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1 TAPSTROKE: A novel intelligent authentication system using tap frequencies
TAPSTROKE: رویکرد سیستم احراز هویت هوشمند با استفاده از فرکانسهای آهسته-2019
Emerging security requirements lead to new validation protocols to be implemented to recent authen- tication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time- domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (STFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%–2.12% and 2.01%–3.21% equal error rates (EER) with mSTFT; while with regular STFT the classifiers produced quite higher EER, 7.49%–11.95% and 6.93%–10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches.
Keywords: Tapstroke | Keystroke | Authentication | Biometrics | Frequency | Short time Fourier transformation | Support vector machines
مقاله انگلیسی
2 Image quality recognition technology based on deep learning
فن آوری تشخیص کیفیت تصویر مبتنی بر یادگیری عمیق-2019
Image plays an important role in today’s society and is an important information carrier. However, due to the problems in shooting or processing, image quality is often difficult to be guaranteed, and low-quality images are often difficult to identify, which results in the waste of information. How to effectively identify low-quality images has become a hot research topic in today’s society. Deep learning has a good application in image recognition. In this paper, it is applied to low-quality image recognition. An image quality recognition technology based on deep learning is studied to effectively realize low-quality image recognition. Firstly, in the stage of image preprocessing, a low-quality image enhancement method is proposed, which uses non-linear transformation to enhance image contrast image, restore image details and enhance image quality. Secondly, the convolutional neural network is used to extract image features, and the L2 regularization method is introduced to optimize the over-fitting problem. Finally, SVM is used to recognize the output of convolutional neural network to realize low quality image recognition. Through simulation analysis, it is found that the image enhancement method proposed in the preprocessing stage can effectively enhance the image quality, and deep learning can effectively realize the recognition of the enhanced image and improve the recognition accuracy.
Keywords: Low quality image | Deep learning | Image recognition | Support vector machines(SVM)
مقاله انگلیسی
3 A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
یک سیستم توصیه گر متا یادگیری برای تنظیم hyperparameter : پیش بینی زمانیکه تنظیم باعث بهبود طبقه بندی های SVM می شود-2019
For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially on larger datasets, while the tuned settings do not always significantly outperform the default values. This paper proposes a recom- mender system based on meta-learning to identify exactly when it is better to use de- fault values and when to tune hyperparameters for each new dataset. Besides, an in-depth analysis is performed to understand what they take into account for their decisions, pro- viding useful insights. An extensive analysis of different categories of meta-features, meta- learners, and setups across 156 datasets is performed. Results show that it is possible to accurately predict when tuning will significantly improve the performance of the induced models. The proposed system reduces the time spent on optimization processes, without reducing the predictive performance of the induced models (when compared with the ones obtained using tuned hyperparameters). We also explain the decision-making pro- cess of the meta-learners in terms of linear separability-based hypotheses. Although this analysis is focused on the tuning of Support Vector Machines, it can also be applied to other algorithms, as shown in experiments performed with decision trees.
Keywords: Meta-learning | Recommender system | Tuning recommendation | Hyperparameter tuning | Support vector machines
مقاله انگلیسی
4 Will aging voting machines cause more voters to experience long waits?
آیا پیرشدگی ماشین های رای گیری باعث خواهد شد تا رای دهنده های بیشتری انتظارات طولانی را تجربه کنند؟-2018
As the majority of voting machines in use today approach or exceed their expected lifetime, an increased number of voting machine failures are expected in upcoming elections. This study examines and quantifies the impact of less reliable voting machines, due to age, on the number of voters waiting longer than 30-min. G/G/s queue approximation and discrete event simulation are used in the analysis. Results show that if reliability measures — mean time between failures, mean time to repair, and availability — are within certain interval ranges, no additional voting machines are needed to ensure that no more than 5% of voters wait for longer than 30 min. However, significantly more voters would have long waits if the reliability of voting machines is poor. Accordingly, less reliable voting machines do not necessarily cause more voters to experience long waits. The proposed closed-form approximation formula and the simulation model are practical tools for election officials to evaluate the impact of less reliable voting machines on voting lines.
keywords: Voting operations |Simulation |Machine failure |Voting lines |Wait time distribution
مقاله انگلیسی
5 الگوریتم بهینه سازی ازدحام ذرات با کنترل هوشمند تعداد ذرات برای طراحی بهینه ماشین های الکتریکی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 23
در این مقاله، یک الگوریتم بهینه سازی ازدحام ذرات (PSO) اصلاح شده پیشنهاد می شود که نسخه ارتقاء یافته الگوریتم PSO معمولی است. برای بهبود دادن عملکرد الگوریتم PSO ، یک روش جدید برای کنترل کردن هوشمندانه تعداد ذرات به کار برده شده است. این روش جدید، مقدار هزینه بهترین جهانی (gbest) در تکرار فعلی نسبت به gbest در تکرار قبلی را با یکدیگر مقایسه می کند. اگر بین دو مقدار هزینه اختلافی وجود داشته باشد، آنگاه الگوریتم پیشنهادی در مرحله اکتشاف عمل می کند و تعداد ذرات را حفظ می کند. اما، وقتی که اختلاف در مقادیر هزینه نسبت به مقادیر تحمل تخصیص یافته توسط کاربر کوچکتر باشد، این الگوریتم پیشنهادی در مرحله استخراج عمل می کند و تعداد ذرات را کاهش می دهد. علاوه بر این، این الگوریتم ، نزدیکترین ذره به بهترین ذره را حذف می کند تا از تصادفی بودنش بر حسب فاصله ی اقلیدسی اطمینان حاصل کند. الگوریتم پیشنهادی با استفاده از پنج تابع آزمون عددی اعتبارسنجی می شود، که تعداد فراخوانی های تابع تا اندازه ای نسبت به PSO معمولی کاهش می یابد. بعد از اعتبار سنجی الگوریتم ، برای طراحی بهینه موتور سنکرون مغناطیس دائم درونی (IPMSM) به کار برده می شود تا اعوجاج هارمونیک کل (THD) نیروی ضد محرکه الکتریکی (back-EMF) کاهش یابد. با در نظر گرفتن شرط عملکرد، طراحی بهینه به دست می آید که back-EMF THD را کاهش داده و مقدار back-EMF را برآورده می کند. نهایتا، یک مدل آزمایشگاهی را ایجاد کرده و آزمایش می کنیم. برای اعتبارسنجی عملکرد طراحی بهینه و الگوریتم بهینه سازی ، یک آزمایش بدون بار انجام می شود. بر اساس نتایج آزمایشگاهی، اثربخشی الگوریتم پیشنهادی بر روی طراحی بهینه یک ماشین الکتریکی تایید می شود.
کلمات کلیدی: طراحی بهینه | الگوریتم بهینه سازی | بهینه سازی ذرات ذرات | ماشین الکتریکی | موتور همگام مگنت دائمی.
مقاله ترجمه شده
6 Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر قطار: چشم انداز تحلیل داده های بزرگ-2018
Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
Keywords: Railway network ، Train Delay Prediction systems ، Big data analytics ، Extreme learning machines ، Shallow architecture ، Deep architecture
مقاله انگلیسی
7 Traffic noise and pavement distresses: Modelling and assessment of input parameters influence through data mining techniques
سر و صدای ترافیکی و ناراحتی های پیاده رو : مدل سازی و ارزیابی پارامترهای ورودی تاثیر از طریق تکنیک های داده کاوی -2018
Traffic noise affects greatly health and well-being of people, consequently the knowledge and control of the factors affecting it is very important. In this study models to predict tyre-pavement noise acoustic and psy choacoustic indicators based on type of pavement, texture, pavement distresses and speed were developed and used to assess the importance of each factor. By applying data mining techniques, in particular artificial neural networks and support vector machines, models with good predictive capacity of both acoustic and psychoa coustic noise indicators were obtained, constituting a precious tool to reduce the tyre-pavement noise. Moreover, the proposed models allowed for the assessment of the influence of the input parameters controlling noise such as: type of pavement, texture, speed and pavement distresses for the first time. It was found that pavement distresses and, as expected, speed influence strongly tyre-pavement noise. In this way it is clearly shown that preventive maintenance of road pavements by authorities, which eliminates distresses, can have an important effect on tyre-road noise, promoting the well-being of the populations.
Keywords: Tyre-pavement noise ، Acoustic and psychoacoustic indicators ، Pavement distresses ، Data mining ، Support vector machines ، Artificial neural networks
مقاله انگلیسی
8 Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines
تاثیر ویژگی های محصول روی رضایت مشتری: یک تحلیل روی بازدیدهای آنلاین برای ماشین های لباسشویی-2018
Online reviews are an important information source for companies analysing users’ demands. We conducted a study of online reviews to measure how product attributes impact customer satisfaction. First, we attempted to infer through sentiment analysis whether a customer is satisfied with a purchase according to their review. Second, a logistic regression model was developed to estimate the impact of various product properties on customer satisfaction scores. Our estimates indicated that customer satisfaction is influenced by drainage mode, loading type, frequency conversion, type, display, colour, and capacity. We further investigate the impact of price and find that customers who buy cheap products should be treated differently from purchasers of expensive items because the relevance of design features on their satisfaction is different. Additionally, we observed that although customers are concerned about noise, perceived noise is not consistent with actual noise levels. We analysed specific reviews and then obtained more detailed information on customer attitudes.
keywords: Customer behavior |Customer satisfaction |Online reviews |Product attributes |Product design
مقاله انگلیسی
9 Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection
رویکرد داده های بزرگ برای نظارت بر فرآیند دسته ای: تشخیص و تشخیص همزمان خطایی با استفاده از ویژگی های مبتنی بر بردار پشتیبانی غیرخطی-2018
This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark data set which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the pre-aligned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.
Keywords: Process monitoring ، Data-driven modeling ، Big data ،Feature selection ، Support vector machines
مقاله انگلیسی
10 یک سیستم کلونی مورچه ها صرفه جویی کننده انرژی برای جاگذاری ماشین مجازی در محاسبات ابری
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 63
جاگذاری ماشینی مجازی (VMP) و بهره وری انرژی، موضوعات قابل توجهی در تحقیقات حوزه محاسبات ابری هستند. در این مقاله، از محاسبات تکاملی برای VMP و جهت به حداقل رساندن تعداد سرورهای فیزیکی فعال به منظور زمان بندی سرورهای کمتر ازحد کافی استفاده شده، برای صرفه جویی در انرژی استفاده می شود. با الهام از عملکرد نویدبخش الگوریتم سیستم کلونی مورچه ای (ACS) برای مسائل ترکیبی، یک دیدگاه مبتنی بر ACS جهت دستیابی به هدف VMP توسعه می یابد. الگوریتم حاصل که به صورت جفت شده با روشهای جستجوی موضعی تبادل ترتیب و مهاجرت (OEM) می باشد، یک OEMACS نامیده می شود. این الگوریتم به صورت موثری تعداد سرورهای فعال استفاده شده برای جاگذاری ماشین های مجازی (VMs) را از یک نقطه نظر بهینه سازی عمومی و ازطریق یک راهبرد جدید برای انتشار فرومون که مورچه های مصنوعی را به سمت راه حل های نویدبخشی که ماشین های مجازی داوطلب را گروه بندی می کنند، هدایت می کند، به حداقل می رساند. از OEMACS برای مسائل مختلف VMP که دارای اندازه های مختلفی برای ماشین مجازی در محیط های ابری سرورهای همگن و ناهمگن هستند استفاده می شود. نتایج نشان می دهد که OEMACS عموما" نسبت به دیدگاههای سنتی غیرمستدل و سایر دیدگاههای مبتنی بر تکامل، عملکرد بهتری به ویژه روی VMP هایی با خصوصیات منابع تنگراهی دارد، و صرفه جویی های قابل توجهی از انرژی و نیز استفاده کارآمدتر از منابع مختلف را به ارمغان می آورد.
کلمات شاخص : سیستم کلونی مورچه ها | محاسبات ابری | جاگذاری ماشین مجازی
مقاله ترجمه شده
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