OpenRaiser/Intern-Atlas huggingface dataset:
Model-Based Cloud Resource Management with TOSCA and OCCI
Venue: arXiv cs.SE 2020
Year: 2020

With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable cloud system. Several standards have been proposed to address this challenge and provide a unified interface to cloud resources. The Open Cloud Computing Interface (OCCI) thereby focuses on the standardization of a common API for Infrastructure-as-a-Service (IaaS) providers while the Topology and Orchestration Specification for Cloud Applications (TOSCA) focuses on the standardization of a template language to enable the proper definition of the topology of cloud applications and their orchestrations on top of a cloud system. TOSCA thereby does not define how the application topologies are created on the cloud. Therefore, we analyse the conceptual similarities between the two approaches and we study how we can integrate them to obtain a complete standard-based approach to manage both cloud infrastructure and cloud application layers. We propose an automated extensive mapping between the concepts of the two standards and we provide TOSCA Studio, a model-driven tool chain for TOSCA that conforms to OCCI. TOSCA Studio allows to graphically design cloud applications as well as to deploy and manage them at runtime using a fully model-driven cloud orchestrator based on the two standards. Our contribution is validated by successfully designing and deploying three cloud applications: WordPress, Node Cellar and Multi-Tier.
Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints
Venue: arXiv cs.CV 2020
Year: 2020

Statistical shape models (SSMs) are state-of-the-art medical image analysis tools for extracting and explaining features across a set of biological structures. However, a principled and robust way to combine shape and pose features has been illusive due to three main issues: 1) Non-homogeneity of the data (data with linear and non-linear natural variation across features), 2) non-optimal representation of the $3D$ motion (rigid transformation representations that are not proportional to the kinetic energy that move an object from one position to the other), and 3) artificial discretization of the models. In this paper, we propose a new framework for dynamic multi-object statistical modelling framework for the analysis of human joints in a continuous domain. Specifically, we propose to normalise shape and dynamic spatial features in the same linearized statistical space permitting the use of linear statistics; we adopt an optimal 3D motion representation for more accurate rigid transformation comparisons; and we provide a 3D shape and pose prediction protocol using a Markov chain Monte Carlo sampling-based fitting. The framework affords an efficient generative dynamic multi-object modelling platform for biological joints. We validate the framework using a controlled synthetic data. Finally, the framework is applied to an analysis of the human shoulder joint to compare its performance with standard SSM approaches in prediction of shape while adding the advantage of determining relative pose between bones in a complex. Excellent validity is observed and the shoulder joint shape-pose prediction results suggest that the novel framework may have utility for a range of medical image analysis applications. Furthermore, the framework is generic and can be extended to n$>$2 objects, making it suitable for clinical and diagnostic methods for the management of joint disorders.
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Venue: arXiv cs.NI 2020
Year: 2020

Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases. This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. The aim is to maximize the time average rate per vehicular user (VUE) while ensuring a target minimum rate for all VUEs with low signaling overhead. We first formulate the user (vehicle) association problem as a discrete non-convex optimization problem. Then, by leveraging tools from machine learning, specifically distributed deep reinforcement learning (DDRL) and the asynchronous actor critic algorithm (A3C), we propose a low complexity algorithm that approximates the solution of the proposed optimization problem. The proposed DDRL-based algorithm endows every road side unit (RSU) with a local RL agent that selects a local action based on the observed input state. Actions of different RSUs are forwarded to a central entity, that computes a global reward which is then fed back to RSUs. It is shown that each independently trained RL performs the vehicle-RSU association action with low control overhead and less computational complexity compared to running an online complex algorithm to solve the non-convex optimization problem. Finally, simulation results show that the proposed solution achieves up to 15\% gains in terms of sum rate and 20\% reduction in VUE outages compared to several baseline designs.
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification
Venue: arXiv cs.LG 2020
Year: 2020

Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep. Meanwhile, for some graph learning scenarios, e.g., nodes with text/image attributes or graphs with long-distance node correlations, deep graph neural networks will be necessary for effective graph representation learning. In this paper, we propose a new graph neural network, namely DIFNET (Graph Diffusive Neural Network), for graph representation learning and node classification. DIFNET utilizes both neural gates and graph residual learning for node hidden state modeling, and includes an attention mechanism for node neighborhood information diffusion. Extensive experiments will be done in this paper to compare DIFNET against several state-of-the-art graph neural network models. The experimental results can illustrate both the learning performance advantages and effectiveness of DIFNET, especially in addressing the "suspended animation problem".
augKlimb: Interactive Data-Led Augmentation of Bouldering Training
Venue: arXiv cs.HC 2020
Year: 2020

Climbing is a popular and growing sport, especially indoors, where climbers can train on man-made routes using artificial holds. Both strength and good technique is required to successfully reach the top of a climb, and often coaches work to improve technique so less strength is required, enabling a climber to ascent more difficult climbs. Various aspects of adding computer-interaction to climbing have been studied in recent years, but there is a large space for research into lightweight tools to aid recreational intermediate climbers, both with trickier climbs and to improve their own technique. In this CS Masters final project, I explored which form of data-capture and output-features could improve a climber's training, and analysed how climbers responded to viewing their data throughout a climbing session, then conducted a user-centred design to build a lightweight mobile application for intermediate climbers. A variety of hardware and software solutions were explored, tested and developed through series of surveys, discussions, wizard-of-oz studies and prototyping, resulting in a system that most closely meets the needs of local indoor boulderers given the project's time scope. This consists of an iteratively developed interactive mobile app that: can record, graph, and score the acceleration of a climber, as both a training tool and gamification incentive for good technique; can link a video recording to the acceleration graph, to enable frame-by-frame inspection of weaknesses; is fully approved and distributed on the Google play Store and currently being regularly used by 15 local climbers. Then I conducted a final usability study, comprising a thematic analysis of forty minutes's worth of interview transcripts, to gain a deep understanding of the app's impact on the climbers using it, along with its benefits and limitations.
On Solving Cooperative MARL Problems with a Few Good Experiences
Venue: arXiv cs.LG 2020
Year: 2020

Cooperative Multi-agent Reinforcement Learning (MARL) is crucial for cooperative decentralized decision learning in many domains such as search and rescue, drone surveillance, package delivery and fire fighting problems. In these domains, a key challenge is learning with a few good experiences, i.e., positive reinforcements are obtained only in a few situations (e.g., on extinguishing a fire or tracking a crime or delivering a package) and in most other situations there is zero or negative reinforcement. Learning decisions with a few good experiences is extremely challenging in cooperative MARL problems due to three reasons. First, compared to the single agent case, exploration is harder as multiple agents have to be coordinated to receive a good experience. Second, environment is not stationary as all the agents are learning at the same time (and hence change policies). Third, scale of problem increases significantly with every additional agent. Relevant existing work is extensive and has focussed on dealing with a few good experiences in single-agent RL problems or on scalable approaches for handling non-stationarity in MARL problems. Unfortunately, neither of these approaches (or their extensions) are able to address the problem of sparse good experiences effectively. Therefore, we provide a novel fictitious self imitation approach that is able to simultaneously handle non-stationarity and sparse good experiences in a scalable manner. Finally, we provide a thorough comparison (experimental or descriptive) against relevant cooperative MARL algorithms to demonstrate the utility of our approach.
Tuneful: An Online Significance-Aware Configuration Tuner for Big Data Analytics
Venue: arXiv cs.DC 2020
Year: 2020

Distributed analytics engines such as Spark are a common choice for processing extremely large datasets. However, finding good configurations for these systems remains challenging, with each workload potentially requiring a different setup to run optimally. Using suboptimal configurations incurs significant extra runtime costs. %Furthermore, Spark and similar platforms are gaining traction within data-scientists communities where awareness of such issues is relatively low. We propose Tuneful, an approach that efficiently tunes the configuration of in-memory cluster computing systems. Tuneful combines incremental Sensitivity Analysis and Bayesian optimization to identify near-optimal configurations from a high-dimensional search space, using a small number of executions. This setup allows the tuning to be done online, without any previous training. Our experimental results show that Tuneful reduces the search time for finding close-to-optimal configurations by 62\% (at the median) when compared to existing state-of-the-art techniques. This means that the amortization of the tuning cost happens significantly faster, enabling practical tuning for new classes of workloads.
Optimal Multistage Group Testing Algorithm for 3 Defectives
Venue: arXiv cs.IT 2020
Year: 2020

Group testing is a well-known search problem that consists in detecting of $s$ defective members of a set of $t$ samples by carrying out tests on properly chosen subsets of samples. In classical group testing the goal is to find all defective elements by using the minimal possible number of tests in the worst case. In this work, a multistage group testing problem is considered. Our goal is to construct a multistage search procedure, having asymptotically the same number of tests as the optimal adaptive algorithm. We propose a new approach to designing multistage algorithms, which allows us to construct a 5-stage algorithm for finding 3 defectives with the optimal number $3\log_2t(1+o(1))$ of tests.
Security and Privacy in Vehicular Social Networks
Venue: arXiv cs.CR 2020
Year: 2020

We surveyed and presented the state-of-the-art VC systems, security and privacy architectures and technologies, emphasizing on security and privacy challenges and their solutions for P2P interactions in VSNs towards standardization and deployment. We note that beyond safety applications that have drawn a lot of attention in VC systems, there is significant and rising interest in vehicle-to-vehicle interaction for a range of transportation efficiency and infotainment applications, notably LBS as well as a gamut of services by mobile providers. While this enriches the VC systems and the user experience, security and privacy concerns are also intensified. This is especially so, considering (i) the privacy risk from the exposure of the users to the service providers, and (ii) the security risk from the interaction with malicious or selfish and thus misbehaving users or infrastructure. We showed existing solutions can in fact evolve and address the VSN-specific challenges, and improve or even accelerate the adoption of VSN applications.
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset
Venue: arXiv cs.CE 2020
Year: 2020

Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In this research, a test-proven deep learning architecture is applied to predict the performance under various restrictions and search for better shapes by optimizing the learned prediction function. The major challenge is the vast amount of data points Deep Neural Network (DNN) demands, which is improvident to simulate. To remedy this drawback, a Frequentist active learning is used to explore regions of the output space that DNN predicts promising. This operation reduces the number of data samples demanded from ~8000 to 625. The final stage, a user interface, made the model capable of optimizing with given user input of minimum area and viscosity. Flood fill is used to define a boundary area function so that the optimal shape does not bypass the minimum area. Stochastic Gradient Langevin Dynamics (SGLD) is employed to make sure the ultimate shape is optimized while circumventing the required area. Jointly, shapes with extremely low drags are found explored by a practical user interface with no human domain knowledge and modest computation overhead.
Pruning CNN's with linear filter ensembles
Venue: arXiv cs.LG 2020
Year: 2020

Despite the promising results of convolutional neural networks (CNNs), their application on devices with limited resources is still a big challenge; this is mainly due to the huge memory and computation requirements of the CNN. To counter the limitation imposed by the network size, we use pruning to reduce the network size and -- implicitly -- the number of floating point operations (FLOPs). Contrary to the filter norm method -- used in ``conventional`` network pruning -- based on the assumption that a smaller norm implies ``less importance'' to its associated component, we develop a novel filter importance norm that is based on the change in the empirical loss caused by the presence or removal of a component from the network architecture. Since there are too many individual possibilities for filter configuration, we repeatedly sample from these architectural components and measure the system performance in the respective state of components being active or disabled. The result is a collection of filter ensembles -- filter masks -- and associated performance values. We rank the filters based on a linear and additive model and remove the least important ones such that the drop in network accuracy is minimal. We evaluate our method on a fully connected network, as well as on the ResNet architecture trained on the CIFAR-10 dataset. Using our pruning method, we managed to remove $60\%$ of the parameters and $64\%$ of the FLOPs from the ResNet with an accuracy drop of less than $0.6\%$.
Managing IEC 61850 Message Exchange for SDN-Controlled Cognitive Communication Resource Allocation in the Smart Grid
Venue: arXiv cs.NI 2020
Year: 2020

The IEC 61850 standard is being largely used in the Smart Grid (SG) context mainly due to its ability to address communication, interoperability and migration issues. IEC 61850 currently aims at internal substation communication. Nevertheless, there is a demand to generalize its use for distributed SG systems like Home Energy Management Systems (HEMS) and Advanced Monitoring Infrastructure (AMI) Communication which potentially involves distributed substations or distributed SG components. IEC 61850- based systems require constrained timing requirements for communication and the common approach is to allocate static link bandwidth resources leading in some cases to over dimensioning. This paper presents the Substation Cognitive Communication Resource Management (IC2RM), that aims the management of bandwidth allocation for IEC messages using a cognitive approach for its provisioning and the SDN/OpenFlow for its deployment. By dynamically deploying bandwidth for IEC messages, IC2RM optimizes links between SG substations and systems and potentially reduces the operational costs (OPEX).
A Power-Efficient Audio Acquisition System for Smart City Applications
Venue: arXiv cs.NI 2020
Year: 2020

Acoustic noise has adverse effects on human activities. Aside from hearing impairment and stress-related illnesses, it can also interfere with spoken communication, reduce human performance and affect the quality of life. As urbanization is intensifying, the potential benefits of reducing noise pollution in smart-city environments are extensive. Noise levels can be collected and analyzed using a wireless sensor network which can monitor the noise level by using microphones. However, every wireless system struggles in terms of the battery requirements needed for continuous data collection and monitoring. In this paper, the design of a testbed for a smart microphone system is presented. To save power, a microcontroller and an Analog-to-Digital Converter (ADC) dynamically switch between high and low power modes in response to environmental noise. Specifically, the high powered components are triggered by a spike in the acoustic noise level. Three wireless technologies, WiFi (2.4 GHz), Bluetooth Low Energy (BLE) 4.0 and Zigbee were examined. According to the results, the power consumption of a node can be lowered by 97% when idle based on the testbed.
Opportunities and Limitations of Mixed Reality Holograms in Industrial Robotics
Venue: arXiv cs.RO 2020
Year: 2020

This paper introduces two case studies combining the field of industrial robotics with Mixed Reality (MR). The goal of those case studies is to get a better understanding of how MR can be useful and what are the limitations. The first case study describes an approach to visualize the digital twin of a robot arm. The second case study aims at facilitating the commissioning of industrial robots. Furthermore, this paper reports the experiences gained by implementing those two scenarios and discusses the limitations.
Design and Implementation of a Multiprotocol IoT Gateway
Venue: arXiv cs.NI 2020
Year: 2020

In the coming years, the interconnection of a large number of devices is expected, which will lead to a new form of interaction between the real and the virtual world. In this promising scenario, known as the Internet of Things, it is expected that different objects, such as sensors, industrial robots, cars, appliances, will be connected to the Internet. One of the main challenges of the Internet of Things is the interoperability of highly heterogeneous devices, mainly in terms of the communication capabilities and network protocols used. As consequence, the interconnection model of the different devices involves an intermediary device, known as gateway. This gateway is a centralized element for the management of the devices that make up an IoT application. In addition, it is essential for the transmission of information to the Internet, especially when many IoT devices are not IP-based. This paper describes the implementation of an IoT gateway that allows the exchange of data through different wireless technologies and forwarding of such data to the Internet. The proposed gateway has important advantages such as: supporting for multiprotocol interconnectivity; remote configuration of wireless nodes for sensor and actuators management; a flexible algorithm to translate the data obtained by sensors into a uniform format for transmission to a cloud server; low energy consumption due to efficient data transfer over the MQTT protocol. In order to demonstrate the usefulness of the developed gateway, a proof-of-concept test was implemented. The implemented scenario consists of 2 wireless nodes responsible for sensing environmental variables and transmitting data to the gateway node through different communication protocols. The obtained results show the feasibility for simultaneous data transmission from the remote wireless nodes to the gateway.
SDN-controlled and Orchestrated OPSquare DCN Enabling Automatic Network Slicing with Differentiated QoS Provisioning
Venue: arXiv cs.NI 2020
Year: 2020

In this work, we propose and experimentally assess the automatic and flexible NSs configurations of optical OPSquare DCN controlled and orchestrated by an extended SDN control plane for multi-tenant applications with differentiated QoS provisioning. Optical Flow Control (OFC) protocol has been developed to prevent packet losses at switch sides caused by packet contentions.Based on the collected resource topology of data plane, the optical network slices can be dynamically provisioned and automatically reconfigured by the SDN control plane. Meanwhile, experimental results validate that the priority assignment of application flows supplies dynamic QoS performance to various slices running applications with specific requirements in terms of packet loss and transmission latency. In addition, the capability of exposing traffic statistics information of data plane to SDN control plane enables the implementation of load balancing algorithms further improving the network performance with high QoS. No packet loss and less than 4.8 us server-to-server latency can be guaranteed for the sliced network with highest priority at a load of 0.5.
Discovering Salient Anatomical Landmarks by Predicting Human Gaze
Venue: arXiv cs.CV 2020
Year: 2020

Anatomical landmarks are a crucial prerequisite for many medical imaging tasks. Usually, the set of landmarks for a given task is predefined by experts. The landmark locations for a given image are then annotated manually or via machine learning methods trained on manual annotations. In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images. Specifically, we consider landmarks that attract the visual attention of humans, which we term visually salient landmarks. We illustrate the method for fetal neurosonographic images. First, full-length clinical fetal ultrasound scans are recorded with live sonographer gaze-tracking. Next, a convolutional neural network (CNN) is trained to predict the gaze point distribution (saliency map) of the sonographers on scan video frames. The CNN is then used to predict saliency maps of unseen fetal neurosonographic images, and the landmarks are extracted as the local maxima of these saliency maps. Finally, the landmarks are matched across images by clustering the landmark CNN features. We show that the discovered landmarks can be used within affine image registration, with average landmark alignment errors between 4.1% and 10.9% of the fetal head long axis length.