Traditionally, business and management subjects have been linked to what is known by one’s culture and business traditions in terms of how business dealings are operated and managed according to given and proven business and managerial theories, concepts, ideas, and overall best practices. However, as more and more technology has been introduced into today’s modern workplace, changes to the overall business landscape have gone from domestic to global to now virtual opportunities. Consequently, many of the traditional business and management fields have been undergoing a metamorphosis in order to complete with the growing needs in today’s busines world. In particular, the field of Human Resource Management (HRM) has been transitioning from the role of just managing the human capital (employees) in an organiztion over to becoming more of a strategic partnership. Thus, HRM professionals today have to master more content knowledge, skils skills, and be prepared for additonal roles in the workpace. This paper will overview how one of these new roles in the area of finance is being placed into the realm of HRM professionals and how educational educations are meeting the need of teaching finance basics to non-financial HR managers.

Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms.
 Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.

Abstract— There is no doubt that provision of security and safety is from the main objectives of the new urban planning, due to its impact on the individual and society, because of that the architects try to solve this problem throw an environmental design which called crime prevention through environmental design (CPTED). We will discuss in this study the design strategies for this way of design starting from the single family homes passing throw all neighborhood's units tell we reach to the economic and industrial buildings. We will discuss too some exterior and interior physical security procedures, modern technologies used to protect people against intruders, the procedures of the government to secure residential areas and how can the architecture help them doing their work. After finishing the design and implementation procedures, we list some of CPTED implementation benefits and problems, and offer some additional recommendations for people who will live in these places to enhance their feeling with safety and security.
 We find after consider all CPTED design benefits that it offer more safe and beautiful place for people to live in and it reduce the total cost of buildings since it use saving lighting system, building material and we will need less amount of physical security systems (CCTV, EAC,..) which are very expensive. The researcher taking into his account a lot of CPTED principals during design Future City project to make safe and secure city.

Abstract—Copper is one of the oldest and highest traded commodities on the Indian commodity market. Its price is based on demand and supply. With the ‘Make in India’ and ‘Smart Cities’ project in process there is a large amount of copper requirement in speculation, which in turn shall cause a sudden increase in demand and bring volatility in copper prices. Therefore, there is a need to study the price behaviour of copper spot prices in India. The study uses data from April 2007 to September 2016 of copper spot prices on Multi-Commodity Exchange. We conduct Autoregressive Integrated Moving Average (ARIMA) method and Multi-layer Prediction (MLP) Artificial Neural Network (ANN) method for predicting volatility in copper prices. The study finds MLP neural network provides better forecasting accuracy compared to ARIMA on the basis of Root Mean Square (RMS) errors and forecast errors.