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Sign Language (SL) is the main language for handicapped and disabled people. Each country has its own SL that is different from other countries. Each sign in a language is represented with variant hand gestures, body movements, and facial expressions. Researchers in this field aim to remove any obstacles that prevent the communication with deaf people by replacing all device-based techniques with vision-based techniques using Artificial Intelligence (AI) and Deep Learning. This article highlights two main SL processing tasks: Sign Language Recognition (SLR) and Sign Language Identification (SLID). The latter task is targeted to identify the signer language, while the former is aimed to translate the signer conversation into tokens (signs). The article addresses the most common datasets used in the literature for the two tasks (static and dynamic datasets that are collected from different corpora) with different contents including numerical, alphabets, words, and sentences from different SLs. It also discusses the devices required to build these datasets, as well as the different preprocessing steps applied before training and testing. The article compares the different approaches and techniques applied on these datasets. It discusses both the vision-based and the data-gloves-based approaches, aiming to analyze and focus on main methods used in vision-based approaches such as hybrid methods and deep learning algorithms. Furthermore, the article presents a graphical depiction and a tabular representation of various SLR approaches.

Nowadays, the automobile sector is one of the hottest applications, where vehicles can be intelligent by using IoT technology. But unfortunately, these vehicles suffer from many crimes. Hence it has become a big challenge for the IoT to avoid such these crimes from professional thieves. This paper presents a proposal for the development of a vehicle guard and alarm system using biometric authentication based on IoT technology. Whereas, for vehicle security issues; the proposed system VSS − IoT gives only full access for authorized vehicle’s driver based on the interface of a Raspberry Pi 3 Model B+ development board, Pi camera, PIR sensor, and smart-phone. Therefore, if the proposed system detects an unauthorized person inside the vehicle, then the system will notify and send his image to vehicle’s owner and/or to a police workstation through the Internet, as well as, its location in case the vehicle is stolen or damaged. The proposed system is tested on two datasets that are ORL dataset and our dataset. The experimental results of the VSS − IoT showed that the accuracy is 98.2% on ORL dataset, whereas 99.6% when applied on our dataset. Besides, the VSS − IoT enhances the sensitivity to 97.7% which is important for real-time. As well as the result demonstrated that the proposed system took shorter time 0.152 sec under different illumination conditions, when the value of the threshold is 3 * 103 and 3.50 * 103. Therefore, the VSS − IoT is very robust and reliable for face recognition when deployed on the low-power processor.

Permeable pavement is considered one of the sustainable management options for roadway networks, which mitigates a number of problems associated with stormwater, ground water pollution, and traffic safety. In this study, the economic feasibility, vehicle operation, and environmental implications of implementing permeable pavement in Nablus, Palestine have been determined by selecting the local roadways that satisfy the permeable pavement requirement, such as low traffic volume, grade less than 5%, speed limit up to 50 km/h, and subgrade with good permeability. The total costs of construction and maintenance for both conventional asphalt and permeable pavement have also been compared based on the life cycle cost analysis (LCCA). Finally, the environmental implications such as the expected increase in the amount of ground water and the reduction in water pollutants have been investigated. The results of the analysis show that the permeable pavement is applicable for the local roadways that have satisfied the requirements, which are 61 roadways. Furthermore, it could lead to an annual significant increase in ground water by 107,404.7 m3 and slightly reduce the cost of construction and maintenance by up to 1,912,000 ILS during its life period compared to conventional asphalt pavement. Moreover, applying porous asphalt could enhance vehicular traffic safety by improving skid resistance. [ABSTRACT FROM AUTHOR]

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches. Index Terms--Information extraction, Web mining, wrapper, wrapper induction.