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3rd International conference on Smart Energy Systems and Technologies (SEST)

作者： Dias, Tiago Sampaio, Pedro Cardoso, Diogo Prates, Claudio Jesus, Ricardo Costa, Tiago Inst Politecn Lisboa IPL Inst Super Engn Lisboa ISEL Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781728147017

This paper presents an open-source wireless Smart Power Outlet (SPO) system to improve the control and monitoring of household appliances in IoT-based smart homes. The system supports not only the most typical SPO functionalities, such as remote on/off control or real-time monitoring of the current consumption, but also other important functionalities, like programming of the power supply time schedule, automatic interruption of vampire currents, or prevention of electrocutions. Moreover, the devised system architecture allows to connect multiple SPOs, even with different capabilities, in an ieee 802.15.4 Sub-1 GHz Personal Area network (PAN). This network is managed by a Controller that also acts as a border router, providing remote access to the SPOs. A prof-of-concept prototype of the system has been implemented and tested. The considered SPO and Controller were based on SimpleLink Sub-1 GHz CC1310 wireless microcontrollers running the Contiki OS. The obtained results demonstrate the basic functionalities offered by the proposed open-source SPO and suggest that it can be used to make the next generation of homes smarter at lower costs.

关键词： IoT Smart home Power outlet system CoAP ieee 802.15.4 Contiki OS

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Non-architectural improvements for ECG classification using deep neural network

Non-architectural improvements for ECG classification using ...

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Ivannikov Ispras open conference (ISPRAS)

作者： Andreev, Pavel Ananev, Vladislav Avetisyan, Aram Makarov, Vladimir Gliner, Vadim Schuster, Assaf Karpulevich, Evgeny Russian Acad Sci Ivannikov Inst Syst Programming Moscow Russia Novgorod State Univ Veliky Novgorod Russia Kurchatov Inst Natl Res Ctr Moscow Russia Moscow Inst Phys & Technol Genome Technol & Bioinfonnat Res Ctr Dolgoprudnyi Russia Technion IIT Comp Sci Dept Haifa Israel Skolkovo Inst Sci & Technol Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia Lomonosov Moscow State Univ Moscow Russia

ISBN: (纸本)9781665412919

Due to the latest advances in machine learning algorithms new deep learning-based approaches to the interpretation of 12-lead electrocardiograms have been developed, demonstrating the quality of diagnostics comparable to the expert one. In this paper, we propose several techniques increasing the quality of ECG classification by a deep neural network. The techniques include patient metadata incorporation, signal denoising and self-adaptive model training. The experimental validation of the approaches was carried out on a novel dataset containing 64198 standard ECG recordings obtained during routine medical practice. The conducted experiments demonstrated statistically significant quality growth compared to the baseline, supporting the further application of our findings.

关键词： ECG Classification Convolutional Neural network Deep Learning Denoising Self-Adaptive Learning

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Enriching programming Student Feedback with Audio Comments 42

Enriching Programming Student Feedback with Audio Comments

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42nd ACM/ieee International conference on Software Engineering - Companion Proceedings (ICSE-Companion) / 42nd ACM/ieee International conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Renzella, Jake Cain, Andrew Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9781450371247

Introductory programming is challenging for many students, requiring them to engage with a deep approach to learning concepts in order to succeed. These challenges compound for online students who do not have direct face-to-face interactions with teaching staff. With the growing demand for online education, we need to examine approaches that assist in building supportive learning environments for these students. A growing body of work from other education disciplines indicates that audio feedback provides an opportunity for developing stronger relationships with students. Further studies recommend an integrated implementation of audio recording into the virtual learning environment. To evaluate audio feedback for use in programming education, we developed an integrated, cross-browser audio feedback feature into the open-source Doubtfire learning management system. Doubtfire is used to support and scale a task-oriented teaching and learning system built upon the principles of constructive alignment and has been shown to help students engage with programming concepts in campus-only units. Our findings from experimental and observational activities indicate that programming tutors can use a blended approach of audio and text feedback via the learning management system to better support student learning. The blended approach provides more nuanced feedback, conveying personality and feelings of connectedness to students, while retaining the benefits of specificity for code-specific issues.

关键词： Learning management system introductory programming formative feedback audio feedback online students

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Architecture of Segmentation Service of Software Defined networks 9

Architecture of Segmentation Service of Software Defined Net...

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9th Mediterranean conference on Embedded Computing (MECO)

作者： Perepelkin, Dmitry Tsyganov, Ilya Ivanchikova, Maria Ryazan State Radio Engn Univ Fac Comp Engn Ryazan Russia

ISBN: (纸本)9781728169491

Software defined networks (SDN) - is an innovative network architecture that provides centralized management of networks with a complex structure SDN consists of three layers: the infrastructure layer of network equipment, the network management layer (represented by SDN controllers) and the application layer. SDN controllers provide open programming interfaces that allow network applications to manage the network. Architecture and software implementation of the service for the development and implementation of SDN segmentation algorithms are proposed in paper. The aim of creating the presented service is to optimize the network resources usage through the use of an additional layer of abstraction - a layer of network segments.

关键词： software defined networks network services segmentation architecture controller northbound interface

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Balancing Data Freshness and Distortion in Real-time Status Updating with Lossy Compression

Balancing Data Freshness and Distortion in Real-time Status ...

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39th ieee International conference on Computer Communications (ieee INFOCOM)

作者： Hu, Shaoling Chen, Wei Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781728186955

Real-time status updating plays a pivotal role in automated control, situational awareness, and networked monitoring. However, how to balance data freshness and its distortion due to lossy compression remains open. In this paper, we are interested in the optimal cross-layer policy that minimizes the Age-of-Information (AoI) and distortion simultaneously over an ON/OFF channel. Specifically, we formulate a hierarchical framework to jointly schedule the lossy compression in the application layer and the packet scheduling in the physical layer. In the application layer, we characterize the compression loss using age or forgetting factor weighted distortion. The optimal tradeoff between the age reduction and compression loss is then revealed via convex optimizations. It allows us to further determine how many packets to send in the physical layer based on a probabilistic scheduling policy. To this end, a Constrained Markov Decision Process (CMDP) problem is formulated and solved by Linear programming (LP), which gives the optimal tradeoff between the data freshness and distortion, as well as, the optimal cross-layer strategy to strike the AoI-distortion tradeoff.

关键词： Linear programming

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NCSbench Demo: Reproducible Benchmarking Platform for networked Control Systems 17

NCSbench Demo: Reproducible Benchmarking Platform for Networ...

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ieee 17th Annual Consumer Communications and networking conference (CCNC)

作者： Zoppi, Samuele Ayan, Onur Molinari, Fabio Music, Zenit Gallenmiiller, Sebastian Carle, Georg Kellerer, Wolfgang TUM Dept Elect & Comp Engn Chair Commun Networks Munich Germany TU Berlin Control Syst Grp Dept Elect Engn & Comp Sci Berlin Germany TUM Dept Informat Chair Network Architectures & Serv Munich Germany

ISBN: (纸本)9781728138930

Cyber-Physical Systems (CPS) are widely spreading thanks to fast-paced technological breakthroughs of micro-controllers and communication networks. Among them, networked Control Systems (NCS) gained notable attention thanks to their application in industrial operations. In NCS, the interconnection of a control logic with sensors and actuators used to steer a physical system occurs over a communication network. Despite large research interest on NCS, the reproducibility and comparison of experimental results are difficult to achieve. This is caused by the lack of well-established models and methodologies that combine the theoretical and practical aspects of NCS. We tackle this problem by proposing and demonstrating NCSbench: the first open-source reproducible benchmarking platform for NCS. NCSbench enables the benchmarking of research experiments using a networked two-wheeled inverted pendulum robot. For each benchmarking experiment, a set of values is measured and used to quantify the key performance indicators (KPIs) of the NCS. In our demonstration, we visualize in real-time the evolution of the benchmarking KPIs on a web-based Graphical User Interface.

关键词： CPS NCS open-source Benchmarking Reproducibility KPI QoS Delay Packet loss QoC Real-time GUI

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Towards Pulverised architectures for Collective Adaptive Systems through Multi-Tier programming

Towards Pulverised Architectures for Collective Adaptive Sys...

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Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), ieee International conference on

作者： Gianluca Aguzzi Roberto Casadei Danilo Pianini Guido Salvaneschi Mirko Viroli Alma Mater STUDIORUM–Università di Bologna Cesena Italy University of St.Gallen St.Gallen Switzerland

Engineering large-scale Cyber-Physical Systems - like robot swarms, augmented crowds, and smart cities - is challenging, for many issues have to be addressed, including specifying their collective adaptive behaviour and managing the connection of the digital and physical parts. In particular, some approaches propose self-organising mechanisms to actually program global behaviour while fostering decentralised, asynchronous execution. However, most of these approaches couple behavioural specifications to specific network architectures (e.g., peer-to-peer), and therefore do not promote flexible exploitation of the underlying infrastructure. Conversely, pulverisation is a recent approach that enables self-organising behaviour to be defined independently of the available infrastructure while retaining functional correctness. However, there are currently no tools to formally specify and verify concrete architectures for pulverised applications. Therefore, we propose to combine pulverisation with multi-tier programming, a paradigm that supports the specification of the architecture of distributed systems in a single code base, and enables static checks for the correctness of actual deployments. The approach can be implemented by combining the ScaFi aggregate computing toolchain with the ScalaLoci multi-tier programming language, paving the path to support the development of self-organising cyber-physical systems, addressing both functional (behaviour) and non-functional concerns (deployment) in a single code base and modular fashion.

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Towards programming in Natural Language: Learning New Functions from Spoken Utterances

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INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2020年第2期14卷 249-272页

作者： Weigelt, Sebastian Steurer, Vanessa Hey, Tobias Tichy, Walter F. Karlsruhe Inst Technol Inst Program Struct & Data Org Karlsruhe Germany

Systems with conversational interfaces are rather popular nowadays. However, their full potential is not yet exploited. For the time being, users are restricted to calling predefined functions. Soon, users will expect to customize systems to their needs and create own functions using nothing but spoken instructions. Thus, future systems must understand how laypersons teach new functionality to intelligent systems. The understanding of natural language teaching sequences is a first step toward comprehensive end-user programming in natural language. We propose to analyze the semantics of spoken teaching sequences with a hierarchical classification approach. First, we classify whether an utterance constitutes an effort to teach a new function or not. Afterward, a second classifier locates the distinct semantic parts of teaching efforts: declaration of a new function, specification of intermediate steps, and superfluous information. For both tasks we implement a broad range of machine learning techniques: classical approaches, such as Naive Bayes, and neural network configurations of various types and architectures, such as bidirectional LSTMs. Additionally, we introduce two heuristic-based adaptations that are tailored to the task of understanding teaching sequences. As data basis we use 3168 descriptions gathered in a user study. For the first task convolutional neural networks obtain the best results (accuracy: 96.6%);bidirectional LSTMs excel in the second (accuracy: 98.8%). The adaptations improve the first-level classification considerably (plus 2.2% points).

关键词： programming in natural language natural language understanding end-user programming conversational interfaces spoken language understanding natural language processing computational linguistics naturalistic programming machine learning neural networks intelligent systems artificial intelligence

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Background Data Resampling for Outlier-Aware Classification

Background Data Resampling for Outlier-Aware Classification

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Yi Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92103 USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

The problem of learning an image classifier that allows detection of out-of-distribution (OOD) examples, with the help of auxiliary background datasets, is studied. While training with background has been shown to improve OOD detection performance, the optimal choice of such dataset remains an open question, and challenges of data imbalance and computational complexity make it a potentially inefficient or even impractical solution. Targeted at balancing between efficiency and detection quality, a dataset resampling approach is proposed for obtaining a compact yet representative set of background data points. The resampling algorithm takes inspiration from prior work on hard negative mining, performing an iterative adversarial weighting on the background examples and using the learned weights to obtain the subset of desired size. Experiments on different datasets, model architectures and training strategies validate the universal effectiveness and efficiency of adversarially resampled background data. Code is available at https://***/JerryYLi/bg-resample-ood.

关键词： The problem of learning an image classifier that allows detection of out-of-distribution (OOD) examples, with the help of auxiliary background datasets, is studied. While training with background has been shown to improve OOD detection performance, the optimal choice of such dataset remains an open question, and challenges of data imbalance and computational complexity make it a potentially inefficient or even impractical solution. Targeted at balancing between efficiency and detection quality, a dataset resampling approach is proposed for obtaining a compact yet representative set of background data points. The resampling algorithm takes inspiration from prior work on hard negative mining, performing an iterative adversarial weighting on the background examples and using the learned weights to obtain the subset of desired size. Experiments on different datasets, model architectures and training strategies validate the universal effectiveness and efficiency of adversarially resampled background data. Code is available at https://***/JerryYLi/bg-resample-ood.

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CNN Model & Tuning for Global Road Damage Detection 8

CNN Model & Tuning for Global Road Damage Detection

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8th ieee International conference on Big Data (Big Data)

作者： Vishwakarma, Rahul Vennelakanti, Ravigopal Hitachi Amer Ltd Res & Dev Big Data Analyt & Solut Lab Santa Clara CA 95054 USA

ISBN: (纸本)9781728162515

This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of ieee International conference on Big Data 2020. We assess single and multi-stage network architectures for object detection and provide a benchmark using popular state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5. Data preparation for provided Road Damage training dataset, captured using smartphone camera from Czech, India and Japan is discussed. We studied the effect of training on a per country basis with respect to a single generalizable model. We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual network (Resnet) and Feature Pyramid network (FPN) backbone. Additionally, we compare this to a one-stage Yolov5 model with Cross Stage Partial network (CSPNet) backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones respectively. This shows the generalizability of the Resnet-50 model when compared to its more complex counterparts. Experiments were conducted using Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU. A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg F1 score to is made available at https://***/vishwakarmarhl/rdd2020

关键词： road damage detection computer vision convolution neural network yolo faster r-cnn

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