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The article describes an approach to build a search index for fuzzy objects recognition in scientific and technical papers. A fuzzy object is explained as an entity described by some physical parameters. A physical parameter consists of two parts: a value, represented in various forms of digits, and a physical unit, represented as text with some digits. The approach presented in the article allows users to search not by ordinary substrings, but by physical parameters with range, less than or greater than operators. The current realisation of the approach is a web service that has functionality to perform all stages for extraction of physical parameters of a fuzzy object. These stages are the extraction of sentences, the extraction of physical parameters and the normalisation of physical parameters values. The web service can be exploited in the synchronous and asynchronous modes. The synchronous mode helps to troubleshoot the software. The asynchronous mode is designed to process papers using queues in the distributed computer systems. The current work is aimed to develop a search engine which will address the challenge of structuring information of a continuously growing number of scientific and technical papers. The search index will help to perform robust navigation among a huge number of documents for scientific workers and engineers. [ABSTRACT FROM AUTHOR]

The omnipresent pervasiveness of personal devices will expand the applicability of the Dynamic Data Driven Application Systems (DDDAS) paradigm in innumerable ways. While every single smartphone or wearable device is potentially a sensor with powerful computing and data capabilities, privacy and security in the context of human participants must be addressed to leverage the infinite possibilities of dynamic data driven application systems. We propose a security and privacy preserving framework for next generation systems that harness the full power of the DDDAS paradigm while (1) ensuring provable privacy guarantees for sensitive data; (2) enabling field-level, intermediate, and central hierarchical feedback-driven analysis for both data volume mitigation and security; and (3) intrinsically addressing uncertainty caused either by measurement error or security-driven data perturbation. These thrusts will form the foundation for secure and private deployments of large scale hybrid participant-sensor DDDAS systems of the future. [ABSTRACT FROM AUTHOR]

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [Copyright &y& Elsevier]

At present, universities are facing a critical period of transition from digital campus to smart campus. Because cloud service data has the characteristics of diversity, in addition to structured data, there are also unstructured data, which play an important role in the study and life of the majority of students. Therefore, it is very important to improve the construction level of smart campus cloud service platform. At this stage, there are great differences between the service platform under the traditional model and the smart campus platform under the big data computer system. Under the traditional mode, the construction method of smart campus cloud service platform in the city has some shortcomings. Therefore, it is urgent to build a smart campus cloud service platform. Based on this, this paper compares the smart campus cloud service platform of the traditional service platform and the big data computer system. The results show that the latter has a timely response speed and can bring high-quality and efficient data services to users. At the same time, it also analyzes the ideas and goals of building an intelligent cloud service platform, and puts forward corresponding construction methods. [ABSTRACT FROM AUTHOR]

Abstract: Planning is motivated by acting. Most of the existing work on automated planning underestimates the reasoning and deliberation needed for acting; it is instead biased towards path-finding methods in a compactly specified state-transition system. Researchers in this AI field have developed many planners, but very few actors. We believe this is one of the main causes of the relatively low deployment of automated planning applications. In this paper, we advocate a change in focus to actors as the primary topic of investigation. Actors are not mere plan executors: they may use planning and other deliberation tools, before and during acting. This change in focus entails two interconnected principles: a hierarchical structure to integrate the actorʼs deliberation functions, and continual online planning and reasoning throughout the acting process. In the paper, we discuss open problems and research directions toward that objective in knowledge representations, model acquisition and verification, synthesis and refinement, monitoring, goal reasoning, and integration. [Copyright &y& Elsevier]

Abstract: Two categories of mathematical models were compared for the simulation of consolidation of paper coating structures, that is for the packing of pigments on a paper substrate under dewatering conditions. The first category uses probabilistic methods, relying on a random number generator to either determine the initial position of the pigments or their motion. The second category uses deterministic methods based on force balances. In this work, two probabilistic models and two deterministic models are described and their respective advantages and drawbacks are critically reviewed. Simulation results obtained using three of these methods are compared for the case of monodisperse and bidisperse spherical suspensions. Porosity calculations of the numerical packings obtained with the (deterministic) discrete element method (DEM) and two probabilistic methods, the Monte-Carlo (MCD) and the steepest descent (SDD) deposition methods, are compared with experimental data from the literature. These calculations reveal significant differences in the pore volume obtained with these three models. An analysis based on the bridging and relaxation phenomena that prevail in the flow of such particulate systems provide an explanation for these differences and show the strong potential of the discrete element method. The choice of the simulation method depends on the objective of the simulations. DEM will provide more accurate predictions of macroscopic quantities such as the porosity or the roughness, but requires very long computational times. MCD or SDD will only provide qualitative trends, but is computationally far less intense. A combination of strategies might be appropriate, using MCD (or SDD) to provide guidelines and DEM to enhance the results predicted by MCD. [Copyright &y& Elsevier]

Nowadays, the complexity of embedded computing systems is increasing due to applications with increasing functional and nonfunctional requirements, e.g., performance, reliability, and energy efficiency. Organic computing has emerged as a research area for handling complex distributed embedded computer systems and is inspired by the organization of biological entities. It provides improved adaptability, robustness and has reduced development efforts. However, organic computing has drawbacks in terms of determinism, composability, and dependability, which are key features for safety-critical applications. Distributed computing systems using time-triggered communication networks have these characteristics and therefore offer distinct advantages for safety-critical systems. By combining organic computing, including artificial DNA and hormone features, with time-triggered communications, we can make these systems more safe, reliable, and suitable for safety-critical applications. Therefore, in this paper, we present a time-triggered organic computing architecture in combination with a simulation tool, in which tasks of an application are executed according to a predefined schedule. Our paper introduces multiple use cases to demonstrate the concepts and the system model. The obtained results from simulation show improved temporal predictability and fault containment of the system. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process. [ABSTRACT FROM AUTHOR]

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to the input before it is captured and converted to a image inside the vision system, when compared to digital adversarial attacks. In this paper, we focus on physical adversarial attacks and further classify them into invasive and non-invasive. Optical-based physical adversarial attack techniques (e.g. using light irradiation) belong to the non-invasive category. The perturbations can be easily ignored by humans as the perturbations are very similar to the effects generated by a natural environment in the real world. With high invisibility and executability, optical-based physical adversarial attacks can pose a significant or even lethal threat to real systems. This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques. [ABSTRACT FROM AUTHOR]

The paper presents a new concept of a yacht Radio-communication system for Support Decision of the yacht captain in case of a collision situation. This has an impact on reducing the risk of loss of life of the yacht crew and the loss of property, including the yacht. The key problem in a collision situation is to be able to contact with SAR Maritime Rescue Centres. This paper proposes a model of an intelligent system based on the method of artificial neural networks ANN, which allows to support in making the right decision by the captain in case of life threatening danger on board of the yacht. The paper presents an example of The Decision Support System for a specific yacht location in relation to the existing RCC-SAR zones A1, A2, A-GSM and the propagation range of VHF, GSM and MF radio waves, with a worked out decision of the possible closest distance from the zone, allowing the use of these devices to call for help and carry out an effective rescue operation. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for multi-track transmission processing of signals from mechanical-navigation sensors describing the state of the ship's motion vector by using a neural network computer system. The integration of mechanical systems with navigation systems on a sea vessel is aimed at creating a uniform decision-making module for the crew steering the ship and supervising propulsion systems in order to minimize the level of risk in controlling a sea vessel in hazardous situations. The concept of integrating radio devices with navigation and engineering systems will enable effective ship management based on sources coming from external and internal sensors of the ship. The processing of navigational and mechanical signals proposed in the article will allow to build an effective prediction of the developing situation in case of danger according to the proposed algorithm for the officer of the watch in charge of the ship. The paper presents a model of mathematical processing of signals from navigational-mechanical systems and illustrates its operation in an exemplary situation of a threat of collision of a sea vessel [ABSTRACT FROM AUTHOR]

Recently, smart data has attracted great attention in the smart city community since it can provide valuable information to support intelligent services such as planning, monitoring, and decision making. However, it imposes a big challenge to explore smart data from big data gathered from smart city with various advanced fusion and analysis approaches. This paper proposes an incremental tensor-based fuzzy c-means approach (IT-FCM) for obtaining smart data from continuously generated big data. Specifically, a weighted version of the tensor-based fuzzy c-means approach (T-FCM) is firstly proposed to cluster the dataset that combines the previous cluster centroids and the new generated data. Aiming to improve the clustering efficiency, the old data objects are represented by the centroids to avoid repeat clustering. Furthermore, this paper presents an edge-cloud-aided clustering scheme to fuse big data from different sources and perspectives and further to implement co-clustering on the fused datasets for exploring smart data. Finally, the proposed IT-FCM approach is evaluated by comparing with T-FCM regarding clustering accuracy and efficiency on two different datasets in the experiments. The results state that IT-FCM outperforms T-FCM in clustering streaming big data in terms of accuracy and efficiency for obtaining smart data. • An edge-cloud-aided computing system for co-clustering is presented. • An edge-cloud-aided computing system for co-clustering is presented. • Experiments are conducted to evaluate the presented approach. [ABSTRACT FROM AUTHOR]

With the rapid growth of internet connected devices and the tremendous amount of data that could be generated and exchanged in IoT environment, we need to reconsider in the current IoT architecture that based on Cloud computing system, to avoid the issues related to performance and scalability. Message Queue Telemetry Transport (MQTT) is one of promising protocol for data exchange in IoT that could encounter such issues because it relies on central broker located in Cloud, and this may lead to increase network congestion, performance overhead or bottleneck. Therefore, we need to leverage Fog computing by developing a distributed architecture for MQTT that contain multiple brokers. In this case, IoT services can be coordinated and managed between Fog computing and Cloud computing. However, this will open new security challenges for several reasons. Firstly, security procedures need to be modified because MQTT that based on distributed architecture require additional multiple brokers and different communication standards that may increase security threats and increase security management complexity. Secondly, MQTT is inherently lacking efficient security features because it performs username/password-based authentication in a plain text, that protected by cryptographic protocol SSL/TSL which is not consider as lightweight protocol for resources constrained devices. This paper will present taxonomy and realization process of IoT authentication scheme. In addition, the paper will discuss challenges of applying authentication mechanisms for IoT systems that based on distributed MQTT brokers. [ABSTRACT FROM AUTHOR]

The paper considers the possibility of constructing a specialized computing system oriented at the transmission of data through information channels, that are determined taking into account the semantics of the selected data. In the process of computations the data is connected with semantic characteristics that describe the channel of computations, which can be considered as a method of semantic data enrichment. The system of information channels as a whole can be considered as an information graph describing the structuring of the processed data. The information graph supports the data model in the form of a network, the framework of which are objects and the relationships between them. The paper proposes language tools for determining the information graph and interpretation tools that provide practical computations. The set of information channels that make up the information graph can be considered as a low-level tool for data enrichment. The paper studies the possibility of determining tools of higher level. An applicative type language is proposed for defining information graphs, the syntax and semantics of the language are specified. The proposed language can be considered as an intermediate level tool for defining semantics. A procedure is proposed for compiling the language into a low-level construct, preserving the semantics of the language. The supporting system for the proposed computing system includes a low-level language interpreter, as well as an intermediate-level language compiler into a low-level language. The supporting system is implemented in an applicative programming environment. Some elements of the supporting system were tested when developing applied information systems in the field of jurisprudence. [ABSTRACT FROM AUTHOR]

Depression is one of the leading forms of mental health issues encountered by individuals of diverse age groups today worldwide. Like any other mental health concerns, depression too poses diagnostic challenges for medical practitioners and clinical experts, given obvious social reservations and lack of awareness and acceptance in the society. Since long researchers have been looking for methods to identify symptoms of depression among individuals from their speech and responses, by utilizing automation systems and computers. In this paper, we propose an audio based depression detection method, which relies on neural networks for audio spectrogram based feature extraction as well as classification between speech/response patterns of depressed vs. non-depressed persons. We adopt a multi-modal approach in our work, by combining Mel-Frequency Cepstral Coefficients (MFCC) features, as well as Spectrogram features extracted from an audio file, by a novel CNN network. Our CNN model demonstrates optimized residual blocks and the "glorot uniform" kernel initializer. The proposed method's performance is assessed in both multi-modal and multi-feature trials. We show our results on standard benchmark datasets DAIC-WOZ and MODMA, which provide repositories of questionnaire and patient responses, relevant in identification of depressive symptoms. We have also tested our model on standard emotion recognition audio dataset, RAVDESS. The proposed model achieves detection accuracy of over 90% in DAIC-WOZ and MODMA, and over 85% in RAVDESS, which is proven to surpass the present state-of-the-art. • We introduce a novel deep neural network for depression detection through audio signals. • We used a multi-modal concept considering mfcc and spectrogram to detect the depression. • This paper shows optimized residual block to extract the detailed information to enhance the performance of the model. • Our model gets better results on the three benchmark datasets. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy. • A survey on the current state of imaging spectroscopy. • Pre-processing techniques and data acquisition methods are highlighted. • We provide applications of imaging spectroscopy in various domains. • Publicly available databases and software tools for spectral data are presented. [ABSTRACT FROM AUTHOR]

The paper presents the development of a method of computer-aided diagnosis of sarcoidosis based on chest X-ray images, for particular stages of the disease. For this purpose, the research material, which consisted of 98 chest X-rays, was analyzed. The datasets included images for healthy cases and for first, second and third degree sarcoidosis. The research material was pre-processed, after which, on the basis of framing, the regions of interest (ROIs) were extracted from the images for individual cases. Next, the analysis of the selected ROIs was carried out, resulting in discriminatory characteristics describing the properties of the images. For the obtained sets, due to their multidimensionality, extraction and selection of features were carried out. Based on the analysis of the obtained results, a selection of features was selected to reduce the data dimension. Three methods were used to carry it out. In the case of heuristic identification of variables, datasets counting respectively for set X-ray2: 34, X-ray3: 47 textural features were obtained. On the basis of the obtained sets, classifiers were built using the supervised learning method. As a result, one model was obtained, based on a single classifier, for the X-ray2 dataset, with a classification error equal to zero. For the X-ray3 dataset, one model was obtained, which was based on an aggregated classifier consisting of two component classifiers and for which the classification error was also equal to zero. The resulting models were proposed as a final solution. The resulting feature vectors and models obtained during the research can be used to build a computer system that will carry out the diagnostic process automatically. The developed solution allows us to classify images for X-ray imaging, depending on the degree of sarcoidosis, into two categories: healthy or sick. This makes it possible to build a system that improves the work of the diagnostician in the process of diagnosing the disease, by reducing the time and cost of performing image analysis, as well as for the patient's condition, thanks to faster referral to advanced clinical trials. [ABSTRACT FROM AUTHOR]

Energy efficiency has emerged as one of the most essential design parameters in contemporary computing system design. Approximate computing is a new computing paradigm to achieve energy efficiency by trading-off energy/area/latency improvements with accuracy for error-resilient applications. This paper proposes R econfigurable E nergy-efficient A pproximate D ivider (READ) that achieves several energy–quality configurable modes using fixed restoring array divider architecture. Conventional approximate binary dividers require various divider hardware configurations to achieve distinct energy–quality trade-off points, which decreases the hardware flexibility, especially for modern embedded systems. READ accomplishes energy efficiency while meeting the dynamically varying accuracy requirements of the targeted application. READ uses reconfigurable subtractor cells that can work in either accurate or approximate mode using a subtractor cell controller logic. The paper also introduces the design of overflow detector using minimal hardware resources. A comprehensive accuracy and hardware evaluation on CMOS 45-nm technology node are performed for the proposed dividers as well as other state-of-the- art divider designs. Compared to the accurate 16 ∕ 8 divider design, the proposed divider shows an improvement of 49% in terms of energy efficiency and is 1.26x faster, while introducing minimal errors. The proposed divider design is demonstrated for its efficacy in image processing tasks and shows nominal effect on the output quality. • A fixed restoring array based approximate divider is proposed and presented with detailed error and circuit analysis for CMOS-45 nm technology node. • The approximate divider presented is reconfigurable in runtime which enables accuracy configurability by varying the design parameter. • Reconfigurable half and full subtractor cells are designed and proposed which achieves dual mode of operation — accurate and exact with energy savings. • Overflow detector circuit for divider design is proposed and presented which is scalable and hardware efficient. • The approximate divider designs are evaluated for image processing applications and found to be satisfactory in terms of Quality of Services (QoS). [ABSTRACT FROM AUTHOR]

This paper considers the data-driven fault isolation and estimation problem for linear time-invariant systems with unknown dynamic matrices and multiple actuator faults. In most of existing fault isolation methods, how to accurately identify the types of faults has not been solved well when the system matrices are unknown. To deal with this problem, a neural network-based fault isolation method is proposed by analyzing and extracting features of different fault models in terms of constructing sparse vectors and function libraries using the available input–output data. Then, a fault estimator is designed to estimate the fault signals within the data-driven framework, where its parameters are computed by the system's Markov parameters and the identified types of faults. Finally, two examples are used to verify the advantages and effectiveness of the proposed fault isolation and estimation approach. • A data-driven fault diagnosis approach is developed for unknown linear systems. • A neural network-based fault isolation method is used to isolate actuator faults. • The faults features of different fault models are analyzed and extracted. • A fault estimator is designed for both single and simultaneously occurring faults. • H 2 index is used to attenuate the effects of noises on fault estimation error. [ABSTRACT FROM AUTHOR]

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is prevalent in various areas and enables a variety of smart applications such as smart factory, smart home, and smart health. The IoT corresponds to a network of physical objects, fostering the collection and exchange of data over the Internet. Each physical object is equipped with sensors, actuators, and other technologies, and is uniquely identifiable through its embedded computing system. Business Process Management (BPM), in turn, is concerned with the modeling, execution, discovery, analysis, and monitoring of business processes. By enriching business processes with real-world IoT data and IoT-driven business rules, process automation as well as real-time process monitoring and decision making can be enhanced. Despite the increasing interest in IoT-enhanced business process support, IoT-driven business rules have not been well integrated with business process execution so far. In this context, providing integrated lifecycle support for IoT-driven business rules in BPM systems constitutes a challenge. This paper discusses how to integrate lifecycle support of IoT-driven business rules with BPM systems covering the modeling, execution, monitoring, analysis, and evolution of IoT-driven business rules. Furthermore, all lifecycle phases need to be covered by holistic approaches to IoT-driven business rules. For each lifecycle phase, we elaborate on the requirements emerging in this context and discuss existing approaches. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach based on multi-agent reinforcement learning for spacecraft formation flying reconfiguration tracking problems. In this scheme, spacecrafts learn the control strategy via transfer learning. For this matter, a new generalized discounted value function is introduced for the tracking problems. Due to the digital nature of spacecraft computer systems, local optimal controllers are developed for the spacecrafts in discrete-time. The stability of the controller is proven. Two Q-learning algorithms are proposed, in each of which the optimal control solution is learned on-line without knowledge about the system dynamics. In the first algorithm, each agent learns the optimal control independently. In the second one, each agent shares the learned information with other agents. Next, the collision avoidance capability is provided. The effectiveness of the presented schemes is verified through simulations and compared with each other. [ABSTRACT FROM AUTHOR]

Important scientific and engineering applications need high performance computing. This can be provided by massively parallel computer (MPC) systems. A sensitive step of maintaining such systems is the interconnection network used to interconnect the computing nodes. The topology used effects the network costs and performance significantly. Hierarchal interconnection networks (HIN) were introduced having several attractive features including low latency, low cost, and high fault tolerance. This paper proposes a new HIN called Midimew connect Torus Network (MTN) that provides constant node degree, high arc connectivity, high fault tolerance and a reasonable bisection width. Static network performance evaluation for the proposed MTN has been conducted and compared with other networks. The comparison included conventional topologies such as 2D Mesh and 2D Torus and hierarchal ones which are TESH and TTN. The comparison of result shows that with the cost of extra communication links MTN attains higher fault tolerance than that of TESH and mesh networks, and equal to that of TTN, and less than that of torus network. Also, the hierarchical networks such as MTN, TTN, and TESH yield moderate bisection width; and the bisection width of MTN is lower than that of mesh and torus networks. This paper shows the superiority of the proposed MTN by comparative study of static network performance. [ABSTRACT FROM AUTHOR]

The research in the field of Cyber Security has raised the need to address the issue of cybercrimes that have caused the requisition of the intellectual properties such as break down of computer systems, impairment of important data, compromising the confidentiality, authenticity, and integrity of the user. Considering these scenarios, it is essential to secure the computer systems and the user using an Intrusion Detection System (IDS). The performance of IDS studied by developing an IDS dataset, consisting of network traffic features to learn the attack patterns. Intrusion detection is a classification problem, wherein various Machine Learning (ML) and Data Mining (DM) techniques applied to classify the network data into normal and attack traffic. Moreover, the types of network attacks changed over the years, and therefore, there is a need to update the datasets used for evaluating IDS. This paper list the different IDS datasets used for the evaluation of IDS model. The paper presents an overview of the ML and DM techniques used for IDS along with the discussion on CIC-IDS-2017 and CSE-CIC-IDS-2018. These are recent datasets consisting of network attack features and include new attacks categories. This paper discusses the recent advancement in the IDS datasets that can be used by various research communities as the manifesto for using the new IDS datasets for developing efficient and effective ML and DM based IDS. [ABSTRACT FROM AUTHOR]

• The digital twin concept is investigated using a discrete damped dynamic system model. • The concept of "slow time" is proposed to differentiate the evolution of the digital twin from its dynamics. • Stiffness and mass variation in the discrete system is considered. • Sensor errors are modeled in the update process of the digital twin. • The digital twin is expressed as a benchmark closed form solution. This paper employs a discrete damped dynamic system to investigate the emerging concept of a digital twin. Dynamic systems are well understood across engineering and science domains, and represent a familiar and convenient platform for exploring the various aspects of a digital twin design. The aim is to create a framework accessible to engineering sciences related to the aerospace, electrical, mechanical and computational area. The virtual model of the physical system is expressed as a differential equation in two-time scales, with the concept of a slow time being used to separate the evolution of the system properties from the instantaneous time. Cases involving stiffness variation and mass variation are considered, individually and together. It is assumed that the damped natural frequency and the time response are measured through sensors placed on the physical system. Issues of errors and reduced sampling rate in sensor measurements on the digital twin are investigated. The digital twin is expressed as an analytical solution through closed-form expressions and the effect of sensor errors is brought out through the simulations. Several key concepts introduced in this paper are summarized and ideas for urgent future research needs are proposed. The current work breaks free from the qualitative description of digital twins pervading the literature and can be used as benchmark solutions to validate digital twin of experimental dynamic systems and their implementation using sensors, the internet of things and deployment on the cloud computing systems. [ABSTRACT FROM AUTHOR]

The reliability, real-time performance, and quality of service of fog computing are dramatically affected by deadlock. In this paper, a new Deadlock Management System for a fog computing environment is proposed to achieve the best task scheduling while considering deadlock prediction. It contains five modules; the receiver, the matchmaker, the deadlock predictor, the dispatcher, and the ranking module. The receiver is responsible for incoming tasks, assigning their initial priority and task envelope, and calculating server computational distance. The match-making is a fuzzy inference algorithm used to calculate the server matchmaking degree. This determines the best fog server to perform the task. The deadlock module calculates the deadlock prediction risk degree while The dispatcher module dispatches the task depending on first-fit or best-fit modes. The ranking module is responsible for ranking all suitable fog servers and choosing the most suitable one to implement the task. This paper contributes to providing a new load-balancing strategy in fog computing while taking deadlock into consideration. Thus, resolving drawbacks such as load and complexity that strain the network, and computation latency. The results show that the proposed strategy outperforms other load-balancing techniques reported in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge computing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable-length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Applicative computing systems and technologies have taken a strong position in modern computing. In this paper, the basic applicative system, whether it is a lambda calculus or a system of combinators, is considered as a prototype concept system, using which it is possible to build individual systems that are practically significant for mathematics, computing, or programming. They are families of computational models that have both their own semantics and applied areas. This conceptualization/individualization technique is characteristic of the field of semantic studies. As it turns out, the applicative approach forms a metatheoretical framework that provides the basis for cognitive systems that consider abstract objects and interpret their properties and behavior in the environment of modern computing. [ABSTRACT FROM AUTHOR]

Lipreading is the ability to recognize words or sentences from the mouth movements of a speaking person. This process is also known as Visual Speech Recognition (VSR). Lipreading has two main advantages: facilitate communication for people with hearing or speaking problems and aid speech recognition in noisy environments. In this paper, we propose a lipreading computing system capable of recognizing ten common Arabic words by performing word extraction from the mouth movements. The system receives a video of a person uttering an Arabic word as an input and outputs the text of the predicted word. During the implementation stage of the proposed system, three deep learning and neural network architectures are alternatively used to train, validate, and test the system using a locally collected and preprocessed dataset. The dataset contains 1051 videos and will be made available upon request. Moreover, a voting model that combines the three architectures is proposed. The highest testing accuracy (i.e. 82.84%) is achieved by leveraging the voting model. [ABSTRACT FROM AUTHOR]

• A new model of electron emission processes in strong electromagnetic fields is formulated. • The model takes into account the structure, properties and actual geometry of the emitter. • The factors (aperture, frequency range, direction diagrams) influencing the parameters of the electron beams are calculated. • The numerical procedure combines methods: control volumes, particle-in-cells and large smoothed particles. • The parallel implementation integrates domain decomposition, parallelization on particles and load balancing. The aim of this work is modeling processes of field electron emission in strong electromagnetic fields. This problem is relevant for many technical and medical applications. At present time, electrical devices that combine a large value of field, a powerful relativistic effect and an ultra-short time interval of action are in demand. They find their application in the treatment of the surfaces with inorganic, organic and mixed structures. Modeling of such devices encounters certain difficulties due to the complexity of the mathematical description of the emission processes. In this paper, an approach using the method of large smoothed particles in combination with grid calculation of fields based on Maxwell's equations is proposed. The study was carried out within the framework of the problem of calculating the field emission of electrons from the surface of axisymmetric metal cathodes on Cartesian and unstructured curved meshes. To implement the approach, a complex mathematical model, a parallel numerical algorithm and its software realization have been developed. The elaborated software is focused on the use of multiprocessor computing systems with a central architecture. Test calculations confirmed the correctness of the proposed approach and the high efficiency of its software implementation. [ABSTRACT FROM AUTHOR]

Real-time virtualization is currently a hot topic, and there is much ongoing research on real-time Virtual Machines and hypervisors. However, most of the previous research focused either on reducing the latencies introduced by the virtualization stack (hypervisor, host Operating System, Virtual Machine scheduling, etc...) or analyzing the virtual CPU scheduling algorithms. Only a few works investigated the impact of the guest Operating System architecture on real-time performance or considered multiple performance metrics (latency, schedulability, startup times, resource consumption) at the same time. This paper compares various features of different virtualization technologies and guest Operating Systems, evaluating their suitability for serving real-time applications. The results indicate that solutions based on KVM (and an appropriate microvm) and the OSv unikernel can be considered viable alternatives to more traditional VMs or containers. [ABSTRACT FROM AUTHOR]

Modern edge real-time automotive applications are becoming more complex, dynamic, and distributed, moving away from conventional static operating environments to support advanced driving assistance and autonomous driving functionalities. This shift necessitates formulating more complex task models to represent the evolving nature of these applications aptly. Modeling of real-time automotive systems is typically performed leveraging Architectural Languages (ALs) such as Amalthea, which are commonly used by the industry to describe the characteristics of processing platforms, operating systems, and tasks. However, these architectural languages are originally derived for classical automotive applications and need to evolve to meet the needs of next-generation applications. This paper proposes an automatic framework for the modeling and automatic code generation of dynamic automotive applications under the QNX RTOS. To this end, we extend Amalthea to describe chains of communicating tasks with multiple operating modes and to consider the QNX's reservation-based scheduler, called APS, which allows providing temporal isolation between applications co-located on the same hardware platform. Finally, an evaluation is presented to compare different implementation alternatives under QNX that are automatically generated by our code generation framework. [ABSTRACT FROM AUTHOR]

Asymmetric Multicore Processors (AMP) are widely used in both advanced and basic computing systems due to their fundamental flexibility and outstanding computing possibilities. Among the subcategories of AMP, Performance Asymmetric Multicore (PAM) Architectures are unique in that they include various micro-architecture cores into a single chip. Nevertheless, the complex interplay between heterogeneous cores and a diverse range of applications presents a formidable challenge in determining the optimal hardware configuration, encompassing core selection and reduced energy consumption for each application. To tackle these multifaceted problems, this paper introduces a pioneering model for core prediction and resource allocation based on the Dual Branch Recurrent Neural Network (DB-RNN), specifically for AMP. The DB-RNN model encompasses weight sharing facilitated by a hybrid optimization algorithm named African Vulture with Aquila Optimizer (AVAO), and a core prediction module. The proposed model evaluates the Energy-Delay Product (EDP) to find the performance of each core. In the final phase, DB-RNN dynamically predicts the most suitable cores for individual workloads at runtime, thereby elevating both energy efficiency and overall system performance. The experimental results show that the model achieved prediction accuracy up to 98.76 %. This innovative approach paves the way for enhanced efficiency and performance in PAM systems. [ABSTRACT FROM AUTHOR]

With the rise of smart city applications, the accessibility of users' location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis. [ABSTRACT FROM AUTHOR]

Haze usually causes severe interference to image visibility. Such degradation on images troubles both human observers and computer vision systems. To seek high-quality images from degraded ones, a large number of image dehazing algorithms have been proposed from different perspectives like image enhancement, restoration, and fusion. Especially in recent years, with the rapid development of deep learning, CNN-based methods have already dominated the mainstream of image dehazing and gained significant progress on benchmark datasets. This paper firstly presents a comprehensive survey of existing image dehazing methods, and then conducts both qualitative and quantitative comparisons among representative methods, from classic methods to recent advanced approaches. We expect the literature survey and benchmark analysis could help readers better understand the advantages and limitations of existing dehazing methods. Moreover, a discussion on possible trends in single image dehazing is put forward to innovate further works. • Image dehazing methods via enhancement, restoration and fusion are surveyed. • The datasets of image dehazing and widely used metrics are summarized and discussed. • Experiments of 19 methods over 8 metrics are conducted to evaluate the performance. • Discussion of opening problems and future trends are given to innovate future works. [ABSTRACT FROM AUTHOR]

This paper researches mean-square bounded consensus for a nonlinear multi-agent system subjected to randomly occurring deception attack, process and measurement noises. Considering the measurement tampered by the attacker, an estimator is presented to obtain relative accurate state estimation, where the gain is acquired by a recursive algorithm. On this basis, a type of centralized controller is designed combined with cloud computing system. Moreover, from perspective of the defender, a detector is proposed at the side of agent to detect whether the current actuator input is attacked. Using linear matrix inequality, sufficient conditions are given for achieving mean-square bounded consensus and an upper boundary is derived. Finally, validity of the proposed method is illustrated via two simulation examples. • An estimator with attack compensation is presented. • A centralized controller is designed to lower effect of network attack. • A detector is proposed to detect whether the current actuator input is attacked. • Sufficient conditions of stability and consensus are derived. [ABSTRACT FROM AUTHOR]

Recently, a new class of energy autonomous systems has emerged. They scavenge regenerative energy from the environment which allows them perpetual operation. In this paper we focus on systems with energy harvesting and DPM (Dynamic Power Management) capabilities under real-time requirements expressed in terms of deadlines. The classical Earliest Deadline First (EDF) scheduler is optimal when dedicated to deadline-constrained job sets in systems with no energy limitation. However, greediness in consuming the energy makes EDF not always the most appropriate solution to energy harvesting conditions. The purpose of this paper is to answer the following question: How to choose a scheduler for the design of a real-time energy harvesting application where the real-time jobs execute on a single processor? We consider a system model composed of a uniprocessor platform provided with energy harvester (e.g. solar panel) and energy reservoir (e.g. battery or capacitor). Firstly, we investigate three distinct schedulers based on the earliest deadline rule, namely ED-H, EH-EDF and EDF. They are respectively proved optimal for clairvoyant, idling and non-idling settings. Secondly, we evaluate the relative performance of the proposed scheduling algorithms based on simulation experiments. We bring to light the effect of clairvoyance and idling capabilities of the scheduler on the improvement in quality of service which is related to deadline success rate and energy storage capacity requirement for maintaining zero deadline miss rate. In addition, this paper gives a selection guide that leads any software designer to find the scheduler that is most pertinent in accordance to operational specifications. [ABSTRACT FROM AUTHOR]

• A survey of kernel clustering methods is provided. • Inspiring from the mechanism of flight guidance in particle swarm a fast kernel clustering method. • Presenting a framework for an automatic parameter tuning for the proposed method. • Application of the proposed method to image and video segmentation tasks. • Developing enhancement strategies to improve image and video segmentation results. Machine learning has become a pillar of today's expert and intelligent systems where a special attention has been drawn to unsupervised methods. Support vector data description is one of the most interesting methods proposed in this context that addresses two main challenges: the absence of ground truth labels and the presence of outliers in data. It is well known that the ultimate problem has a great impact on a system performance since it typically prevents and limits its generalization and hence its failure. Even though the strengths of the Support vector data description method, its application into multi class and large scale context, as computer vision systems, raises bottleneck issues due to its runtime complexity and mono-class context application. We propose, in this paper, an approach inspired from the movement behavior of particle swarm and relies on the use of core set paradigm to overcome the aforementioned drawbacks of Support vector data description method. Indeed, since the proposed method uses some parameters corresponding to kernel function and swarm's movement formulation, we present and study in this paper some strategies to ensure a dynamic adaptation of these parameters. The main qualities of the proposed method is that it provides a fast scheme for clustering a set of points without neither a prior knowledge on the number of naturally occurring cluster in the data nor an assumption on clusters shapes. A quantitative and comparative performance assessment is carried out over the Berkeley and BuffaloXiph datasets. The results witness the efficiency of the method in terms of both classification accuracy and computational performance. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas. [ABSTRACT FROM AUTHOR]

Abstract This paper presents seven concurrent hash table implementations in Haskell, ranging from low-level synchronization mechanisms to high-level ones such as transactional memories. The hash tables were compared using different initial sizes, load factors, data types and hash functions. We also present a case study on implementing a color palette algorithm using the hash tables. The result of the comparison between the algorithms shows that the implementation using the STM Haskell transactional memory library and fine-grain synchronization provides the best performance and good scalability, followed by the implementation using lock striping and MVars. Highlights • The paper describes seven concurrent hash table implementations in Haskell. • Different abstractions were used: MVars, compare and swap, and transactional memory. • Detailed performance comparison of the hash tables is presented. • STM Haskell with fine-grain synchronization shows the best performance in overall. [ABSTRACT FROM AUTHOR]