OpenRaiser/Intern-Atlas huggingface dataset:
From Bare Metal to Virtual: Lessons Learned when a Supercomputing Institute Deploys its First Cloud
Venue: arXiv cs.DC 2018
Year: 2018

As primary provider for research computing services at the University of Minnesota, the Minnesota Supercomputing Institute (MSI) has long been responsible for serving the needs of a user-base numbering in the thousands. In recent years, MSI---like many other HPC centers---has observed a growing need for self-service, on-demand, data-intensive research, as well as the emergence of many new controlled-access datasets for research purposes. In light of this, MSI constructed a new on-premise cloud service, named Stratus, which is architected from the ground up to easily satisfy data-use agreements and fill four gaps left by traditional HPC. The resulting OpenStack cloud, constructed from HPC-specific compute nodes and backed by Ceph storage, is designed to fully comply with controls set forth by the NIH Genomic Data Sharing Policy. Herein, we present twelve lessons learned during the ambitious sprint to take Stratus from inception and into production in less than 18 months. Important, and often overlooked, components of this timeline included the development of new leadership roles, staff and user training, and user support documentation. Along the way, the lessons learned extended well beyond the technical challenges often associated with acquiring, configuring, and maintaining large-scale systems.
Training Humans and Machines
Venue: arXiv cs.NE 2018
Year: 2018

For many years, researchers in psychology, education, statistics, and machine learning have been developing practical methods to improve learning speed, retention, and generalizability, and this work has been successful. Many of these methods are rooted in common underlying principles that seem to drive learning and overlearning in both humans and machines. I present a review of a small part of this work to point to potentially novel applications in both machine and human learning that may be worth exploring.
Joint Offloading and Resource Allocation in Vehicular Edge Computing and Networks
Venue: arXiv cs.NI 2018
Year: 2018

The emergence of computation intensive on-vehicle applications poses a significant challenge to provide the required computation capacity and maintain high performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a high potential to improve vehicular services by offloading computation-intensive tasks to the VEC servers. Nevertheless, as the computation resource of each VEC server is limited, offloading may not be efficient if all vehicles select the same VEC server to offload their tasks. To address this problem, in this paper, we propose offloading with resource allocation. We incorporate the communication and computation to derive the task processing delay. We formulate the problem as a system utility maximization problem, and then develop a low-complexity algorithm to jointly optimize offloading decision and resource allocation. Numerical results demonstrate the superior performance of our Joint Optimization of Selection and Computation (JOSC) algorithm compared to state of the art solutions.
Random Sampling Applied to the MST Problem in the Node Congested Clique Model
Venue: arXiv cs.DS 2018
Year: 2018

The Congested Clique model proposed by Lotker et al.[SICOMP'05] was introduced in order to provide a simple abstraction for overlay networks. Congested Clique is a model of distributed (or parallel) computing, in which there are $n$ players with unique identifiers from set [n], which perform computations in synchronous rounds. Each round consists of the phase of unlimited local computation and the communication phase. While communicating, each pair of players is allowed to exchange a single message of size $O(\log n)$ bits. Since, in a single round, each player can communicate with even $Θ(n)$ other players, the model seems to be to powerful to imitate bandwidth restriction emerging from the underlying network. In this paper we study a restricted version of the Congested Clique model, the Node Congested Clique (NCC) model, proposed by Augustine et al.[arxiv1805], in which a player is allowed to send/receive only $O(\log n)$ messages per communication phase. More precisely, we provide communication primitives that improve the round complexity of the MST algorithm by Augustine et al. [arxiv1805] to $O(\log^3 n)$ rounds, and give an $O(\log^2 n)$ round algorithm solving the Spanning Forest (SF) problem. Furthermore, we present an approach based on the random sampling technique by Karger et al.[JACM'95] that gives an $O(\log^2 n \log Δ/ \log \log n)$ round algorithm for the Minimum Spanning Forest (MSF) problem. Besides the faster SF/ MSF algorithms we consider the key contributions to be - an efficient implementation of basic protocols in the NCC model - a tighter analysis of a special case of the sampling approach by Karger et al.[JACM'95] and related results by Pemmaraju and Sardeshmukh [FSTTCS'16] - efficient k-sparse recovery data structure that requires $O((k +\log n)\log n\log k)$ bits and provides recovery procedure that requires $O((k +\log n)\log k)$ steps
Identity Preserving Face Completion for Large Ocular Region Occlusion
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel deep learning approach to synthesize complete face images in the presence of large ocular region occlusions. This is motivated by recent surge of VR/AR displays that hinder face-to-face communications. Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity. At the core of our method is a novel generative network with dedicated constraints to regularize the synthesis process. To preserve the identity, our network takes an arbitrary occlusion-free image of the target identity to infer the missing content, and its high-level CNN features as an identity prior to regularize the searching space of generator. Since the input reference image may have a different pose, a pose map and a novel pose discriminator are further adopted to supervise the learning of implicit pose transformations. Our method is capable of generating coherent facial inpainting with consistent identity over videos with large variations of head motions. Experiments on both synthesized and real data demonstrate that our method greatly outperforms the state-of-the-art methods in terms of both synthesis quality and robustness.
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Venue: arXiv cs.CV 2018
Year: 2018

While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limits the use of depth prediction in augmented and virtual reality applications, that aim at scene exploration by synthesizing the scene from a different vantage point, or at diminished reality. To address this issue, we shift the focus from conventional depth map prediction to the regression of a specific data representation called Layered Depth Image (LDI), which contains information about the occluded regions in the reference frame and can fill in occlusion gaps in case of small view changes. We propose a novel approach based on Convolutional Neural Networks (CNNs) to jointly predict depth maps and foreground separation masks used to condition Generative Adversarial Networks (GANs) for hallucinating plausible color and depths in the initially occluded areas. We demonstrate the effectiveness of our approach for novel scene view synthesis from a single image.
One for All, All for One: A Heterogeneous Data Plane for Flexible P4 Processing
Venue: arXiv cs.DC 2018
Year: 2018

The P4 community has recently put significant effort to increase the diversity of targets on which P4 programs can be implemented. These include fixed function and programmable ASICs, FPGAs, NICs, and CPUs. However, P4 programs are written according to the set of functionalities supported by the target for which they are compiled. For instance, a P4 program targeting a programmable ASIC cannot be extended with user-defined processing modules, which limits the flexibility and the abstraction of P4 programs. To address these shortcomings, we propose a heterogeneous P4 programmable data plane comprised of different targets that together appear as a single logical unit. The proposed data plane broadens the range of functionalities available to P4 programmers by combining the strength of each target. We demonstrate the feasibility of the proposed P4 data plane by coupling an FPGA with a soft switch which emulates a programmable ASIC. The proposed data plane is demonstrated with the implementation of a simplified L2 switch. The emulated ASIC match-table capacity is extended by the FPGA by an order of magnitude.The FPGA also integrates a proprietary module using a P4 extern.
Traffic-Aware Backscatter Communications in Wireless-Powered Heterogeneous Networks
Venue: arXiv cs.IT 2018
Year: 2018

With the emerging Internet-of-Things services, massive machine-to-machine (M2M) communication will be deployed on top of human-to-human (H2H) communication in the near future. Due to the coexistence of M2M and H2H communications, the performance of M2M (i.e., secondary) network depends largely on the H2H (i.e., primary) network. In this paper, we propose ambient backscatter communication for the M2M network which exploits the energy (signal) sources of the H2H network, referring to traffic applications and popularity. In order to maximize the harvesting and transmission opportunities offered by varying traffic sources of the H2H network, we adopt a Bayesian nonparametric (BNP) learning algorithm to classify traffic applications (patterns) for secondary user (SU). We then analyze the performance of SU using the stochastic geometrical approach, based on a criterion for optimal traffic pattern selection. Results are presented to validate the performance of the proposed BNP classification algorithm and the criterion, as well as the impact of traffic sources and popularity.
Anomaly Detection of Complex Networks Based on Intuitionistic Fuzzy Set Ensemble
Venue: arXiv cs.SI 2018
Year: 2018

Ensemble learning for anomaly detection of data structured into complex network has been barely studied due to the inconsistent performance of complex network characteristics and lack of inherent objective function. In this paper, we propose the IFSAD, a new two-phase ensemble method for anomaly detection based on intuitionistic fuzzy set, and applies it to the abnormal behavior detection problem in temporal complex networks. First, it constructs the intuitionistic fuzzy set of single network characteristic which quantifies the degree of membership, non-membership and hesitation of each of network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose an Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistent of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally we carried out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
The model of methodical system and learning objectives of the foundations of mathematical informatics for students of technical universities
Venue: arXiv cs.CY 2018
Year: 2018

Introduction. Development of methodical system for training course "Foundations of mathematical informatics" plays a key role in forming the students' of technical universities competencies in mathematical informatics. So it is very important to analyze of the components of methodical system, identify the weaknesses and problems that can significantly impair its quality and which can not be overcome without its further development. Purpose. Develop a model of methodical system of training course "Foundations of mathematical informatics" for students of technical universities and specify its target component. Methods. Using cloud technologies at learning the foundations of mathematical informatics requires the construction technology training, which results in the selection of appropriate cloudoriented forms of organization and teaching methods. On the other hand, the theory, methods and tools for cloud significantly affect the primary content of learning and its goals. Thus, the cloud technology theory, methods and tools are the basis for constructing methodical system of training course "Foundations of mathematical informatics". Results. Development of methodical system of training course "Foundations of mathematical informatics" plays a leading role in forming the students' of technical universities competencies in mathematical informatics due to its fundamental impact and technology improvement. Conclusion. The model of methodical system of training course "Foundations of mathematical informatics" includes content, objectives and learning technology. The last one contains forms of organization, methods and teaching tools, including leading cloud technologies. The main purpose of training course "Foundations of mathematical informatics" is developing of students competencies of technical universities in mathematical informatics.
Example Mining for Incremental Learning in Medical Imaging
Venue: arXiv cs.CV 2018
Year: 2018

Incremental Learning is well known machine learning approach wherein the weights of the learned model are dynamically and gradually updated to generalize on new unseen data without forgetting the existing knowledge. Incremental learning proves to be time as well as resource-efficient solution for deployment of deep learning algorithms in real world as the model can automatically and dynamically adapt to new data as and when annotated data becomes available. The development and deployment of Computer Aided Diagnosis (CAD) tools in medical domain is another scenario, where incremental learning becomes very crucial as collection and annotation of a comprehensive dataset spanning over multiple pathologies and imaging machines might take years. However, not much has so far been explored in this direction. In the current work, we propose a robust and efficient method for incremental learning in medical imaging domain. Our approach makes use of Hard Example Mining technique (which is commonly used as a solution to heavy class imbalance) to automatically select a subset of dataset to fine-tune the existing network weights such that it adapts to new data while retaining existing knowledge. We develop our approach for incremental learning of our already under test model for detecting dental caries. Further, we apply our approach to one publicly available dataset and demonstrate that our approach reaches the accuracy of training on entire dataset at once, while availing the benefits of incremental learning scenario.
Dermoscopic Image Analysis for ISIC Challenge 2018
Venue: arXiv cs.CV 2018
Year: 2018

This short paper reports the algorithms we used and the evaluation performances for ISIC Challenge 2018. Our team participates in all the tasks in this challenge. In lesion segmentation task, the pyramid scene parsing network (PSPNet) is modified to segment the lesions. In lesion attribute detection task, the modified PSPNet is also adopted in a multi-label way. In disease classification task, the DenseNet-169 is adopted for multi-class classification.
Expansion-Based QBF Solving Without Recursion
Venue: arXiv cs.LO 2018
Year: 2018

In recent years, expansion-based techniques have been shown to be very powerful in theory and practice for solving quantified Boolean formulas (QBF), the extension of propositional formulas with existential and universal quantifiers over Boolean variables. Such approaches partially expand one type of variable (either existential or universal) and pass the obtained formula to a SAT solver for deciding the QBF. State-of-the-art expansion-based solvers process the given formula quantifier-block wise and recursively apply expansion until a solution is found. In this paper, we present a novel algorithm for expansion-based QBF solving that deals with the whole quantifier prefix at once. Hence recursive applications of the expansion principle are avoided. Experiments indicate that the performance of our simple approach is comparable with the state of the art of QBF solving, especially in combination with other solving techniques.
Free software in electronic learning future teachers of mathematics, physics and computer science
Venue: arXiv cs.CY 2018
Year: 2018

Popularity of the use of free software in the IT industry is much higher than its popular use in educational activities. Disadvantages of free software and problems of its implementation in the educational process is a limiting factor for its use in the education system, however, openness, accessibility and functionality are the main factors for the introduction of free software in the educational process. Nevertheless, for future teachers of mathematics, physics and informatics free software is designed as well as possible because of the specificity of its creation, and therefore, there is a question of the system analysis of the possibilities of using open source software in e-learning for future teachers of mathematics, physics and computer science.
Implementation strategy of free software in the process of preparation of teachers of mathematics, physics and computer science
Venue: arXiv cs.CY 2018
Year: 2018

Information processes in the society encourage the formation of a revision of the forms and methods of learning; involve the use of didactic capabilities of information and communication technologies in teaching. No less important in this context, the problem of professionals training who are able to use modern possibilities of computer technology. Training of highly qualified teachers is only possible using advanced technologies that cover the entire range of existing opportunities. The analysis used in the formation of the software has showed insufficient use of a whole class of software - free software in the educational process. To overcome this problem, the proposed implementation strategy of free software in the preparation of teachers of mathematics, physics and computer science is proposed.
Slots-Memento : A System Facilitating Intergenerational Story Sharing and Preservation of Family Mementos
Venue: arXiv cs.HC 2018
Year: 2018

Family mementos document events shaping family life, telling a story within and between family members. The elderly collected some mementos for children, but never recorded stories related to those objects. In this paper, in order to understand the status quo of memento storytelling and sharing of elderly people, contextual inquiry was conducted, which further helped us to identify design opportunities and requirements. Resulting design was defined after brainstorm and user consultation, which was Slots- Memento, a system consisting a slot machine-like device used by the elderly and a flash drive used by the young. The Slots machine-like device utilizes with the metaphor of slots machine, which integrates functions of memento photo displaying, story recording, and preservation. In the flash disk, the young could copy memento photos to it. The system aims to facilitate memento story sharing and preservation within family members. Preliminary evaluation and user test were conducted in evaluation section, the results showed that Slots-Memento was understood and accepted by the elderly users. Photos of mementos were easy to recall memories. It enabled the elderly people to be aware of the stories of the family mementos, as well as aroused their desire to share them with family members. Related research methodology includes contextual inquiry, brainstorming, prototyping, scenario creation, and user test.
Symplectic Isometries of Stabilizer Codes
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we study the equivalence of quantum stabilizer codes via symplectic isometries of stabilizer codes. We define monomially and symplectically equivalent stabilizer codes and determine how different the two notions can be. Further, we show that to monomial maps correspond local Clifford operators. We relate the latter with the LU-LC Conjecture.
Finite Time Adaptive Stabilization of LQ Systems
Venue: arXiv cs.SY 2018
Year: 2018

Stabilization of linear systems with unknown dynamics is a canonical problem in adaptive control. Since the lack of knowledge of system parameters can cause it to become destabilized, an adaptive stabilization procedure is needed prior to regulation. Therefore, the adaptive stabilization needs to be completed in finite time. In order to achieve this goal, asymptotic approaches are not very helpful. There are only a few existing non-asymptotic results and a full treatment of the problem is not currently available. In this work, leveraging the novel method of random linear feedbacks, we establish high probability guarantees for finite time stabilization. Our results hold for remarkably general settings because we carefully choose a minimal set of assumptions. These include stabilizability of the underlying system and restricting the degree of heaviness of the noise distribution. To derive our results, we also introduce a number of new concepts and technical tools to address regularity and instability of the closed-loop matrix.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon. A large body of previous research studied long-term recommendation through dimensionality reduction techniques applied to the historical user-item interactions. A recently introduced session-based recommendation setting highlighted the importance of modeling short-term user preferences. In this task, Recurrent Neural Networks (RNN) have shown to be successful at capturing the nuances of user's interactions within a short time window. In this paper, we evaluate RNN-based models on both short-term and long-term recommendation tasks. Our experimental results suggest that RNNs are capable of predicting immediate as well as distant user interactions. We also find the best performing configuration to be a stacked RNN with layer normalization and tied item embeddings.
Residual Network based Aggregation Model for Skin Lesion Classification
Venue: arXiv cs.CV 2018
Year: 2018

We recognize that the skin lesion diagnosis is an essential and challenging sub-task in Image classification, in which the Fisher vector (FV) encoding algorithm and deep convolutional neural network (DCNN) are two of the most successful techniques. Since the joint use of FV and DCNN has demonstrated proven success, the joint techniques could have discriminatory power on skin lesion diagnosis as well. To this hypothesis, we propose the aggregation algorithm for skin lesion diagnosis that utilize the residual network to extract the local features and the Fisher vector method to aggregate the local features to image-level representation. We applied our algorithm on the International Skin Imaging Collaboration 2018 (ISIC2018) challenge and only focus on the third task, i.e., the disease classification.