Due to the increasing amount of video data, a lot of research has been done in the field of retrieving and categorizing this type of data. On the other hand, with the growing popularity of football and the increasing number of its audiences, the importance of automatic and real-time extraction of statistics and information about soccer matches has increased. One of the critical and challenging tasks in soccer video analysis is the detection of players’ blobs and regions, along with identifying the teams related to the players. This task encounters many challenges, including grass loss in the playfield, the presence of playfield lines and players' shadows, the overlapping of players with objects and other players, and different shapes of players in different situations. This paper proposes a framework for detecting players and their related teams. For this purpose, an object-sieve-based method is used to detect players’ blobs, and a genetic algorithm is used to identify their related teams. Each chromosome of the genetic algorithm is a window that lies on one blob whose fitness function shows how much its color and shape characteristics fit with the uniforms of each of the two teams. The proposed method was evaluated by 50 different frames of broadcast soccer videos, including 563 players, and 40 different sub-images, including 84 players. The results show 98% and 91.6% precision for player detection and labeling, respectively.

In recent years, Plagiarism has been easier through increasing development of internet and online papers. Plagiarism is to reuse or copy a text without referencing to the original author. Plagiarism or fraud in schools and universities will be a stimulating factor for researchers. If plagiarism was not identified correctly, cheaters and Plagiarists could get results that are not deserved. This paper presents a method based on the semantic role labeling (SRL) and Genetic Algorithm (GA). The Proposed method works on English texts. Results of the experiments on PAN-PC-9 corpus demonstrate that the proposed method improves values of evaluation parameters such as recall, precision and F-measure, comparing with previous approaches in plagiarism detection.

Data mining is a powerful new technology to extract hidden information from data warehouses. Data mining analyzes data from different perspectives and  finds useful patterns and knowledge from large volumes of raw data. Clustering is one of the main methods of data mining. K-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. One of the challenges of clustering is to identify the appropriate label for each cluster. The selection of a label is done so as to provide a proper description of cluster records. In some cases, choosing an appropriate label is not easy due to the results and structure of each cluster. The aim of this study is to present an algorithm based on the K-means clustering in order to facilitate the allocation of labels to each cluster.

A difference labeling of a graph G is an injective function f: V (G) → N ∪ {0} together with the weight function f∗ on E(G) given by f∗(uv) = |f(u)-f(v)| for every edge uv in G. The collection of subgraphs induced by the edges of the same weight is a decomposition of G and is called the common weight decomposition of G induced by f. Let ϒf denote the collection of all the paths taken from each member of the common weight decomposition induced by f. A difference labeling f of G is said to be a graphoidal difference labeling if ϒf is an acyclic graphoidal decomposition of G. This paper initiates a study on this concepts.

Let G be a graph and f: V (G)! {1, 2, 3, . . . . . |V (G)|} be a bijection. Let puv = f(u)f(v) and duv =        hf(u) f(v) i if f(u)  f(v) hf(v) f(u)i if f(v)  f(u) for all edge uv 2 E(G). For each edge uv assign the label 1 if gcd(puv, duv) = 1 or 0 otherwise. f is called PD-prime cordial labeling if |ef (0) − ef (1)|  1 where ef (0) and ef (1) respectively denote the number of edges labelled with 0 and 1. A graph with admit a PD-prime cordial labeling is called PD-prime cordial graph.

Relation between religion and sociological theories is the most important concern of scholars who study in the field of social sciences.Different views have been expressed in this regard. To draw them on a line one must put on one side those views which reject the existence of any relation between them. On the other side those who believe that faith can take part in this area. Islam as a complete religion has solutions both for spiritual guidance of man and for man’s social life. We should just discover these solutions. New theories such as sociological theories guide us to discover these laws.The present paper lies at the middle of this line between two sides of it. The author has a middle approach. He starts his research with a little question in the field of sociology namely social-labeling theory and then explores different aspects of this theory and highlights its most important points and finally makes a comparison between them and hadith sources trying to find an answer.

Let G be a graph with vertex set V (G) and edge set E(G), a vertex labeling f: V (G)! Z2 induces an edge labeling f+: E(G)! Z2 defined by f+(xy) = f(x) + f(y), for each edge xy 2 E(G). For each i 2 Z2, let vf (i) = |{u 2 V (G): f(u) = i}| and ef+(i) = |{xy 2 E(G): f+(xy) = i}|. A vertex labeling f of a graph G is said to be friendly if |vf (1) − vf (0)|  1. The friendly index set of the graph G, denoted by FI(G), is defined as {|ef+(1) − ef+(0)|: the vertex labeling f is friendly}. The full friendly index set of the graph G, denoted by FFI(G), is defined as {ef+(1) − ef+(0): the vertex labeling f is friendly}. A graph G is cordial if − 1, 0 or 1 2 FFI(G). In this paper, by introducing labeling subgraph embeddings method, we determine the cordiality of a family of cubic graphs which are double-edge blow-up of P2 × Pn, n  2. Consequently, we completely determined friendly index and full product cordial index sets of this family of graphs.