TSWAP solving.
A Hierarchical Deep Learning Natural Language Parser for Fashion
Venue: arXiv cs.IR 2018
Year: 2018

This work presents a hierarchical deep learning natural language parser for fashion. Our proposal intends not only to recognize fashion-domain entities but also to expose syntactic and morphologic insights. We leverage the usage of an architecture of specialist models, each one for a different task (from parsing to entity recognition). Such architecture renders a hierarchical model able to capture the nuances of the fashion language. The natural language parser is able to deal with textual ambiguities which are left unresolved by our currently existing solution. Our empirical results establish a robust baseline, which justifies the use of hierarchical architectures of deep learning models while opening new research avenues to explore.
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
Venue: arXiv cs.CV 2018
Year: 2018

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires sequential data from monocular endoscopic videos and a multi-view stereo reconstruction method, e.g. structure from motion, that supervises learning in a sparse but accurate manner. Consequently, our method requires neither manual interaction, such as scaling or labeling, nor patient CT in the training and application phases. We demonstrate the performance of our method on sinus endoscopy data from two patients and validate depth prediction quantitatively using corresponding patient CT scans where we found submillimeter residual errors.
How to Read a Research Compendium
Venue: arXiv cs.GL 2018
Year: 2018

Researchers spend a great deal of time reading research papers. Keshav (2012) provides a three-pass method to researchers to improve their reading skills. This article extends Keshav's method for reading a research compendium. Research compendia are an increasingly used form of publication, which packages not only the research paper's text and figures, but also all data and software for better reproducibility. We introduce the existing conventions for research compendia and suggest how to utilise their shared properties in a structured reading process. Unlike the original, this article is not build upon a long history but intends to provide guidance at the outset of an emerging practice.
Technology, Propaganda, and the Limits of Human Intellect
Venue: arXiv cs.GL 2018
Year: 2018

"Fake news" is a recent phenomenon, but misinformation and propaganda are not. Our new communication technologies make it easy for us to be exposed to high volumes of true, false, irrelevant, and unprovable information. Future AI is expected to amplify the problem even more. At the same time, our brains are reaching their limits in handling information. How should we respond to propaganda? Technology can help, but relying on it alone will not suffice in the long term. We also need ethical policies, laws, regulations, and trusted authorities, including fact-checkers. However, we will not solve the problem without the active engagement of the educated citizen. Epistemological education, recognition of self biases and protection of our channels of communication and trusted networks are all needed to overcome the problem and continue our progress as democratic societies.
Learning-based Feedback Controller for Deformable Object Manipulation
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present a general learning-based framework to automatically visual-servo control the position and shape of a deformable object with unknown deformation parameters. The servo-control is accomplished by learning a feedback controller that determines the robotic end-effector's movement according to the deformable object's current status. This status encodes the object's deformation behavior by using a set of observed visual features, which are either manually designed or automatically extracted from the robot's sensor stream. A feedback control policy is then optimized to push the object toward a desired featured status efficiently. The feedback policy can be learned either online or offline. Our online policy learning is based on the Gaussian Process Regression (GPR), which can achieve fast and accurate manipulation and is robust to small perturbations. An offline imitation learning framework is also proposed to achieve a control policy that is robust to large perturbations in the human-robot interaction. We validate the performance of our controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo-control for general deformable objects with a wide variety of goal settings.
Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
Venue: arXiv cs.CV 2018
Year: 2018

In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning technique that can remove the noises of the CT images obtained in the low-dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not possible due to the differences in the underlying heart structure in two phases, the images in the two phases are closely related so that we propose a cycle-consistent adversarial denoising network to learn the non-degenerate mapping between the low and high dose cardiac phases. Experimental results showed that the proposed method effectively reduces the noise in the low-dose CT image while the preserving detailed texture and edge information. Moreover, thanks to the cyclic consistency and identity loss, the proposed network does not create any artificial features that are not present in the input images. Visual grading and quality evaluation also confirm that the proposed method provides significant improvement in diagnostic quality.
Adaptive guaranteed-performance consensus design for high-order multiagent systems
Venue: arXiv cs.MA 2018
Year: 2018

The current paper addresses the distributed guaranteed-performance consensus design problems for general high-order linear multiagent systems with leaderless and leader-follower structures, respectively. The information about the Laplacian matrix of the interaction topology or its minimum nonzero eigenvalue is usually required in existing works on the guaranteed-performance consensus, which means that their conclusions are not completely distributed. A new translation-adaptive strategy is proposed to realize the completely distributed guaranteed-performance consensus control by using the structure feature of a complete graph in the current paper. For the leaderless case, an adaptive guaranteed-performance consensualization criterion is given in terms of Riccati inequalities and a regulation approach of the consensus control gain is presented by linear matrix inequalities. Extensions to the leader-follower cases are further investigated. Especially, the guaranteed-performance costs for leaderless and leader-follower cases are determined, respectively, which are associated with the intrinsic structure characteristic of the interaction topologies. Finally, two numerical examples are provided to demonstrate theoretical results.
Discovering Flaws in Security-Focused Static Analysis Tools for Android using Systematic Mutation
Venue: arXiv cs.CR 2018
Year: 2018

Mobile application security has been one of the major areas of security research in the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and performance, and are hence soundy. Unfortunately, the specific unsound choices or flaws in the design of these tools are often not known or well-documented, leading to a misplaced confidence among researchers, developers, and users. This paper proposes the Mutation-based soundness evaluation ($μ$SE) framework, which systematically evaluates Android static analysis tools to discover, document, and fix, flaws, by leveraging the well-founded practice of mutation analysis. We implement $μ$SE as a semi-automated framework, and apply it to a set of prominent Android static analysis tools that detect private data leaks in apps. As the result of an in-depth analysis of one of the major tools, we discover 13 undocumented flaws. More importantly, we discover that all 13 flaws propagate to tools that inherit the flawed tool. We successfully fix one of the flaws in cooperation with the tool developers. Our results motivate the urgent need for systematic discovery and documentation of unsound choices in soundy tools, and demonstrate the opportunities in leveraging mutation testing in achieving this goal.
Cross-position Activity Recognition with Stratified Transfer Learning
Venue: arXiv cs.CV 2018
Year: 2018

Human activity recognition aims to recognize the activities of daily living by utilizing the sensors on different body parts. However, when the labeled data from a certain body position (i.e. target domain) is missing, how to leverage the data from other positions (i.e. source domain) to help learn the activity labels of this position? When there are several source domains available, it is often difficult to select the most similar source domain to the target domain. With the selected source domain, we need to perform accurate knowledge transfer between domains. Existing methods only learn the global distance between domains while ignoring the local property. In this paper, we propose a \textit{Stratified Transfer Learning} (STL) framework to perform both source domain selection and knowledge transfer. STL is based on our proposed \textit{Stratified} distance to capture the local property of domains. STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive experiments on three public activity recognition datasets demonstrate the superiority of STL. Furthermore, we extensively investigate the performance of transfer learning across different degrees of similarities and activity levels between domains. We also discuss the potential applications of STL in other fields of pervasive computing for future research.
Limited Evaluation Evolutionary Optimization of Large Neural Networks
Venue: arXiv cs.NE 2018
Year: 2018

Stochastic gradient descent is the most prevalent algorithm to train neural networks. However, other approaches such as evolutionary algorithms are also applicable to this task. Evolutionary algorithms bring unique trade-offs that are worth exploring, but computational demands have so far restricted exploration to small networks with few parameters. We implement an evolutionary algorithm that executes entirely on the GPU, which allows to efficiently batch-evaluate a whole population of networks. Within this framework, we explore the limited evaluation evolutionary algorithm for neural network training and find that its batch evaluation idea comes with a large accuracy trade-off. In further experiments, we explore crossover operators and find that unprincipled random uniform crossover performs extremely well. Finally, we train a network with 92k parameters on MNIST using an EA and achieve 97.6 % test accuracy compared to 98 % test accuracy on the same network trained with Adam. Code is available at https://github.com/jprellberg/gpuea.
Formal Verification of Usage Control Models: A Case Study of UseCON Using TLA+
Venue: arXiv cs.LO 2018
Year: 2018

Usage control models provide an integration of access control, digital rights, and trust management. To achieve this integration, usage control models support additional concepts such as attribute mutability and continuity of decision. However, these concepts may introduce an additional level of complexity to the underlying model, rendering its definition a cumbersome and prone to errors process. Applying a formal verification technique allows for a rigorous analysis of the interactions amongst the components, and thus for formal guarantees in respect of the correctness of a model. In this paper, we elaborate on a case study, where we express the high-level functional model of the UseCON usage control model in the TLA+ formal specification language, and verify its correctness for <=12 uses in both of its supporting authorisation models.