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

Spams and spamming methods are increasing vastly and getting complicated due to the rapid growth in networks, communications and technologies. Therefore, spam filters need to be tested continuously to evaluate their capabilities and efficiency in detecting and preventing spams. This paper discusses spams filtering problem using Bayesian classifier. It shows how using a combination of black and white lists and a customized spam filter based on users’ feedback can enhance the performance of Bayesian classifier. The paper evaluates three models of spam filters which are Static Bayesian Spam Filter, Light Adaptive Bayesian Spam Filter, and Enhanced Adaptive Bayesian Spam Filter. The experiments demonstrate that Enhanced Adaptive Bayesian Spam Filter, which is the one that uses black/white lists and users’ feedback, has the highest performance.

/pdf/an-evaluation-and-analysis-of-static-and-adaptive-bayesian-3rtx3yja2q.pdf

An Evaluation and Analysis of Static and Adaptive Bayesian Spam Filters

With the advent of Web 2.0, numerous collaborative annotation systems have been developed in an effort to enable distant users to annotate the same multimedia resources such as texts, audio, images, and videos. However, the existing systems do not support the semantic aspect of the data available on the Web and ignore the convergence aspect when executing concurrent annotations. Based on the technologies of Semantic Web, this article presents a new conflict‐free replicated data type called OAC‐Set, which extends Open Annotation Collaboration data model to enable concurrent annotations while guaranteeing convergence, causality, and intention preservation criteria. The experimental results show that our approach is efficient and effective.

A conflict‐free replicated data type for collaborative annotation systems

This paper reviews the research articles published in the years between 2016 and 2017 with a focus on using Linked Data technologies for enhancing learning. For this purpose, a survey was conducted and articles were selected according to two main criteria: (1) the paper must be in the technology-enhanced learning domain and published in one of the well-recognized journals indexed by the Institute for Scientific Information (ISI) and listed in the Social Science Citation Index (SSCI), and (2) the paper must clearly show how to consume, produce, or both consume and produce Linked Data in the learning context. The survey results show the importance of using Linked Data as a powerful educational technology.

Trends in Linked Data-Based Educational Studies: A Review of Contributions in SSCI Journals

Auto management and controlling road traffic while identifying abnormal driving behavior is one of the key challenges faced by the traffic authorities. In most of the cities, the traffic violations are detected manually by placing sergeants at various regions on the road. Placing sergeants is not economical and does not cover all the metropolitan area. Only in modern countries, traffic authorities have developed systems that use static road cameras to monitor real-time city traffic for identification of major traffic violations. However, these cameras just cover limited areas of the cities, such as, intersections, signals, roundabouts, and main streets. Therefore, in this paper, we have proposed a real-time traffic violation detection model by using vehicular camera along with the edge device in order to control and manage the road traffic. The edge device is equipped with the graphics processing unit (GPU), deployed inside the vehicle, and directly attached to the vehicle camera. The camera monitors every vehicle ahead, whereas, the edge device identifies the suspected driving violation. As a use case, we have tested our model by considering a wrong U-turn as a traffic violation. We designed a wrong U-turn detection algorithm and deployed it on the GPU-enabled edge device. In order to evaluate the feasibility of the system, we considered the efficiency measurements corresponding to the video generation rate and data size. The results show that the system is able to identify violations far faster than the video generation time.

Real-time Traffic Management Model using GPUenabled Edge Devices

The analysis of large-scale data for the purpose of extracting patterns is applicable to several research fields. In this paper, a dataset of climate related historical data from Jordan is collected. The main focus is to study evolution criteria of weather attributes in Jordan over the evaluated period of time and how it is connected to climate change. Results showed that humidity and dew point weather attributes are going to face a significant increase in the future. Such an increase is expected to have a direct, as well as an indirect impact on human life.

Yaser Jararweh

Papers

An Evaluation and Analysis of Static and Adaptive Bayesian Spam Filters

A conflict‐free replicated data type for collaborative annotation systems

Trends in Linked Data-Based Educational Studies: A Review of Contributions in SSCI Journals

Real-time Traffic Management Model using GPUenabled Edge Devices

The analysis of large-scale climate data: Jordan case study