다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

National Institute of Standards and Technology における超伝導研究及び生活

Matrix Factorization Techniques for Recommender Systems

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.

/pdf/fake-reviews-detection-a-survey-1fi4wybvm1.pdf

Fake Reviews Detection: A Survey

Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today's fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM.

A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation

Providing an acceptable level of security for Internet of Things (IoT)-based critical infrastructures, such as the connected vehicles, considers as an open research issue. Nowadays, blockchain overcomes a wide range of network limitations. In the context of IoT and blockchain, Byzantine Fault Tolerance (BFT)-based consensus protocol, that elects a set of authenticated devices/nodes within the network, considers as a solution for achieving the desired energy efficiency over the other consensus protocols. In BFT, the elected devices are responsible for ensuring the data blocks’ integrity and preventing the concurrently appended blocks that might contain some malicious data. In this paper, we evaluate the fault-tolerance with different network settings, i.e., the number of connected vehicles. We verify and validate the proposed model with MATLAB/Simulink package simulations. The results show that our proposed hybrid scenario performed over the non-hybrid scenario taking throughput and latency in the consideration as the evaluated metrics.

Blockchain Solution for IoT-based Critical Infrastructures: Byzantine Fault Tolerance

Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing (NLP) and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online (especially, on social networks). Most of the current works on SA focus on the English language and work on the sentence-level or the document-level. This work focuses on the less studied version of SA, which is aspect-based SA (ABSA) for the Arabic language. Specifically, this work considers two ABSA tasks: aspect category determination and aspect category polarity determination, and makes use of the publicly available human annotated Arabic dataset (HAAD) along with its baseline experiments conducted by HAAD providers. In this work, several lexicon-based approaches are presented for the two tasks at hand and show that some of the presented approaches significantly outperforms the best known result on the given dataset.

Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches

This paper presents an FPGA architecture for a new version of the Advanced Encryption Standard (AES) algorithm. The efficient hardware that implements the algorithm is also proposed. The new algorithm (AES-512) uses input block size and key size of 512-bits which makes it more resistant to cryptanalysis with tolerated area increase. AES-512 will be suitable for applications with high security and throughput requirements and with less chip area constrains such as multimedia and satellite communication systems. An FPGA architectural for AES-512 was developed using VHDL, and synthesized using Virtix-6 and Virtex-7 chips. AES-512 show tremendous throughput increase of 230% when compared with the implementation of the original AES-128.

Yaser Jararweh

Papers

Fake Reviews Detection: A Survey

A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation

Blockchain Solution for IoT-based Critical Infrastructures: Byzantine Fault Tolerance

Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches

AES-512: 512-bit Advanced Encryption Standard algorithm design and evaluation