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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 |
Deep Reinforcement Learning With Quantum-Inspired Experience Replay
یادگیری تقویتی عمیق با تکرار تجربه کوانتومی-2022 In this article, a novel training paradigm inspired
by quantum computation is proposed for deep reinforcement
learning (DRL) with experience replay. In contrast to the traditional experience replay mechanism in DRL, the proposed DRL
with quantum-inspired experience replay (DRL-QER) adaptively
chooses experiences from the replay buffer according to the
complexity and the replayed times of each experience (also
called transition), to achieve a balance between exploration and
exploitation. In DRL-QER, transitions are first formulated in
quantum representations and then the preparation operation
and depreciation operation are performed on the transitions.
In this process, the preparation operation reflects the relationship between the temporal-difference errors (TD-errors) and the
importance of the experiences, while the depreciation operation is
taken into account to ensure the diversity of the transitions. The
experimental results on Atari 2600 games show that DRL-QER
outperforms state-of-the-art algorithms, such as DRL-PER and
DCRL on most of these games with improved training efficiency
and is also applicable to such memory-based DRL approaches
as double network and dueling network.
Index Terms: Deep reinforcement learning (DRL) | quantum computation | quantum-inspired experience replay (QER) | quantum reinforcement learning. |
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 |
Efficient Floating Point Arithmetic for Quantum Computers
محاسبات ممیز شناور کارآمد برای کامپیوترهای کوانتومی-2022 One of the major promises of quantum computing is the realization of SIMD (single
instruction - multiple data) operations using the phenomenon of superposition. Since the dimension of the
state space grows exponentially with the number of qubits, we can easily reach situations where we pay less
than a single quantum gate per data point for data-processing instructions, which would be rather expensive
in classical computing. Formulating such instructions in terms of quantum gates, however, still remains
a challenging task. Laying out the foundational functions for more advanced data-processing is therefore a
subject of paramount importance for advancing the realm of quantum computing. In this paper, we introduce
the formalism of encoding so called-semi-boolean polynomials. As it turns out, arithmetic Z=2nZ ring
operations can be formulated as semi-boolean polynomial evaluations, which allows convenient generation
of unsigned integer arithmetic quantum circuits. For arithmetic evaluations, the resulting algorithm has been
known as Fourier-arithmetic. We extend this type of algorithm with additional features, such as ancillafree in-place multiplication and integer coefficient polynomial evaluation. Furthermore, we introduce a
tailor-made method for encoding signed integers succeeded by an encoding for arbitrary floating-point
numbers. This representation of floating-point numbers and their processing can be applied to any quantum algorithm that performs unsigned modular integer arithmetic. We discuss some further performance
enhancements of the semi boolean polynomial encoder and finally supply a complexity estimation. The
application of our methods to a 32-bit unsigned integer multiplication demonstrated a 90% circuit depth
reduction compared to carry-ripple approaches.
INDEX TERMS: Quantum arithmetic | quantum computing | floating point arithmetic. |
مقاله انگلیسی |
6 |
Efficient Hardware Implementation of Finite Field Arithmetic AB + C for Binary Ring-LWE Based Post-Quantum Cryptography
اجرای سخت افزار کارآمد محاسبات میدان محدود AB + C برای رمزنگاری پس کوانتومی مبتنی بر حلقه باینری-LWE-2022 Post-quantum cryptography (PQC) has gained significant attention from the community
recently as it is proven that the existing public-key cryptosystems are vulnerable to the attacks launched from
the well-developed quantum computers. The finite field arithmetic AB þ C, where A and C are integer polynomials and B is a binary polynomial, is the key component for the binary Ring-learning-with-errors (BRLWE)-
based encryption scheme (a low-complexity PQC suitable for emerging lightweight applications). In this paper,
we propose a novel hardware implementation of the finite field arithmetic AB þ C through three stages of interdependent efforts: (i) a rigorous mathematical formulation process is presented first; (ii) an efficient hardware
architecture is then presented with detailed description; (iii) a thorough implementation has also been given
along with the comparison. Overall, (i) the proposed basic structure (u ¼ 1) outperforms the existing designs,
e.g., it involves 55.9% less area-delay product (ADP) than [13] for n ¼ 512; (ii) the proposed design also offers
very efficient performance in time-complexity and can be used in many future applications.
INDEX TERMS: Binary ring-learning-with-errors | finite field arithmetic | FPGA platform | hardware design | post-quantum cryptography |
مقاله انگلیسی |
7 |
Efficient Quantum State Preparation for the Cauchy Distribution Based on Piecewise Arithmetic
آماده سازی حالت کوانتومی کارآمد برای توزیع کوشی بر اساس حساب تکه ای-2022 The benefits of the quantum Monte Carlo algorithm heavily rely on the efficiency of the
superposition state preparation. So far, most reported Monte Carlo algorithms use the Grover–Rudolph state
preparation method, which is suitable for efficiently integrable distribution functions. Consequently, most reported works are based on log-concave distributions, such as normal distributions. However, non-log-concave
distributions still have many uses, such as in financial modeling. Recently, a new method was proposed
that does not need integration to calculate the rotation angle for state preparation. However, performing
efficient state preparation is still difficult due to the high cost associated with high precision and low error
in the calculation for the rotation angle. Many methods of quantum state preparation use polynomial Taylor
approximations to reduce the computation cost. However, Taylor approximations do not work well with
heavy-tailed distribution functions that are not bounded exponentially. In this article, we present a method
of efficient state preparation for heavy-tailed distribution functions. Specifically, we present a quantum
gate-level algorithm to prepare quantum superposition states based on the Cauchy distribution, which is a
non-log-concave heavy-tailed distribution. Our procedure relies on a piecewise polynomial function instead
of a single Taylor approximation to reduce computational cost and increase accuracy. The Cauchy distribution is an even function, so the proposed piecewise polynomial contains only a quadratic term and a constant
term to maintain the simplest approximation of an even function. Numerical analysis shows that the required
number of subdomains increases linearly as the approximation error decreases exponentially. Furthermore,
the computation complexity of the proposed algorithm is independent of the number of subdomains in the
quantum implementation of the piecewise function due to quantum parallelism. An example of the proposed
algorithm based on a simulation conducted in Qiskit is presented to demonstrate its capability to perform
state preparation based on the Cauchy distribution.
INDEX TERMS: Algorithms | gate operations | quantum computing. |
مقاله انگلیسی |
8 |
Eigen-Spectrum Estimation and Source Detection in a Massive Sensor Array Based on Quantum Assisted Hamiltonian Simulation Framework
تخمین طیف ویژه و تشخیص منبع در یک آرایه حسگر عظیم بر اساس چارچوب شبیهسازی همیلتونی به کمک کوانتومی-2022 In this work, we propose quantum assisted eigenvalue estimation and target detection algorithms for a large
sensor array via Hamiltonian simulation. Quantum algorithms
provide complexity advantage of a certain class of problems on
a quantum computer with fewer physical resources as compared
to their classical counterparts. The proposed algorithms make
use of the quantum phase estimation (QPE) as its core computing component. We have introduced an analytical quantum
framework to map from classical to quantum in the context of
target detection. Target detection involves an appropriate choice
of threshold based on the probability of detection or false alarm.
We exploited the massive sensor array structure and invoked
the random matrix theory to propose an optimal threshold.
It also takes into account the quantum measurement noise in the
framework. Numerical simulations are performed to ascertain
the efficacy of the proposed framework. The results suggest near
term applications of the quantum algorithm for large-scale linear
systems.
Index Terms: Quantum signal processing | quantum eigenvalue estimation | quantum phase estimation | Hamiltonian simulation | array signal processing. |
مقاله انگلیسی |
9 |
Tuning of grayscale computer vision systems
تنظیم سیستم های بینایی کامپیوتری در مقیاس خاکستری-2022 Computer vision systems perform based on their design and parameter setting. In computer vision systems
that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of
the systems in terms of both results quality and computational costs. Appropriate setting of the weights for
the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision
system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or
no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the
grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the
system and subsystem to small changes in the distribution of data in a color space of the processed images.
We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the
advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show
that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies.
The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer
vision system with the robustness of its performance against variable input data.
keywords: Computer vision | Parameter optimization | Performance evaluation | WECIA graph | Weighted means grayscale conversion |
مقاله انگلیسی |
10 |
EP-PQM: Efficient Parametric Probabilistic Quantum Memory With Fewer Qubits and Gates
EP-PQM: حافظه کوانتومی احتمالی پارامتریک کارآمد با کیوبیت ها و گیت های کمتر-2022 Machine learning (ML) classification tasks can be carried out on a quantum computer (QC)
using probabilistic quantum memory (PQM) and its extension, parametric PQM (P-PQM), by calculating
the Hamming distance between an input pattern and a database of r patterns containing z features with
a distinct attributes. For PQM and P-PQM to correctly compute the Hamming distance, the feature must
be encoded using one-hot encoding, which is memory intensive for multiattribute datasets with a > 2. We
can represent multiattribute data more compactly by replacing one-hot encoding with label encoding; both
encodings yield the same Hamming distance. Implementing this replacement on a classical computer is
trivial. However, replacing these encoding schemes on a QC is not straightforward because PQM and P-PQM
operate at the bit level, rather than at the feature level (a feature is represented by a binary string of 0’s and
1’s). We present an enhanced P-PQM, called efficient P-PQM (EP-PQM), that allows label encoding of data
stored in a PQM data structure and reduces the circuit depth of the data storage and retrieval procedures.
We show implementations for an ideal QC and a noisy intermediate-scale quantum (NISQ) device. Our
complexity analysis shows that the EP-PQM approach requires O(z log2(a)) qubits as opposed to O(za)
qubits for P-PQM. EP-PQM also requires fewer gates, reducing gate count from O(rza) to O(rz log2(a)).
For five datasets, we demonstrate that training an ML classification model using EP-PQM requires 48% to
77% fewer qubits than P-PQM for datasets with a > 2. EP-PQM reduces circuit depth in the range of 60% to
96%, depending on the dataset. The depth decreases further with a decomposed circuit, ranging between 94%
and 99%. EP-PQM requires less space; thus, it can train on and classify larger datasets than previous PQM
implementations on NISQ devices. Furthermore, reducing the number of gates speeds up the classification
and reduces the noise associated with deep quantum circuits. Thus, EP-PQM brings us closer to scalable ML
on an NISQ device.
INDEX TERMS: Efficient encoding | label encoding | quantum memory. |
مقاله انگلیسی |