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1 |
Intelligent authentication of 5G healthcare devices: A survey
احراز هویت هوشمند دستگاه های مراقبت بهداشتی 5G: یک مرور-2022 The dynamic nature of wireless links and the mobility of devices connected to the Internet of
Things (IoT) over fifth-generation (5G) networks (IoT-5G), on the one hand, empowers pervasive
healthcare applications. On the other hand, it allows eavesdroppers and other illegitimate
actors to access secret information. Due to the poor time efficiency and high computational
complexity of conventional cryptographic methods and the heterogeneous technologies used,
it is easy to compromise the authentication of lightweight wearable and healthcare devices.
Therefore, intelligent authentication, which relies on artificial intelligence (AI), and sufficient
network resources are extremely important for securing healthcare devices connected to IoT-
5G. This survey considers intelligent authentication and includes a comprehensive overview of
intelligent authentication mechanisms for securing IoT-5G devices deployed in the healthcare
domain. First, it presents a detailed, thoughtful, and state-of-the-art review of IoT-5G, healthcare
technologies, tools, applications, research trends, challenges, opportunities, and solutions. We
selected 20 technical articles from those surveyed based on their strong overlaps with IoT,
5G, healthcare, device authentication, and AI. Second, IoT-5G device authentication, radiofrequency fingerprinting, and mutual authentication are reviewed, characterized, clustered,
and classified. Third, the review envisions that AI can be used to integrate the attributes
of the physical layer and 5G networks to empower intelligent healthcare devices. Moreover,
methods for developing intelligent authentication models using AI are presented. Finally, the
future outlook and recommendations are introduced for IoT-5G healthcare applications, and
recommendations for further research are presented as well. The remarkable contributions and
relevance of this survey may assist the research community in understanding the research gaps
and the research opportunities relating to the intelligent authentication of IoT-5G healthcare
devices.
keywords: اینترنت اشیا (IoT) | امنیت اینترنت اشیا | احراز هویت دستگاه | هوش مصنوعی | امنیت مراقبت های بهداشتی | شبکه های 5g | InternetofThings(IoT) | InternetofThingssecurity | Deviceauthentication | Artificialintelligence | Healthcaresecurity | 5Gnetworks |
مقاله انگلیسی |
2 |
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 |
3 |
IoTracker: A probabilistic event tracking approach for data-intensive IoT Smart Applications
IoTracker: یک رویکرد ردیابی رویداد احتمالی برای برنامههای هوشمند اینترنت اشیا با داده های فشرده-2022 Smart Applications for cities, industry, farming and healthcare use Internet of Things (IoT)
approaches to improve the general quality. A dependency on smart applications implies that any
misbehavior may impact our society with varying criticality levels, from simple inconveniences
to life-threatening dangers. One critical challenge in this area is to overcome the side effects
caused by data loss due to failures in software, hardware, and communication systems, which
may also affect data logging systems. Event traceability and auditing may be impaired when an
application makes automated decisions and the operating log is incomplete. In an environment
where many events happen automatically, an audit system must understand, validate, and
find the root causes of eventual failures. This paper presents a probabilistic approach to track
sequences of events even in the face of logging data loss using Bayesian networks. The results of
the performance analysis with three smart application scenarios show that this approach is valid
to track events in the face of incomplete data. Also, scenarios modeled with Bayesian subnets
highlight a decreasing complexity due to this divide and conquer strategy that reduces the
number of elements involved. Consequently, the results improve and also reveal the potential
for further advancement.
Keywords: Smart applications | Event tracker | Probabilistic tracker | Bayesian networks |
مقاله انگلیسی |
4 |
DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks
DQRA: عامل مسیریابی کوانتومی عمیق برای مسیریابی درهم تنیده در شبکه های کوانتومی-2022 Quantum routing plays a key role in the development of the next-generation network system. In
particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping
among particles (e.g., photons) associated with nodes in the network. From another side of computing,
machine learning has achieved numerous breakthrough successes in various application domains, including
networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum
networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model
for quantum networks that employs machine learning architectures to construct the routing path for the
maximum number of demands (source–destination pairs) within a time window. Specifically, we present a
deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA
utilizes an empirically designed deep neural network that observes the current network states to accommodate
the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training
process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated
requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a
rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in
extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the
model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum
networks.
INDEX TERMS: Deep learning | deep reinforcement learning (DRL) | machine learning | next-generation network | quantum network routing | quantum networks. |
مقاله انگلیسی |
5 |
High-accuracy in the classification of butchery cut marks and crocodile tooth marks using machine learning methods and computer vision algorithms
دقت بالا در طبقه بندی علائم برش قصابی و علائم دندان تمساح با استفاده از روش های یادگیری ماشین و الگوریتم های بینایی کامپیوتری-2022 Some researchers using traditional taphonomic criteria (groove shape and presence/absence of microstriations) have cast some doubts about the potential equifinality presented by crocodile tooth marks and
stone tool butchery cut marks. Other researchers have argued that multivariate methods can efficiently
separate both types of marks. Differentiating both taphonomic agents is crucial for determining the earliest evidence of carcass processing by hominins. Here, we use an updated machine learning approach
(discarding artificially bootstrapping the original imbalanced samples) to show that microscopic features
shaped as categorical variables, corresponding to intrinsic properties of mark structure, can accurately
discriminate both types of bone modifications. We also implement new deep-learning methods that
objectively achieve the highest accuracy in differentiating cut marks from crocodile tooth scores (99%
of testing sets). The present study shows that there are precise ways of differentiating both taphonomic
agents, and this invites taphonomists to apply them to controversial paleontological and archaeological
specimens.
keywords: تافونومی | علائم برش | علائم دندان | فراگیری ماشین | یادگیری عمیق | شبکه های عصبی کانولوشنال | قصابی | Taphonomy | Cut marks | Tooth marks | Machine learning | Deep learning | Convolutional neural networks | Butchery |
مقاله انگلیسی |
6 |
Efficient Quantum Network Communication Using Optimized Entanglement Swapping Trees
ارتباطات شبکه کوانتومی کارآمد با استفاده از درختان درهم تنیدگی بهینه-2022 Quantum network communication is challenging, as the no-cloning theorem in the quantum
regime makes many classical techniques inapplicable; in particular, the direct transmission of qubit states
over long distances is infeasible due to unrecoverable errors. For the long-distance communication of
unknown quantum states, the only viable communication approach (assuming local operations and classical
communications) is the teleportation of quantum states, which requires a prior distribution of the entangled
pairs (EPs) of qubits. The establishment of EPs across remote nodes can incur significant latency due to the
low probability of success of the underlying physical processes. The focus of our work is to develop efficient
techniques that minimize EP generation latency. Prior works have focused on selecting entanglement paths;
in contrast, we select entanglement swapping trees—a more accurate representation of the entanglement
generation structure. We develop a dynamic programming algorithm to select an optimal swapping tree for a
single pair of nodes, under the given capacity and fidelity constraints. For the general setting, we develop an
efficient iterative algorithm to compute a set of swapping trees. We present simulation results, which show
that our solutions outperform the prior approaches by an order of magnitude and are viable for long-distance
entanglement generation.
INDEX TERMS: Quantum communications | quantum networks (QNs). |
مقاله انگلیسی |
7 |
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 |
8 |
Epsilon-Nets, Unitary Designs, and Random Quantum Circuits
شبکه های اپسیلون، طرح های واحد و مدارهای کوانتومی تصادفی-2022 Epsilon-nets and approximate unitary t-designs are
natural notions that capture properties of unitary operations
relevant for numerous applications in quantum information
and quantum computing. In this work we study quantitative
connections between these two notions. Specifically, we prove
that, for d dimensional Hilbert space, unitaries constituting
δ-approximate t-expanders form -nets for t d5/2 and δ
3d/2 d2. We also show that for arbitrary t, -nets can be used
to construct δ-approximate unitary t-designs for δ t, where
the notion of approximation is based on the diamond norm.
Finally, we prove that the degree of an exact unitary t design
necessary to obtain an -net must grow at least as fast as 1 (for
fixed dimension) and not slower than d2 (for fixed ). This shows
near optimality of our result connecting t-designs and nets.
We apply our findings in the context of quantum computing.
First, we show that that approximate t-designs can be generated
by shallow random circuits formed from a set of universal twoqudit gates in the parallel and sequential local architectures
considered in (Brandão et al., 2016). Importantly, our gate sets
need not to be symmetric (i.e., contains gates together with
their inverses) or consist of gates with algebraic entries. Second,
we consider compilation of quantum gates and prove a nonconstructive Solovay-Kitaev theorem for general universal gate
sets. Our main technical contribution is a new construction of
efficient polynomial approximations to the Dirac delta in the
space of quantum channels, which can be of independent interest.]
Index Terms: Unitary designs, epsilon nets | random quantum circuits | compilation of quantum gates | unitary channels. |
مقاله انگلیسی |
9 |
A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
بررسی حملات خصمانه در بینایی کامپیوتر: طبقه بندی، تجسم و جهت گیری های آینده-2022 Deep learning has been widely applied in various fields such as computer vision, natural language pro-
cessing, and data mining. Although deep learning has achieved significant success in solving complex
problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, result-
ing in models that fail to perform their tasks properly, which limits the application of deep learning
in security-critical areas. In this paper, we first review some of the classical and latest representative
adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowl-
edge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze
the subject development in this field based on the information of 5923 articles from Scopus. In the
end, possible research directions for the development about adversarial attacks are proposed based on
the trends deduced by keywords detection analysis. All the data used for visualization are available at:
https://github.com/NanyunLengmu/Adversarial- Attack- Visualization . keywords: یادگیری عمیق | حمله خصمانه | حمله جعبه سیاه | حمله به جعبه سفید | نیرومندی | تجزیه و تحلیل تجسم | Deep learning | Adversarial attack | Black-box attack | White-box attack | Robustness | Visualization analysis |
مقاله انگلیسی |
10 |
Disintegration testing augmented by computer Vision technology
آزمایش تجزیه با فناوری Vision کامپیوتری تقویت شده است-2022 Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry.
Disintegration testing is often the first step of commercialization and large-scale production of these dosage
forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia
(USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This infor-
mation is subjective, variable, and prone to human error due to manual or physical data collection methods via
the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been
made to automate the analysis of the disintegration process using digital lens and other imaging technologies.
This would provide a non-invasive method to quantitatively determine disintegration time through computer
algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces
through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to
be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and
distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or
other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze
and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not
only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of
disintegration rates and mechanisms in addition to duration. keywords: از هم پاشیدگی | اشکال خوراکی جامد | تست تجزیه | یادگیری ماشین | شبکه های عصبی | Disintegration | Oral Solid Dosage Forms | Disintegration Test | Machine Learning | Neural Networks |
مقاله انگلیسی |