The proceedings contain 28 papers. The topics discussed include: DeCrypto Pro: deep learning based cryptomining malware detection using performance counters;understanding uncertainty in self-adaptive systems;assessing...
ISBN:
(纸本)9781728172774
The proceedings contain 28 papers. The topics discussed include: DeCrypto Pro: deep learning based cryptomining malware detection using performance counters;understanding uncertainty in self-adaptive systems;assessing adaptations based on change impacts;automated management of collections of autonomic systems;self-patch: beyond patch Tuesday for containerized applications;how far should I watch? quantifying the effect of various observational capabilities on long-range situational awareness in multi-robot teams;taming resource heterogeneity in distributed ml training with dynamic batching;automating GUI testing with image-based deep reinforcement learning;and coevolutionary learning of neuromodulated controllers for multi-stage and gamified tasks.
The proceedings contain 183 papers. The topics discussed include: design of direct read from sparse segments in MPI-IO;descriptive and predictive analysis of aggregating functions in serverless clouds: the case of vid...
ISBN:
(纸本)9781728176499
The proceedings contain 183 papers. The topics discussed include: design of direct read from sparse segments in MPI-IO;descriptive and predictive analysis of aggregating functions in serverless clouds: the case of video streaming;a proactive uncertainty driven model for data synopses management in pervasive applications;on-line traffic scheduling optimization in ieee 802.1Qch based time-sensitive networks;multi-layer and heterogeneous resource management in SDN-based space-terrestrial integrated networks;structure preserved graph reordering for fast graph processing without the pain;an efficient approach to vectorize the hybrid breadth-first search;a novel developer portrait model based on Bert-capsule network;job placement strategy with opportunistic resource sharing for distributed deep learning clusters;and batched pattern-aware cache management strategy for astronomical time series sub-images retrieval.
The healthcare system is a distributed collaborative system and the sensitivity of the medical data is one of the most important requirements. Preventing unauthorized access to healthcare information and data sharing ...
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ISBN:
(数字)9781665468190
ISBN:
(纸本)9781665468206
The healthcare system is a distributed collaborative system and the sensitivity of the medical data is one of the most important requirements. Preventing unauthorized access to healthcare information and data sharing security in the healthcare environment are critical processes that affect the credibility of the system. To achieve this goal and to meet the requirements of the healthcare system, access control is an important measure to realize the safe sharing of resources. The attribute-based access control (ABAC) model meets the complex security requirements of large and complex systems and provides a dynamic, flexible and scalable solution. The main obstacle to deploying ABAC is the precise development of ABAC policies. Manually developing access control policies is tedious, time-consuming and error prone. Most systems have high-level requirement specifications, which are written in natural language. These natural language (NL) documents have the intended access control policies for the systems. In this paper, we propose a new approach towards extracting policies from natural language documents. By fully taking advantage of Bidirectional Encoder Representations from Transformers (BERT) and Semantic role labeling (SRL), we are able to correctly identify access control policy (ACP) sentences with an average F1 score of 85% and correctly extract rules with an average F1 score of 72%, which outperforms the state-of-the-art and leads to a performance improvement of 7% and 2% respectively over the previously reported results.
Kyber, an IND-CCA-secure key encapsulation mechanism (KEM) based on the MLWE problem, has been shortlisted for the third round evaluation of the NIST Post-Quantum Cryptography Standardization. In this paper, we explor...
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Novel mobile information systems are being developed with the use of smartphones. The traditional applications of wireless sensor networks such as environmental monitoring, health care, and transportation are being re...
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ISBN:
(纸本)9781728127910
Novel mobile information systems are being developed with the use of smartphones. The traditional applications of wireless sensor networks such as environmental monitoring, health care, and transportation are being realized by many mobile social network applications. Smartphones forward various onboard sensor data using a store-carry-forward way to the central repositories over the internet. In this paper, inter contact time distribution of each smartphone user with other users is utilized for efficient sensor data routing. A novel threshold-based approach is used to estimate future interconnection times. Human mobility based Weighted Relaying Algorithm (Hm-WRA) is proposed for routing in opportunistic mobile sensing systems. The proposed protocol performs almost 15% to 20% better compared to an intercontact time based shortest path routing algorithm named CHARON in terms of the data delivery ratio with proper selection of threshold value at each node.
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems. While recent advan...
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ISBN:
(纸本)9781728183169
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems. While recent advances exploit graph neural networks (GNN) to better learn spatial and temporal dependencies between sensors, they cannot model time-evolving spatio-temporal correlation (STC) between sensors, and require pre-defined graphs, which are neither always available nor totally reliable, and target at only a specific type of sensor data at one time. Moreover, since the form of time-series fluctuation is varied across sensors, a model needs to learn fluctuation modulation. To tackle these issues, in this work, we propose a novel GNN-based model, Attention-adjusted Graph Spatio-Temporal Network (AGSTN). In AGSTN, multi-graph convolution with sequential learning is developed to learn time-evolving STC. Fluctuation modulation is realized by a proposed attention adjustment mechanism. Experiments on three sensor data, air quality, bike demand, and traffic flow, exhibit that AGSTN outperforms the state-of-the-art methods.
The paper considers metrological characteristics of distributed mobile measuring systems used for characteristics control of objects distributed in space. Mobile measuring systems travel along the prescribed routes an...
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This paper presents the development of a versatile software framework enabling the multiform and seamless evaluation of distributedsystems: from fast and flexible simulation to actual system execution. The OpenStack ...
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Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud computing have accelerated the...
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ISBN:
(纸本)9781728105703
Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the four layers of the architecture model that are the sensor Layer, the Network Layer, the Service Layer and the Application Layer. A discussion is also conducted upon the challenges that smart farming monitoring systems face.
Fog computing has been identified as an enabler for many modern technologies like connected vehicles and the Industrial Internet of Things (IIoT). Such technologies are characterized by the integration of applications...
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ISBN:
(纸本)9781728182544
Fog computing has been identified as an enabler for many modern technologies like connected vehicles and the Industrial Internet of Things (IIoT). Such technologies are characterized by the integration of applications with different levels of criticality on shared platforms, which are referred to as mixed-criticality systems. Mixed-criticality systems typically use static scheduling for critical tasks;however, static scheduling is not suitable for scenarios where fog nodes run dynamic non-critical applications that implement, e.g., maintenance checks and data analytics. To address this challenge, in this paper, we differentiate between critical tasks that are statically allocated (called "native") and dynamic non-critical tasks that may migrate across fog nodes (called "temporary"). We propose a static scheduling approach that maximizes the number of temporary tasks that can be added at runtime, without negatively impacting the already scheduled native tasks. This approach enables fog nodes to become more suitable for IIoT environments by configuring them with extensible schedules for the native tasks. To evaluate our approach, we perform experiments considering several test cases, which show that given a number of native tasks, the generated extensible schedules enable the fog nodes to run a larger number of temporary tasks at the same time. Furthermore, the extensible schedules exhibit 7.8% less missed deadlines (on average), compared to the non-extensible schedules.
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