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ردیف | عنوان | نوع |
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1 |
Parallel Time-Delay Reservoir Computing With Quantum Dot Lasers
محاسبات مخزن تاخیر زمانی موازی با لیزرهای کوانتومی-2022 A semiconductor laser with optical feedback and
optical injection is an appealing scheme to construct the
time-delay reservoir computing (TDRC) networks. Quantum
dot (QD) lasers are compatible to the silicon platform, and hence
is helpful to develop fully on-chip TDRCs. This work theoretically
demonstrates a parallel TDRC based on a Fabry-Perot QD laser
with multiple longitudinal modes. These modes act as connected
physical neurons, which process the input signal in parallel.
The interaction strength of the modes is characterized by the
cross-gain saturation effect. We show that the neuron interaction
strength affects the performance of various benchmark tasks,
including the memory capacity, time series prediction, nonlinear
channel equalization, and spoken digit recognition. In comparison
with the one-channel TDRC with the same number of nodes, the
parallel TDRC runs faster and its performance is improved on
multiple benchmark tasks.
Index Terms: Reservoir computing | quantum dot lasers | optical neural network | optical feedback | optical injection |
مقاله انگلیسی |
2 |
A Quantum-Inspired Classifier for Early Web Bot Detection
یک طبقه بندی الهام گرفته از کوانتومی برای تشخیص زودهنگام ربات وب-2022 This paper introduces a novel approach, inspired
by the principles of Quantum Computing, to address web bot
detection in terms of real-time classification of an incoming
data stream of HTTP request headers, in order to ensure the
shortest decision time with the highest accuracy. The proposed
approach exploits the analogy between the intrinsic correlation
of two or more particles and the dependence of each HTTP
request on the preceding ones. Starting from the a-posteriori
probability of each request to belong to a particular class, it is
possible to assign a Qubit state representing a combination of the
aforementioned probabilities for all available observations of the
time series. By leveraging the underlying mathematical details
of superposition and entanglement on specific subsequences,
it is possible to devise a measure of membership to each class,
thus enabling the system to take a reliable decision when a
sufficient level of confidence is met or to continue with additional
observations. The results reported in this paper objectively
show the effectiveness of our quantum-inspired algorithm which
outperforms other state-of-the-art approaches, including our own
one based on the Sequential Probability Ratio Test.
Index Terms— Quantum-inspired computing | bot detection | sequential classification | early decision | multinomial classification | multivariate sequence classification. |
مقاله انگلیسی |
3 |
Evaluation of printable character-based malicious PE file-detection method
ارزیابی روش تشخیص فایل PE مخرب مبتنی بر کاراکتر قابل چاپ-2022 Printable characters extracted from portable executable (PE) files are a common surface analysis
feature. String extraction is a supplemental feature for malware analysis. Recent developments
in natural language processing techniques have enabled the rapid detection of malicious PE files.
Previously, we proposed a method for detecting malicious PE files using printable characters
using two language models for feature extraction and machine-learning. In this study, we
evaluated the method using the latest FFRI dataset consisting of 400,000 benign and 400,000
malicious samples between 2019 and 2021. To the best of our knowledge, this is the first study
to consider the time series of both malicious and benign samples. According to the results,
specific tokens in the printable characters were effective in detecting the latest malicious PE
files. The most practical combination was of the Doc2vec and multilayer perceptron, which
achieved an F1 score of 0.981. Each run time showed an almost linear increase with increasing
dataset size.
Keywords: Malware | Machine learning | Natural language processing |
مقاله انگلیسی |
4 |
Modelling corporate bank accounts
مدل سازی حساب های بانکی شرکت ها-2021 We discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer
a principled treatment of a genuine industrial problem. The corporate bank accounts in our study
constitute spare, irregularly-spaced time series that may take both positive and negative values. We
thus builds on previous models where the underlying is real-valued. We describe an intra-monthly
effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and
end of each month and highest in the middle. However, our theory also allows for the opposite effect
to occur. In-sample applications demonstrate the statistical significance of the hypothesized monthly
effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA
approach.
keywords: حساب های بانکی شرکت | فناوری | پیش بینی برنامه های کاربردی | یادگیری ماشین | Corporate bank accounts | Fin Tech | Forecasting applications | Machine learning |
مقاله انگلیسی |
5 |
A Requiem for ‘‘Blame It on Beijing” interpreting rotating global current account surpluses
مرثیه ای برای «سرزنش آن به گردن پکن» که در حال تفسیر جهانی در حال چرخش است مازاد حساب جاری-2021 Global current account imbalances have reappeared, although the extent and distribution
of these imbalances are noticeably different from those experienced in the middle of the
last decade. What does that recurrence mean for our understanding of the origin and nature of such imbalances? Will imbalances persist over time? Informed by empirical estimates of the determinants of current account imbalances encompassing the period after
the global recession, we find that – as before – the observable manifestations of the factors
driving the global saving glut have limited explanatory power for the time series variation
in imbalances. Fiscal factors determine imbalances, and have accounted for a noticeable
share of the recent variation in imbalances, including in the U.S. and Germany. For
advanced economies, the financial component of the current account has been playing
an increasing role in determining the movements of the account. Examining observable
policy actions, it is clear that net official flows have been associated with some share of
imbalances, although tracing out the motivations for intervention is difficult. Looking forward, it is clear that policy can influence global imbalances, although some component of
the U.S. deficit will likely remain given the U.S. role in generating safe assets.
keywords: تعادل حساب جاری | دارایی های خارجی خالص | صرفه جویی | مروریسم | محافظت از خود | دارایی های ایمن | fi flows | Current account balance | Net foreign assets | Saving glut | Mercantilism | Self-protection | Safe assets | Official flows |
مقاله انگلیسی |
6 |
Nonlinear analysis and active management of production-distribution in nonlinear supply chain model using sliding mode control theory
تحلیل غیرخطی و مدیریت فعال تولید-توزیع در مدل غیر خطی زنجیره تامین با استفاده از تئوری کنترل حالت کشویی-2021 This paper deals with system dynamics approach for dynamical behaviors and control
synthesis of supply chain system by utilizing three-stage production-distribution model.
The presented approach offers systematic tools for determining fundamental relationships
between multi-echelons in the supply chain dynamics by using eigenvalues, bifurcation,
and time history investigation. By exploring system dynamics on time series analysis, it
is found that system performance has suffered severely from the bullwhip effect under
impacts of model uncertainties and perturbed demand. The novel fractional-order sliding
mode control algorithm has been presented based on adaptation mechanism, ensuring that
the shipment flows are robustly stable in supply chain networks against disruptions. This
is a smarter way of getting sufficient strength to sustain existing competitive market for
mitigating the risks and improving the supply chain performance. The system stability has
been thoroughly analyzed by using Routh-Hurwitz criterion and Lyapunov theory. Extensive numerical simulations have been conducted to obtain insights into the system behaviors and to validate effectiveness of active control policies by matching the shipment
sent to customer demand, ensuring supply chains resilience. Finally, it is found that the
presented approach can help decision-makers develop more efficient supply chain management system against severe market disruptions. Keywords: System dynamics | Supply chain management | Production-distribution model | Fractional order | Sliding mode control | Adaptive law |
مقاله انگلیسی |
7 |
Forecasting third-party mobile payments with implications for customer flow prediction
پیش بینی پرداخت های تلفن همراه شخص ثالث با پیامدهای پیش بینی جریان مشتری-2020 Forecasting customer flow is key for retailers in making daily operational decisions, but
small retailers often lack the resources to obtain such forecasts. Rather than forecasting
stores’ total customer flows, this research utilizes emerging third-party mobile payment
data to provide participating stores with a value-added service by forecasting their share
of daily customer flows. These customer transactions using mobile payments can then be
utilized further to derive retailers’ total customer flows indirectly, thereby overcoming
the constraints that small retailers face. We propose a third-party mobile-paymentplatform
centered daily mobile payments forecasting solution based on an extension
of the newly-developed Gradient Boosting Regression Tree (GBRT) method which can
generate multi-step forecasts for many stores concurrently. Using empirical forecasting
experiments with thousands of time series, we show that GBRT, together with a strategy
for multi-period-ahead forecasting, provides more accurate forecasts than established
benchmarks. Pooling data from the platform across stores leads to benefits relative to
analyzing the data individually, thus demonstrating the value of this machine learning
application. Keywords: Analytics | Big data | Customer flow forecasting | Machine learning | Forecasting many time series | Multi-step-ahead forecasting strategy |
مقاله انگلیسی |
8 |
The effects of Chile’s 2005 traffic law reform and in-country socioeconomic differences on road traffic deaths among children aged 0-14 years: A 12-year interrupted time series analysis
اثرات اصلاح قانون راهنمایی و رانندگی در سال 2005 شیلی و اختلافات اقتصادی و اجتماعی درون کشور در مورد مرگ و میر در جاده های کودکان در سن 0-14 سال: تجزیه و تحلیل قطع 12 ساله سری های زمانی -2020 Objectives: This study assessed the effect of Chile’s 2005 traffic law reform (TLR) on the rates of road traffic
deaths (RTD) in children aged 0–14 years, adjusting for socioeconomic differences among the regions of the
country.
Methods: Free-access sources of official and national information provided the data for every year of the study
period (2002–2013) and for each of the country’s 13 upper administrative divisions with respect to RTD in child
pedestrians and RTD in child passengers (dependent variables), and the following control variables: the number
of road traffic tickets processed, investment in road infrastructure, poverty, income inequality, insufficient
education, unemployment, population aged 0–14 years, and prevalence of alcohol consumption in the general
population. Interrupted time series analyses (level and slope change impact model), using generalized estimating
equation methods, were conducted to assess the impact of the TLR (independent variable) on the dependents
variables.
Results: There was a significant interaction between time and Chile’s 2005 TLR for a reduction in child pedestrians
(incidence rate ratio [IRR] 0.87, 95% confidence interval [CI] 0.79-0.96) and passengers RTD (IRR for
interaction 0.80, 95% CI 0.67-0.96) trends. In addition, in child pedestrians, RTD rates were affected by poverty
(IRR 1.04, 95% CI 1.02–1.05), income inequality (IRR 1.02, 95% CI 1.00–1.04), and unemployment (IRR 0.94,
95% CI 0.90-0.98), whereas in the case of child passengers, poverty (IRR 1.05, 95% CI 1.01–1.08) and income
inequality (IRR 0.93, 95% CI 0.91-0.95) were significant.
Conclusions: Large-scale legislative actions can be effective road safety measures if they are aimed at promoting
behavioral change in developing countries, improving the safety of children on the road. Additionally, regional
socioeconomic differences are associated with higher RTD rates in this population, making this an argument in
favor of road safety policies that consider these inequalities. The number of road traffic tickets processed and the
investment in road infrastructure were not significant. Keywords: Safety management | Child | Traffic accidents | Mortality | Socioeconomic factors |
مقاله انگلیسی |
9 |
Effectiveness of implementing the criminal administrative punishment law of drunk driving in China: An interrupted time series analysis, 2004-2017
اثربخشی اجرای قانون مجازات اداری کیفری رانندگی مست در چین: تجزیه و تحلیل سری های زمانی قطع شده ، 2004-2017-2020 In 2011, a more severe drunk driving law was implemented in China, which criminalized driving under the
influence of alcohol for the first time and increased penalties for drunk driving. The present study aimed to assess
effectiveness of the drunk driving law in China in reducing traffic crashes, injuries, and mortality. Data used in
this study was obtained from the Traffic Management Bureau of the Ministry of Public Security of the People’s
Republic of China. An interrupted time series analysis was conducted to analyze annual data from 2004 to 2017,
including the number of road traffic crashes, deaths, and injuries caused by drunk driving in China. The average
annual incidences of crashes, mortality, and injuries have decreased after the promulgation of drunk driving law
in 2011. In the post-intervention period, the increased slope for crashes, mortality and injury rates were, respectively,
-0.140 to -0.006, -0.052 to -0.005 and -0.150 to -0.008, indicating a weaker downward trend of
dependent variables. The more stringent drunk driving law is not as effective as expected. Drunk driving is still a
severe traffic safety problem to be addressed in China. Both legislation and other prevention programs should be
adopted to reduce road traffic injuries caused by drunk driving in China. Keywords: Drunk driving | Interrupted time series analysis | Road traffic law | Injury | Evaluation | China |
مقاله انگلیسی |
10 |
Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
آنتروپی پویای نمادین چند متغیره کامپوزیت تصفیه شده و کاربرد آن در تشخیص خطای ماشین چرخشی-2020 Accurate and efficient identification of various fault categories, especially for the big data and multisensory
system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive
multivariate data, extracting discriminative and stable features with high efficiency is the significant
step. This paper proposes a novel feature extraction method, called Refined Composite multivariate
Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate
multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic
entropy can capture more identification information from multiple sensors with superior computational
efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method
to measure the complexity of multivariate time series and identify the signals with different components
are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed
method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball
bearing dataset under time-varying speed condition are applied. Compared with the existing methods,
the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively.
Meanwhile, the proposed method saves at least half of the computational time. The result shows that the
proposed method is effective to improve the efficiency and classification accuracy dealing with the massive
multivariate signals. Keywords: Multivariate multiscale symbolic dynamic | entropy | Random forest | Time-varying speed conditions | Fault diagnosis |
مقاله انگلیسی |