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ردیف | عنوان | نوع |
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1 |
Decentralization Using Quantum Blockchain: A Theoretical Analysis
تمرکززدایی با استفاده از بلاک چین کوانتومی: یک تحلیل نظری-2022 Blockchain technology has been prominent recently due to its applications in cryptocurrency. Numerous decentralized blockchain applications have been possible due to blockchains’ nature of
distributed, secured, and peer-to-peer storage. One of its technical pillars is using public-key cryptography
and hash functions, which promise a secure, pseudoanonymous, and distributed storage with nonrepudiation.
This security is believed to be difficult to break with classical computational powers. However, recent
advances in quantum computing have raised the possibility of breaking these algorithms with quantum
computers, thus, threatening the blockchains’ security. Quantum-resistant blockchains are being proposed
as alternatives to resolve this issue. Some propose to replace traditional cryptography with postquantum
cryptography—others base their approaches on quantum computer networks or quantum internets. Nonetheless, a new security infrastructure (e.g., access control/authentication) must be established before any of
these could happen. This article provides a theoretical analysis of the quantum blockchain technologies
that could be used for decentralized identity authentication. We put together a conceptual design for a
quantum blockchain identity framework and give a review of the technical evidence. We investigate its
essential components and feasibility, effectiveness, and limitations. Even though it currently has various
limitations and challenges, we believe a decentralized perspective of quantum applications is noteworthy and
likely.
INDEX TERMS: Blockchains | consensus protocol | decentralized applications | identity management systems | quantum computing | quantum networks. |
مقاله انگلیسی |
2 |
AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022 Modern businesses benefit significantly from advances in computer vision technology, one of the
important sectors of artificially intelligent and computer science research. Advanced computer
vision issues like image processing, object recognition, and biometric authentication can benefit
from using deep learning methods. As smart devices and facilities advance rapidly, current net-
works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing
demand. Classification of images, object classification, and facial recognition software are some
of the most difficult computer vision problems that can be solved using deep learning methods. As
a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently
been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to
solve the above challenges by introducing a new methodology named Optimizing Computer
Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al-
gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s
outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper
to recognize pattern recognition and intelligent management systems and provide driven meth-
odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can
summarize the significant areas for future research and potential solutions, including image
enhancement, machine vision, and access control. keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication |
مقاله انگلیسی |
3 |
Mapping foodborne pathogen contamination throughout the conventional and alternative poultry supply chains
نقشه برداری از آلودگی پاتوژن ناشی از مواد غذایی در سراسر زنجیره های تأمین مرغ های متداول و جایگزین-2021 Recently, there has been a consumer
push for natural and organic food products. This
has caused alternative poultry production, such as
organic, pasture, and free-range systems, to grow in
popularity. Due to the stricter rearing practices of
alternative poultry production systems, different
types of levels of microbiological risks might be
present for these systems when compared to conventional production systems. Both conventional
and alternative production systems have complex
supply chains that present many different
opportunities for flocks of birds or poultry meat to
be contaminated with foodborne pathogens. As
such, it is important to understand the risks
involved during each step of production. The purpose of this review is to detail the potential routes
of foodborne pathogen transmission throughout the
conventional and alternative supply chains, with a
special emphasis on the differences in risk between
the two management systems, and to identify gaps
in knowledge that could assist, if addressed, in
poultry risk-based decision making.
Key words: broilers | alternative broiler production | Salmonella | Campylobacter | organic |
مقاله انگلیسی |
4 |
A Tokenless Cancellable Scheme for Multimodal Biometric Systems
طرح غیر قابل لغو برای سیستم های بیومتریک چند حالته-2021 Biometric template protection (BTP) is an open problem for biometric identity management systems. Cancellable biometrics is commonly designed to protect biometric templates with two input factors i.e., biometrics and a token used in template replacement. However, the token is often required to be kept secretly; otherwise, the protected template could be vulnerable to several security attacks and breaches of privacy. In this paper, we propose a tokenless cancellable biometrics scheme called Multimodal Extended Feature Vector (M•EFV) Hashing that employs an improved XOR encryption/decryption notion to operate on the transformation key. We stress on multimodal biometrics where the real-valued face and fingerprint vectors are fused and embedded into a binarized cancellable template. Specifically, M•EFV hashing consists of three stages of transformation: 1) normalization and bio- metric fusion; 2) randomization and binarization; and 3) cancellable template generation. To evaluate the proposed scheme, several benchmarking datasets, i.e., FVC2002, FVC2004 for fingerprint and LFW for face are used in experiments. The verification performance is vali- dated by employing the FVC matching protocol. Various attacks are simulated and analysed in the worst-case scenario. Lastly, unlinkability and revocability properties are examined experimentally.© 2021 Elsevier Ltd. All rights reserved. Keywords: Feature-level Fusion | Multimodal Biometrics | Tokenless Cancellable Biometrics | Privacy and security | XOR encryption/decryption |
مقاله انگلیسی |
5 |
The role of digital innovation in knowledge management systems: A systematic literature review
نقش نوآوری دیجیتال در سیستم های مدیریت دانش: مرور ادبیات سیستماتیک-2021 This article investigates the literary corpus on digital innovation in knowledge management systems (KMS) to
understand its role in business governance.
The study introduces a broad survey of the scientific literature on this topic to understand how digital
innovation promotes new business models through the optimization of new knowledge.
We carried out a bibliometric analysis on a database, including 46 articles published in the last three decades
(1990–2020). All the articles were written in English.
The results show that research published on the topic reveals interesting implications for business models and
business performance. These findings especially highlight the links between innovation and sustainability,
revealing that digital transformation tools contribute over the long-term to the value creation process. This
research contributes to the existing literature analyzing the KMS topic by considering it from the digital inno-
vation processes perspective, pointing out the need to implement new knowledge creation and to share measures
which support global and inclusive growth. keywords: تحول دیجیتال (DT) | مدیریت دانش (کیلومتر) | مدل کسب و کار (BM) | عملکرد پایدار (SP) | Digital Transformation (DT) | Knowledge Management (KM) | Business Model (BM) | Sustainable Performance (SP) |
مقاله انگلیسی |
6 |
A data management framework for strategic urban planning using blue-green infrastructure
یک چارچوب مدیریت داده برای برنامه ریزی شهری استراتژیک با استفاده از زیرساخت های آبی سبز-2021 Spatial planning of Blue-Green Infrastructure (BGI) should ideally be based on well-evaluated and context
specific solutions. One important obstacle to reach this goal relates to adequate provisioning of data to ensure
good governance of BGI, i.e., appropriate planning, design, construction, and maintenance. This study explores
the gap between data availability and implementation of BGI in urban planning authorities in Sweden. A multi
method approach including brainstorming, semi-structured interviews with urban planners and experts on BGI
and Geographical Information System (GIS), and validating workshops were performed to develop a framework
for structured and user-friendly data collection and use. Identified challenges concern data availability, data
management, and GIS knowledge. There is a need to improve the organisation of data management and the skills
of trans-disciplinary cooperation to better understand and interpret different types of data. Moreover, different
strategic goals require different data to ensure efficient planning of BGI. This calls for closer interactions between
development of strategic political goals and data collection. The data management framework consists of three
parts: A) Ideal structure of data management in relation to planning process, data infrastructure and organisa-
tional structure, and B) A generic list of data needed, and C) The development of structures for data gathering
and access. We conclude that it is essential to develop pan-municipal data management systems that bridge
sectors and disciplines to ensure efficient management of the urban environment, and which is able to support
the involvement of citizens to collect and access relevant data. The framework can assist in such development. keywords: زیرساخت آبی سبز | مدیریت اطلاعات | برنامه ریزی فضایی | برنامه ریزی استراتژیک | مدیریت طوفان | انطباق تغییرات اقلیمی | فضاهای سبز شهری | Blue-green infrastructure | Data management | Spatial planning | Strategic planning | Stormwater management | Climate change adaptation | Urban green spaces |
مقاله انگلیسی |
7 |
Pathways to individual performance: Examining the interplay between knowledge bases and repository kms use
راه های عملکرد فردی: بررسی تعامل بین پایگاه های دانش و استفاده از مخزن KMS-2021 The aims of this study are to gain further insight into the contingent performance effects of repository knowledge
management systems (KMS) use. While prior research has laid the foundations, a nuanced understanding of the
interplay between the individual’s personal and social knowledge bases and KMS usage behaviors is missing.
Drawing from prior information systems literature, we identify two components of KMS use, usage frequency and
usage intensity. We examine the influence of knowledge bases on individual performance, and the moderating
influence of the usage behaviors. We test the hypothesized research model on usage and performance data from
18,219 real estate agents. We find support for different pathways to individual performance based on configu-
rations of knowledge bases and usage behavior. Overall, the study provides an integrated view of the interplay
between the three constituents of technology-based learning – cognition, behavior, and performance. keywords: مدیریت دانش | سیستم های مدیریت دانش | استفاده از آن | مخازن دانش | فرکانس استفاده | شدت استفاده | تجربه حرفه ای | عضویت تیمی | Knowledge management | Knowledge management systems | It use | Knowledge repositories | Usage frequency | Usage intensity | Professional experience | Team membershipintroduction |
مقاله انگلیسی |
8 |
Optimal feature selection-based biometric key management for identity management system: Emotion oriented facial biometric system
مدیریت کلیدی بیومتریک مبتنی بر انتخاب ویژگی بهینه برای سیستم مدیریت هویت: سیستم بیومتریک صورت احساسات گرا-2021 Identity management systems with biometric key binding make digital transactions secure and reliable. A novel methodology is proposed to develop an intelligent key management system using facial emotions. Key binding with facial emotions makes use of an intrinsic user specific trait facilitating a more natural computer to human interaction. The proposed system utilizes metaheuristic swarm intelligence based optimization techniques to extract optimal features. The work demonstrates key binding by encrypting an image with a secret key bound to optimal features extracted from facial emotions. Efficiency and correctness of proposed key management is validated by successful decryption at receiving end with any one of the enrolled emotions given as input. Deer Hunting Optimization Algorithm and Chicken Swarm Optimization are merged to select optimal features from facial emotions. The derived algorithm is called Fitness Sorted Deer Hunting Optimization Algorithm with Rooster Update. Seven facial emotions — anger, disgust, fear, happiness, sadness, surprise and neutral are used to extract optimal features from Japanese Female Facial Expressions and Yale Facial datasets to train the neural network. Proposed work achieved better performance results over state-of-art optimization algorithms such as whale optimization algorithm, grey wolf optimization, chicken swarm optimization and deer hunting optimization algorithm. Accuracy of proposed model is 2.2% better than deer hunting optimization algorithm and 12.3% better than chicken swarm optimization for a key length 80. Keywords: Identity management system | Facial emotions | Metaheuristic optimization |
مقاله انگلیسی |
9 |
The use of big data and data mining in nurse practitioner clinical education
استفاده از داده های بزرگ و داده کاوی در آموزش بالینی پزشکان -2020 Nurse practitioner (NP) faculty have not fully used data collected in NP clinical education for data mining. With
current advances in database technology including data storage and computing power, NP faculty have an
opportunity to data mine enormous amounts of clinical data documented by NP students in electronic clinical
management systems. The purpose of this project was to examine the use of big data and data mining from NP
clinical education and to establish a foundation for competency-based education. Using a data mining knowledge
discovery process, faculty are able to gain increased understanding of clinical practicum experiences to
inform competency-based NP education and the use of entrusted professional activities for the future. Keywords: Big data | Data mining | Nurse practitioner clinical education | Competency-based education | Nurse Practitioner Core Competencies | Entrustable professional activities |
مقاله انگلیسی |
10 |
Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building
هماهنگی منابع ظرفیتی تجدید پذیر وسایل نقلیه به خانه برای مدیریت انرژی در ساختمان انعطاف پذیر و خود شفایی-2020 The home energy management is an efficient tool to manage energy in the buildings that organizes
different technologies and mathematical techniques to minimize energy cost. Home energy management
often utilizes renewable energy resources to supply load demand in the building. Current home energy
management systems utilize one or several of the available hardware-software capacity resources to deal
with energy consumption in the buildings. However, a comprehensive model including various hardware
and software capacity resources may increase the flexibility of the model. In this regard, this paper
studies an efficient paradigm for home energy management in the building connected to electric grid.
The proposed model forms an energy hub including the hardware resources (i.e., vehicle-to-home, wind
turbine, and diesel generator) and software tools (i.e., demand response program). All the capacity resources
and grid power are optimally adjusted to minimize the daily operational cost of the building as
well as improvement of resiliency and self-healing. Wind energy and load uncertainty are modeled
through stochastic programming. The seasonal pattern is considered for loads, prices, and wind energy.
Simulation results demonstrate that operating all capacity resources minimizes the daily operational
cost. When the wind energy, demand response program, vehicle-to-home, and diesel generator are not
utilized, the cost is increased by 900, 230, 84, and 322%, respectively. It is also confirmed that the
building not only can operate when one of the components is not connected, but also it is able to supply
the demand under off-grid operation. Keywords: Demand response program | Home energy management | Resiliency | Stochastic mixed integer binary model | Vehicle to home | Wind turbine |
مقاله انگلیسی |