Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy

Recent years have witnessed the emergence of the Internet of Things (IoT) systems that incorporate blockchain (BC) elements in their architecture. Due to discrepancies between the requirements of IoT systems and the characteristics of BC networks, the motivations and design of these blockchain-enabled IoT systems (BC-IoT) are not only intriguing from a research perspective but also invaluable in practice

The complexity of systems now under consideration (be they biological, physical, chemical, social, etc), together with the technicalities of experimentation in the real-world and the non-linear nature of system dynamics, means that computational modelling is indispensible in the pursuit of furthering our understanding of complex systems. Agent-based modelling and simulation is rapidly increasing in

Induced damage during biocomposites drilling is significantly different to that produced on composites based on synthetic reinforcement such as carbon or glass fibers. In composites reinforced with carbon or glass fibers, induced damage increases with feed rate, however damage was observed to decrease with feed rate in biocomposites reinforced with natural fibers. This work is focused on the explanation

The Water Evaluation and Planning (WEAP) system, a modeling and simulation tool, offers certain advantages for studying water systems. These systems are modeled as a collection of supply, demand, and transmission entities. Domain experts can construct whole water system models from template model parts (entities) with built-in constraints for combining them. The parameterized entities are simulated

Cognitive Radio (CR) technology was introduced to effectively exploit the under-utilization portions of the licensed spectrum. A primary challenge in enabling CR networking is how to coordinate the CR communications, in which CR users need to exchange their control information before data exchange can take place. Providing a reliable coordination mechanism in CR networks (CRNs) is not an easy task

Automation in computing systems has always been considered a valuable solution to unburden the user. Internet of Things (IoT) technology best suits automation in different domains, such as home automation, retail, industry, and transportation, to name but a few. While these domains are strongly characterized by implicit user interaction, more recently, automation has been adopted also for the provision

Interference management has always been a research hotspot in the field of wireless communications. Currently, researches based on interference management mostly focus on an interference management technology, and few studies have combined multiple interference management technologies. This paper proposes an interference management technology that combines Interference Alignment (IA) and Successive

Information security standards are influential tools in society today. The validity claim of standards is based on what is considered “best practice.” We unveil the negotiations that take place when “best practice” is constructed during standard development. By using discourse analysis, we investigate how power operates in national and international contexts of de jure information security standard

The sharing and retrieval of medical data, such as medical imaging data, via cloud systems can facilitate timely medical/disease diagnosis. While encryption can be used to ensure that patients’ private and medical information are not accessible by unauthorized individuals, it is challenging for cloud servers to search for and locate medical images (e.g. those relating to similar medical conditions)

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems

Optimal closeness query in social networks requires obtaining the social datasets from each user so that he/she finds out a shortest social distance with any target user. For example, we can make friends in terms of the most similar social relationship of family background, education level and hobbies etc. Unfortunately, social data concerning user’s attributes might reveal personal sensitive information

Graphics Processing Units technology (GPU) and CUDA architecture are one of the most used options to adapt machine learning techniques to the huge amounts of complex data that are currently generated. Biclustering techniques are useful for discovering local patterns in datasets. Those of them that have been implemented to use GPU resources in parallel have improved their computational performance.

With the construction of smart cities entering a new phase, it is necessary to broaden the requirements of traditional roads to provide efficient operation and maintenance (O&M) services. Existing road infrastructure systems fail to meet intelligent functional O&M requirements since they do not incorporate a unified method to organize and integrate road life-cycle spatio-temporal data. Capitalizing

The attractive benefits of cloud computing environment motivate Information Technology (IT) companies to move their IT infrastructure on the cloud server. In a cloud environment, companies must depend on the untrusted Cloud Service Provider (CSP), which raises data security issues due to the presence of numerous hackers and malicious users. Apart from data security, high searching time of data owner

5G-NB-IoT, cellular assisted low power wide area IoT technology, has become an emerging technology for smart farming applications. As NB-IoT offers a significant link budget improvement by utilizing repeated transmissions and varies the repetition order based on the path loss in communications. Hence can cater to the farming geographical requirements effectively. However, these repetitions inevitably

Pervasive edge computing refers to one kind of edge computing that merely relies on edge devices with sensing, storage and communication abilities to realize peer-to-peer offloading without centralized management. Due to lack of unified coordination, users always pursue profits by maximizing their own utilities. However, on one hand, users may not make appropriate scheduling decisions based on their

Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. However, prior work regarding colocating games either ignores the performance interference between games or uses simple performance model to charaterize it, which may make inefficient game

Large-scale datacenters often host latency-sensitive services that have stringent Quality-of-Service requirement and experience diurnal load pattern. Co-locating best-effort applications that have no QoS requirement with the latency-sensitive services has been widely used to improve the resource utilization of datacenters with careful shared resource management. However, existing co-location techniques

The Internet of Things (IoT) is a series of recording devices, mechanical and automated machines, documents, creatures, or individuals that have one of the user identifiers and the ability to substitute information over a network without requiring human-to-human connections. The IoT technology is used in different applications to transfer information between two or more stations or devices using actuators/sensors

Assisted Reproductive Technology (ART) is a set of medical procedures primarily used to address infertility. Success Rate of ART is very low because it is affected by large number of variables. Machine Learning Techniques are now applied to predict ART outcome and to find strategies to improve success rate. For this, determining the best performing classifier for ART is very important. Previously,

This paper introduces an efficient and scalable cloud-based privacy preserving model using a new optimal cryptography scheme for anomaly detection in large-scale sensor data. Our proposed privacy preserving model has maintained a better tradeoff between reliability and scalability of the cloud computing resources by means of detecting anomalies from the encrypted data. Conventional data analysis methods

A new compact 1×4 microstrip patch antenna array design for future 5G applications is presented in this paper. The proposed antenna array consists of square slot loaded with four radiating patch elements. The corporate feed network has been implemented for the excitation of the array. The feed line is connected to the square slot patch through a quarter-wave transformer matching network. The proposed

Next generation sequencing technologies have noticeably improved in the last decade. Time and cost of whole genome sequencing are important challenges that must be reduced, opening unprecedented opportunities to various research and development areas. The alignment or mapping of small reads produced by sequencing machines to reference genomes of billions of nucleotides is a fundamental task in this

A novel bipolar photon-controlled generalized memristor model with an avalanche photodiode (APD) passive quenching circuit is presented in this paper. The SPICE model of the circuit is established and its fingerprints are analyzed by the pinched hysteresis loops with different bipolar periodic stimuli. The dynamical characteristics of the proposed circuit model are investigated both theoretically and

In the class of the boost converters, the conventional DC–DC boost converters are in common practice but their limited boost capabilities at higher duty ratios are one of the concerns. The isolated and non-isolated step-up DC–DC converters are one of the remedies of the above issue. The presence of switched inductor and switched capacitors in the circuit of non-isolated configuration can provide considerable

Wireless Sensor Networks (WSNs) include small battery-based self-governing devices that are deployed in a distributed manner to supervise the environmental or physical circumstances. The routers and gateways are connected to the deployed nodes to support many real-time applications. Due to open access, the security issue arises in WSN. In this circumstance, the external users can be verified by securing

Many security measures designed for large-scale compute hardware (e.g., workstations and servers) are not optimized for embedded systems. One such measure, Relocation Read-Only (RelRO), protects binary relocation sections from tampering after dynamic linking; however, RelRO depends on the presence of memory management hardware that not all embedded systems include. More generally, the latest hardware

Network On Chip is determined as an On-chip packet-switched communication in the study of multi-processors. As the number of components is proliferating, there were many issues regarding the performance of a network. By taking into consideration of this problem, in this paper, we proposed an efficient core mapping algorithm named BMAP, used for customizing the NoC platforms and performing the SPLASH-2

Speech enhancement works towards improvising the quality of speech through various post processing algorithms. Intelligibility enhancement along with overall perceptual quality score improvement is the main objective of many speech signal processing techniques. Generative Adversarial Networks (GAN) aims to generate a new set of data with the help of training set statistics and is seen to be impressive

As a hotspot of machine learning research, deep learning is applied in many fields. Embedded systems are becoming more and more complex and networked, so the real-time performance of embedded systems and the security of network embedded devices face severe challenges. Based on this, this paper studies the real-time task scheduling problem for complex embedded systems and the security of embedded network

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"Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers."

"Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication."

"Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication."

This study examined the transition from Traditional Vehicles (TVs) to Autonomous Vehicles (AVs) using microsimulation modelling approaches. For this purpose, the study first optimised the parameters of human driving behaviours to find out what would happen if AVs could drive with modified human behaviours. Then, the study acquired the optimised driving behaviours to evaluate how efficient AVs can be

Cloud computing provides a new infrastructure for the research of complex system simulation (CSS). However, insufficient computing resource allocation results in lower performance of a CSS application. On the other hand, excessive computing resource allocation will lead to the increase of simulation communication overhead and simulation synchronous computing. Therefore, accurate computing resource

We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The open-loop input-output behavior of the underlying dynamical system is used as the supervising signal

Lithium-ion batteries are increasingly being deployed in liberalised electricity systems, where their use is driven by economic optimisation in a specific market context. However, battery degradation depends strongly on operational profile, and this is particularly variable in energy trading applications. Here, we present results from a year-long experiment where pairs of batteries were cycled with

In this paper, we investigate a prescribed-time and fully distributed Nash Equilibrium (NE) seeking problem for continuous-time noncooperative games. By exploiting pseudo-gradient play and consensus-based schemes, various distributed NE seeking algorithms are presented over either fixed or switching communication topologies so that the convergence to the NE is reached in a prescribed time. In particular

We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications given as Signal Temporal Logic (STL) formulae. RNNs can store information of a system over time, thus, enable us to determine satisfaction of the dynamic temporal requirements specified in STL formulae. Given a STL formula, a

We examine a control problem where the states of the components of a system deteriorate after a disruption, if they are not being repaired by an entity. There exist a set of dependencies in the form of precedence constraints between the components, captured by a directed acyclic graph (DAG). The objective of the entity is to maximize the number of components whose states are brought back to the fully

The motivation of our research is to establish a Laplace-domain theory that provides principles and methodology to analyze and synthesize systems with nonlinear dynamics. A semigroup of composition operators defined for nonlinear autonomous dynamical systems---the Koopman semigroup and its associated Koopman generator---plays a central role in this study. We introduce the resolvent of the Koopman generator

We investigate the problem of monitoring multiple targets using a single mobile sensor, with the goal of minimizing the maximum estimation error among all the targets over long time horizons. The sensor can move in a network-constrained structure, where it has to plan which targets to visit and for how long to dwell at each node. We prove that in an optimal observation time allocation, the peak uncertainty

The process of simultaneously mapping the environment in three dimensional (3D) space and localizing a moving vehicle's pose (orientation and position) is termed Simultaneous Localization and Mapping (SLAM). SLAM is a core task in robotics applications. In the SLAM problem, each of the vehicle's pose and the environment are assumed to be completely unknown. This paper takes the conventional SLAM design

Uncertain partially observable Markov decision processes (uPOMDPs) allow the probabilistic transition and observation functions of standard POMDPs to belong to a so-called uncertainty set. Such uncertainty sets capture uncountable sets of probability distributions. We develop an algorithm to compute finite-memory policies for uPOMDPs that robustly satisfy given specifications against any admissible

Constrained Markov Decision Processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm which leverages the linear programming formulation of finite-horizon CMDP for repeated optimistic planning

Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different

Engineering projects are notoriously hard to complete on-time, with project delays often theorised to propagate across interdependent activities. Here, we use a novel dataset consisting of activity networks from 14 diverse, large-scale engineering projects to uncover network properties that impact timely project completion. We provide the first empirical evidence of the infectious nature of activity

The suicide of Indian actor Sushant Singh Rajput in the midst of the COVID-19 lockdown triggered a media frenzy of prime time coverage that lasted several months and became a political hot button issue. Using data from Twitter, YouTube, and an archive of debunked misinformation stories, we found two important patterns. First, that retweet rates on Twitter clearly suggest that commentators benefited

Graph sparsification aims to reduce the number of edges of a network while maintaining its accuracy for given tasks. In this study, we propose a novel method called GSGAN, which is able to sparsify networks for community detection tasks. GSGAN is able to capture those relationships that are not shown in the original graph but are relatively important, and creating artificial edges to reflect these

Should social media platforms intervene when communities repeatedly break rules? What actions can they consider? In light of this hotly debated issue, platforms have begun experimenting with softer alternatives to outright bans. We examine one such intervention called quarantining, that impedes direct access to and promotion of controversial communities. Specifically, we present two case studies of

Visual analysis of temporal networks comprises an effective way to understand the network dynamics, facilitating the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming

We consider an approach for community detection in time-varying networks. At its core, this approach maintains a small sketch graph to capture the essential community structure found in each snapshot of the full network. We demonstrate how the sketch can be used to explicitly identify six key community events which typically occur during network evolution: growth, shrinkage, merging, splitting, birth

Annotation is the labeling of data by human effort. Annotation is critical to modern machine learning, and Bloomberg has developed years of experience of annotation at scale. This report captures a wealth of wisdom for applied annotation projects, collected from more than 30 experienced annotation project managers in Bloomberg's Global Data department.

Information networks are ubiquitous and are ideal for modeling relational data. Networks being sparse and irregular, network embedding algorithms have caught the attention of many researchers, who came up with numerous embeddings algorithms in static networks. Yet in real life, networks constantly evolve over time. Hence, evolutionary patterns, namely how nodes develop itself over time, would serve

The present paper is devoted to clustering geometric graphs. While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalization, which we call higher-order spectral clustering. It resembles in concept the classical spectral clustering method but uses for partitioning the eigenvector associated with a higher-order eigenvalue. We establish the weak

The A64FX processor from Fujitsu, being designed for computational simulation and machine learning applications, has the potential for unprecedented performance in HPC systems. In this paper, we evaluate the A64FX by benchmarking against a range of production HPC platforms that cover a number of processor technologies. We investigate the performance of complex scientific applications across multiple