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1 |
A Tokenless Cancellable Scheme for Multimodal Biometric Systems
طرح غیر قابل لغو برای سیستم های بیومتریک چند حالته-2021 Biometric template protection (BTP) is an open problem for biometric identity management systems. Cancellable biometrics is commonly designed to protect biometric templates with two input factors i.e., biometrics and a token used in template replacement. However, the token is often required to be kept secretly; otherwise, the protected template could be vulnerable to several security attacks and breaches of privacy. In this paper, we propose a tokenless cancellable biometrics scheme called Multimodal Extended Feature Vector (M•EFV) Hashing that employs an improved XOR encryption/decryption notion to operate on the transformation key. We stress on multimodal biometrics where the real-valued face and fingerprint vectors are fused and embedded into a binarized cancellable template. Specifically, M•EFV hashing consists of three stages of transformation: 1) normalization and bio- metric fusion; 2) randomization and binarization; and 3) cancellable template generation. To evaluate the proposed scheme, several benchmarking datasets, i.e., FVC2002, FVC2004 for fingerprint and LFW for face are used in experiments. The verification performance is vali- dated by employing the FVC matching protocol. Various attacks are simulated and analysed in the worst-case scenario. Lastly, unlinkability and revocability properties are examined experimentally.© 2021 Elsevier Ltd. All rights reserved. Keywords: Feature-level Fusion | Multimodal Biometrics | Tokenless Cancellable Biometrics | Privacy and security | XOR encryption/decryption |
مقاله انگلیسی |
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Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020 Physical world integration with cyber world opens the opportunity of creating smart environments; this
new paradigm is called the Internet of Things (IoT). Communication between humans and objects has
been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications
in business applications focusing in interoperability between machines (i.e., IIoT is a subset
from the IoT). Number of daily life things and objects connected to the Internet has been in increasing
fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity,
dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete,
and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart
transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources
reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge
extraction, and search techniques based on Information Communication Technologies (ICT), such as
data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and
leverage its value. This research presents a comprehensive review to study state-of-the-art challenges
and recommended technologies for enabling data analysis and search in the future IoT presenting a
framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and
presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications
due to ICT integration. Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing |
مقاله انگلیسی |
3 |
Data mining of customer choice behavior in internet of things within relationship network
داده کاوی رفتار انتخاب مشتری در اینترنت اشیایی که در شبکه ارتباطی قرار دارند-2020 Internet of Things has changed the relationship between traditional customer networks, and traditional information
dissemination has been affected. Smart environment accelerates the changes in customer behaviors.
Apparently, the new customer relationship network, benefitted from the Internet of Things technology, will
imperceptibly influence customer choice behaviors for the cyber intelligence. In this work, we selected 298
customers click browsing records as training data, and collected 50 customers who used the platform for the first
time as research objects. and use the smart customer relationship network correspond to cyber intelligence to
build the customer intelligence decision model in Internet of Things. The results showed that the MAE (Mean
Absolute Deviation) of the customer trust evaluation model constructed in this study is 0.215, 45% improvement
over the traditional equal assignment method. In addition, customers consumer experience can be enhanced
with the support of data mining technology in cyber intelligence. Our work indicated the key to build eliminates
confusion in customer choice behavior mechanism is to establish a consumer-centric, effective network of
customers and service providers, and to be supported by the Internet of Things, big data analysis, and relational
fusion technologies. Keywords: Internet of things | Customer relationship network | Decision making | Recommendation | Fusion algorithm |
مقاله انگلیسی |
4 |
Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals
تجزیه و تحلیل عاطفی داده های بزرگ چند متغیره: یک مرور جامع با استفاده از سیگنال های متنی ، صوتی ، تصویری و فیزیولوژیکی-2020 Affective computing is an emerging multidisciplinary research field that is increasingly drawing the attention of
researchers and practitioners in various fields, including artificial intelligence, natural language processing,
cognitive and social sciences. Research in affective computing includes areas such as sentiment, emotion, and
opinion modelling. The internet is an excellent source of data required for sentiment analysis, such as customer
reviews of products, social media, forums, blogs, etc. Most of these data, called big data, are unstructured and
unorganized. Hence there is a strong demand for developing suitable data processing techniques to process these
rich and valuable data to produce useful information. Early surveys on sentiment and emotion recognition in the
literature have been limited to discussions using text, audio, and visual modalities. So far, to the authors
knowledge, a comprehensive survey combining physiological modalities with these other modalities for affective
computing has yet to be reported. The objective of this paper is to fill the gap in this surveyed area. The usage of
physiological modalities for affective computing brings several benefits in that the signals can be used in different
environmental conditions, more robust systems can be constructed in combination with other modalities, and it
has increased anti-spoofing characteristics. The paper includes extensive reviews on different frameworks and
categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications,
trends and future directions to serve as guidelines for readers towards this emerging research area. Keywords: Affective computing | Multimodal fusion | Sentiment databases | Sentiment analysis | Affective applications |
مقاله انگلیسی |
5 |
Multi-model ensemble with rich spatial information for object detection
اثر گروهی چند مدلی با اطلاعات مکانی غنی برای ردیابی شی-2020 Due to the development of deep learning networks and big data dimensionality, research on ensemble deep learning is receiving an increasing amount of attention. This paper takes the object detection task as the research domain and proposes an object detection framework based on ensemble deep learning. To guarantee the accuracy as well as real-time detection, the detector uses a Single Shot MultiBox Detector (SSD) as the backbone and combines ensemble learning with context modeling and multi-scale feature representation. Two modes were designed in order to achieve ensemble learning: NMS Ensembling and Feature Ensembling. In addition, to obtain contextual information, we used dilated convolution to ex- pand the receptive field of the network. Compared with state-of-the-art detectors, our detector achieves superior performance on the PASCAL VOC set and the MS COCO set. Keywords: Ensemble learning | Object detection | Dilated convolution | Feature fusion |
مقاله انگلیسی |
6 |
Conflict management in the fusion of complementary segmentations of deformed kidneys and nephroblastoma
مدیریت تعارض در همجوشی بخش های مکمل کلیه های ناقص شده و نفروبلاستوما-2020 The fusion of multiple segmentations aims to improve their accuracy in order to make them exploitable. However, conflicts may appear. In this paper, two conflict-management models are proposed for the fu- sion of complementary segmentations. This conflict-management and fusion procedure, integrated into the SAIAD project, carries out the fusion of deformed kidneys and nephroblastoma using the combination of six independent methods. These methods are based on different criteria, like the adjacent segmented slices, the variation of information, the Dice, the neighbouring labels, the pixel intensity by scanner im- ages, and the fully connected CRFs. The performances of our fusion models was evaluated on 139 scans for three patients with nephroblastoma, and the results demonstrate its effectiveness and the improve- ment of the resulting segmentations. Keywords: Fusion | Conflict management | Segmentation | Cancer tumour |
مقاله انگلیسی |
7 |
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition-2019 Heterogeneous face recognition (HFR) is still a challenging problem in computer vision community due to large
appearance difference between near infrared (NIR) and visible light (VIS) modalities. Recently, breakthroughs
have been made for traditional face recognition by applying deep learning on a huge amount of labeled
VIS face samples. However, the same deep learning approach cannot be simply applied to HFR task due
to large domain difference as well as insufficient pairwise images in different modalities during training. In
general, the pooling layer of deep network can play the role of feature reduction, but also lead to the loss
of useful face information, resulting in a decrease in the performance of HFR problem. It is important to
eliminate modal-related information and retain more facial identity information. In this paper, we propose
a novel method called Discriminant Deep Feature Learning Based on Joint Supervision Loss and Multi-layer
Feature Fusion (DDFLJM) for HFR task. In most of the available CNNs, the softmax loss function is used as
the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply
learned features, this paper proposes a new loss function called Scatter Loss (SL), which embeds both interand
intra-class information for effectively training the deep model. To make full use of the various layers
of the deep network, a Dimension Reduction Block (DRB) is designed to effectively extract the auxiliary
features on multiple mid-level layers. An orthogonality constraint is introduced to the DRB block to reduce
spectrum variations of two different modalities. The proposed SL is applied to multiple layers of network
for joint supervision training, which enables multiple layers of the network to obtain discriminative identity
features. Moreover, a Modified Gate Two-stream Neural Network (MGTNN) is adopted to fuse multiple-layer
features. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0
and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method. Keywords: Heterogeneous face recognition | Deep learning | Joint supervision loss | Feature fusion |
مقاله انگلیسی |
8 |
Fusing pattern discovery and visual analytics approaches in tweet propagation
کشف الگوی فیوژن و تحلیل های بصری در انتشار صدای جیر جیر-2019 Over the past several years, social networks have become a major channel for information delivery. At present,
social networks are being used to obtain more followers and exert influence over people during political campaigns.
However, the propagation of a social network post is dependent on numerous factors. Some of these are
known; for example, the post contents, the time when it was posted, and the person or entity by whom it was
posted. However, other factors remain unknown, such as what makes a post more successful than others, and
how posts from similar profiles evolve and propagate differently over time. The main subject of this work is
addressing these types of questions. Our approach relies on a three-fold methodology for studying the influence
and propagation of posts: graph-based, semantic, and contrast pattern recognition analysis. The results obtained
are complemented by a dynamic visualization that encompasses all of the variables involved. In order to corroborate
our results, we collected all posts from the Twitter accounts of the most prominent Mexican political
figures and analyzed the influence and propagation of each post issued. Keywords: Social networks | Twitter | Pattern recognition | Influence modeling | Visual analytics |
مقاله انگلیسی |
9 |
Big data requirements in current and next fusion research experiments
الزامات داده های بزرگ در آزمایش های فعلی و بعدی همجوشی-2018 The present and future data management
requirements for fusion experiments are presented along with the
currently adopted solutions. Even if the presented solution fulfil
the requirements of the current experiments, the next generation
fusion devices are likely to produce/require an unpreceded
amount of data. For this reason, the solutions adopted nowadays,
and also foreseen for the experiments under construction, might
prove not enough scalable. Information Technology already
provides efficient solutions for big data management, successfully
employed for large cloud applications and social media. In
particular, MongoDB, Cassandra and Hadoop represent
promising candidates for the next generation experiments
because their combined usage covers the specific data
requirements for fusion research.
Keywords: Big Data ; Nuclear Fusion Experiment ; Data Acquisition ; Databases |
مقاله انگلیسی |
10 |
Big Data Analytics Architecture for Internet-of-Vehicles Based on the Spark
معماری تحلیل داده های بزرگ برای اینترنت وسایل نقلیه بر اساس spark-2018 Internet-of-Vehicles (IoV) technology is the development trend of the intelligent traffic management system, it is also an effective means to ease traffic and improve traffic efficiency. This article used big data analysis technology to build a big data analysis platform of intelligent transportation, the platform is decomposed into infrastructure layer, data analysis layer and application layer; All vehicle information are acquired by several in-vehicle sensors on ECUs or roadside, and the acquired sensor data and received information are for information fusion, processing, trajectory prediction and risk assessment. The platform can solve the problem of storage, analysis and multi terminal distribution of mass data, provide traffic information services to traffic management departments and the public, it is a useful attempt to apply advanced information technology to the transportation industry.
Keywords : Sensors, Big-data, Internet-of-Vehicles, Fusion |
مقاله انگلیسی |