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ردیف | عنوان | نوع |
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1 |
Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification
خوشهبندی کاربردی کوانتومی K-Means: تحلیل عملکرد و کاربردها در طبقهبندی شبکه انرژی-2022 In this work, we aim to solve a practical use-case of unsupervised clustering that has applications in predictive maintenance in the energy operations sector using quantum computers. Using only cloud
access to quantum computers, we complete thorough performance analysis of what some current quantum
computing systems are capable of for practical applications involving nontrivial mid-to-high-dimensional
datasets. We first benchmark how well distance estimation can be performed using two different metrics
based on the swap-test, using angle and amplitude data embedding. Next, for the clustering performance
analysis, we generate sets of synthetic data with varying cluster variance and compare simulation to physical
hardware results using the two metrics. From the results of this performance analysis, we propose a general,
competitive, and parallelized version of quantum k-means clustering to avoid some pitfalls discovered due
to noisy hardware and apply the approach to a real energy grid clustering scenario. Using real-world German
electricity grid data, we show that the new approach improves the balanced accuracy of the standard quantum
k-means clustering by 67.8% with respect to the labeling of the classical algorithm.
INDEX TERMS: Cloud quantum computing | quantum clustering | quantum computing | quantum distance estimation. |
مقاله انگلیسی |
2 |
A Secure Anonymous D2D Mutual Authentication and Key Agreement Protocol for IoT
پروتکل ایمن تأیید هویت متقابل D2D و قرارداد کلیدی برای اینترنت اشیا-2022 Internet of Things (IoT) is a developing technology in our time that is prone to security problems
as it uses wireless and shared networks. A challenging scenario in IoT environments is Device-to-
Device (D2D) communication where an authentication server, as a trusted third-party, does not
participate in the Authentication and Key Agreement (AKA) process and only cooperates in the
process of allocating and updating long-term secret keys. Various authentication protocols have
been suggested for such situations but have not been able to meet security and efficiency re-
quirements. This paper examined three related protocols and demonstrated that they failed to
remain anonymous and insecure against Key Compromise Impersonation (KCI) and clogging at-
tacks. To counter these pitfalls, a new D2D mutual AKA protocol that is anonymous, untraceable,
and highly secure was designed that needed no secure channel to generate paired private and
public keys in the registration phase. Formal security proof and security analysis using BAN logic,
Real-Or-Random (ROR) model, and Scyther tool showed that our proposed protocol satisfied
security requirements. The communication and computation costs and energy consumption
comparisons denoted that our design had a better performance than existing protocols. keywords: تأیید اعتبار و توافقنامه کلید (AKA) | ارتباط دستگاه به دستگاه (D2D) | اینترنت اشیا (IoT) | حمله جعل هویت کلیدی (KCI) | Authentication and Key Agreement (AKA) | Device to Device (D2D) communication | Internet of Things (IoT) | Key Compromise Impersonation (KCI) attack |
مقاله انگلیسی |
3 |
In-home Health Monitoring using Floor-based Gait Tracking
نظارت بر سلامت در خانه با استفاده از ردیابی راه رفتن مبتنی بر کف-2022 Gait assessments are commonly used for clinical evaluations of neurocognitive disease progression and general wellness. However, gait measurements in clinical settings do not accurately
reflect gait in daily life. We present a non-wearable and unobtrusive method of detecting
gait parameters in the home through the vibrations in the floor created by footfalls. Gait
characteristics and gait asymmetry are estimated despite a low sensor density of 6.7 m2/sensor.
Features from each footfall vibration signal is extracted and used to estimate gait parameters
with gradient boosting regression and probabilistic models. Temporal gait asymmetry, locations
of the footfalls, and peak tibial acceleration asymmetry can be predicted with a root mean
square error of 0.013 s, 0.42 m, and 0.34 g respectively. This system allows for continuous
at-home monitoring of gait which aids in early detection of gait anomalies.
keywords: Gait monitoring | Smart home | Signal processing | Localization | Ground reaction force |
مقاله انگلیسی |
4 |
Combining computer vision with semantic reasoning for on-site safety management in construction
ترکیب بینایی ماشین با استدلال معنایی برای مدیریت ایمنی در هر دو سو در ساخت -2021 Computer vision has been utilized to extract safety-related information from images with the advancement of
video monitoring systems and deep learning algorithms. However, construction safety management is a
knowledge-intensive task; for instance, safety managers rely on safety regulations and their prior knowledge
during a jobsite safety inspection. This paper presents a conceptual framework that combines computer vision
and ontology techniques to facilitate the management of safety by semantically reasoning hazards and corre-
sponding mitigations. Specifically, computer vision is used to detect visual information from on-site photos while
the safety regulatory knowledge is formally represented by ontology and semantic web rule language (SWRL)
rules. Hazards and corresponding mitigations can be inferred by comparing extracted visual information from
construction images with pre-defined SWRL rules. Finally, the example of falls from height is selected to validate
the theoretical and technical feasibility of the developed conceptual framework. Results show that the proposed
framework operates similar to the thinking model of safety managers and can facilitate on-site hazard identi-
fication and prevention by semantically reasoning hazards from images and listing corresponding mitigations.
1. Introduction keywords: بینایی ماشین | هستی شناسی | استدلال معنایی | شناسایی ریسک | مدیریت ایمنی ساخت | Computer vision | Ontology | Semantic reasoning | Hazard identification | Construction safety management |
مقاله انگلیسی |
5 |
The effect of WeChat-based training on improving the knowledge of tuberculosis management of rural doctors
تأثیر آموزش مبتنی بر وی چت بر ارتقای دانش مدیریت سل در پزشکان روستایی-2021 Computer vision has been utilized to extract safety-related information from images with the advancement of
video monitoring systems and deep learning algorithms. However, construction safety management is a
knowledge-intensive task; for instance, safety managers rely on safety regulations and their prior knowledge
during a jobsite safety inspection. This paper presents a conceptual framework that combines computer vision
and ontology techniques to facilitate the management of safety by semantically reasoning hazards and corresponding mitigations. Specifically, computer vision is used to detect visual information from on-site photos while
the safety regulatory knowledge is formally represented by ontology and semantic web rule language (SWRL)
rules. Hazards and corresponding mitigations can be inferred by comparing extracted visual information from
construction images with pre-defined SWRL rules. Finally, the example of falls from height is selected to validate
the theoretical and technical feasibility of the developed conceptual framework. Results show that the proposed
framework operates similar to the thinking model of safety managers and can facilitate on-site hazard identification and prevention by semantically reasoning hazards from images and listing corresponding mitigations.
keywords: سل | مدیریت | آموزش مبتنی بر وی چت | پزشکان روستایی | چین | Tuberculosis | Management | WeChat-based training | Rural doctors | China |
مقاله انگلیسی |
6 |
Computer vision approaches for detecting missing barricades
رویکردهای بینایی ماشین برای تشخیص موانع گمشده-2021 The installation of barricades effectively prevents falls from height (FFH) on construction sites. Common approaches for detecting missing barricades (e.g., manual inspection of the site or three-dimensional models) are not practical due to two inherent challenges: (1) these approaches are labor-intensive and time-consuming; and(2) FFH hazards are dynamic and changing as construction work progresses. To address these challenges, two computer vision-based detection approaches, including Masks Comparison Approach (MCA) and Missing Object Detection Approach (MODA), are developed in this study to automatically detect missing barricade. The performance of the proposed approaches and their benefits and implementation challenges were evaluated through a case study. The results demonstrate that MODA can achieve better performance and have several implementation advantages over MCA. The average precision and average recall for MODA were 57.9% and 73.6%, respectively. These two approaches can help site managers take action promptly to reduce the risks of FFH accidents. Keywords: Falls from height | Safety | Computer vision | Unsafe behavior | Deep learning |
مقاله انگلیسی |
7 |
Capturing causality and bias in human action recognition
ثبت علیت و سوگیری در تشخیص عمل انسان-2021 Human action recognition using various sensors is a mandatory component of autonomous vehicles, humanoid robots, and ambient living environments. A particular interest is the detection and recognition of falls. In this paper, we propose the use of temporal convolution networks guided by knowledge distilla- tion for detecting falls and recognizing types of falls using accelerometer data. Tri-axial accelerometers attached to the body measure the acceleration of the body joints when an action occurs. These data are used for pattern analysis and body action recognition. We demonstrate the existence of biases caused by soft biometrics when recognizing human body actions. We introduce a causal network to capture the influences of biases on system performance and illustrate how knowledge distillation can be applied to mitigate the bias effect. Crown Copyright © 2021 Published by Elsevier B.V. All rights reserved. Keywords: Machine learning | Decision support | Human action recognition | Machine reasoning | Belief networks |
مقاله انگلیسی |
8 |
Behavioral biometrics & continuous user authentication on mobile devices: A survey
بیومتریک رفتاری و احراز هویت مداوم کاربر در دستگاه های تلفن همراه: یک مرور-2021 This paper offers an up-to-date, comprehensive, extensive and targeted survey on Behavioral Biometrics and Continuous Authentication technologies for mobile devices. Our aim is to help interested researchers to effectively grasp the background in this field and to avoid pitfalls in their work. In our survey, we first present a classification of behavioral biometrics technologies and continuous authentication for mobile devices and an analysis for behavioral biometrics collection methodologies and feature extraction techniques. Then, we provide a state-of-the-art literature review focusing on the machine learning models performance in seven types of behavioral biometrics for continuous authentication. Further, we conduct another review that showed the vulnerability of machine learning models against well-designed adversarial attack vectors and we highlight relevant countermeasures. Finally, our discussions extend to lessons learned, current challenges and future trends. Keywords: Machine Learning | Behavioral Biometrics | Continuous Authentication | Mobile Devices | Attacks | Defense | Survey |
مقاله انگلیسی |
9 |
Collection weeding: Innovative processes and tools to ease the burden
جمع آوری علفهای هرز : فرایندها و ابزارهای نوآورانه برای کاهش بار-2020 Evaluating collections and ultimately removing content poses a variety of difficult issues, including choosing
appropriate deselection criteria, communicating with stakeholders, providing accountability, and managing the
overall timetable to finish projects on time. The Science and Engineering librarians at Brigham Young University
evaluated their entire print collection of over 350,000 items within one year, significantly reducing the number
of items kept on the open shelves and the physical collection footprint. Keys to accomplishing this project were
extensive preparation, tracking progress and accountability facilitated by Google Sheets and an interactive GIS
stacks map, and stakeholder feedback facilitated by a novel web-based tool. This case study discusses guidelines
to follow and pitfalls to avoid for any organization that is considering a large- or small-scale collection evaluation
project. Keywords: Weeding | Academic libraries | Collection management | Deselection of library materials | Collection evaluation |
مقاله انگلیسی |
10 |
Weathering of ignitable liquids at elevated temperatures: A thermodynamic model, based on laws of ideal solutions, to predict weathering in structure fires
هوازدگی مایعات قابل اشتعال در دماهای بالا: مدل ترمودینامیکی ، براساس قوانین راه حلهای ایده آل ، برای پیش بینی هوازدگی در آتش سوزی سازه-2020 This manuscript provides experimental evidence and a strong theoretical basis that weathering at elevated
temperatures (up to 210 °C) results in significantly different distributions of the weathered residues compared to
room temperature weathering, especially when the extent of weathering is held constant. A nine-component
artificial gasoline mixture enabled quantitative comparisons between the residues predicted by a mathematical
model and those measured in temperature-controlled evaporations. The simple mathematical model employs
iterative fractional losses (e.g. 5% each step) of the mixture components in proportion to their theoretical partial
pressures. The partial pressures of the constituents are determined using either: 1) Raoult’s law and Antoine
constants from the literature, or 2) Henry’s law.
The model supports the experimental observations in that the composition of weathered residues as a function
of time—or extent of weathering—is significantly different at different temperatures. For example, toluene falls
below the limits of detection at 90% weathering and 30 °C but is still readily observable at ~1% of the total ion
chromatogram (TIC) at 98% weathering and 210 °C. Such behavior could help explain why ignitable liquids that
are highly weathered at elevated temperatures in structure fires are likely to resemble those weathered in the
laboratory to a lesser extent at room temperature. Given a chromatogram of a pristine ignitable liquid, the model
based on Raoult’s law predicts the peak area of each weathered compound with a root mean squared error of
prediction (RMSEP) of ~3% when the liquid is weathered up to 98% and 210 °C. Keywords: Fire debris | Ignitable liquids | Weathering | Evaporation |
مقاله انگلیسی |