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This study aims to investigate the factors that perceive citizens’ intention to adopt smart city technologies in the Arab world. A self-administered questionnaire that included 312 end users as citizens in Amman, Jordan’s capital city, was used in this study. This study uses advanced statistical techniques to test an expanded technology acceptance model (TAM) that incorporates the determinants of perceived usefulness, perceived ease of use, security and privacy, ICT infrastructure and inadequate Internet connectivity, social influence, and demographic profiles. Based on the results, perceived ease of use and ICT infrastructure and Internet connectivity showed positive association with the intention of citizens to adopt smart city services in Jordan. By recognizing the factors that predict citizens’ adoption of smart city services, this study presents some theoretical implications and practical consequences related to smart city service adoption.

Language is one of the most important features that distinguish people from other creatures. It provides communication either individually or massively. Language, which is never stationary, is the basic carrier of cultures. It is an important communication tool that enables people from society to get along and get close to each other. Current increase in international tourism, trade and cultural engagement and Turkey's current position suggests that Turkish will be a very important language. The main obstacles in learning another language are due to vocal and oral factors. These are mainly: reading, writing, listening and speaking. It is obvious that all of those are obstacles for beginners in learning Turkish. Learning any language is like encounter with another world. This meeting requires a preparation process. Although there will be some complication, it cannot be impossible for somebody to learn another language. Foreign students who learn Turkish language always face some difficulties and spend enormous amount of effort to overcome these difficulties. As a result, the richness of the collections' motherhood is proportional to the contribution of these collections to the culture. Sociologically and psychologically, it is the result of a healthy motherhood and the ability of native speakers to be able to be creative and healthy. We can see and understand this hundredfold as the language is based on the richness of the language.

<div>Object detection is considered a hot research topic in applications of artificial intel-ligence and computer vision. Historically, object detection was widely used in var-ious fields like surveillance, fine-grained activities and robotics. All studies focus on improving accuracy for object detection using images, whether indoor or outdoor scenes. Therefore, this paper took a shot by improving the doable features extraction and proposing crossed sliding window approach using exiting classifiers for object de-tection. In this paper, the contribution includes two parts: First, improving local depth pattern feature along side SIFT and the second part explains a new technique presented by proposing crossed sliding window approach using two different types of images (colored and depth). Two types of features local depth patterns for detection (LDPD) and scale-invariant feature transform (SIFT) were merged as one feature vector. The RGB-D object dataset has been used and it consists of 300 different objects and in-cludes thousands of scenes. The proposed approach achieved high results comparing to other features or separated features that are used in this paper. All experiments and comparatives were applied on the same dataset for the same objective. Experimental results report a high accuracy in terms of detection rate, recall, precision and F1 scorein RGB-D scenes.</div>

Public health responses to the COVID-19 pandemic since March 2020 have led to lockdowns and social distancing in most countries around the world, with a shift from the traditional work environment to virtual one. Employees have been encouraged to work from home where possible to slow down the viral infection. The massive increase in the volume of professional activities executed online has posed a new context for cybercrime, with the increase in the number of emails and phishing websites. Phishing attacks have been broadened and extended through years of pandemics COVID-19. This paper presents a novel approach for detecting phishing Uniform Resource Locators (URLs) applying the Gated Recurrent Unit (GRU), a fast and highly accurate phishing classifier system. Comparative analysis of the GRU classification system indicates better accuracy (98.30%) than other classifier systems.