Partitioning A Graph In Alliances And Its Application To Data Clustering

Any reasonably large group of individuals, families, states, and parties exhibits the phenomenon of subgroup formations within the group such that the members of each group have a strong connection or bonding between each other. The reasons of the formation of these subgroups that we call alliances differ in different situations, such as, kinship and friendship (in the case of individuals), common economic interests (for both individuals and states), common political interests, and geographical proximity. This structure of alliances is not only prevalent in social networks, but it is also an important characteristic of similarity networks of natural and unnatural objects. (A similarity network defines the links between two objects based on their similarities). Discovery of such structure in a data set is called clustering or unsupervised learning and the ability to do it automatically is desirable for many applications in the areas of pattern recognition, computer vision, artificial intelligence, behavioral and social sciences, life sciences, earth sciences, medicine, and information theory. In this dissertation, we study a graph theoretical model of alliances where an alliance of the vertices of a graph is a set of vertices in the graph, such that every vertex in the set is adjacent to equal or more vertices inside the set than the vertices outside it. We study the problem of partitioning a graph into alliances and identify classes of graphs that have such a partition. We present results on the relationship between the existence of such a partition and other well known graph parameters, such as connectivity, subgraph structure, and degrees of vertices. We also present results on the computational complexity of finding such a partition. An alliance cover set is a set of vertices in a graph that contains at least one vertex from every alliance of the graph. The complement of an alliance cover set is an alliance free set, that is, a set that does not contain any alliance as a subset. We study the properties of these sets and present tight bounds on their cardinalities. In addition, we also characterize the graphs that can be partitioned into alliance free and alliance cover sets. Finally, we present an approximate algorithm to discover alliances in a given graph. At each step, the algorithm finds a partition of the vertices into two alliances such that the alliances are strongest among all such partitions. The strength of an alliance is defined as a real number p, such that every vertex in the alliance has at least p times more neighbors in the set than its total number of neighbors in the graph). We evaluate the performance of the proposed algorithm on standard data sets

Virtual Three-Dimensional Blackboard: Three-Dimensional Finger Tracking With A Single Camera

We present a method for three-dimensional (3D) tracking of a human finger from a monocular sequence of images. To recover the third dimension from the two-dimensional images, we use the fact that the motion of the human arm is highly constrained owing to the dependencies between elbow and forearm and the physical constraints on joint angles. We use these anthropometric constraints to derive a 3D trajectory of a gesticulating arm. The system is fully automated and does not require human intervention. The system presented can be used as a visualization tool, as a user-input interface, or as part of some gesture-analysis system in which 3D information is important. © 2004 Optical Society of America

Machine Learning Empowered Efficient Intrusion Detection Framework

In modern era security is becoming major and basic need of any system. Protecting of a system from unauthorized access is very important for a network system. Network security is turning out to be an influential subject in information technology territory.  Hackers and squatters commit uncountable successful attempts to intrude into networks. Intrusion Detection System plays a vital role in a network security to identify and detect the anomalies in a security system of network. The performance of IDS can be measured through its intelligence, efficiency and accurate detection of unknown and known attacks. The greater the gain concept give the best possible detection rate of anomalies. This study proposed a machine learning framework based on MLP classifier with accuracy 99.98%. This work is further validated through 10-fold and JackKnife cross validation. Key metrics to see the impact on accuracy and other performance measured metrics such as Sensitivity, Specificity and Matthew’s Correlation Coefficient. All the metrics gained their highest ratio, which means MLP is the best classification technique. The accuracy, sensitivity, specificity and MCC rate of the suggested model computed 99.99% from whole dataset of UNSW-NB15. These results show the improvement in accuracy while applying different perceptron topologies. K-fold and JackKnife topologies are capable to earn the 99.99% accurac

Trackable CEMB-Klean Cotton Transgenic Technology: Afforadable Climate Neutral Agri-biotech Industrialization for Developing Countries

Background: Transgenic technology reflects the incorporation of novel useful traits in crop plants like cotton for economic benefits by overcoming the problems including insects' pests and weeds in special. The present study is the success story of the continuous effort of CEMB team started back in the 1990s. Methods: This study includes characterization of a large number of Bacillus thuringiensis (Bt) strains taken from local soil and subjected to direct transformation of isolated BT genes into local cotton cultivars. Protocols for transformation into cotton plants were optimized and validated by the development of double gene codon optimized (Cry1Ac and Cry2A) transgenic cotton varieties. Results: The resulting GMOs in the form of CEMB-33, CA-12, CEMB-66 have been approved by Punjab Seed Council in 2013 and 2016 respectively. Double Bt and weedicide resistant cotton harboring CEMB-Modified and codon optimized cp4EPSPS (GTGene). These varieties can tolerate glyphosate spray @ 1900ml per acre without the appearance of necrotic spots/shedding and complete removal of all surrounding weeds in the cotton field is a significant advance to boost cotton production without spending much on insecticides and herbicides. Conclusion: In the current report, two unique sets of primers which amplify 1.1 Kb for CEMB-double Bt genes and 660 bp product for CEMB-Modified cp4EPSPS (GTGene) were tested. CEMB cotton variety CKC-01 is specially designed as low cost and easy to use by local farmer's technology has the potential to revolutionize the cotton growing culture of the country.Higher Education Commission (HEC) of PakistanOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]