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1 |
Head tremor in cervical dystonia: Quantifying severity with computer vision
لرزش سر در دیستونی دهانه رحم: کمی کردن شدت با دید کامپیوتری-2022 Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective
observation. Technological developments offer alternatives for measuring HT severity that are objective and
amenable to automation.
Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-
based software system) to quantify oscillatory and directional aspects of HT from video recordings during a
clinical examination and to test its convergent validity with clinical rating scales.
Methods: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video
recordings from an examination segment in which participants were instructed to let their head drift to its most
comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used
Spearman’s correlation to measure the agreement between CMOR and clinical ratings.
Results: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis
was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants.
One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t =
12.89, p < 0.0001) HT axes. CMOR’s HT severity measure positively correlated with the HT item on the Toronto
Western Spasmodic Torticollis Rating Scale-2 (Spearman’s rho = 0.54, p < 0.001).
Conclusions: We demonstrate a new objective method to measure HT severity that requires only conventional
video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity
ratings. keywords: لرزش سر | ویدیو | بینایی کامپیوتر | درجه بندی شدت | TWSTRS | Head tremor | Video | Computer vision | Severity rating | TWSTRS |
مقاله انگلیسی |
2 |
Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022 Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture,
engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However,
research on KG updates in the industry is scarce, with most current research focusing on text-based KG
updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the
potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex-
plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to
propose a novel framework called Image2Triplets that can extract explicit visual relationships from images
to update the construction activity KG. Comprehensive experiments on the images of architectural dec-
oration processes have been performed to validate the proposed framework. The results and insights will
contribute new knowledge and evidence to human-object interaction detection, KG update and construc-
tion informatics from the theoretical perspective.
© 2022 Elsevier B.V. All rights reserved. keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph |
مقاله انگلیسی |
3 |
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 |
4 |
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 |
5 |
Deep unsupervised methods towards behavior analysis in ubiquitous sensor data
روش های عمیق بدون نظارت برای تجزیه و تحلیل رفتار در داده های حسگر همه جا حاضر-2022 Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of
features for modeling user-specific characteristics. These characteristics, in turn, can be used for a
number of tasks including resource management, power efficiency, and smart home applications.
In recent years, the employment of topic models for BA has been found to successfully extract the
dynamics of the sensed data. Topic modeling is popularly performed on text data for mining
inherent topics. The task of finding the latent topics in textual data is done in an unsupervised
manner. In this work we propose a novel clustering technique for BA which can find hidden
routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently
works on high dimensional data for BA without performing any computationally expensive
reduction operations. We evaluate three different techniques namely Latent Dirichlet Allocation
(LDA), the Non-negative Matrix Factorization (NMF), and the Probabilistic Latent Semantic
Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using
performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets
namely, the Intel Lab, Kyoto, and MERL. Through rigorous experiments, we achieve silhouette
scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset, and 0.8312 over the
MERL dataset for clustering. In these cases, however, it is di cult to validate the results obtained as
the datasets do not contain any ground truth information. Towards that, we investigate a self-
supervised method that will be capable of capturing the inherent ground truths that are avail-
able in the dataset. We design a self-supervised technique which we apply on datasets containing
ground truth and also without. We see that our performance on data without ground truth differs
from that with ground truth by approximately 8% (F-score) hence showing the efficacy of self-
supervised techniques towards capturing ground truth information. keywords: تحلیل داده های فراگیر | تحلیل رفتار | یادگیری خود نظارتی | Ubiquitous data analysis | Behavior analysis | Self supervised learning |
مقاله انگلیسی |
6 |
Examining the internet of educational things adoption using an extended unified theory of acceptance and use of technology
بررسی پذیرش اینترنت از اشیا آموزشی با استفاده از یک نظریه یکپارچه گسترده پذیرش و استفاده از فناوری-2022 The purpose of this study is to examine the adoption of IoT applications for educational purposes
by focusing on students’ perspectives. To validate the internet of educational things (IoET) ap-
plications’ acceptance and usage, the main constructs of UTAUT2 theory were integrated with
innovativeness and social support constructs. This study adopts a quantitated study method and
examined empirically through smart PLS-SEM software, an online questionnaire is established
and disseminated to Taibah university students. Results revealed that social support, facilitated
conditions, innovativeness, and effort expectancy of UTAUT2 constructs had the strongest effect
on IoET applications’ acceptance and usage respectively. Whereas performance expectancy, and
perceived usefulness had the weakest effect on IoET adoption respectively. On the other hand, the
relationship between perceived ease of use and IoET behavioral intention were not supported
because of insignificant relationships. The results demonstrated in this study potentially assist the
universities to understand the main determinists of using IoET applications acceptance and usage
from students’ perspectives to integrate the IoT concept in teaching and learning. keywords: اینترنت اشیا آموزشی | حمایت اجتماعی | نوآوری | عربستان سعودی | Internet of educational things | UTAUT2 | Social support | Innovativeness | Saudi Arabia |
مقاله انگلیسی |
7 |
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 |
8 |
Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance
طبقه بندی خودکار جانوران در عکس های بستر دریا: تأثیر اندازه مجموعه داده های آموزش و اعتبار سنجی ، با ملاحظاتی برای عدم تعادل کلاس-2021 Machine learning is rapidly developing as a tool for gathering data from imagery and may be useful in identifying (classifying) visible specimens in large numbers of seabed photographs. Application of an automated classifi- cation workflow requires manually identified specimens to be supplied for training and validating the model. These training and validation datasets are generally generated by partitioning the available manual identified specimens; typical ratios of training to validation dataset sizes are 75:25 or 80:20. However, this approach does not facilitate the desired scalability, which would require models to successfully classify specimens in hundreds of thousands to millions of images after training on a relatively small subset of manually identified specimens. A second problem is related to the ‘class imbalance’, where natural community structure means that fewer spec- imens of rare morphotypes are available for model training. We investigated the impact of independent variation of the training and validation dataset sizes on the performance of a convolutional neural network classifier on benthic invertebrates visible in a very large set of seabed photographs captured by an autonomous underwater vehicle at the Porcupine Abyssal Plain Sustained Observatory. We tested the impact of increasing training dataset size on specimen classification in a single validation dataset, and then tested the impact of increasing validation set size, evaluating ecological metrics in addition to computer vision metrics. Computer vision metrics (recall, precision, F1-score) indicated that classification improved with increasing training dataset size. In terms of ecological metrics, the number of morphotypes recorded increased, while diversity decreased with increasing training dataset size. Variation and bias in diversity metrics decreased with increasing training dataset size. Multivariate dispersion in apparent community composition was reduced, and bias from expert-derived data declined with increasing training dataset size. In contrast, classification success and resulting ecological metrics did not differ significantly with varying validation dataset sizes. Thus, the selection of an appropriate training dataset size is key to ensuring robust automated classifications of benthic invertebrates in seabed photographs, in terms of ecological results, and validation may be conducted on a comparatively small dataset with confidence that similar results will be obtained in a larger production dataset. In addition, our results suggest that automated classification of less common morphotypes may be feasible, providing that the overall training dataset size is sufficiently large. Thus, tactics for reducing class imbalance in the training dataset may produce improvements in the resulting ecological metrics. Keywords: Computer vision | Deep learning | Benthic ecology | Image annotation | Marine photography | Artificial intelligence | Convolutional neural networks | Sample size |
مقاله انگلیسی |
9 |
Accounting and auditing of credit loss estimates: The hard and the soft
حسابداری و حسابرسی تخمین زیان اعتباری: سخت و نرم-2021 A key goal of financial reporting is to address information asymmetries, which are amplified in the
case of banks given their credit, maturity and liquidity transformation and complex, judgmental
accounting standards dealing with expected credit losses (ECL).
The paper explores the role of bank management in estimating and recognizing ECL, and how
external auditors challenge the resulting figures. Based on analysis of G-SIB disclosures, it concludes that management and auditors tend to prioritize observable and verifiable, hard information
to reduce challenge to their reported estimates and protect against the threat of legal liability.
Emphasis on such information facilitates loss deferral, damaging the reliability of banks’ financial
reporting, obscuring their safety and soundness picture and jeopardizing financial stability.
Based on these conclusions, the paper seeks to open a new path to the research and policy analysis of credit loss recognition, introducing proposals to address the procyclicality of credit loss
accounting by tackling inappropriate incentives that decouple risk taking from its translation onto
banks’ financial statements.
keywords: انتظارات اعتباری انتظار می رود | عدم تقارن اطلاعات | افشای | عوارض جانبی | ثبات اقتصادی | پروسیکیت | Expected Credit Losses | Information asymmetries | Disclosures | Externalities | Financial stability | Procyclicality |
مقاله انگلیسی |
10 |
Research on prepaid account financing model based on embedded system and Internet of Things
تحقیق در مورد مدل تامین مالی پیش پرداخت بر اساس سیستم جاسازی شده و اینترنت اشیا-2021 Internet of Things (IoT) network interconnection to create objects and things will play the Internet to play an
active role in the global network in the future. For the Internet of Things, which is widely adopted through
funding models, it must be trusted in the IoT security infrastructure. Efficiently and Securely IoT is very
important to define how each other can communicate with remote servers and get Exchange account informa-
tion. Prepayments for effective financial management and an important choice for financial IoT for service
providers and customers. However, it must be supported by real-time credit checking and costing. Internet re-
sources are consumed by these real-time action stuff providers and impose high costs on the old system. To solve
this problem, to propose the K Means Algorithm scalable accounting solutions, where the user is hosted each
occupies a prepaid account, constitute the components of embedded systems. Based on each of our prepaid
billing components’ supervision, it is at the same time consumed by the embedded system of all services, based
on the calculation of the service packages consumed by the customer. Prepaid accounts are reassigned when the
customer had sufficient credit to supplement their use and are allocated based on IoT services’ consumption. This
work aims to reduce the cost of pre-paid services and ensure that service delivery is not to interfere with the
charging unit. Also, embedded systems’ theoretical and experimental analysis shows that this work can store
long-lived services on the Internet of Things to provide inexpensive accounting solutions.
keywords: الگوریتم میانگین کا | سیستم های جاسازی شده | اینترنت اشیا | مدیریت مالی | سیستم حسابداری پیش پرداخت | K means algorithm | Embedded systems | Internet of Things | Financial management | Prepaid accounting system |
مقاله انگلیسی |