با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
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 |
Intelligent context-aware fog node discovery
کشف گره مه آگاه از زمینه هوشمند-2022 Fog computing has been proposed as a mechanism to address certain issues in
cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network
near the edge devices, which we called fog consumers. This decreases latency,
network bandwidth, and response time. Discovering the most relevant fog node,
the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed
Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner.
The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In
this study, the KNN, K-d tree, and brute force algorithms are used to discover
fog nodes based on the context-aware criteria of fog nodes and fog consumers.
The proposed framework is simulated using OMNET++, and the performance of
the proposed algorithms is compared based on performance metrics and execution
time. The accuracy and execution time are the major points of consideration in
the selection of an optimal fog search algorithm. The experiment results show
that the KNN and K-d tree algorithms achieve the same accuracy results of 95 %.
However, the K-d tree method takes less time to find the nearest fog nodes than
KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm
in the IDFD to discover the nearest fog nodes very efficiently and quickly.
keywords: Fog node | Discovery | Context-aware | Intelligent | Fog node discovery |
مقاله انگلیسی |
3 |
DOPIV: Post-Quantum Secure Identity-Based Data Outsourcing with Public Integrity Verification in Cloud Storage
DOPIV: برون سپاری داده مبتنی بر هویت امن پس از کوانتومی با تأیید صحت عمومی در فضای ذخیره سازی ابری-2022 Public verification enables cloud users to employ a third party auditor (TPA) to check the data integrity. However, recent
breakthrough results on quantum computers indicate that applying quantum computers in clouds would be realized. A majority of existing
public verification schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with
quantum computers in the near future. Moreover, new security issues need to be solved when an original data owner is restricted or
cannot access the remote cloud server flexibly. In this paper, we propose an efficient identity-based data outsourcing with public integrity
verification scheme (DOPIV) in cloud storage. DOPIV is designed on lattice-based cryptography, which achieves post-quantum security.
DOPIV enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server.
Any TPA can perform data integrity verification efficiently on behalf of the original data owner, without retrieving the entire data set.
Additionally, DOPIV possesses the advantages of being identity-based systems, avoiding complex certificate management procedures.
We provide security proofs of DOPIV in the random oracle model, and conduct a comprehensive performance evaluation to show that
DOPIV is more practical in post-quantum secure cloud storage systems.
Index Terms: Cloud storage | public verification | lattice-based cryptography | identity-based data outsourcing | post-quantum security |
مقاله انگلیسی |
4 |
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 |
5 |
Prediction of total volatile basic nitrogen (TVB-N) and 2-thiobarbituric acid (TBA) of smoked chicken thighs using computer vision during storage at 4 °C
پیشبینی کل نیتروژن بازی فرار (TVB-N) و اسید ۲-تیوباربیتوریک (TBA) ران مرغ دودی با استفاده از بینایی رایانه در طول نگهداری در دمای ۴ درجه سانتیگراد-2022 As the traditional indicators of freshness measurement of meat products, TVB-N and TBA have the disadvantage
of time-consuming, labor-intensive and destructive to the sample. The objective of this study was to investigate
the possibility of computer vision techniques to visualize the variation of TVB-N and TBA during the storage of
smoked chicken thighs. In this study, freshness indicators (TVB-N and TBA) and images of smoked chicken thighs
were obtained simultaneously every 3 days during storage at 4 ◦C. Then, the RGB color space was converted to
HSI and L*a*b* color spaces by color conversion algorithm, and the color parameters (RGB, HSI and L*a*b*)
were correlated with TVB-N and TBA, respectively, for establishing multiple regression models. Finally, visu-
alization maps of the spoilage were established by applying the multiple regression model to each pixel in the
image. The results showed that the multiple linear regression models of TBA and TVB-N based on the color
parameters L*, a*, I, S and R were well correlated (R 2 = 0.993 for TBA and R 2 = 0.970 for TVB-N). Distribution
maps of TBA and TVB-N changed color gradually from blue to red during storage, respectively. In conclusion, this
study demonstrated that distribution maps can be employed as a rapid, objective, and non-destructive method to
predict the TBA and TVB-N values of smoked chicken thighs during storage. keywords: ران مرغ دودی | بینایی کامپیوتر | خنکی | TVB-N | TBA | Smoked chicken thigh | Computer vision | Freshness |
مقاله انگلیسی |
6 |
Computer vision model for estimating the mass and volume of freshly harvested Thai apple ber ( Ziziphus mauritiana L:) and its variation with storage days
مدل بینایی کامپیوتری برای تخمین جرم و حجم سیب تازه برداشت شده تایلندی (Ziziphus mauritiana L:) و تغییرات آن با روزهای نگهداری-2022 The physical properties of fruits are proportional to their mass and volume; this connection is used to determine
the fruit qualities and in designing the novel postharvest machinery. The present study aimed to forecast the
mass and volume of Thai apple ber (Ziziphus mauritiana L.) as a function of its physical properties measured using
image processing techniques at different stages of ripening (1st day, 4th day, 7th day, and 10th day). The mass
and volume models developed and analyzed the single variable regression, multilinear regressions, and mass
regression based on volume. Among these models, linear support vector machine (SVM) was found appropriate.
The experimental data analysis showed that the R2 of the linear SVM model for mass and volume of the projected
area were 0.955 and 0.965, respectively. In contrast, for the multilinear regression model, R2 values were 0.967
and 0.972, respectively. For the mass prediction model, the R2 was 0.970 based on calculated volume showing a
linear relationship. Thus, it was concluded that real-time measurement of physical properties of Thai apple ber
using an image-processing technique to estimate the mass and volume is a precise and accurate approach. keywords: بینایی کامپیوتر | پردازش تصویر | فراگیری ماشین | پسرفت | ماشین بردار پشتیبانی | Computer vision | Image processing | Machine learning | Regression | Support vector machine |
مقاله انگلیسی |
7 |
Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022 Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used
during their storage, transportation purpose. These are responsible factors that decide the freshness of a post
harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the
freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre-
lation has been observed between the colour of the eye and different duration of storage day. It is segmented
from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory
features. These extracted features show a degradation pattern which acts as an indicative parameter to determine
the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The
experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish
freshness. The high accuracy of freshness detection and low computation time makes this non-destructive
methodology efficient for real-world usage in the fish industry and market. keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation |
مقاله انگلیسی |
8 |
Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions
یادگیری ماشینی الهام گرفته از کوانتومی برای 6G: مبانی، امنیت، تخصیص منابع، چالشها و دستورالعملهای تحقیقاتی آینده-2022 Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been
a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and
data structures. Hence, the conventional machine learning approaches in data training and processing have
exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly
dynamic applications and services. In this regard, the fast developing quantum computing with machine
learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the
processing efficiency and exponentially computational speed-up for effective quantum data representation
and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide a comprehensive overview
of its potential, via machine learning approaches. Furthermore, we introduce quantum-inspired machine
learning applications for 6G networks in terms of resource allocation and network security, considering their
enabling technologies and potential challenges. Finally, some dominating research issues and future research
directions for the quantum-inspired machine learning in 6G networks are elaborated.
INDEX TERMS: 6G networks | machine learning | quantum machine learning | quantum security. |
مقاله انگلیسی |
9 |
Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection
الگوریتم بهینه سازی تقریبی کوانتومی مبتنی بر تشخیص حداکثر احتمال-2022 Recent advances in quantum technologies pave the
way for noisy intermediate-scale quantum (NISQ) devices, where
the quantum approximation optimization algorithm (QAOA)
constitutes a promising candidate for demonstrating tangible
quantum advantages based on NISQ devices. In this paper,
we consider the maximum likelihood (ML) detection problem of
binary symbols transmitted over a multiple-input and multipleoutput (MIMO) channel, where finding the optimal solution is
exponentially hard using classical computers. Here, we apply the
QAOA for the ML detection by encoding the problem of interest
into a level-p QAOA circuit having 2p variational parameters,
which can be optimized by classical optimizers. This level-p
QAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately
in p consecutive rounds. More explicitly, we first encode the
optimal solution of the ML detection problem into the ground
state of a problem Hamiltonian. Using the quantum adiabatic
evolution technique, we provide both analytical and numerical
results for characterizing the evolution of the eigenvalues of
the quantum system used for ML detection. Then, for level-
1 QAOA circuits, we derive the analytical expressions of the
expectation values of the QAOA and discuss the complexity
of the QAOA based ML detector. Explicitly, we evaluate the
computational complexity of the classical optimizer used and the
storage requirement of simulating the QAOA. Finally, we evaluate
the bit error rate (BER) of the QAOA based ML detector and
compare it both to the classical ML detector and to the classical
minimum mean squared error (MMSE) detector, demonstrating
that the QAOA based ML detector is capable of approaching the
performance of the classical ML detector.
Index Terms: Quantum technology | maximum likelihood (ML) detection | quantum approximation optimization algorithm (QAOA) | bit error rate (BER). |
مقاله انگلیسی |
10 |
یک مدل ریاضی چند منظوره برای زنجیره تامین داروسازی با توجه به تراکم دارو در کارخانهها
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 47 مدیریت زنجیره تامین ( SCM ) , به روش یکی از مسائل مهم در جنبه مدیریتی , نقش مهمی در مقابله با مسایل انسانی و مشکلات ایفا میکند . به دلیل برخی محدودیتها ( به عنوان مثال , ظرفیت تولید و ظرفیت ذخیرهسازی ) و خواسته ها( به عنوان مثال , کاهش هزینه و افزایش درآمد ) , مدیران زنجیره تامین همیشه به دنبال بهترین پاسخ به مقدار و نوع ارتباط بین سطوح مختلف SCM هستند . در تحقیقات آتی , یک زنجیره تامین دارو ( PSC ) با سه تابع هدف توسعهیافته , با هدف به حداقل رساندن هزینههای کلی , خواستههای برآورده نشده , و کاهش زمان انتظار در ورودی کارخانه . در تحقیقات آتی , موضوع کلی و تحقیقات در مدلسازی PSC و حل مساله مورد بحث قرار گرفتهاند . سپس یک مدل برنامهریزی غیرخطی با تحقیقات قبلی برای حل کاستیهای موجود پیشنهاد شدهاست.
همچنین روشهای تصمیمگیری چند هدفه برای انطباق با اهداف متناقض مدل به طور همزمان استفاده میشوند . سپس نرمافزار تجاری GAMS برای حل مشکل اندازههای مختلف به کار میرود . در نهایت ، تحلیل حساسیت گسترده و ارزیابی نتایج مورد بحث قرار میگیرد و پیشنهادهای توسعه آتی ارایه میشوند. واژه های کاربردی : زنجیره تامین دارو | فسادپذیری | زمانبندی | فهرست | نظریه کیوینگ |
مقاله ترجمه شده |