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نتیجه جستجو - Tax

تعداد مقالات یافته شده: 307
ردیف عنوان نوع
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 A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions
بررسی حملات خصمانه در بینایی کامپیوتر: طبقه بندی، تجسم و جهت گیری های آینده-2022
Deep learning has been widely applied in various fields such as computer vision, natural language pro- cessing, and data mining. Although deep learning has achieved significant success in solving complex problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, result- ing in models that fail to perform their tasks properly, which limits the application of deep learning in security-critical areas. In this paper, we first review some of the classical and latest representative adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowl- edge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze the subject development in this field based on the information of 5923 articles from Scopus. In the end, possible research directions for the development about adversarial attacks are proposed based on the trends deduced by keywords detection analysis. All the data used for visualization are available at: https://github.com/NanyunLengmu/Adversarial- Attack- Visualization .
keywords: یادگیری عمیق | حمله خصمانه | حمله جعبه سیاه | حمله به جعبه سفید | نیرومندی | تجزیه و تحلیل تجسم | Deep learning | Adversarial attack | Black-box attack | White-box attack | Robustness | Visualization analysis
مقاله انگلیسی
3 Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022
Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper exploits the combination of image processing techniques and machine learning models (Random Forests) to assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor- mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma- chine learning components, was able to build effective models for either the classification problem (visual quality level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con- tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be analyzed using traditional tools.
keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction
مقاله انگلیسی
4 A computer vision framework using Convolutional Neural Networks for airport-airside surveillance
چارچوب بینایی کامپیوتری با استفاده از شبکه‌های عصبی کانولوشن برای نظارت در فرودگاه-2022
Modern airports often have large and complex airside environments featuring multiple runways, with changing configurations, numerous taxiways for effective circulation of flights and tens, if not hundreds, of gates. With inherent uncertainties in gate push-back and taxiway routing, efficient surveillance and management of airport-airside operations is a highly challenging task for air traffic controllers. An increase in air traffic may lead to gate delays, taxiway congestion, taxiway incursions as well as significant increase in the workload of air traffic controllers. With the advent of Digital Towers, airports are increasingly being equipped with surveillance camera systems. This paper proposes a novel computer vision framework for airport-airside surveillance, using cameras to monitor ground movement objects for safety enhancement and operational efficiency improvement. The framework adopts Convolutional Neural Networks and camera calibration techniques for aircraft detection and tracking, push-back prediction, and maneuvering monitoring. The proposed framework is applied on video camera feeds from Houston Airport, USA (for maneuvering monitoring) and Obihiro Airport, Japan (for push-back prediction). The object detection models of the proposed framework achieve up to 73.36% average precision on Houston airport and 87.3% on Obihiro airport. The framework estimates aircraft speed and distance with low error (up to 6 meters), and aircraft push-back is predicted with an average error of 3 min from the time an aircraft arrives with the error-rate reducing until the aircraft’s actual push-back event.
keywords: Air traffic control | Convolutional Neural Network | Computer vision
مقاله انگلیسی
5 Lossy Data Compression for IoT Sensors: A Review
فشرده سازی داده های از دست رفته برای حسگرهای اینترنت اشیا: مرور-2022
Internet of Things (IoT) can be considered a suitable platform for industrial applications, enabling large systems that connect a huge number of intelligent sensors and subsequent data collection for analytical applications. This factor is responsible for the substantial increase in the current volume of data generated by IoT devices. The large volume of data generated by IoT sensors can lead to unusual demands on cloud storage and data transmission bandwidths. A suitable approach to address these issues is through data compression approaches. This article presents a systematic review of the literature on lossy data compression algorithms that allows the systems to reduce the data detected by IoT devices. Lossy algorithms have a good compression ratio, preserving data quality and minimizing compression errors. A taxonomy was proposed from the review results, and the main works were classified, analyzed, and discussed.
keywords: فشرده سازی داده ها | اینترنت اشیا IOT | از بین رفتن در فشرده سازی | حسگرها | Data compression | IoT Internet of things | Lossy compression | Sensors
مقاله انگلیسی
6 Publish–Subscribe approaches for the IoT and the cloud: Functional and performance evaluation of open-source systems
رویکردهای انتشار – اشتراک برای اینترنت اشیا و ابر: ارزیابی عملکرد و کارایی سیستم‌های منبع باز-2022
Publish–Subscribe systems facilitate the communication between services or applications. A typical system comprises the publisher, the subscriber, and the broker but, may also feature message queues, databases, clusters, or federations of brokers, apply message delivery policies, communication protocols, security services, and a streaming API. Not all these features are supported by all systems or, others may be optional. As a result, there is no common ground for the comparison of Publish–Subscribe systems. This paper presents a critical survey and taxonomy of Publish–Subscribe systems, of their design features and technologies. The concepts of message queuing, publish–subscribe systems, and publish–subscribe protocols for the cloud and the IoT are discussed and clarified. The respective evaluation is about seven state-of-the-art open-source systems namely, Apache Kafka, RabbitMQ, Orion-LD, Scorpio, Stellio, Pushpin, and Faye. For the sake of fair comparison, a minimum set of common features is identified in all systems. All systems are evaluated and compared in terms of functionality and performance under real-case scenarios.
keywords: صف پیام | انتشار – اشتراک | معیارها | ارزیابی | Message queue | Publish–subscribe | Benchmarks | Evaluation
مقاله انگلیسی
7 A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
بررسی جامع تشخیص حملات سایبری: مجموعه داده‌ها، روش‌ها، چالش‌ ها و جهت‌گیری‌های تحقیقاتی آینده-2022
Rapid developments in network technologies and the amount and scope of data transferred on networks are increasing day by day. Depending on this situation, the density and complexity of cyber threats and attacks are also expanding. The ever-increasing network density makes it difficult for cybersecurity professionals to monitor every movement on the network. More frequent and complex cyberattacks make the detection and identification of anomalies in network events more complex. Machine learning offers various tools and techniques for automating the detection of cyber attacks and for rapid prediction and analysis of attack types. This study discusses the approaches to machine learning methods used to detect attacks. We examined the detection, classification, clustering, and analysis of anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data sets used in each study we examined. We investigated which feature selection or dimension reduction method was applied to the data sets used in the studies. We presented in detail the types of classification carried out in these studies, which methods were compared with other methods, the performance metrics used, and the results obtained in tables. We examined the data sets of network attacks presented as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties encountered in machine learning applications used in network attacks and their solutions
Keywords: Cyber attacks | Machine learning | Deep learning | Geometric deep learning | Cyber security | Adversarial machine learning | Intrusion detection
مقاله انگلیسی
8 Advanced digital signatures for preserving privacy and trust management in hierarchical heterogeneous IoT: Taxonomy, capabilities, and objectives
امضای دیجیتالی پیشرفته برای حفظ حریم خصوصی و مدیریت اعتماد در اینترنت اشیا ناهمگون سلسله مراتبی: طبقه بندی، قابلیت ها و اهداف-2022
Internet of Things (IoT) systems in different areas, such as manufacturing, transportation, and healthcare, are the convergence of several technologies. There are many concerns about security and privacy drawbacks in IoT systems. Apart from confidentiality supported by encryption primitives, authenticity and non-repudiation are of utmost importance. IoT entities generally use conventional digital signature schemes to achieve imperative goals. However, there are some state-of-the-art digital signatures with more functionalities, IoT-friendly properties, and privacy-preserving features.
This survey paper aims to accelerate the adoption of advanced digital signatures. We bridge the gap between the advanced theoretical digital signatures recently designed in cryptographic oriented papers and the applied IoT systems. It aids researchers in achieving more security, privacy as well as some unique functionality aspects. First, we illustrate the benefits of the hierarchical and heterogeneous IoT architecture supporting the end-edge-fog-cloud continuum accompanying blockchain technology. Second, our survey delves into five state-of-the-art digital signatures, including randomizable, keyless, double-authentication-prevention, sanitizable, and redactable schemes, that are aligned with entities in IoT systems. We provide an outline, taxonomy, comparison table, and diverse IoT-based use cases for each of them. Then, the integration of primitives and the relationship diagrams give guidelines to help select the appropriate advanced digital signatures and highlights how researchers can use them with different IoT entities for preserving privacy and management of trust.
keywords: امضای دیجیتالی | حفظ حریم خصوصی اینترنت اشیا | بلاک چین | محاسبات ابری | Digital signature | IoT Privacy-preserving | Blockchain | Cloud computing
مقاله انگلیسی
9 Accounting research and the significance test crisis
تحقیقات حسابداری و بحران آزمون اهمیت-2021
The emerging or at least threatening ‘‘significance test crisis” in accounting has been prompted by a chorus across multiple physical and social sciences of dissatisfaction with conventional frequentist statistical research methods and behaviors, particularly the use and abuse of p-levels. There are now hundreds of published papers and statements, echoing what has been said behind closed doors for decades, namely that much if not most empirical research is unreliable, simply wrong or at worst fabricated. The problems are a mixture of flawed statistical logic (as Bayesians have claimed for decades), ‘‘phacking” by way of fishing for significant results and publications, selective reporting or ‘‘the file drawer problem”, and ultimately the ‘‘agency problem” that researchers charged by funding bodies (their Universities, governments and taxpayers) with conducting disinterested ‘‘objective science” are motivated more by the personal need to publish and please other researchers. Expanding on that theme, the supply of empirical research in the ‘‘market for statistical significance” is described in terms of ‘‘market failure” and ‘‘the market for lemons”.
keywords: تحقیق حسابداری تجربی | آزمایشات | بحران تکثیر | سطحی | گزارش های ثبت شده | Empirical accounting research | Significance tests | Replication crisis | p-levels | p-hacking | Registered reports
مقاله انگلیسی
10 Conceptual MINLP approach to the development of a CO2 supply chain network – Simultaneous consideration of capture and utilization process flowsheets
رویکرد مفهومی MINLP برای توسعه یک شبکه زنجیره تامین CO2 - در نظر گرفتن همزمان صفحه های جریان فرآیند ضبط و استفاده-2021
A large fraction of anthropogenic CO2 emissions comes from large point sources such as power plants, petroleum refineries, and large industrial facilities. A significant decrease of these CO2 emissions can be achieved with CO2 capture, utilization, and storage (CCUS) technologies. This study proposes a conceptually simplified model for the optimization of combined CO2 supply networks and capture and utilization technologies by the mixed-integer non-linear programming (MINLP) approach. The objective is to maximize the profit of CCUS technologies, considering chemisorption using methyl-diethanolamine (MDEA) as a capture technology and conversion of CO2 to CH3OH as a utilization technology. Additionally, avoided tax from reduced CO2 emissions is considered as a revenue. A hypothetical case study of five larger point sources of CO2 was investigated, namely coal power plants, biogas plant, aluminium production plant and two cement plants. Two scenarios were considered: i) Scenario A considering different values of the CO2 tax, and ii) Scenario B considering different flue gas flowrates at different values of the CO2 tax. The results show the potential of model-based optimization in reducing the amount of CO2 in the atmosphere by CCUS technology. Furthermore, the results in Scenario A show that CCUStechnology is only profitable if the price of CO2 emissions is higher than 110 €/t emitted CO2. Moreover, the results in Scenario B show that both the profit and the production of CH3OH depend to a large extent on the flue gas flow.
KEYWORDS: Point sources of CO2 | Carbon capture | Storage and utilization (CCUS) | Supply network optimization | Process optimization | MINLP approach
مقاله انگلیسی
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