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Human Resources HRs are one of the most important elements in the organizations, but it is difficult to manage a large number of human resources, especially in large companies. For this purpose, companies have tended to manage these resources taking advantages of the technology and the Internet. In this research, we will focus on human resources management in different companies and different countries and explore the factors that motivate companies to adopt Electronic Human Resource Management E-HRM as well as the factors that affect its adoption. The results showed that HRM plays a vital role in facilitating organizational processes, save cost and time, it also beneficial for competitive advantages. The findings also explored that E-HRM aspects and factors impact E-HRM adoption are varied from firm to another and from country to another. However, the companies moved towards adopting E-HRM because it mitigates the efforts of HR managers to take a decision.

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques.  The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

In recent days, increasing numbers of Internet and wireless network users have helped accelerate the need for encryption mechanisms and devices to protect user data sharing across an unsecured network. Data security, integrity, and verification may be used due to these features. In internet traffic encryption, symmetrical block chips play an essential role. Data Encryption Standard (DES) and Advanced Encryption Standard (AES) ensure privacy encryption underlying data protection standards. The DES and the AES provide information security. DES and AES have the distinction of being introduced in both hardware and applications. DES and AES hardware implementation has many advantages, such as increased performance and improved safety. This paper provides an exhaustive study of the implementation by DES and AES of field programming gate arrays (FPGAs) using both DES and AES. Since FPGAs can be defined as just one mission, computers are superior to them.

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New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

In this paper, several works has been presented related to the clustering parallel computing for distributed system. The trend of the paper is to focus on the strength points of previous works in this field towards enhancing performance of the distributed systems. This concentration conducted via presenting several techniques where each of them has the weak and strong features. The most challenging points for all techniques vary from increasing the performance of the system to time responding to overcome overhead running of the system. For more specific addressing concurrent computation besides parallel computing classifications for distributed systems, this paper depended comprehensive features study and comparison between SYNC and ASYNC Modes.