The main idea behind social crowdsensing is to leverage social friends as crowdworkers to participate in crowdsensing tasks. A main challenge, however, is the identification and recruitment of well-suited workers. This becomes especially more challenging for large-scale online social networks with potential sparseness of the friendship network which may result in recruiting participants who are not in direct friendship relations with the requester. Such recruitment may increase the possibility of COLLUSION among participants, thus threatening the application security and affecting data quality. In this paper, we propose a COLLUSIONresistant worker selection method which aims to prevent the selection of colluders as suitable participants. For each participant who is considered to be selected as suitable, the proposed method is aimed to prevent any possible COLLUSION. To do so, it determines whether the selection of a new participant may result in the formation of a colluding group among the selected participants. This has been achieved through leveraging the Frequent Itemset Mining technique and defining a set of COLLUSION behavioral indicators. Simulation results demonstrate the efficacy of our proposed COLLUSION prevention method in terms of selecting efficient COLLUSION indicators and detecting the colluding groups.

Bad news is “ any news that adversely and seriously affects an individual’ s view of his or her future, ” 1 and talking about one’ s cancer diagnosis is one of them. Delivering bad news is a complex, necessary communication skill that should be effectively learned, especially by oncologists. Giving bad news has two key parts: transferring information to patients and responding to patients’ emotional reactions...

COLLUSION between the undertakings as one of anti-competitive practices is one of the issues that the EU competition law from the beginning of the emergence (during the half century from 1957) has focused on it. In our country following of adopting of privatization process in national economy and especially under the Article 44 of the Act of Reform to Fourth Economic, Social and Cultural Development Plan of Islamic Republic of Iran and implementation of General Policies of Principle (44) Approved on 1387/3/25, COLLUSION between undertakings which has one or more effects that enumerated in above Article has been banned.Comparative study of the concept of COLLUSION between undertakings and conditions and effects of it on the activities of undertakings in perspective of European and-Iranian competition laws is the subject of this article.

This paper develops a theoretical model in which a processing industry purchases a main productive input in order to convert it into the final commodity, employing other non-main inputs. In this market, concentration coefficient regarding input demand shares among firms has been divided into several factors, imposing their impacts on economic performance indirectly. Theoretically, it turns out that both the price elasticity of main input supply and COLLUSION degree have positive indirect effects on industry profitability due to concentration changes, but their direct effects are negative and positive respectively. Choosing eleven sugar cane industries and applying the approach of pooling time -series and cross- sections data, it results in the degree of COLLUSION at 0.7 levels among industries. It implies that each factory behaves as a monophony in main input market and that this behavior tends to affirm the collective decision making between industries.      

One way of solving welfare loss asymmetric information in insurance is to design informationally consistent contracts. When the contracts are not informationally consistent, adverse selection cause the portfolio insurance to be risky, and consequently the probability of accident and loss will be high. In this paper we investigate Iran's insurance companies, based on informaionlly consistent contracts and Jonh Raily (2001) paper. We find that there are some signals that show Iran automobile COLLUSION contracts are not informationally consistent.