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Multiple clusterings: Recent advances and perspectives Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-26 Guoxian Yu, Liangrui Ren, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
Clustering is a fundamental data exploration technique to discover hidden grouping structure of data. With the proliferation of big data, and the increase of volume and variety, the complexity of data multiplicity is increasing as well. Traditional clustering methods can provide only a single clustering result, which restricts data exploration to one single possible partition. In contrast, multiple
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Deep learning for intelligent demand response and smart grids: A comprehensive survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-14 Prabadevi Boopathy, Madhusanka Liyanage, Natarajan Deepa, Mounik Velavali, Shivani Reddy, Praveen Kumar Reddy Maddikunta, Neelu Khare, Thippa Reddy Gadekallu, Won-Joo Hwang, Quoc-Viet Pham
Electricity is one of the mandatory commodities for mankind today. To address challenges and issues in the transmission of electricity through the traditional grid, the concepts of smart grids and demand response have been developed. In such systems, a large amount of data is generated daily from various sources such as power generation (e.g., wind turbines), transmission and distribution (microgrids
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Sustainable computing across datacenters: A review of enabling models and techniques Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-13 Muhammad Zakarya, Ayaz Ali Khan, Mohammed Reza Chalak Qazani, Hashim Ali, Mahmood Al-Bahri, Atta Ur Rehman Khan, Ahmad Ali, Rahim Khan
The growth rate in big data and internet of things (IoT) is far exceeding the computer performance rate at which modern processors can compute on the massive amount of data. The cluster and cloud technologies enriched by machine learning applications had significantly helped in performance growths subject to the underlying network performance. Computer systems have been studied for improvement in performance
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Fundamental design aspects of UAV-enabled MEC systems: A review on models, challenges, and future opportunities Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-02-06 Mohd Hirzi Adnan, Zuriati Ahmad Zukarnain, Oluwatosin Ahmed Amodu
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Content-driven music recommendation: Evolution, state of the art, and challenges Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-01-30 Yashar Deldjoo, Markus Schedl, Peter Knees
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data – which we refer
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Systematic literature review: Quantum machine learning and its applications Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-01-25 David Peral-García, Juan Cruz-Benito, Francisco José García-Peñalvo
Quantum physics has changed the way we understand our environment, and one of its branches, quantum mechanics, has demonstrated accurate and consistent theoretical results. Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles (photons, electrons, etc.) for subsequent use in performing calculations
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Deep learning for unmanned aerial vehicles detection: A review Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-01-03 Nader Al-lQubaydhi, Abdulrahman Alenezi, Turki Alanazi, Abdulrahman Senyor, Naif Alanezi, Bandar Alotaibi, Munif Alotaibi, Abdul Razaque, Salim Hariri
As a new type of aerial robotics, drones are easy to use and inexpensive, which has facilitated their acquisition by individuals and organizations. This unequivocal and widespread presence of amateur drones may cause many dangers, such as privacy breaches by reaching sensitive locations of authorities and individuals. In this paper, we summarize the performance-affecting factors and major obstacles
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Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet Comput. Sci. Rev. (IF 12.9) Pub Date : 2024-01-03 Ali Asghari, Mohammad Karim Sohrabi
The growing technology of the fifth generation (5G) of mobile telecommunications has led to the special attention of cloud service providers (CSPs) to mobile cloud computing (MCC). Due to the limitations in processing power, storage space and energy capacity of mobile devices, cloud resources can be moved to the edge of the network to improve the quality of service (QoS). Server placement is a crucial
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A survey on algorithms for Nash equilibria in finite normal-form games Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-12-28 Hanyu Li, Wenhan Huang, Zhijian Duan, David Henry Mguni, Kun Shao, Jun Wang, Xiaotie Deng
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing demand on Nash equilibrium computation, especially for Internet economics and multi-agent learning. This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games
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Systematic review on weapon detection in surveillance footage through deep learning Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-12-26 Tomás Santos, Hélder Oliveira, António Cunha
In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action. Despite the human visual structure being highly evolved and able to process
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Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-12-13 Shaili Mishra, Anuja Arora
The exploding usage of physical object properties has greatly facilitated real-time applications such as robotics to perceive exactly as it appears in existence. Changes in the nature and properties of diverse real-time systems are associated with physical properties modification due to environmental factors. These physics-based object properties features attract the researchers’ attention while developing
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Secret sharing: A comprehensive survey, taxonomy and applications Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-11-30 Arup Kumar Chattopadhyay, Sanchita Saha, Amitava Nag, Sukumar Nandi
The emergence of ubiquitous computing and different disruptive technologies caused magnificent development in information and communication technology. Likewise, cybercriminals are also carefully considering different newer ways of attacks. Protecting the confidentiality, integrity, and authentication of sensitive information is the day’s major challenge. Secret sharing is a method that allows a trusted
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Model-based joint analysis of safety and security:Survey and identification of gaps Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-11-07 Stefano M. Nicoletti, Marijn Peppelman, Christina Kolb, Mariëlle Stoelinga
We survey the state-of-the-art on model-based formalisms for safety and security joint analysis, where safety refers to the absence of unintended failures, and security to absence of malicious attacks. We conduct a thorough literature review and – as a result – we consider fourteen model-based formalisms and compare them with respect to several criteria: (1) Modeling capabilities and Expressiveness:
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Flow based containerized honeypot approach for network traffic analysis: An empirical study Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-10-28 Sibi Chakkaravarthy Sethuraman, Tharshith Goud Jadapalli, Devi Priya Vimala Sudhakaran, Saraju P. Mohanty
The world of connected devices has been attributed to applications that relied upon multitude of devices to acquire and distribute data over extremely diverse networks. This caused a plethora of potential threats. In the field of IT security, the concept of digital baits, or honeypots, which are typically network components (computer systems, access points, or switches) launched to be interrogated
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A comprehensive survey on data aggregation techniques in UAV-enabled Internet of things Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-11-01 Asif Mahmud Raivi, Sangman Moh
In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) framework owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be
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IoT systems modeling and performance evaluation Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-10-31 Alem Čolaković
The continuous increase of IoT applications leads to a vast amount of data that needs to be transmitted, stored, and processed. Many IoT applications rely on the Cloud infrastructure to handle these specific application demands. However, the integration of IoT and Cloud poses challenges such as network delays, throughput, energy consumption, reliability, etc. Therefore, a new computing concept is required
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Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-10-05 Manel Khazri Khlifi, Wadii Boulila, Imed Riadh Farah
In the last decade, there has been a significant surge of interest in machine learning, primarily driven by advancements in deep learning (DL). DL has emerged as a powerful solution to address various challenges in numerous fields, including remote sensing (RS). Graph Deep Learning (GDL), a sub-field of DL, has recently gained increasing attention in the RS community. Tasks in RS requiring detailed
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Asynchronous federated learning on heterogeneous devices: A survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-10-04 Chenhao Xu, Youyang Qu, Yong Xiang, Longxiang Gao
Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices
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Blockchain-based solutions for mobile crowdsensing: A comprehensive survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-16 Ruiyun Yu, Ann Move Oguti, Mohammad S. Obaidat, Shuchen Li, Pengfei Wang, Kuei-Fang Hsiao
Mobile crowdsensing (MCS) is an emerging data-driven paradigm that leverages the collective intelligence of the crowd, their mobility, and the crowd-companioned smart mobile devices embedded with powerful sensors to acquire information from the physical environment for crowd intelligence extraction and human-centric service delivery. However, existing MCS systems operate in a centralized manner, giving
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A quest for research and knowledge gaps in cybersecurity awareness for small and medium-sized enterprises Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-12 Sunil Chaudhary, Vasileios Gkioulos, Sokratis Katsikas
The proliferation of information and communication technologies in enterprises enables them to develop new business models and enhance their operational and commercial activities. Nevertheless, this practice also introduces new cybersecurity risks and vulnerabilities. This may not be an issue for large organizations with the resources and mature cybersecurity programs in place; the situation with small
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A systematic review of federated learning incentive mechanisms and associated security challenges Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-13 Asad Ali, Inaam Ilahi, Adnan Qayyum, Ihab Mohammed, Ala Al-Fuqaha, Junaid Qadir
In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine learning (ML) models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed
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Uncertainty in runtime verification: A survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-07 Rania Taleb, Sylvain Hallé, Raphaël Khoury
Runtime Verification can be defined as a collection of formal methods for studying the dynamic evaluation of execution traces against formal specifications. Aside from creating a monitor from specifications and building algorithms for the evaluation of the trace, the process of gathering events and making them available for the monitor and the communication between the system under analysis and the
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A Comprehensive review of ‘Internet of Healthcare Things’: Networking aspects, technologies, services, applications, challenges, and security concerns Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-07 Himanshu Verma, Naveen Chauhan, Lalit Kumar Awasthi
The Internet of Things (IoT) is a smart, internet-connected, and omnipresent network. Healthcare is one of the most critical sectors that could benefit from IoT technology. In the medical sphere, the rise of the IoT transforms traditional healthcare services by encouraging technological, social, and economic factors. This study rigorously analyzes various aspects of the Internet of Healthcare Things
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Distributed ledger technologies for authentication and access control in networking applications: A comprehensive survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-02 Fariba Ghaffari, Emmanuel Bertin, Noel Crespi, Julien Hatin
The accelerated growth of networking technologies highlights the importance of Authentication and Access Control (AAC) as protection against associated attacks. Controlling access to resources, facilitating resource sharing, and managing user mobility are some of the notable capabilities provided by AAC methods. Centralized methods are the most common deployment architectures, that can be threatened
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Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-08-31 Garima Jaiswal, Ritu Rani, Harshita Mangotra, Arun Sharma
Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of spectral bands, providing unparalleled levels of precision and accuracy in data analysis. Another technology gaining popularity in many industries is Autoencoders (AE). AE uses advanced deep learning algorithms for encoding and decoding data, leading to highly precise and efficient neural network-based models. Within
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An empirical investigation of task scheduling and VM consolidation schemes in cloud environment Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-01 Sweta Singh, Rakesh Kumar, Dayashankar Singh
Cloud computing has evolved as a new paradigm in Internet computing, offering services to the end-users and large-organizations, on-demand and pay-per-the-usage basis with high availability, elasticity, scalability and resiliency. In order to improve the performance of the Cloud system, handling multiple heterogeneous tasks concurrently, an appropriate task scheduler is required. To meet the user’s
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A review of IoT security and privacy using decentralized blockchain techniques Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-09-01 Vinay Gugueoth, Sunitha Safavat, Sachin Shetty, Danda Rawat
IoT security is one of the prominent issues that has gained significant attention among the researchers in recent times. The recent advancements in IoT introduces various critical security issues and increases the risk of privacy leakage of IoT data. Implementation of Blockchain can be a potential solution for the security issues in IoT. This review deeply investigates the security threats and issues
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Comprehensive survey of the solving puzzle problems Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-08-25 Seçkin Yılmaz, Vasif V. Nabiyev
Solving puzzle problems using computer-aided methods is becoming more common with applications in forensic science, restoration, banking system, and multimedia. However, only a few surveys have been published on this topic, the most recent being more than a decade old. The scope of 2D puzzle problems is extensive, and the number of computer-aided methods has increased in recent years. In this paper
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Understanding blockchain: Definitions, architecture, design, and system comparison Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-08-16 Mohammad Hossein Tabatabaei, Roman Vitenberg, Narasimha Raghavan Veeraragavan
The explosive advent of the blockchain technology has led to hundreds of blockchain systems in the industry, thousands of academic papers published over the last few years, and an even larger number of new initiatives and projects. Despite the emerging consolidation efforts, the area remains highly turbulent without systematization, educational materials, or cross-system comparative analysis. In this
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Defense strategies for Adversarial Machine Learning: A survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-08-11 Panagiotis Bountakas, Apostolis Zarras, Alexios Lekidis, Christos Xenakis
Adversarial Machine Learning (AML) is a recently introduced technique, aiming to deceive Machine Learning (ML) models by providing falsified inputs to render those models ineffective. Consequently, most researchers focus on detecting new AML attacks that can undermine existing ML infrastructures, overlooking at the same time the significance of defense strategies. This article constitutes a survey
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Systematic review on privacy categorisation Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-08-10 Paola Inverardi, Patrizio Migliarini, Massimiliano Palmiero
In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people’s decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy
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Aspect based sentiment analysis using deep learning approaches: A survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-08-06 Ganpat Singh Chauhan, Ravi Nahta, Yogesh Kumar Meena, Dinesh Gopalani
The wealth of unstructured text on the online web portal has made opinion mining the most thrust area for researchers, academicians, and businesses to extract information for gathering, analyzing, and aggregating human emotions. The extraction of public sentiment from the text at an aspect level has contributed exceptionally to various businesses in the marketplace. In recent times, deep learning-based
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Optimized traffic engineering in Software Defined Wireless Network based IoT (SDWN-IoT): State-of-the-art, research opportunities and challenges Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-06-30 Rohit Kumar, Venkanna U., Vivek Tiwari
Wireless networks have been in focus since the last few decades due to their indispensable role in the future generation networks like the Internet of Things (IoT). However, the associated challenges in wireless network implementation such as distance, line-of-sight, interference, weather, power issues, etc., affect the performance adversely. Software Defined Networking (SDN) is a future generation
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A survey of the European Open Science Cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-06-07 Amanda Calatrava, Hernán Asorey, Jan Astalos, Alberto Azevedo, Francesco Benincasa, Ignacio Blanquer, Martin Bobak, Francisco Brasileiro, Laia Codó, Laura del Cano, Borja Esteban, Meritxell Ferret, Josef Handl, Tobias Kerzenmacher, Valentin Kozlov, Aleš Křenek, Ricardo Martins, Manuel Pavesio, Antonio Juan Rubio-Montero, Juan Sánchez-Ferrero
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Network resource management mechanisms in SDN enabled WSNs: A comprehensive review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-06-01 Vikas Tyagi, Samayveer Singh
Wireless technologies usually have very limited computing, memory, and battery power that require the optimal management of network resources to increase network performance. The optimization of these network resources provides an efficient network topology, traffic control, routing, and data aggregation. This study presents a qualitative and quantitative investigation to evaluate the efficient network
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A survey of set accumulators for blockchain systems Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-06-02 Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa, Laura Ricci
Set accumulators are cryptographic primitives used to represent arbitrarily large sets of elements with a single constant-size value and to efficiently verify whether a value belongs to that set. Accumulators support the generation of membership proofs, meaning that they can certify the presence of a given value among the elements of a set. In this paper we present an overview of the theoretical concepts
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Private set intersection: A systematic literature review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-05-27 Daniel Morales, Isaac Agudo, Javier Lopez
Secure Multi-party Computation (SMPC) is a family of protocols which allow some parties to compute a function on their private inputs, obtaining the output at the end and nothing more. In this work, we focus on a particular SMPC problem named Private Set Intersection (PSI). The challenge in PSI is how two or more parties can compute the intersection of their private input sets, while the elements that
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Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-05-25 Shahnawaz Ahmad, Iman Shakeel, Shabana Mehfuz, Javed Ahmad
In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability
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Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-05-22 Maha Nssibi, Ghaith Manita, Ouajdi Korbaa
The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus
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Leveraging 6G, extended reality, and IoT big data analytics for healthcare: A review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-05-04 Hafiz Farooq Ahmad, Wajid Rafique, Raihan Ur Rasool, Abdulaziz Alhumam, Zahid Anwar, Junaid Qadir
In recent years, the healthcare industry has faced new challenges around staffing, human interaction, and the adoption of telehealth. Technological innovations can improve efficiency, productivity, and patient outcomes, but healthcare has been slow to adopt them. However, the promise of 6G communication, extended reality (XR), and the Internet of Things (IoT) big data analytics may revolutionize healthcare
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Negative selection in anomaly detection—A survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-05-02 Praneet Saurabh, Bhupendra Verma
The remarkable ability to separate and identify self and non-self in a given problem space, makes negative selection a fascinating concept of artificial immune system. Therefore, negative selection has attracted research interest and is studied and explored for complex problem solving across different application areas. Anomaly detection in computer security is a thriving area of research and has witnessed
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A survey of parameterized algorithms and the complexity of edge modification Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-04-26 Christophe Crespelle, Pål Grønås Drange, Fedor V. Fomin, Petr Golovach
The survey is a comprehensive overview of the developing area of parameterized algorithms for graph modification problems. It describes state of the art in kernelization, subexponential algorithms, and parameterized complexity of graph modification. The main focus is on edge modification problems, where the task is to change some adjacencies in a graph to satisfy some required properties. To facilitate
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State of the art on quality control for data streams: A systematic literature review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-04-17 Mostafa Mirzaie, Behshid Behkamal, Mohammad Allahbakhsh, Samad Paydar, Elisa Bertino
These days, endless streams of data are generated by various sources such as sensors, applications, users, etc. Due to possible issues in sources, such as malfunctions in sensors, platforms, or communication, the generated data might be of low quality, and this can lead to wrong outcomes for the tasks that rely on these data streams. Therefore, controlling the quality of data streams has become increasingly
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Towards accessible chart visualizations for the non-visuals: Research, applications and gaps Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-04-17 Mandhatya Singh, Muhammad Suhaib Kanroo, Hadia Showkat Kawoosa, Puneet Goyal
Chart Visualizations (CharVis) such as charts/plots and diagrams are commonly used in documents for representing the underlying quantitative information. However, the inaccessibility of such visualizations exemplify one of the rife challenges of information access for Blind and Visually Impaired People (BVIP). The existing BVIP-related assistive technologies (ATs) are capable enough to provide the
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Research communities in cyber security vulnerability assessments: A comprehensive literature review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-03-30 Fredrik Heiding, Sotirios Katsikeas, Robert Lagerström
Ethical hacking and vulnerability assessments are gaining rapid momentum as academic fields of study. Still, it is sometimes unclear what research areas are included in the categories and how they fit into the traditional academic framework. Previous studies have reviewed literature in the field, but the attempts use manual analysis and thus fail to provide a comprehensive view of the domain. To better
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A survey on GANs for computer vision: Recent research, analysis and taxonomy Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-03-20 Guillermo Iglesias, Edgar Talavera, Alberto Díaz-Álvarez
In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought
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A survey on legal question–answering systems Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-03-16 Jorge Martinez-Gil
Many legal professionals think the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and error-prone. The two main reasons are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal
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Task scheduling in fog environment — Challenges, tools & methodologies: A review Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-03-13 Zahra Jalali Khalil Abadi, Najme Mansouri, Mahshid Khalouie
Even though cloud computing offers many advantages, it can be a poor choice sometimes because of its slow response to existing requests, leading to the need for fog computing. Scheduling tasks in a fog environment is a major challenge. It is important that IoT clients execute their tasks in a timely manner and obtain lower-cost services; however, they are also looking for tasks to be executed in a
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Visual language integration: A survey and open challenges Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-03-02 Sang-Min Park, Young-Gab Kim
With the recent development of deep learning technology comes the wide use of artificial intelligence (AI) models in various domains. AI shows good performance for definite-purpose tasks, such as image recognition and text classification. The recognition performance for every single task has become more accurate than feature engineering, enabling more work that could not be done before. In addition
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Fog computing for next-generation Internet of Things: Fundamental, state-of-the-art and research challenges Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-03-01 Abhishek Hazra, Pradeep Rana, Mainak Adhikari, Tarachand Amgoth
In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance
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Synthetic data generation: State of the art in health care domain Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-02-26 Hajra Murtaza, Musharif Ahmed, Naurin Farooq Khan, Ghulam Murtaza, Saad Zafar, Ambreen Bano
Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors
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Facial expression and body gesture emotion recognition: A systematic review on the use of visual data in affective computing Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-02-22 Sze Chit Leong, Yuk Ming Tang, Chung Hin Lai, C.K.M. Lee
Emotion is an important driver of human decision-making and communication. With the recent rise of human–computer interaction, affective computing has become a trending research topic, aiming to develop computational systems that can understand human emotions and respond to them. A systematic review has been conducted to fill these gaps since previous reviews regarding machine-enabled automated visual
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A survey: When moving target defense meets game theory Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-02-22 Jinglei Tan, Hui Jin, Hongqi Zhang, Yuchen Zhang, Dexian Chang, Xiaohu Liu, Hengwei Zhang
Moving target defense (MTD) can break through asymmetry between attackers and defenders. To improve the effectiveness of cybersecurity defense techniques, defense requires not only advanced and practical defense technologies but effective, scientific decision-making methods. Due to complex attacker–defender interaction, autonomous, automatic, accurate, and effective selection of the optimal strategy
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A comprehensive review on blockchains for Internet of Vehicles: Challenges and directions Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-02-21 Brian Hildebrand, Simra Tabassum, Bharath Konatham, Fathi Amsaad, Mohamed Baza, Tara Salman, Abdul Razaque
Internet of Vehicles (IoVs) consists of smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate wirelessly to provide enhanced transportation services such as improved traffic efficiency and reduced traffic congestion and accidents. Unfortunately, current IoV networks suffer from security, privacy, and trust issues. Blockchain technology emerged as a decentralized
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Web service adaptation: A decade’s overview Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-02-06 Haithem Mezni
With the exponential growth of communication and information technologies, adaptation has gained a significant attention as it becomes a key feature of service-based systems, allowing them to operate and evolve in highly dynamic and uncertain environments. Although several Web service standards and frameworks have been proposed and extended, existing solutions do not provide a suitable architecture
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Virtual Customer Assistants in finance: From state of the art and practices to design guidelines Comput. Sci. Rev. (IF 12.9) Pub Date : 2023-01-18 Andrea Iovine, Fedelucio Narducci, Cataldo Musto, Marco de Gemmis, Giovanni Semeraro
Virtual Customer Assistants (VCAs) are revolutionizing the way users interact with machines. VCAs allow a far more natural interaction, and are gaining an increasingly large role in customer service. The financial domain is especially susceptible of this change, because customer care is of paramount importance. Furthermore, VCAs have the potential of supporting customers in performing routine operations
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Abelian combinatorics on words: A survey Comput. Sci. Rev. (IF 12.9) Pub Date : 2022-12-30 Gabriele Fici, Svetlana Puzynina
We survey known results and open problems in abelian combinatorics on words. Abelian combinatorics on words is the extension to the commutative setting of the classical theory of combinatorics on words. The extension is based on abelian equivalence, which is the equivalence relation defined in the set of words by having the same Parikh vector, that is, the same number of occurrences of each letter
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Studying fake news spreading, polarisation dynamics, and manipulation by bots: A tale of networks and language Comput. Sci. Rev. (IF 12.9) Pub Date : 2022-12-28 Giancarlo Ruffo, Alfonso Semeraro, Anastasia Giachanou, Paolo Rosso
With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets’ credibility. Therefore, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines struggle with an information
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Remarks on some misconceptions about unequal probability sampling without replacement Comput. Sci. Rev. (IF 12.9) Pub Date : 2022-12-26 Yves Tillé
Before computer scientists became interested in unequal probability sampling methods, they were widely studied by survey statisticians. We show that sometimes the same sampling methods have been proposed again without reference to existing methods. We also show that methods that are not correct and that were widely discussed in the 1950s are being proposed again. We review the most common errors and