Biblio

With the proliferation of false redundant information on various e-commerce platforms, ineffective recommendations and other untrustworthy behaviors have seriously hindered the healthy development of e-commerce platforms. Modern recommendation systems often use side information to alleviate these problems and also increase prediction accuracy. One such piece of side information, which has been widely investigated, is trust. However, it is difficult to obtain explicit trust relationship data, so researchers infer trust values from other methods, such as the user-to-item relationship. In this paper, addressing the problems, we proposed a novel trust-based recommender model called UITrust, which uses user-item relationship value to improve prediction accuracy. With the improvement the traditional similarity measures by employing the entropies of user and item history ratings to reflect the global rating behavior on both. We evaluate the proposed model using two real-world datasets. The proposed model performs significantly better than the baseline methods. Also, we can use the UITrust to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

The supply chain network is a complex network with the risk of cascading failure. To study the cascading failure in it, an accurate supply chain network model needs to be established. In this paper, we construct a layered supply chain network model according to the types of companies in real supply chain networks. We first define the similarity between companies in the same layer by studying real-world scenarios in supply chain networks. Then, considering both the node degree and the similarity between nodes in the same layer, we propose preferential attachment probability formulas for the new nodes to join the exist network. Finally, the evolution steps of the model are summarized. We analyze the structural characteristics of the new model. The results show that the new model has scale-free property and small-world property, which conform to the structural characteristics of the known supply chain networks. Compared with the other network models, it is found that the new model can better describe the actual supply chain network.

Recommender system helps people in decision making by asking their preferences about various items and recommends other items that have not been rated yet and are similar to their taste. A traditional recommendation system aims at generating a set of recommendations based on inter-user similarity that will satisfy the target user. Positive preferences as well as negative preferences of the users are taken into account so as to find strongly related users. Weighted entropy is usedz as a similarity measure to determine the similar taste users. The target user is asked to fill in the ratings so as to identify the closely related users from the knowledge base and top N recommendations are produced accordingly. Results show a considerable amount of improvement in accuracy after using weighted entropy and opposite preferences as a similarity measure.

Cloud Computing is considered as a business model for providing IT resources as services through the Internet based on pay-as-you-go principle. These IT resources are provided by Cloud Service Providers (CSPs) and requested by Cloud Service Consumers (CSCs). Selecting the proper CSP to deliver services is a critical and strategic process. According to the work in this paper, a framework for trust management in cloud computing has been introduced. The proposed framework consists of five stages; Filtrating, Trusting, Similarity, Ranking and Monitoring. In the Filtrating stage, the existing CSPs in the system will be filtered based on their parameters. The CSPs trust values are calculated in the Trusting stage. Then, the similarity between the CSC requirements and the CSPs data is calculated in the Similarity stage. The ranking of CSPs will be performed in Ranking stage. According to the Monitoring stage, after finishing the service, the CSC sends his feedbacks about the CSP who delivered the service to be used to monitor this CSP. To evaluate the performance of the proposed framework, a comparative study has been done for the Ranking and Monitoring stages using Armor dataset. According to the comparative results it is found that the proposed framework increases the reliability and performance of the cloud environment.

Physical Unclonable Function is an innovative hardware security primitives that exploit the physical characteristics of a physical object to generate a unique identifier, which play the role of the object's fingerprint. Silicon PUF, a popular type of PUFs, exploits the variation in the manufacturing process of integrated circuits (ICs). It needs an input called challenge to generate the response as an output. In addition, of classical attacks, PUFs are vulnerable to physical and modeling attacks. The performance of the PUFs is measured by several metrics like reliability, uniqueness and uniformity. So as an evidence, the main goal is to provide a complete tool that checks the strength and quantifies the performance of a given physical unconscionable function. This paper provides a tool and develops a set of metrics that can achieve safely the proposed goal.

In Internet of Things (IoT) each object is addressable, trackable and accessible on the Internet. To be useful, objects in IoT co-operate and exchange information. IoT networks are open, anonymous, dynamic in nature so, a malicious object may enter into the network and disrupt the network. Trust models have been proposed to identify malicious objects and to improve the reliability of the network. Recommendations in trust computation are the basis of trust models. Due to this, trust models are vulnerable to bad mouthing and collusion attacks. In this paper, we propose a similarity model to mitigate badmouthing and collusion attacks and show that proposed method efficiently removes the impact of malicious recommendations in trust computation.

This paper introduces a biologically inspired associative data model and structure for finding nearest neighbors and similar patterns. The method can be used as an alternative to the classical approaches to accelerate the search for such patterns using various priorities for attributes according to the Sebestyen measure. The presented structure, together with algorithms developed in this paper can be useful in various computational intelligence tasks like pattern matching, recognition, clustering, classification, multi-criterion search etc. This approach is particularly useful for the on-line operation of associative neural network graphs. Graphs that dynamically develop their structure during learning on training data. The results of experiments show that the associative approach can substantially accelerate the nearest neighbor search and that associative structures can also be used as a model for KNN tasks. Finally, this paper presents how the associative structures can be used to self-organize data and represent knowledge about them in the associative way, which yields new search approaches described in this paper.

Artificial neural networks are complex biologically inspired algorithms made up of highly distributed, adaptive and self-organizing structures that make them suitable for optimization problems. They are made up of a group of interconnected nodes, similar to the great networks of neurons in the human brain. So far, artificial neural networks have not been applied to user modeling in multi-criteria recommender systems. This paper presents neural networks-based user modeling technique that exploits some of the characteristics of biological neurons for improving the accuracy of multi-criteria recommendations. The study was based upon the aggregation function approach that computes the overall rating as a function of the criteria ratings. The proposed technique was evaluated using different evaluation metrics, and the empirical results of the experiments were compared with that of the single rating-based collaborative filtering and two other similarity-based modeling approaches. The two similarity-based techniques used are: the worst-case and the average similarity techniques. The results of the comparative analysis have shown that the proposed technique is more efficient than the two similarity-based techniques and the single rating collaborative filtering technique.

A new dataset is presented composed of music identification matches from Gracenote, a leading global music metadata company. Matches from January 1, 2014 to December 31, 2014 have been curated and made available as a public dataset called Gracenote Music Identification 2014, or GNMID14, at the following address: https://developer.gracenote.com/mid2014. This collection is the first significant music identification dataset and one of the largest music related datasets available containing more than 110M matches in 224 countries for 3M unique tracks, and 509K unique artists. It features geotemporal information (i.e. country and match date), genre and mood metadata. In this paper, we characterize the dataset and demonstrate its utility for Information Retrieval (IR) research.