Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

The traditional mass diffusion recommendation algorithm only relies on the user’s object collection relationship, resulting in poor recommendation performance for users with small purchases (i.e., small-degree user), and it is difficult to balance the accuracy and diversity of the recommendation system. This paper introduces the trust relationship into the resource allocation process of the traditional mass diffusion algorithm and proposes the Dual Wing Mass Diffusion model (DWMD), which constructs a dual wing graph based on trust relationships and object collection relationships. Implicit trust is mined according to the network structure of the trust relationship and integrated into the resource allocation process, and then merging the positive effects of object reputation on a recommendation through tunable scaling parameters. The user controls the tunable scaling parameter to achieve the best recommendation performance. The experimental results show that the DWMD method significantly improves diversity and novelty while ensuring high accuracy and effectively improves the accuracy and diversity balance. The improved recommendation performance for small-degree users proves that the trust relationship can effectively alleviate the generalized cold start problem of the recommendation algorithm for users who collect a small number of objects.

Greedy Based Radio Resource Allocation Algorithm with SARSA Power Control Scheme in D2D Underlaying Communication

Device-to-Device (D2D) underlaying communication system is a solution in reducing the workload of eNodeB and increasing the system data rate. This communication system consists of two users, namely Cellular User Equipment (CUE) and D2D pair, where CUE will share its resources with the D2D pair. This sharing resources also causes interference and should be managed using the resource allocation algorithm. In this work, the resource allocation scheme occurs in a single cell with an uplink communication direction. The resource allocation process uses greedy and joint greedy algorithms. After CUE allocates all of its resources, SARSA algorithm performs the power allocation process. The resource allocation process involves the scheduled CUE and D2D pair. After all the resource and power are allocated, parameter performance of the system is calculated. Based on the work results, joint greedy algorithm with power allocation using SARSA algorithm have performance results 1.375 × 107 bps/Watt in energy efficiency, 43.105 bps/Hz in spectral efficiency, and 0.993 in D2D fairness index.

A resource allocation framework for predisaster resilience management of interdependent infrastructure networks

PurposeResource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events.Design/methodology/approachTaking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized.FindingsThe findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies.Practical implicationsAs the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices.Social implicationsThe proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities.Originality/valueCapitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.

Formation of the schedule at the university with the application of greedy algorithms

A greedy resource allocation algorithm is understood as an algorithm according to which the resource allocation process can be represented as a sequence of steps. At each step, an optimal, under certain conditions, distribution of a part of the resources occurs, which does not change in the future. The problem of improving the quality of the organization of the educational process in a higher educational institution is solved on the basis of the use of greedy algorithms. A well-designed timetable should ensure an even workload of student groups and faculty. The purpose of this work is to develop an algorithm that can improve the quality of the formation of the educational schedule based on the use of greedy algorithms.

A Unified Model for the Two-stage Offline-then-Online Resource Allocation

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.

The Influence of Managerial Orientation in Decision-Making Processes

This research aims to examine managerial preferences in the resource allocation process. This research used an experimental method to test whether resource availability, stakeholder claims, and managers’ affiliations to stockholders can influence the decision-making process of resource allocation. The results show that resource availability, stakeholder claims, and managers’ affiliation could influence the resource allocation process. The results of the research contribute to several things. The first is to show that stakeholder theory can test managerial preferences at the individual level. Secondly, the resources distribution is influenced by behavioral factors associated with normative stakeholder theory.

Resource Allocation by Demand Based Optimization and Machine

In real-time multimedia usage the resource allocation for the modern communication is very much needed in-order to overcome certain problems or degradation happening in the communication channels. The quality of the communication is reduced due to the TVWS (Television White Space), variable BER signal requires variable channel allocation procedures and Qos depends on the various applications. These problems in the OFDM should be corrected continuously by keeping track of channel situation so that to provide a long term video streaming in good QoS. The energy distribution for the video is high the application requirement is higher also the occurrence of multiple BER will leads to the challenging environment to control. The main objective of this paper is to enhance a Game theory based algorithm incorporated with demand optimization algorithm and scheduling algorithm for machine learning to take decision in nonlinear space, which results in a system with good channel awareness and an adaptive resource allocation process. The effect of interference due to this procedure is checked and accordingly allocations are done

Smart Home Surveillance System using IoT with an Efficient Data Analytics

Security in our daily life became one of the basic need for all of us now a days. Drastic growth of the technology results sophisticated life, and other side directly or indirectly it can be applied to save one’s life and to safeguard the properties we earned .But we should know how to use the technology. Home burglary and crime is one of the major problem, when we are not in our home. The whole movable asserts may be taken by thieves because of lake of monitoring systems in our home when gone for out station. There may be possibility of losing the whole property, which we earned for our better future. So we need an effective monitoring system in our home which may be enabled the above said situation. There are so many monitoring systems are available for the same scenario, but In this paper we propose a new efficient monitoring system with dynamic cloud resource allocation process and improved data analytics which gives most accurate results