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1 |
Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105 The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database
systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM)
systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and
control systems are operated by different units at NYCDOT as an independent database or operation system. New York City
experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial
systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all
users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to
develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City.
This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS,
involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and
wireless communication features for data collection and reduction; 2) interface development among existing database and control
systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study
paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from
loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi
and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day.
GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept.
© 2014 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshu
Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data |
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 |
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 |
4 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
5 |
Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision
مناظر معنایی رودخانه: درک و ارزیابی مناظر خطی از تصاویر مایل با استفاده از بینایی کامپیوتری-2022 Traditional approaches for visual perception and evaluation of river landscapes adopt on-site surveys or as-
sessments through photographs. The former is expensive, hindering large-scale analyses, and it is conducted only
on street-level or top-down imagery. The latter only reflects the subjective perception and also entails a laborious
process. Addressing these challenges, this study proposes an alternative: a novel workflow for visual analysis of
urban river landscapes by combining unmanned aerial vehicle (UAV) oblique photography with computer vision
(CV) and virtual reality (VR). The approach is demonstrated with an experiment on a section of the Grand Canal
in China where UAV oblique panoramic imagery has been processed using semantic segmentation for visual
evaluation with an index system we designed. Concurrent surveys, immersive and non-immersive VR, are used to
evaluate these photos, with a total of 111 participants expressing their perceptions across multiple dimensions.
Then, the relationship between the people’s subjective visual perception and the river landscape environment as
seen by computers has been established. The results suggest that using this approach, rivers and surrounding
landscapes can be analyzed automatically and efficiently, and the mean pixel accuracy (MPA) of the developed
model is 90%, which advances state of the art. The results of this study can benefit urban planners in formulating
riverside development policies, analyzing the perception of plans for a future scenario before an area is rede-
veloped, and the method can also aid relevant parties in having a macro understanding of the overall situation of
the river as a basis for follow-up research. Due to simplicity, accuracy and effectiveness, this workflow is
transferable and cost-effective for large-scale investigations of riverscapes and linear heritage. We openly release
Semantic Riverscapes—the dataset we collected and processed, bridging another gap in the field. keywords: ریورساید | باز کردن داده ها | GeoAI | بررسی های هوایی | هواپیماهای بدون سرنشین | واقعیت مجازی | Riverside | Open data | GeoAI | Aerial surveys | Drones | Virtual reality |
مقاله انگلیسی |
6 |
Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022 Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and
mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-
scale topographical information. This paper addresses this issue by providing a novel method for evaluating
surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging)
measurements. The developed method derives topographical properties and runoff accumulation by applying a
semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology
technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the
SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the
proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales
and identify problematic low points that could be susceptible to water ponding. Municipalities and property
owners can use this information to take targeted corrective maintenance actions. keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure |
مقاله انگلیسی |
7 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
8 |
Private Computation of Phylogenetic Trees Based on Quantum Technologies
محاسبات خصوصی درختان فیلوژنتیک بر اساس فناوری های کوانتومی-2022 Individuals’ privacy and legal regulations demand genomic data be handled and studied
with highly secure privacy-preserving techniques. In this work, we propose a feasible Secure Multiparty
Computation (SMC) system assisted with quantum cryptographic protocols that is designed to compute a
phylogenetic tree from a set of private genome sequences. This system significantly improves the privacy
and security of the computation thanks to three quantum cryptographic protocols that provide enhanced
security against quantum computer attacks. This system adapts several distance-based methods (Unweighted
Pair Group Method with Arithmetic mean, Neighbour-Joining, Fitch-Margoliash) into a private setting
where the sequences owned by each party are not disclosed to the other members present in the protocol.
We theoretically evaluate the performance and privacy guarantees of the system through a complexity
analysis and security proof and give an extensive explanation about the implementation details and cryptographic protocols. We also implement a quantum-assisted secure phylogenetic tree computation based on
the Libscapi implementation of the Yao, the PHYLIP library and simulated keys of two quantum systems:
Quantum Oblivious Key Distribution and Quantum Key Distribution. This demonstrates its effectiveness
and practicality. We benchmark this implementation against a classical-only solution and we conclude that
both approaches render similar execution times, the only difference being the time overhead taken by the
oblivious key management system of the quantum-assisted approach.
INDEX TERMS: Genomics | phylogenetic trees | privacy | quantum oblivious transfer | quantum secure multiparty computation | security. |
مقاله انگلیسی |
9 |
CREASE: Certificateless and REused-pseudonym based Authentication Scheme for Enabling security and privacy in VANETs
CREASE: طرح احراز هویت مبتنی بر نام مستعار بدون گواهی و استفاده مجدد برای فعال کردن امنیت و حریم خصوصی در VANET-2022 Due to the customers’ growing interest in using various intelligent and connected devices, we
are surrounded by the Internet of Things (IoT). It is estimated that the number of IoT devices
will exceed 60 billion by 2025. One of the primary reasons for such rapid growth is the Internet
of Vehicles (IoV). Internet of Vehicles (IoV) has evolved into an emerging concept in intelligent
transportation systems (ITS) that integrates VANETs and the IoT to enhance their capabilities.
With the emergence of IoV and the interest shown by customers, Vehicular Ad hoc NETworks
(VANETs) are likely to be widely deployed in the near future. However, for this to happen, wide
participation of vehicle owners in VANET is needed. The primary concerns of vehicle owners
to participate in VANET are privacy and security. In this paper, we present a Certificateless and
REused-pseudonym based Authentication Scheme for Enabling security and privacy (CREASE) in
VANETs. One of the ways to preserve the privacy of vehicles/drivers is to allow vehicles/drivers
to use pseudo identities (pseudonyms) instead of their real identities (such as VIN number or
driving license number) in all communications. The pseudonym used by a vehicle needs to
be changed frequently to prevent the vehicle from being tracked. Our scheme uses Merkle
Hash Tree and Modified Merkle Patricia Trie to efficiently store and manage the pseudonyms
assigned to a vehicle. This enables a vehicle to pick and use a random pseudonym from a
given set of pseudonyms assigned to it as well as change its pseudonym frequently and securely
to ensure privacy. Unlike many of the existing schemes, our scheme does not use certificates
and certificate revocation lists for authentication. Moreover, it allows vehicles to get a set of
pseudonyms only once from the trusted authority. We present a formal proof of correctness of
our scheme and also compare our scheme with some of the other contemporary schemes to
show the effectiveness of our scheme.
Keywords: VANETs | Intelligent transportation systems | Authentication | Security | Privacy-preserving authentication |
مقاله انگلیسی |
10 |
A Modified Key Sifting Scheme With Artificial Neural Network Based Key Reconciliation Analysis in Quantum Cryptography
یک طرح غربال کلید اصلاح شده با تحلیل آشتی کلید مبتنی بر شبکه عصبی مصنوعی در رمزنگاری کوانتومی-2022 Quantum Cryptography emerged from the limitations of classical cryptography. It will play a
vital role in information security after the availability of expected powerful quantum computers. Still many
quantum primitives like quantum money, blind quantum computation, quantum copy protection, etc. are
theoretical as they require a completely functional quantum computer for their implementation. But one
prominent quantum cryptographic primitive, the Quantum Key Distribution (QKD) is possible with current
technology. The QKD is a key establishment system having several stages namely raw key generation, key
sifting, key reconciliation, and privacy amplification. In this paper, an efficient key sifting scheme has been
developed. Successful simulation has shown that the proposed modified key sifting scheme requires less
time to build the sifted key compared to the sifted key in conventional BB84 protocol in most cases. This
paper also represents Tree Parity Machine (TPM) based key reconciliation analysis using different learning
algorithms such as Hebbian, Anti-Hebbian, and Random-Walk. This reconciliation analysis helps to choose
the optimum learning algorithm for Artificial Neural Network (ANN) based key reconciliation in future
Quantum Key Distribution systems.
INDEX TERMS: Artificial neural network | BB84 protocol | key sifting | key reconciliation | learning algorithms | quantum key distribution | quantum cryptography. |
مقاله انگلیسی |